Universidade Federal do Rio Grande do Norte 
Programa de Pós-Graduação em Neurociências 
 
 
Arthur Sérgio Cavalcanti de França 
 
 
 
 
 
 
O papel de oscilações beta2 e de interneurônios OLMα2 da 
região CA1 do hipocampo de camundongos na memória de 
reconhecimento de objetos 
 
 
 
 
 
 
 
Natal 2016 
Universidade Federal do Rio Grande do Norte 
Programa de Pós-Graduação em Neurociências 
 
 
 
 
O papel de oscilações beta2 e de interneurônios OLMα2 da 
região CA1 do hipocampo de camundongos na memória de 
reconhecimento de objetos 
 
 
 
Tese apresentada como requisito parcial à obtenção 
do título de Doutor em Neurociências, do Programa 
de Pós-Graduação em Neurociências, da 
Universidade Federal do Rio Grande do Norte. 
 
 
Orientador: Prof. Dr. Adriano B. L. Tort 
Co-orientador: Prof. Dr. Richardson N. Leão 
Aluno: Arthur S. C. França 
 
 
 
Natal 2016 
    
 Universidade Federal do Rio Grande do Norte - UFRN  
Sistema de Bibliotecas - SISBI 
Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial do Instituto do Cérebro - ICE 
  França, Arthur Sérgio Cavalcanti de.  
   O papel de oscilações beta2 e de interneurônios OLMa2 da 
região CA1 do hipocampo de camundongos na memória de 
reconhecimento de objetos / Arthur Sérgio Cavalcanti de França. - 
Natal, 2016.  
   146 f.: il.  
 
   Universidade Federal do Rio Grande do Norte,    Instituto do 
Cérebro,    Programa de Pós-Graduação em Neurociências   
Orientador: Adriano Bretanha Lopes Tort.  
   Coorientador: Richardson Naves Leão.  
 
 
   1. Hipocampo. 2. CA1. 3. OLMa2. 4. Memórias. 5. 
Reconhecimento de Objeto. I. Tort, Adriano Bretanha Lopes. II. 
Leão, Richardson Naves. III. Título.  
 
RN/UF/Biblioteca Setorial Árvore do Conhecimento - Instituto do 
Cérebro CDU 159.953 
 
 
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Dedico este trabalho aos meus pais, 
que são minha fonte diária de exemplos e  
que me inspiram a ser um ser humano melhor 
  
Agradecimentos 
 
Antes de qualquer coisa, gostaria de agradecer à minha família. Nela encontro todo apoio 
e incentivo necessários para enfrentar qualquer caminho que eu escolha trilhar. 
Sou uma pessoa que gosta de se embebedar de exemplos e sou felizardo de ter tido a 
chance de conviver com pessoas que me dão inúmeros exemplos, em todos os aspectos 
da vida.  
Gostaria de agradecer à minha mãe, meu exemplo de compaixão e da sede por 
conhecimento. Ao meu pai, meu exemplo de dedicação e otimismo. Ter sido criado por 
vocês foi a maior felicidade que poderia ter sonhado, amo vocês de forma imensurável. 
Agradeço de coração às minhas irmãs e minhas queridas avós, seus cuidados e amor são 
sentidos mesmo de longe, amo muito vocês.  
Gostaria de agradecer à minha namorada Mayara Medeiros, minha companheira na dança 
da vida.   
Gostaria de agradecer aos meus pais científicos e a todos que contribuíram ao longo dessa 
jornada. Primeiramente à minha primeira orientadora, Juliana Espada, que me inspirou a 
seguir o caminho da pesquisa. A Bruno Lobão e Sidarta Ribeiro com quem construí e 
trilhei meus primeiros passos na neurociência. Ao meu co-orientador Richardson Leão, o 
maior experimentalista com quem já tive oportunidade de trabalhar. Agradeço também 
ao meu supervisor no exterior Klas Kullander por todo suporte dado durante a minha 
estadia na Suécia, e por sua confiança depositada em mim.  
Quero fazer um agradecimento especial ao meu orientador Adriano Tort. Ele foi e é o 
maior exemplo que já tive no meio científico. Ao longo desses 4,5 anos só aumentou 
minha admiração pelo seu jeito de fazer ciência, pela sua integridade e seriedade. Nunca 
trabalhei em um ambiente tão bom e produtivo, com tanto compartilhamento de 
informação e pessoas se ajudando. Não acredito em sorte, então acredito que você é o 
maior fomentador desse ambiente. Não é à toa que todas as pessoas que saem desse 
laboratório querem continuar o vínculo com você. Parabéns!  
Gostaria de agradecer aos professores Marcos Costa, Katarina Leão, Olavo Amaral e 
Diego Laplagne pelas discussões que geraram impacto direto na construção do meu 
trabalho.  
Agradeço aos meus colegas do laboratório sueco, Julia, Sanja, Samer, Stefano e Fábio. 
Vocês tornaram minha estadia muito mais prazerosa e produtiva do que eu poderia 
imaginar. Sem vocês tudo teria sido mais difícil.  
Gostaria de agradecer aos meus colegas de laboratório, Bryan, Pavão, André, Zé, Alan e 
Hindiael. Obrigado pelas inúmeras discussões científicas e não científicas, pelas festinhas 
e pelo bullying diário. Vocês tornaram meu trabalho extremamente prazeroso e produtivo.  
Agradeço aos meus colegas Vitor, João, Robson e Aron. Ao Neuro Revolución! O grupo 
de pessoas que mais desenvolvi afinidade científica. Vocês são exemplos não só de 
integridade científica, mas também de aspiração por uma sociedade melhor. 
A todos os meus amigos que fizeram parte diretamente ou não da minha construção 
científica, dando suporte pessoal ou científico, Daniela Moura, Marina Siqueira, Annie 
Souza, Thawann Malfatti, Annara Soares, Eduardo Queiroz, Eduardo Leite, Stefano 
Marlon, Felipe Farias, Dyêgo Siqueira e Aderson Stanrley. 
Gostaria de agradecer à Alexandra Elbakyan, graças a ela tive acesso aos artigos 
necessários para embasar boa parte da minha investigação científica. Parabéns por sua 
contribuição para divulgação científica no mundo.   
Por último, agradeço ao governo brasileiro por ter pago meu salário nesses últimos 12 
anos, da graduação ao doutorado. Graças a isso, tive oportunidade de me qualificar e 
espero retornar esse investimento para a sociedade.  
 
 
  
 
 
  
Resumo 
O hipocampo é relacionado com a formação de memórias explicitas e com a capacidade 
de reconhecer novos objetos. No presente trabalho visamos contribuir para uma maior 
compreensão do papel da região CA1 do hipocampo nestes processos. Através da 
aplicação de técnicas de eletrofisiologia, comportamento animal, psicofarmacologia e 
optogenética em camundongos transgênicos e selvagens, encontramos que células 
OLMα2 do CA1 atuam na codificação da representação de objetos em uma tarefa de 
reconhecimento de objetos, e também influenciam a codificação de memórias aversivas 
em uma tarefa associativa de medo ao contexto. Além disso, descrevemos uma nova 
atividade oscilatória no potencial de campo local do CA1 na frequência beta 2 (23-30 
Hz), que é caracteristicamente transitória e ligada à detecção de novos objetos durante 
uma tarefa de reconhecimento de objetos. Estes resultados sugerem potenciais 
mecanismos celulares e de rede neuronal na região CA1 subjacentes ao seu papel na 
formação de memórias e na detecção de novidade.     
 
  
Abstract 
The hippocampus is associated to novelty detection and formation of explicit memories. 
The present work aims at better understanding the role of the CA1 region of the 
hippocampus in these processes. By employing electrophysiology, animal behavior, 
psychopharmacology and optogenetic techniques in transgenic and wild-type mice, we 
found that CA1 OLMα2 cells influence the formation of new object representations in an 
object recognition task, as well as the encoding of aversive memories in a contextual fear 
memory task. Furthermore, we characterized a new oscillatory activity in the local field 
potential of CA1 at beta 2 frequency (23-30 Hz), which was typically transient and linked 
to the amount of novelty in an object recognition task. These results suggest potential 
cellular and network mechanisms that underlie the role of CA1 in memory formation and 
novelty detection. 
 
  
 1 
 
 
Sumário 
1.0 – Introdução .................................................................................................... 2 
1.1 – Neuroanatomia e citoarquitetura da formação hipocampal ..................... 2 
1.2 – Formação hipocampal e memória .......................................................... 12 
1.2.1 – Mecanismos eletrofisiológicos e plásticos da memória ...................... 12 
1.2.2 – Formação hipocampal e a memória reconhecimento ......................... 20 
1.3 – Modulação da codificação de memória de reconhecimento pelas células 
OLMα2 (Artigo 1). ..................................................................................................... 22 
1.3.1 – Sistema Cre-loxp, optogenética e o controle das células OLMα2. ..... 23 
1.3.2 – Células OLMα2 e o controle das entradas do córtex entorrinal ......... 26 
1.4 – Oscilações hipocampais e detecção de novidade (Artigo 2) ................. 29 
2.0 – Artigo 1 ...................................................................................................... 34 
3.0 – Artigo 2 ...................................................................................................... 65 
4.0 – Discussão ................................................................................................... 76 
4.1 – Artigo 1 .................................................................................................. 76 
4.2 – Artigo 2 .................................................................................................. 80 
4.3 – Discussão geral ...................................................................................... 81 
5.0 – Referências ................................................................................................ 85 
6.0 – Anexos ....................................................................................................... 96 
 
 
  
  
 2 
 
 
1.0 – Introdução  
Os artigos que serão apresentados aqui visam aumentar nosso entendimento 
acerca dos mecanismos que permitem ao hipocampo desempenhar seus papeis cognitivos, 
em particular a capacidade de reconhecimento de novidade. Para melhor situar nossa 
contribuição ao campo, a seção de introdução é dividida em diferentes subseções. Será 
feita inicialmente uma breve descrição sobre a neuroanatomia e citoarquitetura da 
formação hipocampal, bem como sobre as principais conexões entre as estruturas que a 
compõem. Iremos trazer também um breve histórico sobre o estudo da memória e os 
correlatos eletrofisiológicos, plásticos e comportamentais que tangem esse campo de 
estudo. Posteriormente, iremos introduzir os assuntos tratados nos dois artigos científicos 
que constituem o corpo de resultados desta tese. Finda a introdução, apresentaremos os 
artigos na íntegra nas seções intuladas “Artigo 1” e “Artigo 2”, e finalizaremos a tese com 
uma discussão geral sobre os resultados apresentados e como estes se inserem na literatura 
atual. Por fim, como anexo desta tese fornecemos a título de registro três outros artigos 
científicos também elaborados durante o período de doutoramento. 
 
1.1 – Neuroanatomia e citoarquitetura da formação hipocampal 
O hipocampo é uma das estruturas chave ligadas a memórias declarativas 
(Scoville and Milner, 1957), bem como a funções de orientação espacial (O’Keefe & 
Dostrovsky, 1971), em conjunto a outras áreas da formação hipocampal como o córtex 
entorrinal (Hafting et al., 2005). A estrutura anatômica da formação hipocampal começou 
a ser detalhadamente investigada a partir do final do século XIX, se destacando nesse 
período os trabalhos de Camilo Golgi, Luigi Sala, Karl Schaffer e Santiago Ramón y 
Cajal (Bentivoglio & Swanson, 2001; López-Muñoz et al., 2006; Szirmai et al., 2012). 
No trabalho intitulado “On the fine structure of the pes Hippocampi major, 1886” Camilo 
Golgi, utilizando a técnica criada por ele de coloração por nitrato de prata em 1873 
(Figura 1), descreveu as camadas que formam o hipocampo, bem como fez a descoberta 
do giro denteado como uma estrutura separada do Corno de Amon (CA; Bentivoglio & 
Swanson, 2001). Posteriormente, trabalhos como o de Karl Schaffer (Szirmai et al., 2012) 
  
 3 
 
 
e Santiago Ramón y Cajal (López-Muñoz et al., 2006) descreveram a conectividade entre 
as áreas do hipocampo. Santiago Ramón y Cajal e em seguida Lorente de Nó, proponente 
das subdivisões do corno de Amon em CA1, CA2 e CA3 (Lorente De Nó, 1934), 
montaram um esquema de conexão entre as áreas do hipocampo e das vias de entrada e 
saída de informação que é muito próximo ao modelo aceito atualmente (López-Muñoz et 
al., 2006).  
Figura 1. Exemplos de neurônios 
da formação hipocampal de coelho 
corados através da técnica de 
nitrato de prata, desenvolvida por 
Camilo Golgi. (Lâmina XIV, 
Golgi 1886).   
 
 
 
 
 
 
 
 
Atualmente o hipocampo é dividido em três subáreas distintas: CA1, CA2 e CA3. 
Além deste, compõem a formação hipocampal o córtex entorrinal, o parasubículo, o 
présubículo, o subículo e o giro denteado (Amaral & Witter, 1989; Schultz & Engelhardt, 
2014). O conjunto de conexões entre essas áreas formam uma unidade funcional 
associada a várias capacidades cognitivas (Amaral & Witter, 1989; Hafting et al., 2005; 
O’Keefe & Dostrovsky, 1971; Szirmai et al., 2012), entre elas a formação de memórias 
declarativas (Scoville & Milner, 1957). Para exercer tal função, a formação hipocampal 
  
 4 
 
 
recebe informação sensorial e multimodal de diversas áreas do encéfalo, bem como envia 
informação processada para diferentes regiões (Figura 2; Andersen et al., 2006) 
Figura 2. Esquema de vias de projeções de 
informações sensoriais e multimodais que 
chegam à formação hipocampal de um roedor 
(Brown & Aggleton, 2001). 
 
 
 
 
Das estruturas mencionadas, o córtex entorrinal é o principal componente de 
entrada e saída de informação de outras áreas do cérebro para a formação hipocampal 
(Dolorfo & Amaral, 1998; Insausti et al., 1997). O córtex entorrinal é dividido nos 
domínios lateral e medial; sua parte lateral recebe projeções multimodais do córtex 
perirrinal, olfatório, insular e da amígdala, enquanto a parte medial recebe projeções do 
córtex occipital, pósrinal e pressubículo (van Groen et al., 2003). No que tange sua 
citoarquitetura, o córtex entorrinal é formado por 6 camadas. As camadas II e III enviam 
projeções para o hipocampo e giro denteado e a camada V e VI recebem projeções do 
hipocampo (Dolorfo & Amaral, 1998; Insausti et al., 1997; van Groen et al., 2003). 
Neurônios piramidais glutamatérgicos compõem os principais neurônios excitatórios do 
córtex entorrinal, e os interneurônios GABAérgicos os principais neurônios inibitórios 
(Andersen et al., 2006; Dolorfo & Amaral, 1998; Insausti et al., 1997). Os neurônios 
piramidais do córtex entorrinal da camada II projetam seus axônios para o giro denteado 
através de um feixe de axônios denominado de via perforante, e seus terminais acabam 
principalmente nas espinhas dendríticas das células granulares (Amaral et al., 2007; 
Dolorfo & Amaral, 1998), enquanto os neurônios e interneurônios da camada III projetam 
bilateralmente para a região CA1 (Basu et al., 2016; van Groen et al., 2003) através de 
uma subdivisão da via perforante nomeada via têmporo-amônica (Remondes & Schuman, 
  
 5 
 
 
2004). Um esquema detalhado com entradas para o córtex entorrinal e saídas para o 
hipocampo pode ser visto na Figura 3. 
  
 
Figura 3. Representações esquemáticas de neurônios e conexões do córtex entorrinal lateral e 
medial. Setas indicam entrada e saída de projeções para as várias camadas do córtex entorrinal. 
Abreviaturas: ACC: córtex cingulado anterior; Amygd: Amígdala; CA1-CA3: subáreas do 
hipocampo; DG: giro denteado; IL: córtex infralímbico; INC: córtex insular; OB: bulbo 
olfatório; OLFC: córtex olfatório; AP: parasubículo; PER: córtex perirrinal; PL: córtex 
prelímbico; POR: córtex posrinal; PPC: córtex parietal posterior; PRS presubiculum; prox: 
proximal; RSC: córtex retroesplenial; sub: subículo; subcort: estruturas subcorticais tais como 
prosencéfalo basal, amígdala; superf: superficial. (Grillner, 2010). 
 
Como dito anteriormente, o giro denteado é um dos principais alvos de projeções 
do córtex entorrinal, recebendo através da via perforante axônios da camada II (Amaral 
et al., 2007; Dolorfo & Amaral, 1998). O giro denteado recebe estas projeções na camada 
molecular, que, junto com as camadas granular e polimórfica, formam sua estrutura 
  
 6 
 
 
citoarquitetônica (Amaral et al., 2007; Andersen et al., 2006). A camada molecular é 
caracterizada pelos dendritos oriundos dos corpos celulares das células granulares do giro 
denteado (Figura 4), bem como pela presença dos axônios oriundos do córtex entorrinal 
e um pequeno número de interneurônios (Amaral et al., 2007). A camada de células 
granulares, como o nome sugere, é onde os corpos celulares das células granulares se 
localizam, formando uma camada densa de neurônios (Amaral et al., 2007). Por último, 
a camada polimórfica é composta principalmente por interneurônios, entre eles as células 
musgosas (Amaral et al., 2007; Kobayashi, 2010). As células musgosas possuem 
dendritos na região hilar, enquanto seus axônios projetam para a camada molecular tanto 
ipsilateral como contralateral do giro denteado, e têm como principal alvo as células 
granulares (Freund & Buzsáki, 1996). O giro denteado também pode ser caracterizado 
através de seu formato, em “V” ou “U”, sendo a porção voltada para a região CA1 
chamada de suprapiramidal, a região abaixo do CA3 é chamada de infrapiramidal, e a 
interseção é chamada de crista (Amaral et al., 2007; Schultz & Engelhardt, 2014). A 
principal saída do giro denteado se dá através das fibras musgosas para a região CA3 do 
hipocampo; os axônios oriundos do giro denteado são particularmente grossos, e capazes 
de desencadear potenciais de ação na região CA3 com um único disparo pré-sináptico 
(Kobayashi, 2010).   
  
 7 
 
 
 
Figura 4. Ilustração original de Karl Schaffer (1892) do giro denteado e região CA3 e CA1 da 
formação hipocampal do coelho. Em destaque a entrada para o giro denteado oriundo do córtex 
entorrinal através da via perforante; a projeção de axônios das células granulares do giro 
denteado formando as fibras musgosas para as os neurônios piramidais da região CA3; as 
conexões intra CA3 através das colaterais recorrentes; e as conexões de CA3 para CA1 via as 
colaterais de Schaffer (Szirmai et al., 2012).      
  
A região CA3 recebe entradas do giro denteado e entradas diretas do córtex 
entorrinal em suas diferentes camadas (Amaral & Witter, 1989; Dolorfo & Amaral, 1998). 
A região CA3 possui em sua citoarquitetura 5 camadas ou estratos, que são similares às 
outras áreas do hipocampo (Andersen et al., 2006): (1) stratum oriens é a camada 
localizada entre o stratum pyramidale e alveus; o stratum oriens é caracterizado por 
possuir os dendritos basais dos neurônios piramidais, e é a região onde há saídas axonais 
(Figura 4). Nessa região também se localiza uma diversidade de interneurônios. (2) 
Stratum pyramidale é a camada em que se localiza os corpos celulares dos neurônios 
  
 8 
 
 
piramidais. (3) Stratum lucidum é uma camada que ocorre exclusivamente na região CA3. 
Essa camada localizada abaixo do stratum pyramidale é caracterizada por projeções das 
fibras musgosas oriundas do giro denteado. (4) Stratum radiatum, camada em que na 
região CA3 é localizada abaixo do stratum lucidum, é caracterizada pela chegada de 
projeções oriundas do próprio CA3 (colaterais recorrentes; Figura 4) e por possuir uma 
variedade de interneurônios. (5) Stratum lacunosum-moleculare, camada localizada infra 
stratum radiatum, é caracterizada por possuir os terminais das conexões oriundas da 
camada II do córtex entorrinal e por possuir uma variedade de interneurônios (Andersen 
et al., 2006; Cherubini & Miles, 2015). 
Subsequente a região CA3, se localiza a região CA2 no hipocampo. O CA2 é uma 
pequena região de transição entre as regiões CA3 e CA1, cuja área total é 
significativamente menor que estas. Porém, possui características singulares como o 
padrão de projeções em relação às outras áreas (Shinohara et al., 2012). A região CA2, 
diferentemente da região CA3, possui somente 4 camadas: (1) stratum oriens; (2) stratum 
pyramidale; (3) stratum radiatum; (4) stratum lacunosum-moleculare (Amaral & Witter, 
1989; Andersen et al., 2006).  
A região CA1 possui as mesmas camadas que a região CA2, porém com diversas 
características próprias. O stratum pyramidale é formado por neurônios piramidais com 
formato mais homogêneos e em geral menores quando comparados com a região CA3 
(Andersen et al., 2006). A região CA1 recebe inputs da região CA3, através das colaterais 
de Schaffer, no stratum radiatum (Amaral et al., 2007). Recebe também inputs da camada 
III do córtex entorrinal, através da via perforante e têmporo-amônica, no stratum 
lacunosum-moleculare (Brun et al., 2008). Na Figura 5 podemos ver um esquema de 
neurônios piramidais que povoam as três regiões do hipocampo. 
 
  
 9 
 
 
 
Figura 5. Representação esquemática dos neurônios piramidais e das camadas encontradas nas 
três regiões do hipocampo (CA1, CA2 e CA3). Podem ser observadas as principais conexões de 
entrada do córtex entorrinal (CE) e giro denteado (GD) para o hipocampo (linhas verde musgo, 
rosa e magenta) e saída (linha verde) entre das regiões hipocampais, bem como as conexões 
intra-hipocampais (linhas azul e laranja).  
 
A atividade dos neurônios piramidais do hipocampo é modulada por vários tipos 
de interneurônios GABAérgicos (Freund & Buzsáki, 1996; Klausberger & Somogyi, 
2008). Da diversidade de interneurônios presentes no hipocampo, podemos citar alguns 
que se diferenciam pela localização, velocidade de atividade neuronal e projeções: (1) 
Células em candelabro (Freund & Buzsáki, 1996) ou células axo-axônicas (Woodruff et 
al., 2010) são um conjunto de interneurônios que possuem seu corpo celular no stratum 
pyramidale, dendritos no stratum radiatum e lacunosum-moleculare (Freund & Buzsáki, 
1996), e projeções axonais que inibem o segmento inicial do axônio das células 
piramidais (Wang et al., 2016; Woodruff et al., 2010). (2) Células em cesto correspondem 
a um grupo diverso de interneurônios, que possuem seus corpos celulares em estratos 
diferentes como o pyramidale e o radiatum, e são caracterizadas por projetarem axônios 
  
 10 
 
 
que inibem a região perisomatica dos neurônios piramidais, e também por dispararem 
rapidamente (fast-spiking) (Freund & Buzsáki, 1996; Klausberger & Somogyi, 2008). (3) 
As células biestratificadas possuem seus corpos celulares no stratum pyramidale, e 
inibem os dendritos basais das células piramidais no stratum oriens, e os dendritos apicais 
no stratum radiatum; possuem ainda disparo regular (regular-spiking) (Ferrante & 
Ascoli, 2015; Klausberger & Somogyi, 2008; Wheeler et al., 2015). (4) As células oriens 
lacunosum-moleculare (OLM) possuem corpos celulares no stratum oriens e enviam 
projeções axonais para o stratum lacunosum-moleculare, inervando os dendritos apicais 
distais das células piramidais, bem como outros interneurônios no stratum radiatum 
(Klausberger & Somogyi, 2008; Leão et al., 2012); elas regulam a atividade oscilatória 
dos neurônios piramidais (Forro et al., 2015). Um esquema da variedade de 
interneurônios e projeções recebidas e enviadas pode ser visto na  Figura 6. 
  
 11 
 
 
 
Figura 6. Esquema mostrando diferentes interneurônios e suas projeções. Podemos ver em azul 
uma representação de neurônios piramidais e suas projeções. Em amarelo e verde podemos ver 
uma variedade de células em cesto. Em marrom é representado a célula biestratificada. Em 
vermelho a célula em candelabro ou axo-axônica. Por último, em magenta vemos a célula OLM 
e sua projeção para os dendritos apicais distais (Klausberger & Somogyi, 2008) 
O subículo, área da formação hipocampal localizada entre o córtex entorrinal e o 
hipocampo, possui três principais camadas: (1) molecular, (2) piramidal e (3) camada 
polimórfica (Ding, 2013). A camada piramidal possui grandes neurônios piramidais que 
têm dendritos apicais na camada molecular e os dendritos basais nas porções mais 
profundas da camada piramidal (O’Mara, 2005). O subículo é uma das estruturas que 
mais recebe saídas do hipocampo, especificamente da região CA1 que projeta para todas 
  
 12 
 
 
as camadas do subículo, e posteriormente projeta para o córtex entorrinal e outras diversas 
áreas corticais (Amaral et al., 1991; Ding, 2013; O’Mara, 2005).  
 
1.2 – Formação hipocampal e memória  
A unidade funcional que compõe a formação hipocampal descrita na seção 1.1 
possui um papel fundamental no processo de formação de memórias declarativas. Apesar 
das primeiras evidências encontradas nos anos 30 (McGaugh, 2000), o indício mais forte 
da participação da formação hipocampal no processo de formação de memórias 
declarativas foi visto duas décadas mais tarde no artigo intitulado “Loss of recent memory 
after bilateral hipocampal lesions” de William Scoville e Brenda Milner (1957). Nesse 
trabalho, os autores descreveram as cirurgias realizadas em 10 pacientes, onde o paciente 
H.M. (caso 1), que teve remoção bilateral da formação hipocampal, se tornou a principal 
evidência da estreita relação entre os processos relacionados à memória e a formação 
hipocampal (Scoville & Milner, 1957). Esse mesmo paciente sofreu de amnésia 
anterógrada, e retrógrada parcial, de memórias declarativas e episódicas (Scoville & 
Milner, 1957), porém manteve a capacidade de aprender e reter habilidades motoras 
(Milner et al., 1968; Scoville & Milner, 1957). 
Após os achados desse artigo, várias descobertas foram feitas ao longo das 
décadas seguintes no que concerne o papel de cada região da formação hipocampal no 
processo de formação e consolidação de memórias, da anatomia funcional de conexões 
entre as regiões hipocampais, passando pelos mecanismos moleculares até a codificação 
específica de informações (Amaral & Witter, 1989; Bliss & Lomo, 1973; Kandel, 2001; 
O’Keefe & Dostrovsky, 1971).  
 
1.2.1 – Mecanismos eletrofisiológicos e plásticos da memória 
As investigações sobre o processo de formação de memórias ao longo do século 
XX parecem ter seguido os preceitos propostos por Donald Hebb no livro intitulado “The 
Organization of Behavior” (Hebb, 1949). Nesse livro, Hebb propõe que a formação de 
  
 13 
 
 
traços de memória se daria por conexões funcionais que guardariam informações em rede. 
Através da facilitação de conexões entre os neurônios, esses formariam conjuntamente 
unidades de informação; essas unidades foram intituladas por Hebb de “assembleias 
neuronais”. Pela ativação sequencial de múltiplas assembleias neuronais, intitulada de 
“sequência de fase”, teríamos a codificação de informações mais complexas (Hebb, 
1949). As ideias propostas por Hebb levaram ao início de uma variedade de investigações 
em diversas áreas das neurociências, incluindo o campo da aprendizagem e memória 
(Brown & Milner, 2003). A posteriori, tais investigações desencadearam a descoberta de 
características das bases bioquímicas e eletrofisiológicas subjacentes à formação dos 
traços de memória (Brown & Milner, 2003).  
O postulado sobre a aprendizagem hebbiana, onde a eficiência da conexão 
funcional entre neurônios seria proporcional ao grau de atividade entre os neurônios pré 
e pós-sináptico, obteve o primeiro correlato biológico na década de 70. O mecanismo 
denominado de potenciação de longa duração (LTP, do inglês long term potentiation) foi 
descrito por Bliss e Lomo em 1973 em um trabalho de estimulação elétrica em coelhos 
anestesiados. Os animais, dentre alguns experimentos realizados, foram estimulados com 
eletrodos de forma rítmica e rápida (estímulos tetânicos de 15 Hz por 10 segundos) na via 
perforante e registrados em duas localizações: na região da camada molecular conectada 
monossinapticamente à região de registro (via experimental), e na camada molecular 
alguns milímetros distantes da região da via experimental (via controle). A estimulação 
na via experimental resultou no aumento de amplitude no potencial pós-sináptico 
excitatório populacional na camada granular do giro denteado, que pôde ser visto horas 
depois do protocolo de estimulação. Porém, a estimulação registrada na via controle não 
resultou no mesmo aumento de amplitude do potencial pós-sináptico (Figura 7; Bliss e 
Lomo, 1973).  
 
  
 14 
 
 
 
Figura 7. Experimento realizado por Bliss e Lomo em coelhos anestesiados. (A,B) Traçados de 
potenciais de campo registrados na via experimental e controle antes do protocolo de 
estimulação (A) e depois do protocolo de estimulação (B). (C) Amplitude dos potenciais pós-
sinápticos excitatórios populacionais registrados na região da via controle (círculos brancos) e 
na região da via experimental (círculos pretos). Os valores representam a porcentagem de 
quanto a amplitude variou em relação aos registros anteriores ao procedimento de estimulação. 
Podemos ver o fenômeno de potenciação de longa duração no grupo experimental (Bliss & 
Lomo, 1973).  
Ao longo das quatro décadas seguintes, houve vários avanços sobre a relação entre 
o LTP e mecanismos de plasticidade celular; porém, somente em 2006 foi mostrado que 
o processo de aprendizagem era capaz de gerar LTP no hipocampo de animais (Whitlock 
et al., 2006). Após um protocolo de aprendizagem com a tarefa de esquiva inibitória em 
ratos, Whitlock e colaboradores demonstraram que a região CA1 do hipocampo responde 
de forma diferenciada, onde pode ser observado aumento, diminuição ou manutenção nos 
potenciais de campo pós-sinápticos excitatórios populacionais (Whitlock et al., 2006). 
  
 15 
 
 
Estes achados puderam ser vistos mesmo horas após a aprendizagem (Figura 8; Whitlock 
et al., 2006). Uma das características vistas neste estudo, em que neurônios diminuem a 
responsividade a outros, é chamada de depressão de longa duração (LTD, do inglês long 
term depression). O LTD foi posteriormente evidenciado como tão importante quanto o 
LTP para a plasticidade de conexões funcionais entre neurônios (Feldman, 2012), apesar 
de não contemplado por Hebb em sua teoria. Para Hebb, o mecanismo de 
enfraquecimento de conexões não era um fenômeno ativo e sim uma resposta passiva ao 
fortalecimento de outras conexões (Hebb, 1949). O fenômeno de LTD foi visto pela 
primeira vez nas fibras musgosas das células de Purkinje do cerebelo (Ito et al., 1982).  
 
Figura 8. Experimento com esquiva inibitória 
realizado por Whitlock et al. (2006). (A) 
Localização dos eletrodos de registro no 
hipocampo. (B) Exemplo de traçados 
registrados por dois eletrodos distintos no 
painel de cima. O painel debaixo mostra a 
porcentagem de variação das inclinações das 
respostas evocadas nos potenciais de campo 
em relação aos registros antes da tarefa 
comportamental. As cores quentes mostram 
registros de eletrodos que exibiram aumento 
das respostas evocadas e as cores frias 
eletrodos que exibiram a diminuição ou 
manutenção das respostas evocadas.  
 
 
Além da ausência de postulados sobre o enfraquecimento de conexões, outra 
característica pouco explorada por Hebb foi a questão do tempo entre disparos como 
variável chave na plasticidade das conexões entre neurônios. Apesar de apresentar uma 
ideia de causalidade temporal ao postular que as assembleias seriam formadas por 
  
 16 
 
 
sequência de ativação de um neurônio após outro, nenhuma ideia nesse sentido foi 
aprofundada. Desta forma, a teoria de aprendizagem hebbiana clássica vem sendo 
atualizada desde sua formulação; podemos enumerar alguns postulados que a teoria mais 
atual trata: (1) a atividade conjunta entre um neurônio pré e pós sináptico; (2) a 
participação de moduladores nesse processo (Frémaux & Gerstner, 2015); (3) a relação 
de tempo entre os potenciais de ação pré e pós sináptico como indutor de plasticidade 
(Figura 9; STDP, do inglês spike-timing-dependent plasticity; Feldman, 2012); (4) o 
enfraquecimento de conexões como mecanismo importante na plasticidade (Caporale & 
Dan, 2008; Feldman, 2012; Frémaux & Gerstner, 2015). 
Figura 9. Mecanismo de plasticidade 
dependente do tempo de disparo. Painel de 
cima mostra dois esquemas de pareamento 
pré-pós sinaptico, onde o neurônio pós-
sináptico dispara depois (esquerda) ou 
antes (direita) do neurônio pré-sináptico. 
Painel abaixo mostra que quando há 
pareamento entre os disparos dos 
neurônios pré e pós há indução de LTP ou 
LTD, a depender do tempo relativo de 
disparo entre eles. A ativação não pareada 
do neurônio pré- ou pós-sináptico não 
induz LTP ou LTD. Painel debaixo mostra 
que a magnitude da plasticidade (em %) é 
inversamente proporcional ao tempo entre 
os disparos dos neurônios pré e pós 
sinápticos (Feldman, 2012). 
 
  
 
 
  
 17 
 
 
As modificações vistas no LTP só são possíveis através de mudanças em cadeias 
bioquímicas que alteram a estrutura funcional do neurônio, indo das variações de níveis 
de cátions e ânions em botões sinápticos, até alterações de transcrições de DNA (Caporale 
& Dan, 2008). Essas diferenciações de mecanismos bioquímicos, genéticos e celulares, 
necessárias para codificação e manutenção dos traços de memória, são denominadas de 
plasticidade sináptica, termo cunhado por Jerzy Konorski (Konorski, 1948). 
Posteriormente, a plasticidade sináptica foi postulada por Hebb como o mecanismo 
responsável para guardar informação no cérebro (Hebb, 1949).    
Um grande número de cascatas bioquímicas (Giese & Mizuno, 2013), 
modificações de expressão de genes (Bliim et al., 2016) até alterações estruturais (Leal et 
al., 2015) relacionadas a codificação e manutenção de traços de memória foram descritas 
ao longo do século XX e XXI (Andersen et al., 2006). As alterações mencionadas 
parecem estar ligadas a modulação do funcionamento de receptores glutamatérgicos do 
tipo AMPA e NMDA, e seus segundos mensageiros, cujas mudanças são induzidas por 
proteínas cinases (Izquierdo & Medina, 1997).  
Bastante resumidamente, após a ativação de receptores glutamatérgicos do tipo 
NMDA, há aumento nos níveis de cálcio intracelular que por sua vez se liga a 
calmodulinas (Izquierdo & Medina, 1997). Essas calmodulinas ativam cinases, sendo 
CAMKII uma das principais cinases envolvidas. Por conseguinte, CAMKII é capaz de 
afetar a atividade de outras proteínas que participam do remodelamento sináptico 
(Okamoto et al., 2009), e na modulação de expressão de genes imediatos (Okuno et al., 
2012). Seus níveis são aumentados durante o aprendizado (Cammarota et al., 1998), e sua 
inativação resulta em prejuízos na formação da memória (Lisman et al., 2002). Outras 
cinases como a família de PKA, C e G, MAPK, ou vias dependentes de segundo 
mensageiros como cAMP (do inglês, cyclic adenosine monophosphate), são igualmente 
importantes no processo de modificação sináptica e regulação de expressão gênica 
(Bernabeu et al., 1997; Giese & Mizuno, 2013). Uma das funções apresentadas pelas 
cinases envolve a modulação da expressão de genes imediatos que são capazes de regular 
a expressão de outros genes, bem como se autorregular (Minatohara et al., 2015). O Zif-
268 e o Arc são dois genes imediatos com papeis conhecidos na indução de plasticidade 
  
 18 
 
 
sináptica relacionada a formação e manutenção de memórias (Izquierdo & Cammarota, 
2004; Minatohara et al., 2015), e tanto a indução de LTP como novas experiências 
resultam no aumento de expressão de Zif-268 (Ribeiro et al., 2007, 2002; Ribeiro & 
Nicolelis, 2004). Outro componente na cadeia de plasticidade sináptica que possui grande 
importância no processo de formação e manutenção de memórias é o fator trófico 
derivado do cérebro (BDNF, do inglês brain-derived neurotrophic factor) (Bekinschtein 
et al., 2014; Leal et al., 2015). Podemos ver um esquema da relação das cinases, genes 
imediatos e fatores neurotróficos na Figura 10.  
 
Figura 10. Figura 
exemplificando, 
numa conexão entre 
CA3 e CA1, 
mecanismos 
plásticos 
relacionados ao 
LTP. É mostrada 
uma sequência de 
modificações que vai 
do botão sináptico 
(PK e CAMKII), 
passando por 
alterações de 
expressão gênicas 
(CREB) até efetores 
que induzem 
modificação 
estrutural (BDNF). 
 
 
  
 19 
 
 
Tão importante quanto o funcionamento de receptores glutamatérgicos, são as 
ações de sistemas de neurotransmissores moduladores, seja através da modulação da 
atividade dos receptores glutamatérgicos como também das cascatas bioquímicas e de 
expressão gênica tratadas acima (Blake et al., 2014; Hansen & Manahan-Vaughan, 2012; 
Knox, 2016; Rossato et al., 2009; Zhang & Stackman, 2015). Sistemas como o 
colinérgico, noradrenérgico, serotonérgico e dopaminérgico possuem projeções para 
formação hipocampal (Andersen et al., 2006), e modulam a atividade de cinases, genes 
imediatos e fatores neurotróficos relacionados a aprendizagem, aquisição e consolidação 
de memórias (Cammarota et al., 2008; Hansen & Manahan-Vaughan, 2012; Rossato et 
al., 2009).  
O sistema dopaminérgico, por exemplo, é implicado tanto na aquisição como na 
manutenção da memória (França et al., 2016, 2015; Rossato et al., 2009). Já foi visto que 
a modulação da atividade de receptores dopaminérgicos da família D1 no hipocampo leva 
a persistência de memórias de longo prazo (Rossato et al., 2009). Essa persistência da 
memória é relacionada à plasticidade induzida por BDNF, na janela de tempo de 12 horas 
após a aquisição da memória (Rossato et al., 2009). Mais especificamente, é relacionada 
à indução de plasticidade nos dendritos apicais de neurônios piramidais da região CA1 
(Navakkode et al., 2012) e à plasticidade induzida em interneurônios parvalbumina 
positivos da região CA1 (Karunakaran et al., 2016). A modulação de receptores 
dopaminérgicos do tipo D2 também é relacionada ao processo de aquisição e 
consolidação das memórias (França et al., 2016, 2015). A injeção de antagonista de 
receptores D2 leva à diminuição em cadeia da cinase dependente de cálcio ao longo de 3, 
6 e 12 horas após a injeção; a diminuição da expressão do gene imediato Zif-268 é vista 
6 horas após a injeção e, por último, a diminuição de BDNF é vista 12 horas após a injeção 
(França et al., 2015). Estes resultados estão de acordo com a janela de tempo da ação de 
BDNF (Navakkode et al., 2012; Rossato et al., 2009). Possivelmente, a ação de receptores 
D2 se dá através do controle do sono REM (França et al., 2015), que é relacionado aos 
processos plásticos da consolidação das memórias (Ribeiro et al., 2007; Ribeiro & 
Nicolelis, 2004).   
  
 20 
 
 
O sistema colinérgico também é relacionado ao processo de aprendizagem (Martí 
Barros et al., 2004) e sua ação é estreitamente ligada ao sistema dopaminérgico, 
ocorrendo através de inputs colinérgicos em núcleos dopaminérgicos (de Kloet et al., 
2015; Subramaniyan & Dani, 2015). Diferentes receptores colinérgicos do tipo nicotínico 
participam de diferentes processos cognitivos (Levin, 2002), e a transmissão colinérgica 
modula a potenciação de longa duração na região CA1 (Al-Onaizi et al., 2016). Os anexos 
1, 2 e 3 desta tese discutem o papel dos sistemas dopaminérgicos e acetilcolinérgicos, 
trazendo evidências comportamentais, moleculares e eletrofisiológicas em intervenções 
psicofarmacológicas em camundongos submetidos a tarefas cognitivas hipocampo-
dependentes.  
     
 1.2.2 – Formação hipocampal e a memória reconhecimento  
Em geral, as investigações sobre memória envolvem a manipulação 
farmacológica (sistêmica ou localizada). ou lesões específicas de estruturas ou vias da 
formação hipocampal (descrita na seção 1.1). Tais manipulações são acompanhadas da 
verificação do comportamento do animal em alguma tarefa e/ou a verificação dos níveis 
de moléculas relacionadas à memória (descritas na seção 1.2).  
Dentro desse contexto, testes de reconhecimento de objetos foram propostos em 
1988, sendo mostrado a tendência natural do roedor de explorar a novidade em detrimento 
da familiaridade (Ennaceur & Delacour, 1988). Posteriormente, esses testes foram 
empregados para investigar a função de regiões, circuitos, células e moléculas na 
formação, consolidação e evocação de memória (Antunes & Biala, 2012; Blaser & 
Heyser, 2015). Vale citar que a utilização do paradigma de reconhecimento de objetos 
também é aplicado para o estudo de doenças relacionadas ao sistema nervoso central, 
como esquizofrenia (Rajagopal et al., 2014), Alzheimer (Bengoetxea et al., 2015), 
Parkinson e autismo (Grayson et al., 2015). 
A capacidade de reconhecimento de padrões, objetos, lugares e contextos está 
ligada à formação hipocampal (Brun et al., 2002a; Daumas et al., 2005; Dere et al., 2007; 
Nakazawa et al., 2002; Suzuki et al., 1997). Como explicitado na seção 1.2.1, o 
  
 21 
 
 
funcionamento de receptores NMDA como indutor de plasticidade, seja por mecanismo 
de LTP ou LTD, está ligado ao processo de manutenção de memória. Foi observado por 
Kem e Manahan-Vaughan (2004) que a exploração de objetos novos facilita o mecanismo 
de LTD e dificulta o mecanismo de LTP no stratum radiatum da região CA1 (Kemp & 
Manahan-Vaughan, 2004). O uso de drogas antagonistas à ação de AMPA ou NMDA, 
aplicadas sistemicamente ou intra-hipocampal, prejudica a aquisição e a consolidação de 
memórias ligadas ao reconhecimento de objetos (Dere et al., 2007). Mais 
especificamente, camundongos knock-out para a subunidade 1 do receptor NMDA (que 
inviabiliza a função do receptor) na região CA1 do hipocampo possuem prejuízo no 
reconhecimento de objetos e na tarefa de associação de medo ao contexto; no entanto, a 
capacidade de reconhecimento é recuperada ao induzir plasticidade na região CA1 através 
de exposição a ambientes enriquecidos (Rampon et al., 2000). Já animais knock-out para 
os receptores NMDA na região CA3 possuem capacidade de reconhecimento e reativação 
de memória espacial; porém, estes animais exibem prejuízo da memória quando há 
diminuição das pistas espaciais no momento da reativação, o que revela o envolvimento 
de CA3 em memórias associativas ao contexto (Nakazawa et al., 2002).  
A modulação de receptores dopaminérgicos influencia na discriminação de 
objetos e contextos (França et al., 2016, 2015; Melichercik et al., 2012; Puma et al., 1999; 
Tian et al., 2015). Animais knock-out total ou parcial para o transportador de recaptação 
da dopamina da fenda sináptica (modelos de hiperdopaminergia) apresentam mudança no 
padrão da procura e interação com objetos novos (Pogorelov et al., 2005), déficit em 
reconhecimento de objetos (França et al., 2016) e no completamento de padrões (Li et al., 
2010). Estes resultados mostram a importância da dopamina balanceada no momento da 
codificação de memórias. A capacidade de discriminação de objetos é recuperada ao 
utilizar antagonista de receptores D2 (haloperidol 0.05 mg/Kg) na fase de codificação da 
memória nos camundongos com knock-out parcial para o transportador dopaminérgico 
(França et al., 2016). Quando o antagonista de receptores D2 (haloperidol 0.3 mg/Kg) é 
utilizado em camundongos selvagens na fase posterior à codificação dos objetos, pode 
ser observado o prejuízo na discriminação de objetos junto a uma diminuição de cascatas 
moleculares relacionadas a traços de memória (França et al., 2015). Drogas que modulam 
  
 22 
 
 
receptores D1/D5 também interferem na discriminação de objetos; por exemplo, um 
agonista D1/D5 injetado pós-treino (SKF 10 mg/Kg) prejudica a discriminação de objetos 
novos (de Lima et al., 2011).  
A modulação da atividade de receptores colinérgicos também pode causar 
prejuízo ou aumentar a discriminação de objetos novos e contextos (Anexo 3; Gould & 
Lommock, 2003; Kutlu & Gould, 2015; Puma et al., 1999; Tian et al., 2015). A nicotina, 
que atua em receptores colinérgicos, leva a um aumento na discriminação de objetos e 
contextos em baixas doses (0.1 – 0.4 mg/Kg) (Gould & Lommock, 2003; Puma et al., 
1999; Rezvani & Levin, 2001), mas em altas doses (1.5 mg/Kg) prejudica a discriminação 
de objetos e espaços (Anexo 3). Camundongos submetidos à dose de 1.5 mg/Kg pré-
treino passam a preferir o objeto familiar em detrimento do objeto novo (Anexo 3). A 
mesma dose injetada após a fase de codificação de memória reverte o padrão de 
exploração (Anexo 3).  
A exploração de contextos novos leva a uma assinatura eletrofisiológica na 
atividade de campo local nas regiões CA1 e CA3 do hipocampo (Berke et al., 2008), 
enquanto a associação contextual leva a um aumento no acoplamento entre a atividade 
oscilatória de teta e gama na região CA3 (Tort et al., 2009). A conexão entre o córtex 
entorrinal e CA1 parece ter papel chave para localização e reconhecimento espacial, já 
que a ausência de CA3 não afeta o reconhecimento de novos ambientes; a conexão entre 
o córtex entorrinal e CA1 parece ser suficiente para manter a codificação de espaço por 
células de lugar em CA1, e a capacidade de reconhecimento de lugar (Brun et al., 2002). 
Tais características relacionadas a atividades oscilatórias serão discutidas na seção 1.4. 
   
1.3 – Modulação da codificação de memória de reconhecimento pelas células 
OLMα2 (Artigo 1). 
Como dito seção 1.1, o hipocampo apresenta uma variedade de interneurônios 
GABAérgicos em diferentes camadas que modulam a atividade das células piramidais 
(Freund & Buzsáki, 1996). As projeções axonais perisomáticas tendem a inibir as saídas 
dos neurônios piramidais, enquanto que as dendríticas inibem as entradas (Klausberger 
  
 23 
 
 
& Somogyi, 2008). Os interneurônios na região CA1 que possuem corpos celulares no 
stratum oriens e projeções axonais enviadas para o stratum lacunosum-moleculare são 
denominadas de células OLM (Figura 5 e 11). As células OLM modulam a atividade dos 
neurônios piramidais através da inibição de seus dendritos apicais distais (Amaral & 
Witter, 1989). As células OLM recebem projeções diretas de neurônios colinérgicos, que 
por sua vez são relacionados a aprendizagem e consolidação de memórias (Bunce et al., 
2004; Easton et al., 2012; Elvander et al., 2004).   
 
 Figura 11. Imagem de reconstrução do 
neurolúcida de dois interneurônios. Em preto o 
soma e dendritos, e em vermelho os axônios de 
um interneurônio IS-III que modula a atividade 
de células OLM. Em verde o corpo celular e os 
dendritos no stratum oriens, e em azul os 
axônios projetados no stratum lacunosum-
moleculare de um célula OLM (Chamberland 
& Topolnik, 2012). 
 
 
1.3.1 – Sistema Cre-loxp, optogenética e o controle das células OLMα2. 
As células OLM foram caracterizadas através de animais transgênicos, e 
ferramentas eletrofisiológicas e optogenéticas por Leão e colaboradores (2012). Nesse 
trabalho foi verificado que a subunidade α2 do receptor colinérgico (Chrnα2) é expressa, 
dentro do hipocampo, exclusivamente nas células OLM do CA1, o que possibilitou usá-
la como marcador molecular desta subpopulação de células OLM (Leão et al., 2012; 
Mikulovic et al., 2015), aqui referida como “células OLMα2”. 
Para conseguir controlar a atividade dos interneurônios OLMα2 de forma 
específica, foi utilizado o sistema de manipulação genético Cre-loxp (Leão et al., 2012). 
Esse sistema foi descoberto no vírus bacteriófago P1, que o utiliza para reparação e 
  
 24 
 
 
estabilização de ADN durante a infecção da bactéria alvo (Sauer, 1998). O Cre-loxp 
utiliza duas variáveis importantes: (1) Cre recombinase, uma enzima que identifica pares 
de sequências específicas de ADN e inverte sua sequência (por exemplo, 3’-5’ para 5’-
3’); e (2) loxp, que é uma sequência de 13-bp invertida identificada pela Cre recombinase 
(Brault et al., 2007; Fenno et al., 2011; Sauer, 1998). O uso desse sistema de identificação 
e inversão da sequência de ADN pode ser associado a uma grande variedade de 
manipulações genéticas (Brault et al., 2007). Por exemplo, através da inserção de genes 
de interesse flanqueados pela sequência de loxp, e pela interação com a Cre recombinase, 
é possível estabelecer o knock-out e o knock-in de genes específicos, ou a expressão de 
genes em células específicas (Brault et al., 2007).  
A optogenética é uma técnica experimental moderna que utiliza rodopsinas para 
manipular a atividade de subtipos específicos de neurônios (Fenno et al., 2011). As 
rodopsinas são estruturas proteicas que incluem canais ou bombas iônicas, cuja 
conformação tridimensional é modificada na presença de luz em faixas comprimento de 
onda específicos (Deisseroth, 2011). Existem diversos canais ou bombas iônicas sensíveis 
à luz; por exemplo, o canal de cátions ChR2 (channelrodopsin-2) é oriundo de uma 
espécie de alga verde, e a bomba de prótons Arch (archaerhodopsin) oriunda de archaea 
bactérias (Madisen et al., 2012; Schneider et al., 2015). O canal de cátions ChR2 é 
sensível ao comprimento de onda de ~473 nm, que corresponde ao azul. Na presença da 
luz azul, sua conformação é modificada e o canal permite a passagem de cátions 
(principalmente sódio) para dentro da célula, provocando a despolarização da membrana 
e facilitando a atividade neuronal (Schneider et al., 2015). Já a bomba de prótons Arch é 
sensível à luz de comprimento de onda 532 nm; na presença de luz verde, Arch retira 
prótons de dentro para fora da célula, provocando hiperpolarização da membrana 
(Madisen et al., 2012).  
Leão e colaboradores (2012) associaram o sistema de Cre-loxp à optogenética 
para manipular especificamente as células OLMα2. Um animal transgênico foi 
desenvolvido utilizando Chrnα2 (a proteína que é expressa no hipocampo exclusivamente 
nas células OLM) como promotor da expressão de Cre recombinase e de um marcador 
bioluminescente vermelho (tomato) para identificar as células Cre+. Dessa forma, 
  
 25 
 
 
somente os neurônios OLM se tornaram Cre+ (Leão et al., 2012). Posteriormente, 
utilizando a estratégia doublefloxed inverted open-reading-frame (DIO; Figura 12), foram 
injetados nos animais Chrnα2-Cre/Tomato vetores virais duplamente flanqueados por lox 
contendo o gene de ChR2. Dessa maneira, o canal ChR2 foi exclusivamente expresso em 
células Chrnα2-Cre/Tomato infectadas (Leão et al., 2012). Um esquema da associação 
entre as ferramentas Cre-loxp e optogenética pode ser visto na Figura 12. 
 
Figura 12. Esquema do funcionamento do sistema Cre-loxp utilizando a estratégia de 
doublefloxed inverted open-reading-frame (DIO). Podemos observar em (a) o esquema da 
injeção viral, que infecta tanto células Cre+ (onde o Cre é expresso) e Cre- (onde não há 
expressão de Cre). A inversão da sequência específica de ADN só ocorre nas células Cre+. (b) 
Esquema do conteúdo da injeção viral. A primeira sequência gênica possui 5 genes: EF1α 
corresponde ao promotor; loxp corresponde à sequência a ser identificada e invertida por Cre; 
lox2722 é a sequência de parada; eYFP é o gene que codifica uma proteína bioluminescente; 
ChR2 é o gene que codifica o canal sensível à luz. Note que a sequência de eYFP e ChR2 está 
invertida. Ao final do processo, Cre identifica o loxp e inverte a sequência de eYFP e ChR2, que 
passam a ser expressas exclusivamente nas células Cre+ (Fenno et al., 2011).  
 
  
 26 
 
 
1.3.2 – Células OLMα2 e o controle das entradas do córtex entorrinal  
Leão e colaboradores (2012) verificaram que as células OLMα2 modulam a 
atividade dos neurônios piramidais inibindo os dendritos apicais distais no stratum 
lacunosum-moleculare, e tal modulação diminui entradas do córtex entorrinal (Figura 13; 
Leão et al., 2012). Essa evidência foi obtida através de protocolos de indução de LTP em 
fatias de hipocampo. Em particular, os autores estimularam eletricamente a via têmporo-
amônica (via de comunicação entre o córtex entorrinal com a região CA1) e registraram 
respostas de potencial de campo no stratum lacunosum-moleculare. Foram investigados 
dois animais transgênicos: (1) animal Chrna2-cre, e (2) animal Chrna2-cre/Viaatloxp/loxp, 
que não possuem transportadores vesiculares GABAérgicos nas células OLMα2, 
inviabilizando a função inibitória destes interneurônios. Ambos animais foram sujeitos à 
injeção hipocampal de vírus contendo o gene de ChR2 flanqueado por lox; portanto, em 
ambos animais as células OLMα2 tornaram-se sensíveis à luz azul. Ao estimular a via 
têmporo-amônica e registrar nas fatias dos animais Chrna2-cre sem incidência 
concomitante de luz, houve um aumento das respostas em relação à linha de base, 
indicando um aumento de excitabilidade característica do fenômeno de LTP (Leão et al., 
2012). Entretanto, ao associar a estimulação elétrica da via têmporo-amônica com a 
incidência da luz azul, a potenciação da resposta pós-sináptica foi significativamente 
menor, indicando que as células OLMα2 – estimuladas optogeniticamente – inibiram a 
indução de LTP (Leão et al., 2012). Por último, ao utilizar animais Chrna2-
cre/Viaatloxp/loxp, pôde-se verificar uma potenciação ainda maior que nas duas condições 
anteriores, corroborando a função inibitória das células OLMα2 sobre a via têmporo-
amônica (Leão et al., 2012). Esse trabalho também mostrou que as células OLMα2 inibem 
interneurônios localizados no stratum radiatum que modulam as entradas de CA3 pela 
via colateral de Schaffer, facilitando assim a conexão CA3-CA1 (Leão et al., 2012). Por 
último, foi verificado que as células OLMα2 se conectam entre si por sinapses elétricas e 
recebem projeções colinérgicas diretas (Leão et al., 2012). Um esquema da conectividade 
apresentada pelas OLM pode ser visto na Figura 14.    
  
 27 
 
 
Figura 13. Experimento de 
estimulação da via temporo-
amônica em três condições 
experimentais: (1) Fatias de 
hipocampo de animais Chrna2-
cre que expressam canais de 
sódio sensíveis à luz (ChR2) 
mas na ausência de luz 
(branco). (2) Como em (1) mas 
na presença de luz azul (preto). 
(3) Fatias de animais Chrna2-
cre que expressam ChR2 mas não expressam transportador vesicular de GABA nas células 
OLMα2 (vermelho). Nesse experimento, a via têmporo-amônica é estimulada e o registro ocorre 
no stratum lacunosum-moleculare. As fatias da condição 2 exibem menor LTP, mostrando que 
as células OLMα2 modulam sinapses do córtex entorrinal para a região CA1. 
 
Figura 14 Esquema de conexões entre 
as células OLMα2 (laranja) e 
neurônios piramidais (preto) da 
região CA1. Pode ser visto as 
conexões elétricas entre as células 
OLMα2, a conexão inibitória entre 
células OLMα2 e interneurônios do 
stratum radiatum, e conexão 
inibitória entre células OLMα2 e 
neurônio piramidal no stratum 
lacunosum-moleculare. Pode ser 
visto também o input oriundo de 
neurônios colinérgicos (verde) a 
células OLMα2 (Leão et al., 2012).   
 
A região CA1 do hipocampo está implicada em aprendizagem e consolidação de 
memórias (Tsien et al., 1996), bem como na codificação de informações do ambiente 
  
 28 
 
 
(Lenck-Santini et al., 2005), reconhecimento de objetos (Rampon et al., 2000), e 
condicionamento ao medo (Ji & Maren, 2008; Lee & Kesner, 2004). Levando em conta 
que (1) lesão da camada do córtex entorrinal que projeta para a região CA1 prejudica a 
localização espacial e a atividade das células de lugar (Brun et al., 2008; Van Cauter et 
al., 2008), e que (2) as células OLM atuam na organização temporal de oscilações na 
frequência de teta (Forro et al., 2015) relacionadas a processos cognitivos hipocampo-
dependentes (Tort et al., 2009), nós hipotetisamos que as células OLMα2 podem controlar 
a formação de memórias hipocampo-dependentes.  
Para testar nossa hipótese, o artigo 1 desta tese investigou a participação das 
células OLMα2 na modulação da codificação da memória de objetos explorados em um 
campo aberto, e também na formação de uma nova memória associativa de medo a um 
contexto. Utilizando camundongos Chrna2-cre e ferramentas optogenética, fomos 
capazes de modular especificamente a atividade das células OLMα2 nas tarefas de 
reconhecimento de objetos (NOR, do inglês novel object recognition) e numa tarefa de 
esquiva contextual passiva. Descobrimos que a ativação optogenética de células OLMα2 
pareada ao momento em que os animais exploram um objeto específico ("objeto 
iluminado") prejudica o seu reconhecimento subsequente, mas não afeta o 
reconhecimento de um segundo objeto que não teve pareamento com a ativação de células 
OLMα2 ("objeto não iluminado"). Por outro lado, a inibição de células OLMα2 aumenta 
seletivamente a memória de reconhecimento para o “objeto iluminado". A estimulação 
de células OLMα2 enquanto os animais não estavam engajados na exploração dos objetos 
não afetou a memória de reconhecimento. Finalmente, descobrimos que a ativação de 
células OLMα2 durante o treino na tarefa de esquiva contextual passiva também prejudica 
a aquisição desta memória associativa. Concluímos que as células OLMα2 possuem papel 
chave no controle da codificação das memórias hipocampo-dependentes, seja de objeto 
ou contextual. Provavelmente esse resultado é devido à capacidade das células OLMα2 
de filtrar informação multimodal transmitida do córtex entorrinal para o hipocampo. 
 
  
 29 
 
 
1.4 – Oscilações hipocampais e detecção de novidade (Artigo 2)  
Como dito na seção 1.2, o processo de aprendizagem, aquisição e consolidação de 
memórias leva a algumas assinaturas eletrofisiológicas (Berke et al., 2008; Hafting et al., 
2005; O’Keefe & Dostrovsky, 1971; Tort et al., 2009). O estudo de oscilações em 
potenciais de campo local (LFPs, do inglês local field potential) é uma abordagem 
importante para o entendimento do funcionamento do sistema nervoso central. A 
compreensão dos ritmos cerebrais pode elucidar diversas características fisiológicas, 
desde atividades sinápticas até os circuitos envolvidos na formação de memórias e na 
transferência de informações entre regiões do cérebro. Diversos estudos ao longo das 
últimas décadas visaram relacionar a atividade oscilatória neuronal com a expressão de 
comportamentos (Brankack et al., 1993; Buzsáki et al., 2012; Buzsáki & Draguhn, 2004; 
Buzsáki & Watson, 2012).  
As oscilações corticais resultam de atividades eletroquímicas das membranas 
excitáveis; há participação de neurônios excitatórios e inibitórios com diferentes pesos na 
formação das oscilações (Buzsáki et al., 2012). As oscilações podem ser detectadas em 
neurônios individuais (Kamondi et al., 1998), mas são comumente investigadas na escala 
mesoscópica do potencial de campo local (Buzsáki & Draguhn, 2004), que representa a 
atividade de um conjunto de neurônios (Figura 15). Entre as funções atribuídas à atividade 
oscilatória, estão a modulação da excitabilidade de neurônios, sincronia de atividade 
neuronal, transferência de informações entre áreas do cérebro e combinação de 
informações (Buzsáki & Draguhn, 2004).  
  
 30 
 
 
 
Figura 15 Exemplo de registros de atividade de neurônio individual e de populações neuronais 
(LFP) (Buzsáki et al., 2012). 
No hipocampo, muitos estudos têm centrado no papel das oscilações teta (5-12 
Hz; Buzsáki, 2002; Vanderwolf, 1969; Winson, 1978), oscilações gama (30-100 Hz; 
Colgin et al., 2009; Csicsvari et al., 2003; Montgomery & Buzsáki, 2007), oscilações de 
alta-frequência (110-160 Hz; Scheffer-Teixeira et al., 2013, 2012; Tort et al., 2013), e 
oscilações ripples (150-250 Hz; Buzsáki et al., 1992; Ego-Stengel & Wilson, 2010; 
Girardeau et al., 2009) em diferentes comportamentos e estados cognitivos (Figura 16). 
Estas oscilações neuronais também variam de acordo com o estágio do ciclo sono e vigília 
(Gervasoni et al., 2004).  
Entre resultados recentes que relacionaram oscilações neuronais com funções 
cerebrais, citamos a modulação de oscilações de altas frequências por oscilações de baixas 
frequências durante tomada de decisão (Tort et al., 2008); o aumento de acoplamento 
entre teta e gama durante uma tarefa de distinção de contextos (Tort et al., 2009); e o 
déficit de memória espacial em ratos através de supressão da atividade de ondas ripples 
do hipocampo (Ego-Stengel & Wilson, 2010; Girardeau et al., 2009).  
  
 31 
 
 
 
Figura 16. Exemplos de traçados de sinais brutos de potenciais de campo local do hipocampo. 
Cores dos traçados correspondem a faixa de frequência indicada na escala de cor abaixo, em 
preto o sinal de potencial de campo local filtrado nas frequências indicadas. 
De relevância para a presente tese, o trabalho de Berke e colaboradores (2008) 
mostrou uma associação entre o aumento de potência da oscilações beta2 (23 -30 Hz) e a 
exploração de ambientes novos. Esse trabalho mostrou que o beta2 aumenta de forma 
transiente no início da exploração de um ambiente novo, e que os neurônios da região 
CA1 e CA3 do hipocampo são acoplados a esta oscilação (Figura 17; Berke et al., 2008). 
Nesse trabalho é colocada a hipótese de que a oscilação beta2 constitui um estado 
funcional discreto do hipocampo que permite a plasticidade durante a aprendizagem de 
novos contextos (Berke et al., 2008). Beta2 seria dependente de transmissão de receptores 
NMDA (Berke et al., 2008), que por sua vez é envolvido com aprendizagem rápida 
(Nakazawa et al., 2003).  
  
 32 
 
 
 
Figura 17 Espectros de potência onde pode ser visto o aumento transitório de potência na faixa 
23-30 Hz em um ambiente novo, mas não em um familiar (Berke et al., 2008). 
De toda forma, se torna curioso o porquê de uma oscilação que é sugerida como 
componente do processo de aprendizagem de novos contextos não ter sido descrita 
anteriormente na literatura, principalmente levando-se em conta que o hipocampo é uma 
estrutura bastante estudada e que, segundo os achados de Berke e colaboradores (2008), 
a oscilações beta2 possuiriam magnitude similar às bem descritas oscilações teta.  
O artigo 2 desta tese investiga o aparecimento de oscilações beta2 na detecção de 
novidade. Através de registros com múltiplos eletrodos isolados no hipocampo, córtex 
motor primário e córtex somato-sensorial primário, caracterizamos as oscilações beta2 
em camundongos durante o teste de reconhecimento de objetos. Encontramos que beta2 
aparece de forma transiente durante a exploração de novos objetos. Verificamos também 
que esta oscilação não está presente no córtex motor primário nem no córtex somato-
sensorial, mas somente no hipocampo. Consistente com esse achado, somente neurônios 
hipocampais são modulados pela fase de beta2, mas não neurônios neocorticais. 
Verificamos também que uma intervenção farmacológica que prejudica a consolidação 
  
 33 
 
 
da memória de reconhecimento é capaz de modificar o padrão de aparecimento das 
oscilações beta2. Estes resultados dão suporte à hipótese de que as oscilações beta2 
participam no processo de detecção de novidade, provavelmente sinalizando períodos 
para a ocorrência de plasticidade na rede.    
  
  
 34 
 
 
2.0 – Artigo 1  
Submetido para Neuron como Report 
OLMα2 cells control encoding of recognition and avoidance memory  
Arthur S. C. França1#,, Samer Siwani2#, Amilcar Reis2, Sanja Mikulovic2, Markus 
M. Hilscher1,2, Steven J. Edwards2, Richardson N. Leão1,2, Adriano B. L. Tort1, 
Klas Kullander2*  
# The first two authors contributed equally to this work.  
Running title: OLMα2 cells control memory encoding  
1Brain Institute, Federal University of Rio Grande do Norte, Brazil.  
2Developmental Genetics, Department of Neuroscience, Uppsala University, 
Uppsala, Sweden.  
*Corresponding author:  
Klas Kullander: klas.kullander@neuro.uu.se  
 
Keywords: OLM cells; Object Recognition; Passive Inhibitory Avoidance; 
Memory.  
  
 35 
 
 
SUMMARY 
 
Learning and memory depend on several brain structures and a variety of 
cell types within these structures. That inhibitory interneurons participate 
in mnemonic processes is undisputed; however, defined roles for identified 
interneuron populations are still scarce. Oriens lacunosum-moleculare 
(OLM) interneurons are positioned to control the flow of information into 
CA1. In particular, a subpopulation of OLM cells genetically defined by the 
expression of the nicotinergic receptor α2 subunit has been shown to gate 
information carried by either the temperoammonic pathway or Schaffer 
collaterals in vitro. Here we set out to determine whether selective 
modulation of OLMα2 cell activity can affect learning and memory in vivo. 
Our data show that OLMα2 cells control encoding of object and contextual 
fear memories in freely moving mice, suggesting that OLMα2 cells are an 
important component in the CA1 microcircuit regulating learning and 
memory processes. 
 
  
  
 36 
 
 
INTRODUCTION 
The hippocampus has a variety of GABAergic interneurons that inhibit 
pyramidal cells in distinct subcellular domains (Freund and Buzsáki, 1996). While 
interneurons targeting perisomatic domains gate pyramidal cell output, dendritic-
targeting interneurons modulate pyramidal cell input (Klausberger and Somogyi, 
2008). Among the latter, oriens lacunosum-moleculare (OLM) cells have soma 
located in stratum oriens and send axonal projections to the distal dendrites of 
pyramidal cells in stratum lacunosum-moleculare, where inputs from the 
temporo-ammonic pathway arrive (Leão et al., 2012). OLM cells are thus 
positioned to control the entrance of multimodal information from the entorhinal 
cortex into the hippocampus.  
The hippocampal CA1 area is involved in the recognition of familiar objects 
(Basu et al., 2016; Rampon et al., 2000) as well as in contextual fear conditioning 
(Basu et al., 2016; Ji and Maren, 2008; Lee and Kesner, 2004), but the underlying 
neuronal circuitry remains largely unknown. In CA1, OLM cells that specifically 
express the nicotine acetylcholine receptor α2 subunit (encoded by the Chrna2 
gene) form a genetically defined population of neurons (Leão et al., 2012), 
referred to as OLMα2 cells. We have previously shown that OLMα2 cells control 
the flow of information into CA1, facilitating transmission from CA3 while inhibiting 
entorhinal cortex inputs (Leão et al., 2012; Mikulovic et al., 2015). Interestingly, 
OLMα2 cells receive direct cholinergic transmission (Leão et al., 2012), and 
acetylcholine has been implicated in learning and memory processes (Elvander 
et al., 2004; Tian et al., 2015). Further, the interconnectivity between the 
neocortex and hippocampus is essential to memory formation (Brun et al., 2008).  
Because multimodal information about objects and contexts reaches CA1 
through the temporoammonic pathway (Amaral and Witter, 1989), we 
hypothesized that OLMα2 cells could play a role in the encoding of new object 
representations and contextual fear memory. To test this hypothesis, we 
activated or silenced OLMα2 cells in freely moving Chrna2-cre mice during 
  
 37 
 
 
training in novel object recognition (NOR) and contextual passive avoidance 
tasks. 
 
RESULTS 
To study the role of OLMα2 cells in memory encoding, we crossbred Chrna2-cre 
with tdTomato reporter mice to visualize red fluorescent Chrna2-cre expressing 
cells in the hippocampus (Figure 1A). Corroborating our previous study (Leão et 
al., 2012), the tomato protein was specifically expressed in OLM cells, that is, 
interneurons with soma in stratum oriens and massive projections to stratum 
lacunosum-moleculare (Figure 1A and Video S1). We delivered the light-gated 
cation channel channelrhodopsin (ChR2) or the proton pump archeorhodopsin 
(Arch) via bilateral injection of double-floxed inverted open-reading-frame adeno-
associated viral vectors to the intermediate hippocampus (Figure 1B and D). 
Successfully infected OLMα2 cells also expressed the virally encoded yellow 
fluorescent protein (EYFP) (Figure 1A-D). We performed patch-clamp recordings 
to corroborate that OLMα2 cells became responsive to light. Rhythmic application 
of blue light at 8 Hz increased spiking in OLMα2 cells expressing ChR2 (Figure 
1C). Conversely, green light inhibited spiking in OLMα2 cells expressing Arch 
(Figure 1E). We also examined the expression of the immediate early gene c-Fos 
following light stimulation of ChR2-expressing OLMα2 cells in freely moving 
animals implanted with optical fibers (Figure 1F and G; see Experimental 
Procedures). The number of cells in the stratum oriens that became 
immunopositive for c-Fos after in vivo light stimulation in ChR2 animals (Chrnα2-
cre mice injected with ChR2 virus) was significantly increased compared to 
control mice (Chrnα2-cre mice injected with a sham virus; Figure 1G). These 
results show that optogenetic tools can control OLMα2 cell activity in freely 
moving animals. 
We next investigated the behavioral consequence of activating OLMα2 
cells in hippocampal-dependent memory tasks. The recognition of novel objects 
  
 38 
 
 
constitutes a test of episodic-like memory and is impaired in rodents with 
hippocampal lesions (Antunes and Biala, 2012; Blaser and Heyser, 2015). We 
used a novel object recognition task in which animals were allowed to explore 
two different objects during 10 minutes in two sessions, named training and test 
sessions. We first subjected mice to experimental protocols in which - in the 
training session - a computer coupled to a detection system delivered blue light 
to both hippocampi of control and ChR2 animals during exploration of one of the 
two objects (lit object) but not during exploration of the other object (non-lit; Video 
S2). In the test session performed 24 hours later, the mice were re-exposed to 
one familiar and one new object. In the protocol named “non-lit object replaced”, 
animals were exposed to the previously lit object and a new object in the test 
session. Control animals spent more time exploring the new object than ChR2 
animals, who explored the new and the previously lit object equally (Figure 2A, 
Tables S1 and S2), indicating impairment in object recognition. In a control 
experiment (“lit object replaced”), animals were exposed to the non-lit object and 
a new object, i.e. two objects not previously associated with OLMα2 cell 
modulation. In this protocol, both ChR2 and control animals spent more time 
exploring the new object, with no difference between groups (Figure 2B, Table 
S1 and S2). Thus, light-stimulation of OLMα2 cells during encoding selectively 
impaired encoding of memory for the lit object. 
Since the length of the intersession interval relates to performance in the 
object recognition test (Hammond et al., 2004), we next investigated if activating 
OLMα2 cells would also impair object recognition with an intersession interval of 
one hour. We found similar results as in the 24-hour protocols: ChR2 animals 
spent equal time with the new and previously lit object, but recognized the non-lit 
object (Figure 2C and D, Tables S1 and S2). We therefore used 1-hour 
intersession intervals in subsequent protocols.  
We next investigated the effect of inhibiting OLMα2 cells on object 
recognition. To that end, we delivered green light during training to mice whose 
OLMα2 cells expressed Arch (Arch animals). In the test session with the non-lit 
  
 39 
 
 
object replaced, both Arch and control animals exhibited preference for the new 
object (Table S2). Remarkably, Arch animals spent significantly more time 
exploring the new object than the previously lit object compared to control animals 
(Figure 2E, Tables S1 and S2). In contrast, replacing the lit object by a new object 
did not result in any significant differences between groups (Figure 2F, Tables S1 
and S2), showing that light-inhibition of OLMα2 cells selectively improved 
discrimination for the lit object. 
In a set of control experiments, we stimulated OLMα2 cells while ChR2 
animals were in the arena but disengaged from object exploration. This had no 
effect on the object preference in the test session (Figure 2E and Tables S1 and 
S2). Likewise, light stimulation of OLMα2 cells after the training session did not 
produce any measurable differences between ChrR2 animals and controls, with 
both groups showing the expected exploration preference for the novel object in 
the test session (Figure 2G and Tables S1 and S2). Interestingly, and consistent 
with our findings above, ChR2 but not control animals explored the lit object 
significantly more when both the lit and non-lit objects were kept in the test 
session (Figure 2I, Table S1 and S2), suggesting that ChR2 animals perceived 
the previously lit object as a new object. 
Previous studies have shown that CA1 is involved in contextual fear 
conditioning (Corcoran et al., 2005; Rudy and Matus-Amat, 2005), and that 
somatostatin positive interneurons play a role in fear learning (Lovett-Barron et 
al., 2014). We next investigated whether OLMα2 cells may also be involved in 
the encoding of contextual fear memories using a passive inhibitory avoidance 
task. We used an arena with a brightly lit and a dark compartment. During training 
sessions, the animals received a foot shock in the dark compartment in order to 
acquire an aversion to this normally preferred compartment in future exposures 
to the arena (test sessions). ChR2 animals that were light-stimulated during 
training (i.e., during foot shock delivery) displayed significantly lower latency to 
enter the dark compartment in the test sessions compared to control animals 
(Figure 3, Table S3 and Video S3). Moreover, ChR2 mice also showed 
  
 40 
 
 
significantly higher velocity compared to controls. In contrast, mice whose OLMα2 
cells expressed Arch and were light-inhibited during the training sessions showed 
no difference in latency or velocity compared to controls (Figure 3, Table S3). 
Thus, whereas inhibition of OLMα2 cells had no effect, activation of OLMα2 cells 
impaired contextual fear memory encoding. 
DISCUSSION 
Our results show that OLMα2 cells can control memory encoding, either 
object or contextual representations. Light-mediated activation of OLMα2 cells 
resulted in impaired learning when animals were tested for object as well as 
emotion-related memories. In contrast, inhibition resulted in improved object 
recognition but did not affect emotion-related memories. 
In this in vivo study, light was applied only when animals were interacting 
with the object, as automatically determined by a computer by snout direction and 
proximity to the object. This ensured objectivity in onset of light-induced activation 
or inhibition of OLMα2 cells. In addition, we have focused on behaviors that were 
readily quantifiable and objectively measured by computer software. Post-
experiment analysis confirmed the proper location of viral injections and insertion 
of the light probe into the intermediate hippocampus. We used stimulation 
frequencies matching those previously measured in vivo in OLMα2 cells (Forro 
et al., 2015; Mikulovic et al, submitted), to as far as possible mimic endogenous 
activity. By using the clarity method (Chung and Deisseroth, 2013), we estimated 
the number of OLMα2 cells reached on each side to 338 out of a total of 3124 in 
the cleared hippocampus, based on our measured light power at ~1.5 mW/mm2 
at 1 mm, which should suffice to induce activity (Video S1; Histed and Maunsell, 
2014; Niyuhire et al., 2007). Corroborating this, we observed robust changes in 
behavior associated to light stimulation. 
In CA1, the intrahippocampal input from CA3 projects on proximal apical 
dendrites of pyramidal cells whereas the direct external input from the entorhinal 
  
 41 
 
 
cortex contacts the distal apical tuft dendrites (Kajiwara et al., 2008; Takahashi 
and Magee, 2009). The intrahippocampal and indirect pathway is primarily 
responsible for pattern separation and completion and association of diverse sets 
of information, whereas the direct pathway seems more important for recognizing 
the novelty of an event or context (Andersen et al., 2006; Lisman and Otmakhova, 
2001). Part of the multimodal information necessary for object and context 
recognition is directly related to the EC-CA1 connections through the 
temporoammonic pathway (Brun et al., 2008; Daumas et al., 2005; van Groen et 
al., 2003). Consistent with this, our findings suggest that when CA1 OLMα2 cells 
are activated and suppress distal direct inputs, learning is severely attenuated 
due to blockade of information mediated by the direct inputs from the entorhinal 
cortex. In further support, in the reverse situation, when OLMα2 cells were 
inactivated during learning, object recognition was improved. This result suggests 
that OLMα2 cells can potentially be used as a target for improving learning. Of 
note, dysfunctional OLM cell activity has been recently associated to memory 
deficits in an animal model of Alzheimer’s disease (Schmid et al., 2016). 
We found that OLMα2 cell activation also affected contextual fear learning. 
In the passive inhibitory avoidance test, mice which received OLMα2 cell 
activation concomitant with a foot shock maintained similar latency to enter in the 
dark compartment (shock associated) as in the training day, while control animals 
increased the latency showing normal encoding for the contextual fear memory. 
Indeed, rodents depend on the integrity of the hippocampus to produce 
contextual fear memories (Rudy and Matus-Amat, 2005; Wiltgen et al., 2006). In 
contrast, however, OLMα2 cell inactivation did not affect context fear memories. 
This is seemingly at odds with a previous study, which demonstrated that the 
inactivation of somatostatin positive interneurons in the oriens layer prevented 
fear learning (Lovett-Barron et al., 2014). This may be explained by the use of 
different genetic mouse lines (Mikulovic et al., 2015), genetic tools or stimulation 
protocols (Shapiro et al., 2012). In particular, it has been reported that the 
dynamic characteristics of the Cl- channel NphR3 can lead to a rebound action 
  
 42 
 
 
potential after release of light (Madisen et al., 2012; Raimondo et al., 2012). This 
could in principle generate activation instead of the intended inactivation. In 
addition, the areas of stimulation differed between the two studies (dorsal and 
intermediate hippocampus). Recent studies have reported on functional 
subdivisions of the hippocampus along both the transverse and dorsoventral axis 
(Cembrowski et al., 2016; Ito and Schuman, 2012). Importantly, nicotine has 
opposite effects in the dorsal and ventral hippocampus: while nicotine 
administered in the dorsal hippocampus impairs contextual fear memory, when 
administered in the ventral hippocampus nicotine enhances it (Kenney et al., 
2012). Since nicotine activates OLMα2 cells (Leão et al., 2012), this could 
indicate why we get similar results as Lovett-Barron through stimulation of 
OLMα2 cells in the intermediate hippocampus as was found by them through 
inhibition of somatostatin positive cells in the dorsal hippocampus. In this respect, 
it is also noteworthy that the number of OLMα2 cells displays a graded 
dorsoventral distribution, with a higher number of cells present ventrally 
(unpublished observations). 
In conclusion, this study provides direct evidence for a role of OLMα2 cells 
in memory formation processes, likely through gating of sensory information 
transmitted from the entorhinal cortex to hippocampus. 
  
  
 43 
 
 
 
EXPERIMENTAL PROCEDURES  
Animals. We used heterozygote Chrnα2-cre transgenic mice bred with 
homozygote tdTomato reporter mice as described in our previous study3. Males 
and females were used as isolated groups and all animals were kept on a 12-
hour light/dark cycle with food and water ad libitum. All procedures were approved 
by Uppsala Animal Ethics Committee, Jordbruksverket (C135/14, C132/13). 
 
Surgical procedures. We delivered channelrhodopsin (ChR2) or 
archeorhodopsin (Arch) using adeno-associated viral vectors (rAAV2/9.EF1a-
DIO-hChR2(H134R)-EYFP.WPRE.hGH, rAAV2/EF1a-DIO-hChR2(H134R)-
EYFP, rAAV9/Flex-ArchT-GFP and rAAV2/EF1a—DIO-eArch3.0-eYFP). Virus 
were bilaterally delivered using a nanofil syringe (World Precision Instruments) 
and a stereotaxic frame at the following coordinates: -3 mm rostrocaudal; -3 and 
3 mm mediolateral; -2.7 and -3.6 mm dorsoventral. After virus injection, optical 
fibers (200 µm diameter) were implanted at the same coordinates at a depth of -
2.7 mm and fixed with dental cement. Before the behavior experiments, animals 
were given three weeks for recovery and viral vector driven expression. 
 
c-Fos analysis. Animals were anesthetized by isoflurane inhalation and 
thereafter deeply anesthetized with injections of a Ketalar/Domitor mix 
(Pfizer/OrionPharma, Sollentuna, Sweden). The mice were then perfused 
transcardially with phosphate buffered saline pH 7.4 (PBS) followed by 4% 
formaldehyde. Brains were removed and placed in 4% formaldehyde at 4°C 
overnight. Brains were washed 3x in PBS for 10 min, thereafter placed in a 
solution of 25% sucrose and allowed to settle overnight.  Following this, brains 
were removed from the sucrose solution and stored at -80°C. For cryostat 
sections, brains were mounted in Tissue-tech™ (Miami, FL, USA) and placed on 
dry ice, then cut in coronal sections of 35 μm, mounted on glass slides and stored 
  
 44 
 
 
at -80°C until use. Brain sections on glass slides were thawed at room 
temperature for 45 min. Slides were washed 4x in PBS for 10 min and blocked 
for 1 hr with blocking solution (2% donkey serum, 1% BSA, 0.1% Triton X-100, 
0.05% Tween 20, 0.01M PBS) (1:10). Primary antibody c-Fos anti-goat (Santa 
Cruz, California, USA) was diluted (1:150) in Supermix (200 ml TBS, 0.5 g gelatin, 
1 ml Triton X-100, heat up to 60 ° C until gelatin dissolves) and slides were 
incubated for 72 hours at 4°C. During the last 24 hours, primary antibody GFP 
anti-rabbit (Abcam, Cambridge, UK) diluted in Supermix (1:1000) was added to 
the slides. Slides were washed 4x in TBS for 10 min. Secondary antibodies 
(Thermofisher Scientific, Massachusetts, USA) Alexa fluor 647 donkey anti-goat, 
Alexa fluor 488 donkey anti-rabbit (1:300) and nuclear marker DAPI (1:100) were 
diluted in Supermix, added to the slides and left to incubate for 1.5 hours at room 
temperature. Finally, slides were washed 4x in TBST (0.01% tween) for 10 min 
and mounted with mowiol. Images were acquired on a Zeiss LSM 510 Meta 
confocal microscope, using 10x and 20x objectives and laser excitation within 
suitable wavelengths, and thereafter processed with Photoshop CS3 (Adobe) to 
create merged panoramic images and adjust input levels and color pallet for 
uniformity across images. Number of c-Fos positive cells was counted along the 
contour of the SO of multiple biological samples (see Figure1f) using ImageJ 
(NIH). Total number of c-Fos positive for control and ChR2 expressing mice was 
plot as mean ± s.e.m. and statistical analysis performed applying Student’s 
t-test. 
 
Electrophysiology. Horizontal hippocampal slices (300 µm) were 
obtained from Chrnα2-cre/R26tdTomato mice carrying hChR2 or Arch. Slices 
were cut using a vibratome (VT1200, Leica, Microsystems). The slices were 
transferred to a submerged chamber and maintained in artificial cerebrospinal 
fluid (aCSF: 126 mM NaCl, 2.5 mM KCl, 1.25 mM NaH2PO4, 2 mM MgCl2, 2 mM 
CaCl2, 26 mM NaHCO3 and 10 mM glucose), constantly bubbled with 95% O2 
and 5% CO2. Patch pipettes from borosilicate glass capillaries (GC150F-10 
  
 45 
 
 
Harvard Apparatus) were pulled on a vertical puller (Narishige, Japan) with 
resistance around 7 MΩ and filled with internal solution containing: 130 mM K-
gluconate, 7 mM NaCl, 0.3 mM MgCl2, 2 mM ATP, 0.5 mM GTP, 10 mM HEPES 
and 0.1 mM EGTA (pH was adjusted to 7.2 using KOH). Current-clamp 
recordings were obtained using a Multiclamp 700B (Molecular Devices) amplifier. 
Data was acquired by a National Instruments DAQ card and WinWCP/WinEDR 
softwares implemented by Dr. J. Dempster (University of Strathclyde, Glasgow, 
UK). 8-Hz sinusoid function (varying from 0 to 4 mW at the tip of the fiber) drove 
a 473-nm laser (Shanghai Dream Lasers analog modulated) to activate ChR2 
expressing cells. Square light pulses (555-nm laser, Shanghai Dream Lasers) 
were used to inhibit Arch expressing cells. 
  
 46 
 
 
Habituation 
Before the behavioral tasks, the animals were handled by the 
experimenter in 5-minute sessions inside the experimental room for three 
consecutive days. During these sessions, they were also habituated to mounting 
of the optic fiber. 
 
Novel object recognition (NOR) 
To assess the encoding of object memory, we used five different protocols 
of the NOR task (listed below). We placed two objects of similar size in a round 
arena (46 cm diameter; Supplementary Video 2). During the training session, 
animals were allowed to explore the arena with the objects for 10 min. The test 
session was performed either 1 hour (Figure 2) or 24 hours (Supplementary 
Figure 1) apart. In the test session, we replaced one of the objects with a novel 
one (except in Figure 2I) and once again allowed mice to explore the arena for 
10 min. The preference ratio was defined as the time exploring the novel object 
divided by time exploring the familiar object (except in Figure 2I, where it was 
defined as the time exploring the lit object divided by the time exploring the non-
lit object).  
Mice were subjected to the following protocols: (1) Non-lit object replaced: 
during the training session, light was delivered for a minimum of 1 second when 
the animal approached one of the objects (lit object), but not the other (non-lit 
object); light delivery was only turned off when the animal stopped exploring the 
object (Supplementary Video 2). In the test session, the non-lit object was 
replaced by a new object. (2) Lit object replaced: the training session was 
performed as described above, but in the test session the lit object was replaced 
by a new object. (3) Arena stimulation: during the training session light was 
delivered when animals were not engaged in object exploration. During the test 
session, one of the objects was replaced by a new object. (4) Stimuli after 
training: no light was delivered during the training session; immediately after 
  
 47 
 
 
training, light was delivered in windows of 10 seconds on and off for 10 minutes. 
In the test session one of the objects was replaced by a new object. (5) Lit and 
non-lit objects maintained: the training session was performed as in protocol 1. 
In the test session, no object was replaced.  
During the training session, light was delivered bilaterally at theta 
frequency (8 Hz) with an intensity of ~4 mW. Light delivery was automated using 
a national instruments board (USB-6351) controlled by Ethovision software (XT, 
versions 9-11) through a custom designed Labview software. The same animal 
was not used in more than two different protocols.  
 
Passive inhibitory avoidance 
 We assessed contextual fear conditioning using the passive inhibitory 
avoidance task. The apparatus consisted of two chambers without roof: one light 
chamber with white walls (~3200 lux) and one dark chamber with black walls 
(closed door ~4 lux, open door ~9 lux). The light chamber had two 3-W lamps 
illuminating the chamber. During training, the animal was placed in the light 
chamber for 30 seconds. Next, the door connecting the two chambers was 
opened (see Supplementary Video 3). When the animal entered the dark 
chamber, the door was closed and a shock was delivered after 5 seconds (1 mA, 
100 ms). The animal was brought to the home cage for two minutes; afterwards, 
the animal was subjected to another trial in the same arena (same shock 
amplitude and duration). The maximum latency to enter the dark chamber was 
90 seconds for the first trial and 270 seconds for the second trial. Animals that 
did not exit the light chamber in the training trials were excluded from further 
analysis. For the data analysis, the latency time to enter in the dark chamber was 
averaged for the two trials in the training session (day 0). During the training 
session, light was delivered bilaterally at theta frequency (8 Hz) with an intensity 
of ~4 mW in periods of 1 second on and 1 second off for the whole duration of 
the task. We performed three test sessions separated by 24 hours (day 1, day 2 
and day 3). In the test session, no shock was delivered and animals were allowed 
  
 48 
 
 
a maximum latency of 270 seconds to enter the dark chamber. Animals that did 
not exit the light chamber after 270 seconds were placed in the dark chamber for 
~10 seconds before being returned to their home cage, so that all mice were 
exposed to the pairing of CS - no US before being retested in a subsequent day. 
This procedure was implemented to minimize differences in memory extinction 
within groups (Myers and Davis, 2006; Niyuhire et al., 2007; Vianna et al., 2001). 
 
Excluded animals 
 In the NOR task, we excluded animals that had the optical fiber unplugged 
during the training session (n = 5). We also excluded one animal in the passive 
inhibitory avoidance task that managed to escape the chamber by jumping out of 
the arena. 
  
Statistical analyses 
 Tracking data were extracted and scored from video recordings using the 
Ethovision software (XT, versions 9-11). The normality distribution test (Shapiro-
Wilk normality test) was applied to each dataset. We performed t-tests to 
compare data with Gaussian distribution, otherwise the Mann-Whitney test was 
used. Data are presented as mean and standard error of the mean (s.e.m.). For 
the passive inhibitory avoidance task, we performed two-way repeated measures 
ANOVA using animal group and day as independent variables. Numerical details 
of all statistical analyses are provided in Tables S1 and S2. 
 
AUTHOR CONTRIBUTIONS 
 
França, ASC. and Siwani, S. designed and performed the behavior and 
optogenetics experiments; Reis, A, Mikulovic, S, Hilscher, MM, Edwards, SJ, 
Leao, RN performed the c-fos, clarity and ephys experiments; Tort, A and 
Kullander, K conceived the study, wrote and edited the final version of the 
manuscript. 
  
 49 
 
 
 
REFERENCES 
Amaral, D.G., Witter, M.P., 1989. The three-dimensional organization of the 
hippocampal formation: a review of anatomical data. Neuroscience 31, 
571–591. 
Andersen, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J., 2006. The 
Hippocampus Book. Oxford University Press. 
Antunes, M., Biala, G., 2012. The novel object recognition memory: neurobiology, 
test procedure, and its modifications. Cogn. Process. 13, 93–110.  
Basu, J., Zaremba, J.D., Cheung, S.K., Hitti, F.L., Zemelman, B.V., Losonczy, A., 
Siegelbaum, S.A., 2016. Gating of hippocampal activity, plasticity, and 
memory by entorhinal cortex long-range inhibition. Science 351, aaa5694.  
Blaser, R., Heyser, C., 2015. Spontaneous object recognition: a promising 
approach to the comparative study of memory. Front. Behav. Neurosci. 9, 
183.  
Bria, A., Iannello, G., 2012. TeraStitcher - A tool for fast automatic 3D-stitching of 
teravoxel-sized microscopy images. BMC Bioinformatics 13, 316.  
Brun, V.H., Leutgeb, S., Wu, H.-Q., Schwarcz, R., Witter, M.P., Moser, E.I., 
Moser, M.-B., 2008. Impaired Spatial Representation in CA1 after Lesion 
of Direct Input from Entorhinal Cortex. Neuron 57, 290–302.  
Cembrowski, M.S., Bachman, J.L., Wang, L., Sugino, K., Shields, B.C., Spruston, 
N., 2016. Spatial Gene-Expression Gradients Underlie Prominent 
Heterogeneity of CA1 Pyramidal Neurons. Neuron 89, 351–368.  
Chung, K., Deisseroth, K., 2013. CLARITY for mapping the nervous system. Nat. 
Methods 10, 508–513.  
Corcoran, K.A., Desmond, T.J., Frey, K.A., Maren, S., 2005. Hippocampal 
Inactivation Disrupts the Acquisition and Contextual Encoding of Fear 
Extinction. J. Neurosci. 25, 8978–8987.  
Daumas, S., Halley, H., Francés, B., Lassalle, J.-M., 2005. Encoding, 
consolidation, and retrieval of contextual memory: Differential involvement 
of dorsal CA3 and CA1 hippocampal subregions. Learn. Mem. 12, 375–
382.  
Elvander, E., Schött, P.A., Sandin, J., Bjelke, B., Kehr, J., Yoshitake, T., Ögren, 
S.O., 2004. Intraseptal muscarinic ligands and galanin: influence on 
hippocampal acetylcholine and cognition. Neuroscience 126, 541–557.  
Ennaceur, A., 2010. One-trial object recognition in rats and mice: Methodological 
and theoretical issues. Behav. Brain Res., Special issue on episodic 
memory 215, 244–254.  
  
 50 
 
 
Ennaceur, A., Delacour, J., 1988. A new one-trial test for neurobiological studies 
of memory in rats. 1: Behavioral data. Behav. Brain Res. 31, 47–59. 
Forro, T., Valenti, O., Lasztoczi, B., Klausberger, T., 2015. Temporal organization 
of GABAergic interneurons in the intermediate CA1 hippocampus during 
network oscillations. Cereb. Cortex N. Y. N 1991 25, 1228–1240.  
Freund, T. f., Buzsáki, G., 1996. Interneurons of the hippocampus. Hippocampus 
6, 347–470.  
Hammond, R.S., Tull, L.E., Stackman, R.W., 2004. On the delay-dependent 
involvement of the hippocampus in object recognition memory. Neurobiol. 
Learn. Mem. 82, 26–34.  
Histed, M.H., Maunsell, J.H.R., 2014. Cortical neural populations can guide 
behavior by integrating inputs linearly, independent of synchrony. Proc. 
Natl. Acad. Sci. 111, E178–E187.  
Ito, H.T., Schuman, E.M., 2012. Functional division of hippocampal area CA1 via 
modulatory gating of entorhinal cortical inputs. Hippocampus 22, 372–387.  
Ji, J., Maren, S., 2008. Differential roles for hippocampal areas CA1 and CA3 in 
the contextual encoding and retrieval of extinguished fear. Learn. Mem. 
15, 244–251.  
Kajiwara, R., Wouterlood, F.G., Sah, A., Boekel, A.J., Baks-te Bulte, L.T.G., 
Witter, M.P., 2008. Convergence of entorhinal and CA3 inputs onto 
pyramidal neurons and interneurons in hippocampal area CA1—An 
anatomical study in the rat. Hippocampus 18, 266–280.  
Kenney, J.W., Raybuck, J.D., Gould, T.J., 2012. Nicotinic receptors in the dorsal 
and ventral hippocampus differentially modulate contextual fear 
conditioning. Hippocampus 22, 1681–1690.  
Klausberger, T., Somogyi, P., 2008. Neuronal Diversity and Temporal Dynamics: 
The Unity of Hippocampal Circuit Operations. Science 321, 53–57.  
Leão, R.N., Mikulovic, S., Leão, K.E., Munguba, H., Gezelius, H., Enjin, A., Patra, 
K., Eriksson, A., Loew, L.M., Tort, A.B.L., Kullander, K., 2012. OLM 
interneurons differentially modulate CA3 and entorhinal inputs to 
hippocampal CA1 neurons. Nat. Neurosci. 15, 1524–1530.  
Lee, I., Kesner, R.P., 2004. Differential contributions of dorsal hippocampal 
subregions to memory acquisition and retrieval in contextual fear-
conditioning. Hippocampus 14, 301–310.  
Lisman, J.E., Otmakhova, N.A., 2001. Storage, recall, and novelty detection of 
sequences by the hippocampus: elaborating on the SOCRATIC model to 
account for normal and aberrant effects of dopamine. Hippocampus 11, 
551–568.  
Lovett-Barron, M., Kaifosh, P., Kheirbek, M.A., Danielson, N., Zaremba, J.D., 
Reardon, T.R., Turi, G.F., Hen, R., Zemelman, B.V., Losonczy, A., 2014. 
  
 51 
 
 
Dendritic Inhibition in the Hippocampus Supports Fear Learning. Science 
343, 857–863.  
Madisen, L., Mao, T., Koch, H., Zhuo, J., Berenyi, A., Fujisawa, S., Hsu, Y.-W.A., 
Garcia, A.J., Gu, X., Zanella, S., Kidney, J., Gu, H., Mao, Y., Hooks, B.M., 
Boyden, E.S., Buzsáki, G., Ramirez, J.M., Jones, A.R., Svoboda, K., Han, 
X., Turner, E.E., Zeng, H., 2012. A toolbox of Cre-dependent optogenetic 
transgenic mice for light-induced activation and silencing. Nat. Neurosci. 
15, 793–802.  
Mikulovic, S., Restrepo, C.E., Hilscher, M.M., Kullander, K., Leão, R.N., 2015. 
Novel markers for OLM interneurons in the hippocampus. Front. Cell. 
Neurosci. 9, 201.  
Myers, K.M., Davis, M., 2006. Mechanisms of fear extinction. Mol. Psychiatry 12, 
120–150.  
Niyuhire, F., Varvel, S.A., Thorpe, A.J., Stokes, R.J., Wiley, J.L., Lichtman, A.H., 
2007. The disruptive effects of the CB1 receptor antagonist rimonabant on 
extinction learning in mice are task-specific. Psychopharmacology (Berl.) 
191, 223–231.  
Raimondo, J.V., Kay, L., Ellender, T.J., Akerman, C.J., 2012. Optogenetic 
silencing strategies differ in their effects on inhibitory synaptic 
transmission. Nat. Neurosci. 15, 1102–1104.  
Rampon, C., Tang, Y.P., Goodhouse, J., Shimizu, E., Kyin, M., Tsien, J.Z., 2000. 
Enrichment induces structural changes and recovery from nonspatial 
memory deficits in CA1 NMDAR1-knockout mice. Nat. Neurosci. 3, 238–
244.  
Rudy, J.W., Matus-Amat, P., 2005. The Ventral Hippocampus Supports a 
Memory Representation of Context and Contextual Fear Conditioning: 
Implications for a Unitary Function of the Hippocampus. Behav. Neurosci. 
119, 154–163.  
Schmid, L.C., Mittag, M., Poll, S., Steffen, J., Wagner, J., Geis, H.-R., Schwarz, 
I., Schmidt, B., Schwarz, M.K., Remy, S., Fuhrmann, M., 2016. 
Dysfunction of Somatostatin-Positive Interneurons Associated with 
Memory Deficits in an Alzheimer’s Disease Model. Neuron 92, 114–125.  
Shapiro, M.G., Frazier, S.J., Lester, H.A., 2012. Unparalleled Control of Neural 
Activity Using Orthogonal Pharmacogenetics. ACS Chem. Neurosci. 3, 
619–629.  
Takahashi, H., Magee, J.C., 2009. Pathway Interactions and Synaptic Plasticity 
in the Dendritic Tuft Regions of CA1 Pyramidal Neurons. Neuron 62, 102–
111.  
Tian, S., Pan, S., You, Y., 2015. Nicotine enhances the reconsolidation of novel 
object recognition memory in rats. Pharmacol. Biochem. Behav. 129, 14–
18.  
  
 52 
 
 
van Groen, T., Miettinen, P., Kadish, I., 2003. The entorhinal cortex of the mouse: 
Organization of the projection to the hippocampal formation. Hippocampus 
13, 133–149.  
Vianna, M.R.M., Szapiro, G., McGaugh, J.L., Medina, J.H., Izquierdo, I., 2001. 
Retrieval of memory for fear-motivated training initiates extinction requiring 
protein synthesis in the rat hippocampus. Proc. Natl. Acad. Sci. 98, 12251–
12254.  
Wiltgen, B.J., Sanders, M.J., Anagnostaras, S.G., Sage, J.R., Fanselow, M.S., 
2006. Context Fear Learning in the Absence of the Hippocampus. J. 
Neurosci. 26, 5484–5491.  
 
  
  
 53 
 
 
 
Figure 1. Optical control of OLMα2 cell activity. (A) Left, Coronal 
section of the hippocampus of a Chrnα2-cre/R26tdTomato mouse. Dapi (blue), 
Tomato (red) and ChR2 (green). Right panels show each marker separately. 
Notice co-expression of Tomato and ChR2 in SO and SLM. (B) Position of the 
optical fiber tip. (C) Patch clamp recordings of OLMα2 cells in hippocampal slices. 
8-Hz blue light induced spiking in OLMα2 cells expressing ChR2. (D) Expression 
of Arch in SO and SLM. (E) Green light inhibited current-induced spiking in 
OLMα2 cells expressing Arch. (F) c-Fos expression assessed after in vivo 
delivery of blue light in ChR2 mice. Arrows in the inset point to cells co-expressing 
ChR2 and c-Fos. (G) Number of c-Fos positive cells in SO after in vivo light 
stimulation for control and ChR2 groups (horizontal traces show mean ± s.e.m.). 
***p<0.001, t-test. Scalebars, 100 μm.  
 
  
  
 54 
 
 
 
Figure 2. OLMα2 cells control object memory encoding. (A-F) 
Schematics show experimental protocol, in which blue (A-D) or green (E-F) light 
was selectively delivered during exploration of one of the objects (Lit) in the 
training session. In A, C and E, the lit object was kept in the test session while 
the non-lit object was replaced by a new one. In B, D and F, the lit object was 
replaced and the non-lit object was kept. ChR2 animals proportionally explored 
the lit object more than controls (A,C), however, when the lit object was replaced, 
there was no significant difference between groups (B,D). Conversely, Arch 
animals proportionally explored the lit object less than controls (E), with no 
difference between groups when the lit object was replaced (F). (G-H) Light 
stimulation while animals were not engaged in object exploration (G), or after the 
training session (H), led to no difference in object exploration compared to 
controls. (I)  When both objects (lit and non-lit) were kept in the test session, 
  
 55 
 
 
ChR2 mice explored the lit object more, while control animals exhibited no object 
preference. Heat maps depict average spatial occupancy during the test session; 
data points show individual animals. The preference ratios were computed for the 
new object during the test session (except in G, where preference is calculated 
for the lit object). *p<0.05, t-test. 
 
  
  
 56 
 
 
 
Figure 3. OLMα2 cell activity impairs contextual fear memory. (A) 
Task scheme depicting training and test sessions. (B-E) Latency to enter in the 
dark compartment (B,D) and velocity in the light compartment (C,E) during a 
passive avoidance task (mean ± s.e.m.). ChR2 mice, but not Arch mice, exhibited 
significantly lower latency and higher velocity than controls. *p<0.05 between 
groups, two-way repeated measures ANOVA. 
 
 
  
  
 57 
 
 
SUPPLEMENTARY INFORMATION 
SUPPLEMENTARY VIDEO LEGENDS 
 
Supplementary Video 1. Video of cleared tissue showing distribution of 
Chrnα2+ cells in the left hippocampus of a Chrnα2-cre/R26tdTomato mouse. 
Note the labeling of cell bodies in the stratum oriens and the massive projections 
in the stratum lacunosum-moleculare, which characterize OLMα2 cells. The video 
also shows the location of the optic fiber tip and an estimation of the light spread 
(green sphere). 
 
Supplementary Video 2. Example of object exploration during a training 
session of the NOR task. One of the objects is paired with light delivery (lit object), 
whereas the other object is not (non-lit object).  
 
Supplementary Video 3. Example of the passive inhibitory avoidance 
task and the behavior of two animals (control and ChR2) during training and test 
sessions. Note that the control animal enters the dark chamber during the training 
session, but not during the test session, while the ChR2 animal enters the dark 
chamber during both training and test sessions.  
  
  
 58 
 
 
SUPPLEMENTARY EXPERIMENTAL PROCEDURES  
 
Tissue clearing and imaging. Dissected hippocampi were cleared using 
a CLARITY protocol (Chung and Deisseroth, 2013). Chrnα2-cre/R26tdTomato 
animals were first perfused and the brain fixed in 4% paraformaldehyde solution 
for 24 hours. Tissues were post-fixed in 10 mL hydrogel solution (Acrylamide 4%, 
VA-044 initiator 0,25%, 1X PBS, 4% PFA, dH2O) and stored at 4°C for three 
days. Samples were degased in a desiccation chamber for 10 min and air in the 
chamber was replaced with nitrogen, followed by polymerization at 37°C shaking 
for three hours. Next, samples were washed and cleared in clearing solution (200 
mM Boric acid, 4% Sodium Dodecyl Sulfate, dH2O, NaOH; pH 8.5) at 37°C for 
two weeks and 45°C for another week. Clearing solution was replaced every third 
day. Cleared samples were washed twice in 1xPBST (0.1% Triton X-100) for 24 
hours. The tissue was refractive index matched through 3x24h incubation in 20%, 
40% and 63% 2,2’-Thiodiethanol (TDE, Sigma-Aldrich) in 1xPBS solution. 
Images were acquired on a Zeiss Light Sheet Z.1 (5x/0.16 objective) and image 
tiles were aligned and stitched using Tetrastitcher (Bria and Iannello, 2012). 
Imaris 8.1 (Bitplane) was used for volume rendering and soma detection. The 
optical sphere in Video S3 is an estimation of blue light spread made through 
theoretical calculations (Histed and Maunsell, 2014). Light power was ~4 mW at 
the tip of the fiber, and considering a wavelength of 473 nm, a core radius of 0.1 
and a numerical aperture (NA) of 0.22, the light power is ~1.5 mW/mm2 at 1 mm 
from the tip (Histed and Maunsell, 2014), which suffices to induce spiking 
activity(Niyuhire et al., 2007). 
 
 
 
 
 
  
 59 
 
 
Supplementary table 1 
NOR - ChR2 Protocols 
Non-Lit Object Replaced 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 1.57 ± 0.25, n=8 
t(13)=2.24, p=0.043 
ChR2: 0.89 ± 0.15, n=7 
1h 
 
% of 
Exploration 
Control: 58.5% ± 3.80, n=8 
t(13)=2.30, p=0.038 
ChR2: 48.32% ± 3.81, n=7 
24h 
 
Preference 
Ratio 
Control: 1.70 ± 0.20, n=9 
t(15)=2.46, p=0.026 
ChR2: 1.01 ± 0.14, n=8 
24h 
 
% of 
Exploration 
Control: 60.9% ± 3.10, n=9 
t(15)=2.57, p=0.021 
ChR2: 48.32% ± 3.81, n=8 
Lit Object Replaced 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 2.54 ± 0.63, n=7 
t(13)=0.93, p=0.36 
ChR2: 1.92 ± 0.27, n=8 
1h 
 
% of 
Exploration 
Control: 66.5% ± 5.20, n=7 
t(13)=0.46, p=0.65 
ChR2: 63.69% ± 3.36, n=8 
24h 
 
Preference 
Ratio 
Control: 1.38 ± 0.15, n=12 
t(21)=1.02, p=0.31 
ChR2: 1.61 ± 0.16, n=11 
24h 
 
% of 
Exploration 
Control: 54.01 ± 3.29, n=12 
t(21)=1.35, p=0.18 
ChR2: 59.96 ± 2.82, n=11 
Arena Stimulation 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 1.60 ± 0.32, n=9 
t(14)=0.56, p=0.58 
ChR2: 1.86 ± 0.31, n=7 
1h 
 
% of 
Exploration 
Control: 57.25 ± 4.47, n=9 
t(14)=0.79, p=0.43 
ChR2: 62.39 ± 4.48, n=7 
Stimulation After Training 
  
 60 
 
 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
24h 
 
Preference 
Ratio 
Control: 1.54 ± 0.21, n=7 
t(15)=0.37, p=0.71 
ChR2: 1.65 ± 0.21, n=10 
24h 
 
% of 
Exploration 
Control: 58.51 ± 4.42, n=7 
U=31, p=0.73 
ChR2: 57.37 ± 4.00, n=10 
Lit and Non-Lit Objects Maintained 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 0.90 ± 0.25, n=7 
t(14)=2.51, p=0.024 
ChR2: 1.84 ± 0.26, n=9 
1h 
 
% of 
Exploration 
Control: 42.45 ± 6.41, n=7 
t(14)=3.03, p=0.008 
ChR2: 62.55 ± 3.08, n=9 
NOR - Arch Protocols 
Non-Lit Object Replaced 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 1.91 ± 0.35, n=12 
t(18)=2.40, p=0.026 
Arch: 3.26 ± 0.43, n=8 
1h 
 
% of 
Exploration 
Control: 59.28 ± 5.19, n=12 
t(18)=2.29, p=0.03 
Arch: 74.75 ± 2.61, n=8 
Lit Object Replaced 
Intersession 
Interval 
Metric Mean ± SEM, n Statistics 
1h 
 
Preference 
Ratio 
Control: 1.86 ± 0.27, n=11 
t(17)=1.28, p=0.21 
Arch: 1.36 ± 0.23, n=8 
1h 
 
% of 
Exploration 
Control: 60.63 ± 4.85, n=11 
t(17)=0.90, p=0.37 
Arch: 53.76 ± 5.92, n=8 
% of Exploration and Preference Ratio were computed in the test 
session relative to the new object, except in Figure 2g protocol, where the 
metrics refer to the lit object. 
 
 
  
 61 
 
 
Supplementary Table 2 
NOR - ChR2 Protocols 
Non-Lit Object Replaced 
Intersession 
Interval 
Mean ± SEM, n Paired t test (against 1) 
1h 
 
Control: 1.57 ± 0.25, n=8 t(7)=2.25, p=0.05 
ChR2: 0.89 ± 0.15, n=7 t(6)=0.76, p=0.47 
24h 
 
Control: 1.70 ± 0.23, n=9 t(8)=3.04, p=0.01 
ChR2: 1.00 ± 0.14, n=8 t(7)=0.05, p=0.95 
Lit Object Replaced 
1h 
 
Control: 2.54 ± 0.63, n=7 t(6)=2.42, p=0.05 
ChR2: 1.92 ± 0.27, n=8 t(7)=3.40, p=0.01 
24h 
 
ChR2: 1.38 ± 0.15, n=12 t(11)=2.51, p=0.02 
ChR2: 1.61 ± 0.16, n=11 t(10)=3.74, p=0.003 
Arena Stimulation 
1h 
 
Control: 1.60 ± 0.32, n=9 t(6)=2.42, p=0.10 
ChR2: 1.86 ± 0.31, n=7 t(6)=3.40, p=0.03 
Stimulation After Training 
24h 
 
Control: 1.54 ± 0.20, n=7 t(6)=2.63, p=0.03 
ChR2: 1.65 ± 0.21, n=10 t(9)=3.07, p=0.01 
Lit and Non-Lit Object Maintained 
1h 
 
Control: 0.89 ± 0.25, n=7 t(6)=0.40, p=0.70 
ChR2: 1.85 ± 0.21, n=9 t(9)=3.19, p=0.01 
NOR - Arch Protocols 
Non-Lit Object Replaced 
1h 
 
Control: 1.91 ± 0.35, n=12 t(11)=2.58, p=0.02 
Arch: 1.65 ± 0.21, n=8 t(7)=5.22, p=0.001 
Lit Object Replaced 
1h 
 
Control: 1.86 ± 0.27, n=11 t(10)=3.09, p=0.01 
Arch: 1.65 ± 0.21, n=7 t(6)=2.60, p=0.04 
 
  
 62 
 
 
 
 
 
Supplementary Table 3 
 Contextual Avoidance – ChR2 Protocol 
Latency to Entry in the Dark Compartment 
Descriptive Statistics 
Session Mean ± SEM, n 
Training 
 
Control: 20.36 ± 9.26, n=11 
ChR2: 13.49 ± 4.74, n=10 
Test Day 1 
 
Control: 99.31 ± 39.85, n=11 
ChR2: 12.52 ± 5.15, n=10 
Test Day 2 
 
Control: 111.60 ± 38.09, n=11 
ChR2: 4.27 ± 1.44, n=10 
Test Day 3 
 
Control: 124.06 ± 41.30, n=11 
ChR2: 32.62 ± 26.30, n=10 
Two-Way Repeated Measures ANOVA 
Source of variation F DF P Value 
Interaction 3.66 (3,57) 0.017 
Day 4.75 (3,57) 0.005 
Group 4.91 (1,57) 0.039 
Velocity (cm/s) 
Descriptive Statistics 
Session Mean ± SEM, n 
Training 
 
Control: 8.42 ± 1.12, n=11 
ChR2: 9.83 ± 1.04, n=10 
Test Day 1 
 
Control: 7.12 ± 1.36, n=11 
ChR2: 10.69 ± 1.19, n=10 
Test Day 2 
 
Control: 6.38 ± 1.16, n=11 
ChR2: 11.64 ± 1.04, n=10 
  
 63 
 
 
Test Day 3 
 
Control: 5.13 ± 1.02, n=11 
ChR2: 8.62 ± 1.08, n=10 
Two-Way Repeated Measures ANOVA 
Source of variation F DF P Value 
Interaction 4.06 (3,57) 0.0108 
Day 7.55 (3,57) 0.0002 
Group 5.55 (1,57) 0.0287 
Contextual Avoidance – Arch Protocol 
Latency to Entry in the Dark Compartment 
Descriptive Statistics 
Session Mean ± SEM, n 
Training 
 
Control: 28.36 ± 8.15, n=20 
Arch: 14.15 ± 3.94, n=14 
Test Day 1 
 
Control: 104.46 ± 27.59, n=20 
Arch: 60.56 ± 25.84, n=14 
Test Day 2 
 
Control: 99.85 ± 27.08, n=20 
Arch: 111.02 ± 33.58, n=14 
Test Day 3 
 
Control: 113.59 ± 27.80, n=20 
Arch: 89.21 ± 28.61, n=14 
Two-Way Repeated Measures ANOVA 
Source of variation F DF P Value 
Interaction 0.40 (3,96) 0.749 
Day 4.63 (3,96) 0.004 
Group 0.99 (1,96) 0.319 
Velocity (cm/s) 
Descriptive Statistics 
Session Mean ± SEM, n 
Training 
 
Control: 7.51 ± 0.76, n=20 
Arch: 8.45 ± 0.98, n=14 
Test Day 1 
 
Control: 7.02 ± 0.91, n=20 
Arch: 7.48 ± 1.04, n=14 
Test Day 2 Control: 5.21 ± 0.76, n=20 
  
 64 
 
 
 Arch: 5.78 ± 0.74, n=14 
Test Day 3 
 
Control: 6.04 ± 0.71, n=20 
Arch: 5.79 ± 0.80, n=14 
Two-Way Repeated Measures ANOVA 
Source of variation F DF P Value 
Interaction 0.55 (3,96) 0.6475 
Day 12.07 (3,96) 0.0001 
Group 0.16 (1,96) 0.6908 
 
  
 65 
 
 
3.0 Artigo 2  
  
 66 
 
 
 
  
 67 
 
 
 
  
 68 
 
 
 
  
 69 
 
 
  
  
 70 
 
 
 
  
 71 
 
 
 
  
 72 
 
 
 
  
 73 
 
 
 
  
 74 
 
 
 
  
 75 
 
 
 
  
 76 
 
 
4.0 – Discussão  
Ao longo desta tese foram levantados diversos aspectos relacionados à memória, 
da descrição de estruturas anatômicas aos processos plásticos envolvidos em sua 
aquisição e manutenção. Como resultados novos, no artigo 1 investigamos a participação 
das células OLMα2 da região CA1 do hipocampo no processo de codificação da 
representação de objetos e também no aprendizado associativo de um contexto a um 
estímulo aversivo. No artigo 2 caracterizamos atividades oscilatórias da região CA1 que 
podem estar relacionadas à detecção de novidade. Abaixo fornecemos uma discussão 
expandida acerca dos resultados encontrados. 
 
4.1 – Artigo 1  
No primeiro capítulo de resultados investigamos a participação das células 
OLMα2 da região CA1 (Figura 1 – Artigo 1) na modulação da codificação de memórias 
contextual e de reconhecimento de objetos. Estes interneurônios inibem os dendritos 
apicais distais das células piramidais, onde chegam projeções do córtex entorrinal 
trazendo informação multimodal. Utilizando ferramentas de optogenética e animais 
Chrna2-cre, modulamos especificamente células OLMα2 expressando 
Channelrhodopsin-2 (ChR2) ou archaerhodopsin (ARCH) (Figura 1 – Artigo 1). A 
ChR2, proteína que forma um canal iônico, muda a conformação de sua estrutura 
possibilitando a passagem de cátions na presença da luz azul (~473 nm); através de uma 
luz nesse comprimento de onda somos capazes de facilitar a atividade da célula alvo 
(Deisseroth, 2011). Da mesma forma, somos capazes de inibir um neurônio alvo que 
expressa ARCH, sensível à luz verde (~532 nm), que por sua vez muda sua conformação 
estrutural bombeando prótons para fora da célula, hiperpolarizando sua membrana (Chow 
et al., 2010). De nota, uma vez que a luz pode aumentar a temperatura de um neurônio e 
modificar o catabolismo e anabolismo da célula, ou mesmo levar à morte celular (Allen 
et al., 2015), neste trabalho tivemos o cuidado de controlar a potência e duração da luz 
incidida.    
  
 77 
 
 
Para acessar a memória do animal, utilizamos a tarefa de reconhecimento de 
objetos. Essa tarefa é baseada na característica apresentada pelos roedores de explorar a 
novidade em detrimento da familiaridade (Antunes and Biala, 2012). Utilizamos dois 
objetos similares em dimensão, textura e formato, que foram expostos em uma arena 
circular. Dividimos a tarefa em duas sessões, treino e teste, separadas por dois tempos 
distintos, 1 hora e 24 horas. Os animais foram submetidos a diferentes protocolos; em 
geral, na sessão de treino a exploração de um dos objetos foi pareada com a modulação 
das células OLMα2. Na sessão teste, ao expor o objeto iluminado junto a um objeto novo, 
os animais demonstraram dois diferentes comportamentos: (1) nenhuma preferência pelos 
objetos quando houve a prévia ativação das células OLMα2 na sessão treino, portanto 
prejuízo na capacidade de reconhecimento; (2) aumento da exploração do objeto novo 
quando houve inativação células OLMα2 na sessão treino, indicando aumento na 
discriminação entre os objetos. Os grupos controles (sem interferência nas células 
OLMα2) expressaram o comportamento natural de explorar a novidade em detrimento da 
familiaridade (Figura 2; Figura suplementar 1 – Artigo 1). Nos protocolos que não houve 
a presença do objeto iluminado, (1) seja quando a estimulação ocorreu fora dos períodos 
de exploração de algum objeto (Arena stimulation), (2) ou quando houve estimulação 
após a sessão de treino (Stimulation after training), ou (3) quando o objeto iluminado foi 
substituído pelo objeto novo (lit-object replaced), os animais exibiram o mesmo 
comportamento dos animais controle, ou seja, exploraram mais o objeto novo do que o 
objeto não iluminado (Figura 2; Figura suplementar 1 – Artigo 1). Por último, quando 
ambos objetos da sessão treino (iluminado e não iluminado) foram mantidos na sessão 
teste, os animais com ativação das células OLMα2 durante o treino exibiram preferência 
ao objeto iluminado. Isto indica que os animais consideraram o objeto previamente 
iluminado como um objeto novo, enquanto o grupo controle não demonstrou preferência 
por nenhum dos objetos (Figura 2 – Artigo 1).  
Outra tarefa utilizada no artigo 1 foi a esquiva contextual passiva, na qual os 
animais aprendem a evitar um contexto previamente associado a um choque. Essa tarefa 
se vale da tendência do animal de evitar ambientes abertos e iluminados, preferindo 
ambientes escuros e fechados. No entanto, na tarefa há associação de um estímulo 
  
 78 
 
 
aversivo ao ambiente escuro (Rudy & Matus-Amat, 2005). Dividimos a tarefa em duas 
fases, treino e teste. A tarefa se deu numa arena que possuía um ambiente claro e um 
ambiente escuro. Durante a fase de treino, os animais são colocados no ambiente claro e 
tendem naturalmente a entrar no ambiente escuro, onde recebem um choque. Repetimos 
a associação do choque com o ambiente escuro em duas sessões de treino. Durante estas 
sessões, as células OLMα2 foram excitadas com luz azul em um grupo de animais 
Chrna2-cre expressando ChR2, ou inibidas com luz verde em outro grupo de animais 
expressando ARCH. A fase de teste consistiu de 3 dias consecutivos ao dia do treino, nos 
quais os animais foram expostos à mesma arena e foi contabilizada a latência que levaram 
para entrar no ambiente escuro. Os animais que tiveram suas células OLMα2 ativadas 
durante o treino não exibiram aversão ao ambiente em que sofreram o choque, indicando 
prejuízo na representação da memória de associação de contexto ao estímulo aversivo. Já 
os animais controles ou os que tiveram inibição da atividade das células OLMα2 evitaram 
o ambiente associado ao choque (Figura 3 – Artigo 1). 
Os resultados do Artigo 1 dão suporte ao papel da região CA1 no processo de 
codificação de memórias (Rampon et al., 2000); porém, parecem ser opostos aos 
resultados recentemente descritos por Lovett-Barron e colaboradores (2014). Nesse 
trabalho, os autores investigaram o papel da inibição de interneurônios somatostatina 
positivos durante uma tarefa de condicionamento de medo ao contexto. Através de 
experimentos com animais presos (head fixed) e em movimento livre, e da inibição da 
atividade dos interneurônios somatostatina positivos com o uso de quimiogenética, foi 
revelado um prejuízo no condicionamento aversivo (Lovett-Barron et al., 2014).   
Apesar de usar uma abordagem semelhante, no que tange o paradigma 
comportamental utilizado, especialmente nos experimentos com animais de movimento 
livre, o trabalho de Lovett-Barron e colaboradores diverge metodologicamente de nosso 
estudo. Primeiramente, nosso trabalho utilizou a subunidade α2 do receptor nicotínico 
que é expresso, no hipocampo, exclusivamente em células OLM de CA1, enquanto 
Lovett-Barron e colaboradores utilizaram como marcador a somatostatina, que é um 
marcador inespecífico, expressa por diferentes tipos de interneurônios (Lovett-Barron et 
al., 2014; Mikulovic et al., 2015). A saber, neurônios marcados por somatostatina podem 
  
 79 
 
 
possuir características eletrofisiológicas distintas (Mikulovic et al., 2015), se localizar em 
estratos diferentes, e apresentar padrão de inervação distintos aos das células OLM (Leão 
et al., 2012; Mikulovic et al., 2015); isto gera uma diferença importante no controle das 
entradas e saídas das células piramidais em comparação com o controle exercido pelas 
células OLM (Mikulovic et al., 2015). Outra distinção importante foi a utilização de 
farmacogenética como ferramenta de modulação da atividade das células alvo, enquanto 
no artigo 1 utilizamos optogenética. O uso de farmacogenética diminui a especificidade 
temporal e espacial da modulação das células alvo comparada com a optogenética. 
Acredita-se que o PSEM utilizado no trabalho de Lovett-Barron e colaboradores atua por 
até 80 minutos, embora possa ser necessário até 24 horas para recuperação completa 
(Shapiro et al., 2012). Desta forma, a modulação pode não só ter atuado durante a fase de 
codificação, como na consolidação e até na fase de reativação. Portanto, junto da falta de 
especificidade da célula alvo, houve perda de especificidade temporal. Outra questão a 
ser levantada é a dinâmica característica do canal de Cl- que leva a um rebote de potencial 
de ação após seu uso (Madisen et al., 2012; Raimondo et al., 2012), que pode gerar uma 
ação modulatória oposta à esperada.  
O estudo apresentado nesta tese e as evidências in vitro das células OLM no 
controle da informação oriunda do córtex entorrinal para região CA1 (Leão et al., 2012; 
Lovett-Barron et al., 2014; Mikulovic et al., 2015) não implicam que este seja o único 
mecanismo de filtro entre essas regiões. A variedade de interneurônios que possuem 
potencial para tal controle, junto a outros sistemas modulatórios, é grande (Freund & 
Buzsáki, 1996; Klausberger & Somogyi, 2008). Mais ainda, há neurônios no próprio 
córtex entorrinal que enviam projeções inibitórias para a região CA1, modulando 
indiretamente a atividade das células piramidais (Basu et al., 2016). Basu e colaboradores 
(2016) demonstraram, através da estimulação de interneurônios LRIPs (long range 
inhibitory projections) do córtex entorrinal, que projetam para interneurônios no stratum 
lacunosum-moleculare, que é possível controlar indiretamente a entrada de informação 
do córtex entorrinal para a região CA1, modulando a discriminação de objetos e 
condicionamento de medo ao contexto (Basu et al., 2016). Desta forma, torna-se 
  
 80 
 
 
necessário saber quais inputs os variados tipos de interneurônios recebem para melhor 
entender as funções desempenhadas. 
 
4.2 – Artigo 2 
Utilizando uma variação do teste de reconhecimento de objetos com quatro 
objetos em cada sessão, e registros de atividade neuronal e de potencial de campo local, 
investigamos no artigo 2 uma atividade organizada na região CA1 que parece surgir 
durante experiências novas: as oscilações beta2. Verificamos que esta atividade 
oscilatória na frequência de 23-30 Hz, originalmente relatada por Berke e colaboradores 
em 2008 durante a exploração de ambientes novos, também ocorre durante a exploração 
de objetos novos (Figura 3 – Artigo 2). Descrevemos também a característica transiente 
desta oscilação; seu pico de atividade ocorre nos primeiros 100 segundos de uma sessão 
de 600 segundos, e sua potência é bastante reduzinda ao final da sessão (Figuras 3 e 4 – 
Artigo 2). A dinâmica transiente do beta2 não é correlacionada com o padrão de 
exploração dos objetos por parte dos animais (Figura 5 – Artigo 2); diferentemente do 
beta2, a exploração dos objetos se mantém ao longo de toda sessão (Figura 2 – Artigo 2). 
Verificamos também que – entre as áreas registradas no trabalho – somente os registros 
do hipocampo exibiram aumento de potência de beta2 no começo das sessões de 
exploração de objetos (Figura 9 – Artigo 2). Do mesmo modo, somente os neurônios 
hipocampais exibiram atividade modulada por beta2 (Figura 9 – Artigo 2). Verificamos 
também que a potência de beta2 parece ter relação com a quantidade de novidade presente 
no ambiente: a atividade beta2 é mais proeminente em um ambiente com 4 objetos novos 
(sessão 1) em relação à sessão onde os animais exploram 2 objetos novos e 2 familiares 
(sessão 2) (Figuras 8 e 10 – Artigo 2). Interessantemente, uma maior potência de beta2 é 
também observada na sessão 2 quando há prejuízo da consolidação da memória através 
de intervenção farmacológica logo após a sessão 1 (Figura 10 – Artigo 2). 
As oscilações beta (15-30 Hz), que abrangem a subfaixa beta2 (23-30 Hz), têm 
sido descritas em diferentes regiões do cérebro, incluindo córtex motor (Lacey et al., 
2014; Roopun et al., 2006), córtex somatossensorial (Haegens et al., 2011; Roopun et al., 
  
 81 
 
 
2006), bulbo olfatório (Ravel et al., 2003), córtex entorrinal e CA1 (Igarashi et al., 2014). 
As oscilações beta do bulbo olfatório vêm sendo relacionadas com tarefas cognitivas de 
distinção de odor (Ravel et al., 2003); oscilações beta também foram descritas nas 
conexões entre a parte lateral do córtex entorrinal e a região CA1 numa tarefa de 
discriminação de contextos com pistas de odor (Igarashi et al., 2014). O nosso trabalho 
mostra que apenas a área CA1 entre as áreas registradas apresentou aumento transiente 
da potência de beta2 (ver Figura 9 – Artigo 2), afastando a possibilidade do beta2 
detectado corresponder às oscilações beta reportadas para as áreas M1 e S1 (Haegens et 
al., 2011; Lacey et al., 2014; Roopun et al., 2006). Porém, o mesmo não pode ser afirmado 
para o beta apresentado pelo bulbo olfatório ou o beta relacionado a discriminação 
olfatória (Igarashi et al., 2014; Ravel et al., 2003). O aumento transiente em beta2, cuja 
potência sobe no início e diminui no final, não foi relacionado ao comportamento da 
exploração dos objetos, que se manteve ao longo da sessão (ver Figura 2 – Artigo 2); esta 
característica difere do aumento de beta na tarefa de Igarashi e colaboradores (2014), que 
ocorre após a exposição ao odor a ser discriminado. Caso fosse o mesmo beta descrito 
neste estudo, esperaríamos a presença de beta ao longo de toda sessão de exploração. Mas 
apesar de provavelmente não se tratar da mesma atividade oscilatória, ambos ritmos 
foram registrados em áreas semelhantes ao longo de uma tarefa de discriminação, seja de 
odor (Igarashi et al., 2014) ou objetos (França et al., 2014), o que pode indicar 
similaridades funcionais.       
 
4.3 – Discussão geral 
As investigações realizadas em ambos os artigos dão suporte a participação do 
hipocampo na capacidade de reconhecimento de padrões, objetos e contextos (Brun et al., 
2002a; Daumas et al., 2005; Dere et al., 2007; Nakazawa et al., 2002; Suzuki et al., 1997). 
O hipocampo, bem como outras regiões do lobo temporal, incluindo a formação 
hipocampal e o córtex perirrinal, participam do processo de aquisição e consolidação de 
memórias declarativas e são relacionados a capacidade de reconhecimento de novidade 
(Amaral & Witter, 1989; Andersen et al., 2006; Bliss & Lomo, 1973; Brown & Aggleton, 
2001; Kandel, 2001; O’Keefe & Dostrovsky, 1971; Scoville & Milner, 1957). A memória 
  
 82 
 
 
de reconhecimento requer a diferenciação do que está sendo experimentado de 
experiências prévias (Mandler, 1980); trata-se, portanto, de uma capacidade de associar 
inúmeros inputs informacionais, do discernimento do objeto em si até a localidade e 
contexto em que ele se apresenta, e envolve inúmeras regiões do cérebro (Brown & 
Aggleton, 2001).  
A região CA1 tem um papel chave na capacidade de reconhecimento de objetos 
(Rampon et al., 2000). Como explicitado na introdução, a aquisição e manutenção de 
traços de memória estão ligados a propriedades plásticas dos neurônios, que vão desde 
modificações nos níveis de cálcio até a modificação de expressão gênica (Bernabeu et al., 
1997; Izquierdo & Cammarota, 2004; Izquierdo & Medina, 1997; Minatohara et al., 2015; 
Okuno et al., 2012). Já foi relacionada à região CA1 a ocorrência de plasticidade sináptica 
ligada a formação de traços de memória (Dere et al., 2007; Rampon et al., 2000), como 
por exemplo a indução de LTP e LTD após o processo de aprendizagem (Whitlock et al., 
2006). A manipulação de receptores NMDA, em animais knock-out para NMDA 
especificamente na região CA1, leva ao prejuízo no reconhecimento de objetos (Rampon 
et al., 2000), enquanto que o mesmo não é visto com animais knock-out para NMDA na 
região CA3 (Nakazawa et al., 2003). De modo semelhante, a modulação dos sistemas 
dopaminérgicos e acetilcolinérgicos leva a modificação na capacidade de discriminação 
de objetos e contextos (França et al., 2016, 2015; Kutlu & Gould, 2015; Melichercik et 
al., 2012; Puma et al., 1999; Tian et al., 2015). 
O papel de CA1 no reconhecimento provavelmente está relacionado às projeções 
do córtex entorrinal que carregam informações multimodais oriundas de diversas partes 
do cérebro (van Groen et al., 2003). Lesões da via entre o córtex entorrinal e a região CA1 
já foram relacionadas a prejuízos de localização espacial (Brun et al., 2008, 2002). A 
retirada dos inputs intra-hipocampais entre as regiões CA3-CA1, mantendo os inputs 
recebidos por CA1 do córtex entorrinal, não prejudica a capacidade de localização 
espacial, nem a memória relacionada ao reconhecimento espacial (Brun et al., 2002). 
Enquanto que a modulação da conexão entre a região CA1 e o córtex entorrinal leva a 
modulação da codificação de memórias de reconhecimento de objetos e contextos (Basu 
et al., 2016; Lovett-Barron et al., 2014). Os resultados mostrados no artigo 1, junto com 
  
 83 
 
 
resultados prévios que demonstram que as células OLMα2 filtram informação do córtex 
entorrinal (Leão et al., 2012), reforçam o papel da conexão córtex entorrinal-CA1 na 
codificação de memórias relacionadas à capacidade de reconhecimento.  
As células OLMα2 inibem os dendritos apicais distais dos neurônios piramidais, 
que recebem projeções do córtex entorrinal (Leão et al., 2012); tal característica é 
esperada para a função de controle do fluxo de informação e, portanto, para a codificação 
de memórias hipocampo-dependentes. As células OLMα2 são inervadas pelo sistema 
colinérgico, em particular possuem receptores nicotínicos (Leão et al., 2012). O sistema 
colinérgico tem sido amplamente ligado ao processo de formação e consolidação de 
memórias (Aren- et al., 1995; Hasselmo, 2006; Levin, 2002; Puma et al., 1999; Tian et 
al., 2015; Anéxo 3), dando suporte ao papel das células OLMα2 reportado no artigo 1. 
Por sua vez, as células OLMα2 projetam seus axônios para interneurônios no stratum 
radiatum que inibem as projeções entre CA3-CA1; desta forma, ao serem ativadas, as 
células OLMα2 inibem as entradas do córtex entorrinal mas facilitam a comunicação pela 
via colateral de Schaffer entre as regiões CA3-CA1, via essa ligada ao processo de 
consolidação de memórias (Sacchetti et al., 2001).  
O sistema dopaminérgico e as células OLMα2 agem em regiões semelhantes, 
apesar de parecer ter características diferentes nas etapas de aquisição e manutenção de 
memórias hipocampo-dependentes. Neurônios dopaminérgicos invervam tanto o córtex 
entorrinal quanto a região CA1 do hipocampo (Andersen et al., 2006). Terminais de 
axônios da área tegmentar ventral e receptores dopaminérgicos são vistos em diversos 
estratos de CA1, incluindo o piramidale, oriens, radiatum e lacunosum-moleculare, bem 
como na região CA3 e nas células musgosas do giro denteado (Gangarossa et al., 2012; 
Otmakhova et al., 2013). Por outro lado, as células OLM agem mais localmente ao 
modular as conexões oriundas do córtex entorrinal nos dendritos apicais distais das 
células piramidais de CA1 (Leão et al., 2012; Lovett-Barron et al., 2014). O sistema 
dopaminérgico está relacionado ao processo de consolidação de informações, sendo 
necessário em etapas chave para manutenção do traço de memória (Rossato et al, 2009; 
França et al, 2015); sua função provavelmente é ligada ao sistema motivacional e de 
recompensa (Otmakhova et al, 2013). Já as células OLM parecem ter o papel ligado a 
  
 84 
 
 
modulação da codificação das memórias, tendo destaque no filtro de informações 
espaciais oriundos do córtex entorrinal (Artigo 1; Brun et al., 2008, 2002; Basu et al., 
2016; Lovett-Barron et al., 2014), provavelmente por intermédio da modulação do 
sistema colinérgico (Leão et al., 2012). Segundo Lisman e Grace (2005), o loop que 
ocorre entre a área tegmentar ventral e a formação hipocampal possui um papel 
importante na manutenção de traços de memória relacionados à novidade (Lisman & 
Grace, 2005). Tanto o modelo proposto por Lisman e Grace como os resultados 
encontrados com as células OLMα2 são consistentes com achados prévios da literatura 
(França et al., 2016, 2015, 2014; Gasbarri et al., 1996; Hansen and Manahan-Vaughan, 
2012; Morice et al., 2007; Rossato et al., 2009; Silva et al., 2012; Basu et al., 2016; Lovett-
Barron et al., 2014; Tian et al, 2015) e não são excludentes entre si, o que ilustra a 
complexidade da dinâmica dos circuitos envolvidos na memória. 
Em suma, os estudos apresentados no artigo 1 e artigo 2, bem como os 3 artigos 
em anexo desta tese dão suporte ao papel da região CA1 no processo de formação de 
memórias, e na capacidade de reconhecimento de novidade. Estes artigos adicionam ao 
nosso conhecimento atual acerca do funcionamento de CA1 ao descrever uma atividade 
oscilatória ligada à detecção de novidade, bem como ao demonstrar que um subtipo 
específico de célula é capaz de modular a codificação de novas memórias.  
 
 
 
 
  
  
 85 
 
 
5.0 – Referências 
Allen, B.D., Singer, A.C., Boyden, E.S., 2015. Principles of designing interpretable 
optogenetic behavior experiments. Learn. Mem. 22, 232–238.  
Al-Onaizi, M.A., Parfitt, G.M., Kolisnyk, B., Law, C.S.H., Guzman, M.S., Barros, D.M., 
Leung, L.S., Prado, M.A.M., Prado, V.F., 2016. Regulation of Cognitive 
Processing by Hippocampal Cholinergic Tone. Cereb. Cortex  p. bhv349.  
Amaral, D.G., Dolorfo, C., Alvarez-Royo, P., 1991. Organization of CA1 projections to 
the subiculum: A PHA-L analysis in the rat. Hippocampus 1, 415–435.  
Amaral, D.G., Scharfman, H.E., Lavenex, P., 2007. The dentate gyrus: fundamental 
neuroanatomical organization (dentate gyrus for dummies). Prog. Brain Res. 163, 
3–22.  
Amaral, D.G., Witter, M.P., 1989. The three-dimensional organization of the 
hippocampal formation: a review of anatomical data. Neuroscience 31, 571–591. 
Andersen, P., Morris, R., Amaral, D., Bliss, T., O’Keefe, J., 2006. The Hippocampus 
Book. Oxford University Press. 
Antunes, M., Biala, G., 2012. The novel object recognition memory: neurobiology, test 
procedure, and its modifications. Cogn. Process. 13, 93–110.  
Aren-, G.W., Sanberg, P.R., Sengstock, G.J., 1995. Nicotine enhances the learning and 
memory of aged rats. Pharmacol. Biochem. Behav. 52, 517–523.  
Basu, J., Zaremba, J.D., Cheung, S.K., Hitti, F.L., Zemelman, B.V., Losonczy, A., 
Siegelbaum, S.A., 2016. Gating of hippocampal activity, plasticity, and memory 
by entorhinal cortex long-range inhibition. Science 351.6269.  
Bekinschtein, P., Cammarota, M., Medina, J.H., 2014. BDNF and memory processing. 
Neuropharmacology 76 Pt C, 677–683.  
Bengoetxea, X., Rodriguez-Perdigon, M., Ramirez, M.J., 2015. Object recognition test 
for studying cognitive impairments in animal models of Alzheimer’s disease. 
Front. Biosci. Sch. Ed. 7, 10–29. 
Bentivoglio, M., Swanson, L.W., 2001. On the fine structure of the pes Hippocampi major 
(with plates XIII-XXIII). Brain Res. Bull. 54, 461–483.  
Berke, J.D., Hetrick, V., Breck, J., Greene, R.W., 2008. Transient 23-30 Hz oscillations 
in mouse hippocampus during exploration of novel environments. Hippocampus 
18, 519–529.  
Bernabeu, R., Bevilaqua, L., Ardenghi, P., Bromberg, E., Schmitz, P., Bianchin, M., 
Izquierdo, I., Medina, J.H., 1997. Involvement of hippocampal cAMP/cAMP-
dependent protein kinase signaling pathways in a late memory consolidation 
phase of aversively motivated learning in rats. Proc. Natl. Acad. Sci. 94, 7041–
7046. 
  
 86 
 
 
Blake, M.G., Krawczyk, M.C., Baratti, C.M., Boccia, M.M., 2014. Neuropharmacology 
of memory consolidation and reconsolidation: Insights on central cholinergic 
mechanisms. J. Physiol. Paris 108, 286–291.  
Blaser, R., Heyser, C., 2015. Spontaneous object recognition: a promising approach to 
the comparative study of memory. Front. Behav. Neurosci. 9, 183.  
Bliim, N., Leshchyns’ka, I., Sytnyk, V., Janitz, M., 2016. Transcriptional regulation of 
long-term potentiation. Neurogenetics.  
Bliss, T.V., Lomo, T., 1973. Long-lasting potentiation of synaptic transmission in the 
dentate area of the anaesthetized rabbit following stimulation of the perforant 
path. J. Physiol. 232, 331–356. 
Brankack, J., Stewart, M., Fox, S.E., 1993. Current source density analysis of the 
hippocampal theta rhythm: associated sustained potentials and candidate synaptic 
generators. Brain Res. 615, 310–327. 
Brault, V., Besson, V., Magnol, L., Duchon, A., Hérault, Y., 2007. Cre/loxP-mediated 
chromosome engineering of the mouse genome. Handb. Exp. Pharmacol. 29–48.  
Brown, M.W., Aggleton, J.P., 2001. Recognition memory: What are the roles of the 
perirhinal cortex and hippocampus? Nat. Rev. Neurosci. 2, 51–61.  
Brown, R.E., Milner, P.M., 2003. The legacy of Donald O. Hebb: more than the Hebb 
Synapse. Nat. Rev. Neurosci. 4, 1013–1019.  
Brun, V.H., Leutgeb, S., Wu, H.-Q., Schwarcz, R., Witter, M.P., Moser, E.I., Moser, M.-
B., 2008. Impaired Spatial Representation in CA1 after Lesion of Direct Input 
from Entorhinal Cortex. Neuron 57, 290–302.  
Brun, V.H., Otnæss, M.K., Molden, S., Steffenach, H.-A., Witter, M.P., Moser, M.-B., 
Moser, E.I., 2002a. Place Cells and Place Recognition Maintained by Direct 
Entorhinal-Hippocampal Circuitry. Science 296, 2243–2246.  
Brun, V.H., Otnass, M.K., Molden, S., Steffenach, H.-A., Witter, M.P., Moser, M.-B., 
Moser, E.I., 2002b. Place cells and place recognition maintained by direct 
entorhinal-hippocampal circuitry. Science 296, 2243–2246.  
Bunce, J.G., Sabolek, H.R., Chrobak, J.J., 2004. Intraseptal infusion of the cholinergic 
agonist carbachol impairs delayed-non-match-to-sample radial arm maze 
performance in the rat. Hippocampus 14, 450–459.  
Buzsáki, G., 2002. Theta Oscillations in the Hippocampus. Neuron 33, 325–340.  
Buzsáki, G., Anastassiou, C.A., Koch, C., 2012. The origin of extracellular fields and 
currents--EEG, ECoG, LFP and spikes. Nat. Rev. Neurosci. 13, 407–420.  
Buzsáki, G., Draguhn, A., 2004. Neuronal oscillations in cortical networks. Science 304, 
1926–1929.  
Buzsáki, G., Horváth, Z., Urioste, R., Hetke, J., Wise, K., 1992. High-frequency network 
oscillation in the hippocampus. Science 256, 1025–1027. 
  
 87 
 
 
Buzsáki, G., Watson, B.O., 2012. Brain rhythms and neural syntax: implications for 
efficient coding of cognitive content and neuropsychiatric disease. Dialogues 
Clin. Neurosci. 14, 345–367. 
Cammarota, M., Bernabeu, R., Levi de Stein, M., Izquierdo, I., Medina, J.H., 1998. 
Learning-specific, time-dependent increases in hippocampal Ca2+/calmodulin-
dependent protein kinase II activity and AMPA GluR1 subunit immunoreactivity. 
Eur. J. Neurosci. 10, 2669–2676. 
Cammarota, M., Bevilaqua, L.R., Medina, J.H., Izquierdo, I., 2008. ERK1/2 and 
CaMKII-mediated events in memory formation: is 5HT regulation involved? 
Behav. Brain Res. 195, 120–128.  
Caporale, N., Dan, Y., 2008. Spike Timing–Dependent Plasticity: A Hebbian Learning 
Rule. Annu. Rev. Neurosci. 31, 25–46.  
Chamberland, S., Topolnik, L., 2012. Inhibitory control of hippocampal inhibitory 
neurons. Front. Neurosci. 6, 165.  
Cherubini, E., Miles, R.M., 2015. The CA3 region of the hippocampus: how is it? What 
is it for? How does it do it? Front. Cell. Neurosci. 9, 19.  
Chow, B.Y., Han, X., Dobry, A.S., Qian, X., Chuong, A.S., Li, M., Henninger, M.A., 
Belfort, G.M., Lin, Y., Monahan, P.E., Boyden, E.S., 2010. High-performance 
genetically targetable optical neural silencing by light-driven proton pumps. 
Nature 463, 98–102.  
Colgin, L.L., Denninger, T., Fyhn, M., Hafting, T., Bonnevie, T., Jensen, O., Moser, M.-
B., Moser, E.I., 2009. Frequency of gamma oscillations routes flow of information 
in the hippocampus. Nature 462, 353–357.  
Csicsvari, J., Jamieson, B., Wise, K.D., Buzsáki, G., 2003. Mechanisms of gamma 
oscillations in the hippocampus of the behaving rat. Neuron 37, 311–322. 
Daumas, S., Halley, H., Francés, B., Lassalle, J.-M., 2005. Encoding, consolidation, and 
retrieval of contextual memory: Differential involvement of dorsal CA3 and CA1 
hippocampal subregions. Learn. Mem. 12, 375–382.  
de Kloet, S.F., Mansvelder, H.D., De Vries, T.J., 2015. Cholinergic modulation of 
dopamine pathways through nicotinic acetylcholine receptors. Biochem. 
Pharmacol. 97, 425–438.  
de Lima, M.N.M., Presti-Torres, J., Dornelles, A., Siciliani Scalco, F., Roesler, R., 
Garcia, V.A., Schröder, N., 2011. Modulatory influence of dopamine receptors on 
consolidation of object recognition memory. Neurobiol. Learn. Mem. 95, 305–
310.  
Deisseroth, K., 2011. Optogenetics. Nat. Methods 8, 26–29.  
Dere, E., Huston, J.P., De Souza Silva, M.A., 2007. The pharmacology, neuroanatomy 
and neurogenetics of one-trial object recognition in rodents. Neurosci. Biobehav. 
Rev. 31, 673–704.  
  
 88 
 
 
Ding, S.-L., 2013. Comparative anatomy of the prosubiculum, subiculum, presubiculum, 
postsubiculum, and parasubiculum in human, monkey, and rodent. J. Comp. 
Neurol. 521, 4145–4162.  
Dolorfo, C.L., Amaral, D.G., 1998. Entorhinal cortex of the rat: Topographic organization 
of the cells of origin of the perforant path projection to the dentate gyrus. J. Comp. 
Neurol. 398, 25–48.  
Easton, A., Douchamps, V., Eacott, M., Lever, C., 2012. A specific role for 
septohippocampal acetylcholine in memory? Neuropsychologia 50, 3156–3168.  
Ego-Stengel, V., Wilson, M.A., 2010. Disruption of ripple-associated hippocampal 
activity during rest impairs spatial learning in the rat. Hippocampus 20, 1–10.  
Elvander, E., Schött, P.A., Sandin, J., Bjelke, B., Kehr, J., Yoshitake, T., Ögren, S.O., 
2004. Intraseptal muscarinic ligands and galanin: influence on hippocampal 
acetylcholine and cognition. Neuroscience 126, 541–557.  
Ennaceur, A., Delacour, J., 1988. A new one-trial test for neurobiological studies of 
memory in rats. 1: Behavioral data. Behav. Brain Res. 31, 47–59. 
Feldman, D.E., 2012. The Spike-Timing Dependence of Plasticity. Neuron 75, 556–571.  
Fenno, L., Yizhar, O., Deisseroth, K., 2011. The Development and Application of 
Optogenetics. Annu. Rev. Neurosci. 34, 389–412.  
Ferrante, M., Ascoli, G.A., 2015. Distinct and synergistic feedforward inhibition of 
pyramidal cells by basket and bistratified interneurons. Front. Cell. Neurosci. 9, 
439.  
Forro, T., Valenti, O., Lasztoczi, B., Klausberger, T., 2015. Temporal organization of 
GABAergic interneurons in the intermediate CA1 hippocampus during network 
oscillations. Cereb. Cortex N. Y. N 1991 25, 1228–1240.  
França, A.S.C., do Nascimento, G.C., Lopes-dos-Santos, V., Muratori, L., Ribeiro, S., 
Lobão-Soares, B., Tort, A.B.L., 2014. Beta2 oscillations (23–30 Hz) in the mouse 
hippocampus during novel object recognition. Eur. J. Neurosci. 40, 3693–3703.  
França, A.S.C., Lobão-Soares, B., Muratori, L., Nascimento, G., Winne, J., Pereira, C.M., 
Jeronimo, S.M.B., Ribeiro, S., 2015. D2 dopamine receptor regulation of learning, 
sleep and plasticity. Eur. Neuropsychopharmacol. 25, 493–504.  
França, A.S.C., Muratori, L., Nascimento, G.C., Pereira, C.M., Ribeiro, S., Lobão-Soares, 
B., 2016. Object recognition impairment and rescue by a dopamine D2 antagonist 
in hyperdopaminergic mice. Behav. Brain Res. 308, 211–216.  
Frémaux, N., Gerstner, W., 2015. Neuromodulated Spike-Timing-Dependent Plasticity, 
and Theory of Three-Factor Learning Rules. Front. Neural Circuits 9, 85.  
Freund, T. f., Buzsáki, G., 1996. Interneurons of the hippocampus. Hippocampus 6, 347–
470.  
Gangarossa, G., Longueville, S., De Bundel, D., Perroy, J., Hervé, D., Girault, J.-A., 
Valjent, E., 2012. Characterization of dopamine D1 and D2 receptor-expressing 
neurons in the mouse hippocampus. Hippocampus 22, 2199–2207.  
  
 89 
 
 
Gasbarri, A., Sulli, A., Innocenzi, R., Pacitti, C., Brioni, J.D., 1996. Spatial memory 
impairment induced by lesion of the mesohippocampal dopaminergic system in 
the rat. Neuroscience 74, 1037–1044. 
Gervasoni, D., Lin, S.-C., Ribeiro, S., Soares, E.S., Pantoja, J., Nicolelis, M.A.L., 2004. 
Global forebrain dynamics predict rat behavioral states and their transitions. J. 
Neurosci. Off. J. Soc. Neurosci. 24, 11137–11147.  
Giese, K.P., Mizuno, K., 2013. The roles of protein kinases in learning and memory. 
Learn. Mem. Cold Spring Harb. N 20, 540–552.  
Girardeau, G., Benchenane, K., Wiener, S.I., Buzsáki, G., Zugaro, M.B., 2009. Selective 
suppression of hippocampal ripples impairs spatial memory. Nat. Neurosci. 12, 
1222–1223.  
Gould, T.J., Lommock, J.A., 2003. Nicotine Enhances Contextual Fear Conditioning and 
Ameliorates Ethanol-Induced Deficits in Contextual Fear Conditioning. Behav. 
Neurosci. 117, 1276–1282.  
Grayson, B., Leger, M., Piercy, C., Adamson, L., Harte, M., Neill, J.C., 2015. Assessment 
of disease-related cognitive impairments using the novel object recognition 
(NOR) task in rodents. Behav. Brain Res. 285, 176–193.  
Haegens, S., Nácher, V., Hernández, A., Luna, R., Jensen, O., Romo, R., 2011. Beta 
oscillations in the monkey sensorimotor network reflect somatosensory decision 
making. Proc. Natl. Acad. Sci. 108, 10708–10713.  
Hafting, T., Fyhn, M., Molden, S., Moser, M.-B., Moser, E.I., 2005. Microstructure of a 
spatial map in the entorhinal cortex. Nature 436, 801–806.  
Hansen, N., Manahan-Vaughan, D., 2012. Dopamine D1/D5 Receptors Mediate 
Informational Saliency that Promotes Persistent Hippocampal Long-Term 
Plasticity. Cereb. Cortex bhs362.  
Hasselmo, M.E., 2006. The role of acetylcholine in learning and memory. Curr. Opin. 
Neurobiol., Motor systems / Neurobiology of behaviour 16, 710–715.  
Igarashi, K.M., Lu, L., Colgin, L.L., Moser, M.-B., Moser, E.I., 2014. Coordination of 
entorhinal-hippocampal ensemble activity during associative learning. Nature 
510, 143–147.  
Insausti, R., Herrero, M.T., Witter, M.P., 1997. Entorhinal cortex of the rat: 
Cytoarchitectonic subdivisions and the origin and distribution of cortical 
efferents. Hippocampus 7, 146–183.  
Ito, M., Sakurai, M., Tongroach, P., 1982. Climbing fibre induced depression of both 
mossy fibre responsiveness and glutamate sensitivity of cerebellar Purkinje cells. 
J. Physiol. 324, 113–134. 
Izquierdo, I., Cammarota, M., 2004. Zif and the Survival of Memory. Science 304, 829–
830.  
  
 90 
 
 
Izquierdo, I., Medina, J.H., 1997. Memory Formation: The Sequence of Biochemical 
Events in the Hippocampus and Its Connection to Activity in Other Brain 
Structures. Neurobiol. Learn. Mem. 68, 285–316.  
Ji, J., Maren, S., 2008. Differential roles for hippocampal areas CA1 and CA3 in the 
contextual encoding and retrieval of extinguished fear. Learn. Mem. 15, 244–251.  
Kamondi, A., Acsády, L., Wang, X.J., Buzsáki, G., 1998. Theta oscillations in somata 
and dendrites of hippocampal pyramidal cells in vivo: activity-dependent phase-
precession of action potentials. Hippocampus 8, 244–261.  
Kandel, E.R., 2001. The Molecular Biology of Memory Storage: A Dialogue Between 
Genes and Synapses. Science 294, 1030–1038.  
Karunakaran, S., Chowdhury, A., Donato, F., Quairiaux, C., Michel, C.M., Caroni, P., 
2016. PV plasticity sustained through D1/5 dopamine signaling required for long-
term memory consolidation. Nat. Neurosci. 19, 454–464.  
Kemp, A., Manahan-Vaughan, D., 2004. Hippocampal long-term depression and long-
term potentiation encode different aspects of novelty acquisition. Proc. Natl. 
Acad. Sci. U. S. A. 101, 8192–8197.  
Klausberger, T., Somogyi, P., 2008. Neuronal Diversity and Temporal Dynamics: The 
Unity of Hippocampal Circuit Operations. Science 321, 53–57.  
Knox, D., 2016. The role of basal forebrain cholinergic neurons in fear and extinction 
memory. Neurobiol. Learn. Mem. 133, 39–52.  
Kobayashi, K., 2010. Hippocampal mossy fiber synaptic transmission and its modulation. 
Vitam. Horm. 82, 65–85.  
Konorski, J., 1948. Conditioned reflexes and neuron organization. Cambridge University 
Press, New York, NY, US. 
Kutlu, M.G., Gould, T.J., 2015. Nicotine modulation of fear memories and anxiety: 
Implications for learning and anxiety disorders. Biochem. Pharmacol. 97, 498–
511.  
Lacey, M.G., Gooding-Williams, G., Prokic, E.J., Yamawaki, N., Hall, S.D., Stanford, 
I.M., Woodhall, G.L., 2014. Spike Firing and IPSPs in Layer V Pyramidal 
Neurons during Beta Oscillations in Rat Primary Motor Cortex (M1) In Vitro. 
PLOS ONE 9, e85109.  
Leal, G., Afonso, P.M., Salazar, I.L., Duarte, C.B., 2015. Regulation of hippocampal 
synaptic plasticity by BDNF. Brain Res., Brain and Memory: Old Arguments and 
New Perspectives 1621, 82–101.  
Leão, R.N., Mikulovic, S., Leão, K.E., Munguba, H., Gezelius, H., Enjin, A., Patra, K., 
Eriksson, A., Loew, L.M., Tort, A.B.L., Kullander, K., 2012. OLM interneurons 
differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. 
Nat. Neurosci. 15, 1524–1530.  
  
 91 
 
 
Lee, I., Kesner, R.P., 2004. Differential contributions of dorsal hippocampal subregions 
to memory acquisition and retrieval in contextual fear-conditioning. 
Hippocampus 14, 301–310.  
Lenck-Santini, P.-P., Rivard, B., Muller, R. u., Poucet, B., 2005. Study of CA1 place cell 
activity and exploratory behavior following spatial and nonspatial changes in the 
environment. Hippocampus 15, 356–369.  
Levin, E.D., 2002. Nicotinic receptor subtypes and cognitive function. J. Neurobiol. 53, 
633–640.  
Li, F., Wang, L.P., Shen, X., Tsien, J.Z., 2010. Balanced dopamine is critical for pattern 
completion during associative memory recall. PloS One 5, e15401.  
Lisman, J., Schulman, H., Cline, H., 2002. The molecular basis of CaMKII function in 
synaptic and behavioural memory. Nat. Rev. Neurosci. 3, 175–190.  
Lisman, J.E., Grace, A.A., 2005. The hippocampal-VTA loop: controlling the entry of 
information into long-term memory. Neuron 46, 703–713.  
López-Muñoz, F., Boya, J., Alamo, C., 2006. Neuron theory, the cornerstone of 
neuroscience, on the centenary of the Nobel Prize award to Santiago Ramón y 
Cajal. Brain Res. Bull. 70, 391–405.  
Lorente De Nó, R., 1934. Studies on the structure of the cerebral cortex. II. Continuation 
of the study of the ammonic system. J. Für Psychol. Neurol. 46, 113–177. 
Lovett-Barron, M., Kaifosh, P., Kheirbek, M.A., Danielson, N., Zaremba, J.D., Reardon, 
T.R., Turi, G.F., Hen, R., Zemelman, B.V., Losonczy, A., 2014. Dendritic 
Inhibition in the Hippocampus Supports Fear Learning. Science 343, 857–863.  
Madisen, L., Mao, T., Koch, H., Zhuo, J., Berenyi, A., Fujisawa, S., Hsu, Y.-W.A., 
Garcia, A.J., Gu, X., Zanella, S., Kidney, J., Gu, H., Mao, Y., Hooks, B.M., 
Boyden, E.S., Buzsáki, G., Ramirez, J.M., Jones, A.R., Svoboda, K., Han, X., 
Turner, E.E., Zeng, H., 2012. A toolbox of Cre-dependent optogenetic transgenic 
mice for light-induced activation and silencing. Nat. Neurosci. 15, 793–802.  
Mandler, G., 1980. Recognizing: The judgment of previous occurrence. Psychol. Rev. 87, 
252–271.  
Martí Barros, D., Ramirez, M.R., dos Reis, E.A., Izquierdo, I., 2004. Participation of 
hippocampal nicotinic receptors in acquisition, consolidation and retrieval of 
memory for one trial inhibitory avoidance in rats. Neuroscience 126, 651–656.  
McGaugh, J.L., 2000. Memory--a Century of Consolidation. Science 287, 248–251.  
Melichercik, A.M., Elliott, K.S., Bianchi, C., Ernst, S.M., Winters, B.D., 2012. Nicotinic 
receptor activation in perirhinal cortex and hippocampus enhances object memory 
in rats. Neuropharmacology 62, 2096–2105.  
Mikulovic, S., Restrepo, C.E., Hilscher, M.M., Kullander, K., Leão, R.N., 2015. Novel 
markers for OLM interneurons in the hippocampus. Front. Cell. Neurosci. 9, 201.  
Milner, B., Corkin, S., Teuber, H.-L., 1968. Further analysis of the hippocampal amnesic 
syndrome: 14-year follow-up study of H.M. Neuropsychologia 6, 215–234.  
  
 92 
 
 
Minatohara, K., Akiyoshi, M., Okuno, H., 2015. Role of Immediate-Early Genes in 
Synaptic Plasticity and Neuronal Ensembles Underlying the Memory Trace. 
Front. Mol. Neurosci. 8, 78.  
Montgomery, S.M., Buzsáki, G., 2007. Gamma oscillations dynamically couple 
hippocampal CA3 and CA1 regions during memory task performance. Proc. Natl. 
Acad. Sci. 104, 14495–14500.  
Morice, E., Billard, J.-M., Denis, C., Mathieu, F., Betancur, C., Epelbaum, J., Giros, B., 
Nosten-Bertrand, M., 2007. Parallel loss of hippocampal LTD and cognitive 
flexibility in a genetic model of hyperdopaminergia. Neuropsychopharmacol. Off. 
Publ. Am. Coll. Neuropsychopharmacol. 32, 2108–2116.  
Nakazawa, K., Quirk, M.C., Chitwood, R.A., Watanabe, M., Yeckel, M.F., Sun, L.D., 
Kato, A., Carr, C.A., Johnston, D., Wilson, M.A., Tonegawa, S., 2002. 
Requirement for Hippocampal CA3 NMDA Receptors in Associative Memory 
Recall. Science 297, 211–218.  
Nakazawa, K., Sun, L.D., Quirk, M.C., Rondi-Reig, L., Wilson, M.A., Tonegawa, S., 
2003. Hippocampal CA3 NMDA Receptors Are Crucial for Memory Acquisition 
of One-Time Experience. Neuron 38, 305–315.  
Navakkode, S., Sajikumar, S., Korte, M., Soong, T.W., 2012. Dopamine induces LTP 
differentially in apical and basal dendrites through BDNF and voltage-dependent 
calcium channels. Learn. Mem. Cold Spring Harb. N 19, 294–299.  
O, D., 1949. The organization of behavior; a neuropsychological theory. Wiley, Oxford, 
England. 
Okamoto, K., Bosch, M., Hayashi, Y., 2009. The roles of CaMKII and F-actin in the 
structural plasticity of dendritic spines: a potential molecular identity of a synaptic 
tag? Physiol. Bethesda Md 24, 357–366.  
O’Keefe, J., Dostrovsky, J., 1971. The hippocampus as a spatial map. Preliminary 
evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175. 
Okuno, H., Akashi, K., Ishii, Y., Yagishita-Kyo, N., Suzuki, K., Nonaka, M., Kawashima, 
T., Fujii, H., Takemoto-Kimura, S., Abe, M., Natsume, R., Chowdhury, S., 
Sakimura, K., Worley, P.F., Bito, H., 2012. Inverse synaptic tagging of inactive 
synapses via dynamic interaction of Arc/Arg3.1 with CaMKIIβ. Cell 149, 886–
898.  
O’Mara, S., 2005. The subiculum: what it does, what it might do, and what neuroanatomy 
has yet to tell us. J. Anat. 207, 271–282.  
Otmakhova, N., Duzel, E., Deutch, A.Y., Lisman, J., 2013. The Hippocampal-VTA Loop: 
The Role of Novelty and Motivation in Controlling the Entry of Information into 
Long-Term Memory 235–254.  
Pogorelov, V.M., Rodriguiz, R.M., Insco, M.L., Caron, M.G., Wetsel, W.C., 2005. 
Novelty seeking and stereotypic activation of behavior in mice with disruption of 
the Dat1 gene. Neuropsychopharmacol. Off. Publ. Am. Coll. 
Neuropsychopharmacol. 30, 1818–1831.  
  
 93 
 
 
Puma, C., Deschaux, O., Molimard, R., Bizot, J.C., 1999. Nicotine improves memory in 
an object recognition task in rats. Eur. Neuropsychopharmacol. J. Eur. Coll. 
Neuropsychopharmacol. 9, 323–327. 
Raimondo, J.V., Kay, L., Ellender, T.J., Akerman, C.J., 2012. Optogenetic silencing 
strategies differ in their effects on inhibitory synaptic transmission. Nat. Neurosci. 
15, 1102–1104.  
Rajagopal, L., W Massey, B., Huang, M., Oyamada, Y., Y. Meltzer, H., 2014. The Novel 
Object Recognition Test in Rodents in Relation to Cognitive Impairment in 
Schizophrenia. Curr. Pharm. Des. 20, 5104–5114. 
Rampon, C., Tang, Y.P., Goodhouse, J., Shimizu, E., Kyin, M., Tsien, J.Z., 2000. 
Enrichment induces structural changes and recovery from nonspatial memory 
deficits in CA1 NMDAR1-knockout mice. Nat. Neurosci. 3, 238–244.  
Ravel, N., Chabaud, P., Martin, C., Gaveau, V., Hugues, E., Tallon-Baudry, C., Bertrand, 
O., Gervais, R., 2003. Olfactory learning modifies the expression of odour-
induced oscillatory responses in the gamma (60–90 Hz) and beta (15–40 Hz) 
bands in the rat olfactory bulb. Eur. J. Neurosci. 17, 350–358.  
Remondes, M., Schuman, E.M., 2004. Role for a cortical input to hippocampal area CA1 
in the consolidation of a long-term memory. Nature 431, 699–703.  
Rezvani, A.H., Levin, E.D., 2001. Cognitive effects of nicotine. Biol. Psychiatry, 
Nicotine Mechanisms in Alzheimer’s Disease 49, 258–267.  
Ribeiro, S., Mello, C.V., Velho, T., Gardner, T.J., Jarvis, E.D., Pavlides, C., 2002. 
Induction of hippocampal long-term potentiation during waking leads to increased 
extrahippocampal zif-268 expression during ensuing rapid-eye-movement sleep. 
J. Neurosci. Off. J. Soc. Neurosci. 22, 10914–10923. 
Ribeiro, S., Nicolelis, M.A.L., 2004. Reverberation, storage, and postsynaptic 
propagation of memories during sleep. Learn. Mem. Cold Spring Harb. N 11, 
686–696.  
Ribeiro, S., Shi, X., Engelhard, M., Zhou, Y., Zhang, H., Gervasoni, D., Lin, S.-C., Wada, 
K., Lemos, N.A.M., Nicolelis, M.A.L., 2007. Novel experience induces persistent 
sleep-dependent plasticity in the cortex but not in the hippocampus. Front. 
Neurosci. 1, 43–55.  
Roopun, A.K., Middleton, S.J., Cunningham, M.O., LeBeau, F.E.N., Bibbig, A., 
Whittington, M.A., Traub, R.D., 2006. A beta2-frequency (20–30 Hz) oscillation 
in nonsynaptic networks of somatosensory cortex. Proc. Natl. Acad. Sci. 103, 
15646–15650.  
Rossato, J.I., Bevilaqua, L.R.M., Izquierdo, I., Medina, J.H., Cammarota, M., 2009. 
Dopamine controls persistence of long-term memory storage. Science 325, 1017–
1020.  
Rudy, J.W., Matus-Amat, P., 2005. The Ventral Hippocampus Supports a Memory 
Representation of Context and Contextual Fear Conditioning: Implications for a 
Unitary Function of the Hippocampus. Behav. Neurosci. 119, 154–163.  
  
 94 
 
 
Sacchetti, B., Lorenzini, C.A., Baldi, E., Bucherelli, C., Roberto, M., Tassoni, G., 
Brunelli, M., 2001. Long-lasting hippocampal potentiation and contextual 
memory consolidation. Eur. J. Neurosci. 13, 2291–2298.  
Sauer, B., 1998. Inducible gene targeting in mice using the Cre/lox system. Methods San 
Diego Calif 14, 381–392.  
Scheffer-Teixeira, R., Belchior, H., Caixeta, F.V., Souza, B.C., Ribeiro, S., Tort, A.B.L., 
2012. Theta phase modulates multiple layer-specific oscillations in the CA1 
region. 1991.  Cereb. Cortex N. Y. N 22, 2404–2414.  
Scheffer-Teixeira, R., Belchior, H., Leão, R.N., Ribeiro, S., Tort, A.B.L., 2013. On high-
frequency field oscillations (>100 Hz) and the spectral leakage of spiking activity. 
J. Neurosci. Off. J. Soc. Neurosci. 33, 1535–1539.  
Schneider, F., Grimm, C., Hegemann, P., 2015. Biophysics of Channelrhodopsin. Annu. 
Rev. Biophys. 44, 167–186.  
Schultz, C., Engelhardt, M., 2014. Anatomy of the hippocampal formation. Front. Neurol. 
Neurosci. 34, 6–17.  
Scoville, W.B., Milner, B., 1957. Loss of recent memory after bilateral hippocampal 
lesions. J. Neurol. Neurosurg. Psychiatry 20, 11–21. 
Shapiro, M.G., Frazier, S.J., Lester, H.A., 2012. Unparalleled Control of Neural Activity 
Using Orthogonal Pharmacogenetics. ACS Chem. Neurosci. 3, 619–629.  
Shinohara, Y., Hosoya, A., Yahagi, K., Ferecskó, A.S., Yaguchi, K., Sík, A., Itakura, M., 
Takahashi, M., Hirase, H., 2012. Hippocampal CA3 and CA2 have distinct 
bilateral innervation patterns to CA1 in rodents. Eur. J. Neurosci. 35, 702–710.  
Silva, W.C.N. da, Köhler, C.C., Radiske, A., Cammarota, M., 2012. D1/D5 dopamine 
receptors modulate spatial memory formation. Neurobiol. Learn. Mem. 97, 271–
275.  
Subramaniyan, M., Dani, J.A., 2015. Dopaminergic and cholinergic learning mechanisms 
in nicotine addiction. Ann. N. Y. Acad. Sci. 1349, 46–63.  
Suzuki, W.A., Miller, E.K., Desimone, R., 1997. Object and Place Memory in the 
Macaque Entorhinal Cortex. J. Neurophysiol. 78, 1062–1081. 
Szirmai, I., Buzsáki, G., Kamondi, A., 2012. 120 years of hippocampal Schaffer 
collaterals. Hippocampus 22, 1508–1516.  
Tian, S., Pan, S., You, Y., 2015. Nicotine enhances the reconsolidation of novel object 
recognition memory in rats. Pharmacol. Biochem. Behav. 129, 14–18.  
Tort, A.B.L., Komorowski, R.W., Manns, J.R., Kopell, N.J., Eichenbaum, H., 2009. 
Theta-gamma coupling increases during the learning of item-context associations. 
Proc. Natl. Acad. Sci. U. S. A. 106, 20942–20947.  
Tort, A.B.L., Kramer, M.A., Thorn, C., Gibson, D.J., Kubota, Y., Graybiel, A.M., Kopell, 
N.J., 2008. Dynamic cross-frequency couplings of local field potential oscillations 
in rat striatum and hippocampus during performance of a T-maze task. Proc. Natl. 
Acad. Sci. U. S. A. 105, 20517–20522.  
  
 95 
 
 
Tort, A.B.L., Scheffer-Teixeira, R., Souza, B.C., Draguhn, A., Brankačk, J., 2013. Theta-
associated high-frequency oscillations (110-160Hz) in the hippocampus and 
neocortex. Prog. Neurobiol. 100, 1–14.  
Tsien, J.Z., Huerta, P.T., Tonegawa, S., 1996. The Essential Role of Hippocampal CA1 
NMDA Receptor–Dependent Synaptic Plasticity in Spatial Memory. Cell 87, 
1327–1338.  
Van Cauter, T., Poucet, B., Save, E., 2008. Unstable CA1 place cell representation in rats 
with entorhinal cortex lesions. Eur. J. Neurosci. 27, 1933–1946.  
van Groen, T., Miettinen, P., Kadish, I., 2003. The entorhinal cortex of the mouse: 
Organization of the projection to the hippocampal formation. Hippocampus 13, 
133–149.  
Vanderwolf, C.H., 1969. Hippocampal electrical activity and voluntary movement in the 
rat. Electroencephalogr. Clin. Neurophysiol. 26, 407–418. 
Wang, Y., Zhang, P., Wyskiel, D.R., 2016. Chandelier Cells in Functional and 
Dysfunctional Neural Circuits. Front. Neural Circuits 10.  
Wheeler, D.W., White, C.M., Rees, C.L., Komendantov, A.O., Hamilton, D.J., Ascoli, 
G.A., 2015. Hippocampome.org: a knowledge base of neuron types in the rodent 
hippocampus. eLife 4.  
Whitlock, J.R., Heynen, A.J., Shuler, M.G., Bear, M.F., 2006. Learning Induces Long-
Term Potentiation in the Hippocampus. Science 313, 1093–1097.  
Winson, J., 1978. Loss of hippocampal theta rhythm results in spatial memory deficit in 
the rat. Science 201, 160–163. 
Woodruff, A.R., Anderson, S.A., Yuste, R., 2010. The Enigmatic Function of Chandelier 
Cells. Front. Neurosci. 4.  
Zhang, G., Stackman, R.W., 2015. The role of serotonin 5-HT2A receptors in memory 
and cognition. Front. Pharmacol. 6, 225.  
 
 
  
  
 96 
 
 
6.0 – Anexos 
 
D2 dopamine receptor regulation of learning,
sleep and plasticity
A.S.C. Françaa,1, B. Lobão-Soaresb,1,n, L. Muratoria,c,1,
G. Nascimentod,e, J. Winnee, C.M. Pereirae,
S.M.B. Jeronimoc, S. Ribeiroa,n
aBrain Institute, Federal University of Rio Grande do Norte (UFRN), 59056-450 Natal, RN, Brazil
bDepartment of Biophysics and Pharmacology, Federal University of Rio Grande do Norte (UFRN), Brazil
cDepartment of Biochemistry, Federal University of Rio Grande do Norte (UFRN), Brazil
dDepartment of Biomedical Engineering, Federal University of Rio Grande do Norte (UFRN), Brazil
eEdmond and Lily Safra International Institute of Neuroscience of Natal (ELS-IINN), Natal, RN, Brazil
Received 2 September 2014; received in revised form 8 January 2015; accepted 16 January 2015
KEYWORDS
REM sleep;
CaMKII;
Zif-268;
BDNF;
Haloperidol;
Object recognition
Abstract
Dopamine and sleep have been independently linked with hippocampus-dependent learning.
Since D2 dopaminergic transmission is required for the occurrence of rapid-eye-movement (REM)
sleep, it is possible that dopamine affects learning by way of changes in post-acquisition REM
sleep. To investigate this hypothesis, we first assessed whether D2 dopaminergic modulation in
mice affects novel object preference, a hippocampus-dependent task. Animals trained in the
dark period, when sleep is reduced, did not improve significantly in performance when tested
24 h after training. In contrast, animals trained in the sleep-rich light period showed significant
learning after 24 h. When injected with the D2 inverse agonist haloperidol immediately after the
exploration of novel objects, animals trained in the light period showed reduced novelty
preference upon retesting 24 h later. Next we investigated whether haloperidol affected the
protein levels of plasticity factors shown to be up-regulated in an experience-dependent manner
during REM sleep. Haloperidol decreased post-exploration hippocampal protein levels at 3 h, 6 h
and 12 h for phosphorylated Ca2+/calmodulin-dependent protein kinase II, at 6 h for Zif-268; and
at 12 h for the brain-derived neurotrophic factor. Electrophysiological and kinematic recordings
showed a significant decrease in the amount of REM sleep following haloperidol injection, while
slow-wave sleep remained unaltered. Importantly, REM sleep decrease across animals was
strongly correlated with deficits in novelty preference (Rho=0.56, p=0.012). Altogether, the
www.elsevier.com/locate/euroneuro
http://dx.doi.org/10.1016/j.euroneuro.2015.01.011
0924-977X/& 2015 Elsevier B.V. and ECNP. All rights reserved.
nCorresponding authors. Tel.: +55 84 9127 7141.
E-mail addresses: brunolobaosoares@gmail.com (B. Lobão-Soares),
sidartaribeiro@neuro.ufrn.br (S. Ribeiro).
1The first three authors contributed equally to this work.
European Neuropsychopharmacology (2015) 25, 493–504
results suggest that the dopaminergic regulation of REM sleep affects learning by modulating
post-training levels of calcium-dependent plasticity factors.
& 2015 Elsevier B.V. and ECNP. All rights reserved.
1. Introduction
The neurotransmitter dopamine is involved with the acquisi-
tion of hippocampal-dependent memories (Rossato et al.,
2009). While most studies have focused on D1/D5 receptors
(Lisman and Grace, 2005; Lemon and Manahan-Vaughan, 2006;
Rossato et al., 2009), a role for D2 receptors in hippocampus-
dependent memory acquisition and/or consolidation has been
proposed (Manahan-Vaughan and Kulla, 2003; De Lima et al.,
2011). D2 receptors are also known to control the sleep–wake
cycle (Dzirasa et al., 2006; Monti and Monti, 2007; Lima et al.,
2007, Morice et al., 2007; Lima et al., 2008). Hyperdopami-
nergic mice knockout for the dopamine transporter display
robust REM sleep (Dzirasa et al., 2006), but REM sleep
disappears when animals are treated with alpha-methyl-p-
tyrosine, a dopamine synthesis inhibitor (Dzirasa et al., 2006).
Importantly, a D2 (but not D1) receptor agonist is able to
rescue REM sleep (Dzirasa et al., 2006). These findings are
consistent with the evidence that blockade of D2 receptors
specifically decreases REM sleep (Lima et al., 2008). The acute
blockade of D2 receptors by haloperidol is known to impair
learning in the novel object preference test in rats (Proença
et al., 2014) and mice (França et al., 2014). Could the effects
of dopamine on learning be mediated by REM sleep?
Hippocampus-dependent learning increases the duration
and number of REM sleep episodes (Binder et al., 2012).
Several studies have reported the up-regulation of calcium-
dependent plasticity factors during post-acquisition REM sleep
(Ribeiro et al., 1999, 2002, 2007; Ulloor and Datta, 2005), in
line with evidence that sleep deprivation impairs cAMP sign-
aling in the hippocampus (Vecsey et al., 2009). Neural pla-
sticity related to learning is associated with both molecular
and electrophysiological events that lead to gradual changes
in synaptic strength and morphology (Kandel et al., 2014;
Whitlock et al., 2006). Key molecular events include the
phosphorylation of Ca2+/calmodulin-dependent protein
kinase II (CaMKII) (Lisman et al., 2002), and up-regulation of
the protein levels of immediate-early gene Zif-268 (Guzowski,
2002) and brain-derived neurotrophic factor (BDNF) (Panja and
Bramham, 2014). The early increase in pCaMKII levels imme-
diately after memory acquisition is followed by a rise of Zif-
268 protein levels after 1–2 h, and then increased BDNF levels
after 12 h (Bekinschtein et al., 2007; Medina et al., 2008).
These events are necessary for the consolidation of long-las-
ting memories (Bozon et al., 2003; Xia and Storm, 2005), and
for the long-term potentiation (LTP) of electrophysiological
responses implicated as the cellular basis of learning and
memory (Jones et al., 2001; Whitlock et al., 2006).
To gain insight into the relation of dopamine, sleep and
learning, we set out to investigate whether the D2 regulat-
ion of REM sleep correlates with changes in the consolidat-
ion of the object recognition task, a hippocampus-depend-
ent task (Piterkin et al., 2008). To this end, we assessed
electrophysiological and behavioral alterations induced by
haloperidol in mice subjected to the object recognition task.
We also investigated how the post-training administration of
haloperidol modulates the hippocampal levels of pCaMKII,
Zif-268 and BDNF. Finally, we combined behavioral, electro-
physiological and kinematic recordings to determine the re-
lationship between learning deficits and haloperidol-induced
changes in sleep. The results suggest that D2 dopaminergic
transmission affects learning by way of changes in REM sleep
and calcium-dependent plasticity factors.
2. Experimental procedures
2.1. Animals
A total of 116 adult male mice were used (C57Bl-6 strain, 2–5 months).
After surgery, animals were housed in cages under a 12 h/12 h light/
dark schedule, with lights on at 07:00, and food and water ad libitum.
Animals were daily handled 10 times for 5 min before the experi-
ments, in order to decrease stress responses. Housing, surgical and
behavioral procedures were in accordance with the guidelines of the
National Institutes of Health, and were approved by the ELS-IINN
Ethics Committee (Protocol number 08/2010).
2.2. Novelty preference task
The task was based on the spontaneous tendency of rodents to
explore novelty (Hughes, 2007). Our task employed 6 different
objects presented over 2 consecutive days; 4 objects were presented
during the initial exploration session (training session, 10 min); and
2 unfamiliar objects replaced 2 familiar objects during the second
exploration session, 24 h later (testing session, 10 min). To evaluate
memory consolidation, an object preference ratio was calculated
(time spent with unfamiliar/familiar objects). Behavioral recordings
began at 10:00 or 22:00 in an open field apparatus (50 cm diameter
and 30 cm high). Following object exploration, animals were injected
with haloperidol or vehicle, and were then allowed to behave freely
in their home cages until the second exploration session (Figure 1A).
For animals subjected to electrophysiological recordings, two
training-testing pairs of session were performed within seven days
of each other, the first with vehicle and the second with haloperidol.
Locomotion was estimated as the total distance traveled per session.
In behavioral sessions, experiments during the dark period were
conducted under white light.
2.3. Single object exposure and perfusion times for
histochemical analyses
For histochemical analyses, we used mice previously exposed to 10
sessions of handling and a single exposure of 10 min to 4 novel objects,
identical to the training session described above. All experiments in
this case were conducted from 8:00 to 10:00; animals (N=7–10 per
group) received 0.1 ml/10 g injections of haloperidol (0.3 mg/kg) or
vehicle (saline) immediately after object exploration. An additional
group (naïve; N=7) was studied in which the mice were immediately
perfused after being removed from their cages, without object
exploration. With the exception of the naïve group, animals were
returned to their home cages after injection and allowed to
cycle freely through waking (WK) and sleep states for 3 h (n=10
A.S.C. França et al.494
VH/haloperidol), 6 h (n=7 VH/haloperidol) or 12 h (n=7 VH/haloper-
idol); at the criterion time, animals were deeply anesthetized and
perfused (Figure 1B).
2.4. Immunohistochemistry
Animals were anesthetized with isoflurane and perfused with
paraformaldehyde 4% in phosphate buffer (PB). The brains were
removed and placed in a solution of 30% sucrose at 4 1C for 24 h.
Subsequently the brains were frozen, frontally sectioned at 30 mm
in a criostat (Zeiss), and thaw-mounted over glass slides. Sections
were incubated as a single batch per plasticity factor in blocking
buffer solution (0.5% fresh skim milk and 0.3% Triton X-100 in 0.1 M
PB) for 30 min, and then incubated overnight at 18 1C in primary
antibody (pCaMKII – 1:200, Millipore; Zif-268–1:100, Santa Cruz
Biotechnology, USA) diluted in blocking buffer. Next, the sections
were washed in PB for 15 min, incubated with a biotinylated
secondary antibody for 2 h, washed again in PB for 15 min, and
then incubated in avidin–biotin–peroxidase solution (Vector Labs,
USA) for another 2 h. Slides were then placed in a solution
containing 0.03% DAB and 0.001% hydrogen peroxide in 0.1 M PB,
dehydrated and cover-slipped with Entellan (Merck, USA). In order
to confirm labeling specificity, the primary antibodies were
replaced by blocking buffer in test sections. For BDNF staining,
some changes were made in the protocol. First, we used an
antigenic recovery protocol that consisted in immersing sections
in borate buffer (0.1 M, pH 9.0) and then heating them in a
microwave oven, for two periods of 30 s. Sections were washed in
PB for 15 min. To block endogenous peroxidase, sections were then
incubated for 30 min in 3% H2O2 diluted in 20% methanol. The
sections were then washed in PB for 15 min, incubated in blocking
buffer for 2 h and incubated for 72 h in primary antibody (BDNF –
1:100, Santa Cruz Biotechnology, USA). After this procedure the
steps were the same as described above (Figure 1B).
Figure 1 Behavioral task, pharmacology, immunohistochemistry and LFP recordings. (A) Behavioral and pharmacological
procedures. C57BL-6 mice were presented to 4 novel objects at light or dark periods (training session). Haloperidol or vehicle
was injected immediately after exploration (I.A.E.) or 6 h after exploration (6 h A.E.) of novel objects. One day after training,
animals were exposed to 2 novel objects and 2 familiar objects. (B) Immunohistochemistry and histology. At 3, 6 or 12 h after
injection, animals were euthanized, and the brains were processed. Frontal sections were cresyl-stained or subjected to
immunohistochemistry for the transcription factor Zif-268, phosphorylated calcium-calmodulin kinase II (pCaMKII), and brain-
derived neurotrophic factor (BDNF). Labeling quantification in hippocampal regions DG, CA3, and CA1 comprised densitometry for
Zif-268, pCaMKII, and BDNF, as well as cell countings for Zif-268, which shows nuclear staining. (C) Local field potential recordings
(LFP) were performed in the primary somatosensory (S1) and motor cortices (M1), and in CA1. Inertial recordings from an
accelerometer were also obtained. (D) Spectral maps used to quantitatively sort the major behavioral states of the sleep–wake cycle
(Gervasoni et al., 2004). Top left panel indicates waking (WK, in black), slow wave sleep (SWS, in red), and rapid-eye-movement
sleep (REM, in green); blue denotes state transitions. Top right panel show accelerometer data. High acceleration (hot colors) was
only observed during WK. Bottom left panel shows LFP power in the theta range (6–12 Hz); peak theta power occurs during REM.
Bottom right panel show LFP power in the delta range (1–4.5 Hz); peak delta power occurs during SWS. (E) Placement of the
microelectrode arrays on neuroanatomical diagram overlaid with cresyl-violet stained section. Note electrode tracks in CA1.
495Dopaminergic regulation of learning, sleep and plasticity
2.5. Staining quantification
Densitometric measurements of pCaMKII, Zif-268 and BDNF staining
were performed with ImageJ software (3 sections/animal, 1.46–
2.06 mm posterior to bregma). Measurements of the corpus callo-
sum were used to subtract staining background values in each
section. Densitometric measurements of the different hippocampal
regions of interest (DG, CA3, CA1) were then normalized by dividing
the mean grey value (in black and white photographs) measured for
each animal by the average of these values from all compared
groups. Unlike pCaMKII and BDNF, which present diffuse cytoplasmic
staining, Zif-268 exhibits nuclear staining. For the cellular quanti-
fication of Zif-268 staining, labeled nuclei were counted using
Stereo Investigator software (MBF bioscience, USA). In each region
of interest (3 sections/animal, 1.46 mm–2.06 mm posterior to
Bregma), labeled cells were counted within 50
 50 μm2 grid
squares. The number of labeled cells per grid (R1) was obtained
for each section. Individual values were then normalized by the
average value of all groups: naïve, VH and haloperidol for each time
point, as well as VH and haloperidol inter-time comparisons
(Figure 1B). Results are presented as the average of the normalized
staining ratio across the three regions of interest (Figure 4), as well
as separated by anatomical region (Supplementary Figure 1).
2.6. Behavioral and kinematic recordings
Behaviors were recorded using a Panasonic camera and AMcap 9.21
free software in behavioral groups. Video recordings were synchro-
nized to local field potentials (LFPs) and kinematic data obtained
with a three axis accelerometer sensor (ADXL330, Analog Devices)
tightly installed over the multi-electrode implant.
2.7. Multi-electrode implantation and recordings
For intracranial LFP recordings (Figure 1C), 9 animals were chronically
implanted with 5 electrodes in the hippocampus, 4 electrodes in the
primary motor cortex (M1), and 4 electrodes in the primary somato-
sensory cortex (S1). Each multi-electrode array was 0.9
 2.10 mm2
with length 1.5 mm, composed of 50 μm diameter tungsten wires
coated with polyamide, attached to an 18-pin connector (Omnetics
A79040-001). Arrays were implanted under isoflurane through an
opening in the skull (Bregma coordinates: 0.55 and 1.65 ML, 0.0 and
2.2 mm AP). An acrylic cap built over the head was secured by
3 screws attached to the skull. One of the screws touched the dura-
mater and was used as recording ground soldered to a silver wire. A 10-
fold pre-amplification circuitry was placed 4 cm distant from the
animal's head, in order to reduce noise. LFP signals sampled at a
1000 Hz were pre-amplified 500
 and recorded in a 32-channel system
for neural recording analysis (MAP, Plexon Inc).
2.8. Identification of wake–sleep states
LFP, video and kinematic recordings were combined to sort the
different sleep–wake states. Online LFP spectral analysis of the
sleep–wake cycle (Gervasoni et al., 2004; Ribeiro et al., 2007) was
used to identify and quantify occurrence of WK, slow wave-sleep
(SWS) and REM sleep (Figure 1D). Animal behavior and LFPs were
continuously observed and recorded in real time for 12 h. The first
4 h of recording were used for comparisons among treatments.
2.9. Haloperidol treatment in behavioral groups
Animals received i.p. injections of haloperidol or vehicle (saline 0.9%).
Haloperidol doses of 0.3 mg/kg i.p. were used, based on previous
studies (Dzirasa et al., 2006; Morice et al., 2007). Depending on the
animal group (Figure 1A), drug or vehicle was applied immediately
after exploration (I.A.E.) or 6 h after exploration (6 h A.E.).
2.10. Haloperidol treatment in electrophysiological
groups
Animals subjected to object exploration received haloperidol
(0.3 mg/kg i.p.) or vehicle I.A.E., defining the Exploration/Halo-
peridol and Exploration/Vehicle groups, respectively. Animals not
subjected to object exploration were injected with haloperidol
(0.3 mg/kg, group Control/Haloperidol) or vehicle (group Control/
Vehicle).
2.11. Histological confirmation of electrode placement
In order to confirm electrode positioning, animals subjected to
multi-electrode implantation were subjected to post-mortem ana-
lysis by Nissl staining. In all cases the electrode tips were located at
the depth of 1.5 mm for cortical electrodes, or in the CA1 layer in
the case of hippocampal electrodes. A representative example is
shown in Figure 1E.
2.12. Statistical analysis
The data were first subjected to the Shapiro–Wilk and Kolmogorov–
Smirnov normality tests. The data showed parametric distributions,
and were expressed as mean7the standard error of mean (SEM),
with statistical significance set at α=0.05. Two-way ANOVA compar-
isons followed by Bonferroni post-hoc tests were used for behavioral
analyses (period and treatment as independent variables, in
Figure 2A), for immunohistochemical analyses (using time of injec-
tion and treatment as independent variables in Figure 4) and in
electrophysiology groups (with object exposure and treatment as
independent variables in Figure 6A). Bonferroni-corrected one-way
ANOVA followed by Bonferroni post-hoc tests were used for multiple
comparisons among the cell counting groups (Figure 5) and the
staining ratio of separate hippocampal regions (Supplementary
Figure 1). Spearman's correlations were used for verifying the
relation between REM sleep duration and novelty preference ratio
(Figure 6C). The descriptive statistics comprise F, p values and
degrees of freedom (DF) of corresponding one or two-way ANOVAs,
followed by mean7SEM and summary of p values for each post-hoc
test (Table 1, Supplementary Tables 1–4).
3. Results
3.1. Time-dependent effect of haloperidol
injection in the object recognition task
First, we measured the effect of haloperidol on performa-
nce of the object recognition task. A two-way ANOVA (Time
of injection: F (1, 28)=22.24, po0.0001; Treatment: F (1,
28)=11.44, p=0.0021; Interaction: F (1,28)=5.4, p=0.027)
revealed that animals injected with haloperidol and subje-
cted to the task during the dark period showed significantly
less object recognition in comparison to animals treated
during the light period (Figure 2A).
Next we compared the effect of haloperidol at two different
time points during the light phase: I.A.E. and 6 h A.E. Figure 2B
shows that the only treatment to impair object recognition
was the administration of haloperidol 0.3 mg/kg I.A.E (two-
way ANOVA: Time of injection: F (1, 28)=2.30, p=0.14, Treat-
ment: F (1, 28)=5.04, p=0.037; Interaction: F (1, 28)=9.5,
A.S.C. França et al.496
p=0.0044). This result indicates that haloperidol impaired
object discrimination within the initial hours after injection.
3.2. Haloperidol has a time-dependent effect on
pCaMKII, Zif-268 and BDNF levels
The protein levels of the plasticity factors assessed varied
substantially across naïve, vehicle-treated and haloperidol-
treated animals (Figure 3). 3D illustrates regions of interest
differentially stained for pCaMKII, Zif-268 and BDNF, respec-
tively. Quantitative results are shown in Figure 4,
Supplementary Figure 1 (densitometry) and Figure 5 (cell
counting, only for zif-268).
Figure 4 shows that pCaMKII levels in pooled hippocampal
data 3 h after injection were lower in the haloperidol and
vehicle groups, in comparison with naïve animals. Haloperidol-
treated animals showed lower levels than naïve and vehicle
animals 6 h after injection. Animals killed 12 h after injection
showed lower levels of pCaMKII in the haloperidol group, in
comparison with the vehicle group (statistics in Table 1). In the
analysis of separate hippocampal regions 3 h after injection, a
significant difference was observed only in the CA3 region
(Supplementary Figure 1A, F=5.58, p=0.049, statistics in
Supplementary Table 2). At 6 h post-injection, a decrease of
pCaMKII levels was detected in the haloperidol group in CA1
(F=33.22; p=0.0003), CA3 (F=28.28; p=0.0003) and DG
(F=11.56; p=0.0027) (Supplementary Figure 1A; statistics in
Supplementary Table 3). At 12 h post-injection, pCaMKII levels
were lower in the haloperidol group in comparison with the
vehicle group in CA1 (F=9.23; p=0.0051), CA3 (F=14.24;
p=0.0003) and DG (F=11.12; p=0.0024; Supplementary
Figure 1A statistics in Supplementary Table 4).
Significant differences in Zif-268 levels were observed only
at 6 h post-injection. The haloperidol group showed lower Zif-
268 levels than the vehicle and naïve groups (two-way ANOVA:
Time: F (2, 45)=0.14, p=0.86, Treatment: F (2, 45)=7.82,
po0.0012; Interaction: F (4, 45)=5.58, po0.0007; see
Table 1). Similar results were observed when we analyzed
the different hippocampal regions: at 6 h post injection,
Zif-268 staining was significantly decreased in the haloperidol
group in CA1 (F=14.27; p=0.0009) and CA3 (F=9.92;
p=0.0054), in comparison with naïve and vehicle groups (Supp-
lementary Figure 1B; statistics in Supplementary Table 3).
The effect of haloperidol in BDNF levels was observed
only at 12 h post injection. Staining in the haloperidol group
was significant lower than in the vehicle group (two-way
ANOVA: Time: F (2, 43)=0.22, p=0.80, Treatment: F (2, 43)
=5.18, po0.009; Interaction: F (4, 43)=0.91, p=0.91, sta-
tistics in Table 1). A non-significant statistical trend was
observed in the DG at 12 h post-injection, with lower levels
in the haloperidol group than in animals injected with
vehicle (F=2.06, p=0.056) (Supplementary Figure 1C, sta-
tistics in Supplementary Table 4).
3.3. VH and haloperidol inter-time comparisons
We then analyzed the data through separate comparisons
over time for the VH and haloperidol groups. For pCaMKII
levels, ANOVA revealed lower immunostaining in the VH 3 h
group, in comparison with the other groups (naive, 6 h and
12 h), in DG (F=8.346; p=0.0009), CA1 (compared only to
6 h and 12 groups; F=10.28; p=0.0002) and CA3 (compared
only to 6 h and 12 h groups; F=9.218; p=0.0004). No
differences were found for the VH inter-time comparisons
of Zif-268 and BDNF staining among naïve, 3 h, 6 h and 12 h
groups (Figure 4). For inter-time comparisons among groups
injected with haloperidol, there was decreased immunor-
eactivity for pCaMKII levels at 6 h compared in DG (F=
6.708; p=0.0026, comparison to naïve), and CA1 (F=16.34;
p=0.0001, comparison to naïve and haloperidol 12 h). In
CA3 there was decreased pCaMKII labeling in haloperidol
3 h and haloperidol 6 h in comparison to the naïve group.
(F=9.400; p=0.0004). For Zif-268 staining, ANOVA revealed
a decrease at 6 h when compared to all other time points, in
both CA1 (F=12.42; Po0.0001) and CA3 (F=9.672; p=
0.0004; Figure 4). For BDNF, ANOVA revealed no differences
in haloperidol inter-time comparisons (Figure 4).
Light Dark
0
1
2
3
Pr
ef
er
en
ce
 R
at
io
***
***
IAE 6h
0.0
0.5
1.0
1.5
2.0
2.5
Vehicle
Halo 
**
Figure 2 Haloperidol modulates memory for novel objects in mice (N=7–8 animals per group) Preference ratio was calculated as
the exploration time of unfamiliar/familiar objects. (A) Animals trained during the light phase (morning) showed clear preference
for novel objects when injected with vehicle immediately after training, but not when injected with haloperidol. Animals trained
during the dark phase (night) failed to show novel object preference for either treatment. Two-way ANOVA followed by Bonferroni
post-hoc test was applied for the comparisons. (B) Effect on preference ratio of haloperidol injected in the light phase. Two-way
ANOVA followed by Bonferroni post-hoc test were applied. Haloperidol impairs learning when injected immediately after training,
but this effect disappeared when animals were injected 6 h after training.*po0.05, **po0.01, ***po0.001.
497Dopaminergic regulation of learning, sleep and plasticity
3.4. IHC quantification of Zif-268 by cell counting
Most of the results obtained by cell counting (Figure 5) were in
accordance with the densitometry measurements. For the 3 h
post-injection time, we observed increased Zif-268 reactivity
in the VH group in CA1 comparison to the naïve group
(F=5.323; p=0.066), and in CA3 when compared to naïve
and haloperidol groups (F=12.59; p=0.003). At 6 h post-
injection, we found a decrease in CA1 in the haloperidol
group, when compared to naïve and vehicle groups (F=24.97;
Table 1 Statistical summary of the significant results in the densitometry analysis for pCaMKII, Zif-268 and BDNF staining.
MANOVA comparisons of labeling measurements in the hippocampus were followed by Bonferroni post-hoc tests. Comparisons
were performed considering two independent variables: treatment and time of injection.
pCaMKII
Source of variation MANOVA
F P value DF
Interaction 7.87 0.0001 (4,54)
Treatment 42.88 0.0001 (2,54)
Time 1.67 0.19 (2,54)
Time Bonferroni
Groups Mean7SEM; N P value
3 h NV
Halo 1.0870.05; N=7 Po0.001
0.8370.05; N=6
NV
VH 1.0870.05; N=6 Po0.05
0.9270.04; N=5
6 h NV
Halo 1.0570.05; N=7 Po0.001
0.7070.02; N=6
VH
Halo 1.1770.01; N=6 Po0.001
0.7070.02; N=6
12 h NV
 halo 1.1470.01; N=7 Po0.01
0.8770.03; N=7
Zif-268
Source of variation MANOVA
F P value DF
Interaction 5.58 0.0007 (4,45)
Treatment 7.82 0.0012 (2,45)
Time 0.14 0.86 (2,45)
Time Bonferroni
Groups Mean7SEM; N P value
6 h NV
Halo 1.1770.05; N=6 Po0.001
0.6570.08; N=6
VH
Halo 1.0970.07; N=6 Po0.001
0.6570.08; N=6
BDNF
Source of variation MANOVA
F P value DF
Interaction 0.24 0.91 (4,43)
Treatment 5.18 0.009 (2,43)
Time 0.22 0.80 (2,43)
Time Bonferroni
Groups Mean7SEM; N P value
12 h VH
Halo 1.1470.06; N=7 Po0.05
0.8970.04; N=7
A.S.C. França et al.498
Po0.0001). At 12 h post-injection, no significant differences
were found among groups in CA1 and CA3. In VH inter-time
comparisons, we observed no differences among naïve, 3 h,
6 h and 12 h groups. Nevertheless, haloperidol-injected ani-
mals presented differential staining at these time points
(ANOVA F=7.689; p=0.004). In CA1, we observed a decrease
in Zif-268 at 6 h when compared to naïve (P40.01) haloper-
idol 3 h (Po0.01) and 12 h groups (Po0.05). At CA3 we
observed a similar reduction in animals injected with haloper-
idol at a 6 h time point (ANOVA F=9.181 and p=0.002), when
compared to 3 h (Po0.05) and 12 h groups (Po0.05), and a
reduction in naïve animals compared to the haloperidol 3 h
group (Po0.05).
3.5. Haloperidol impairs memory recognition and
decreases REM sleep duration
To test the influence of haloperidol on specific sleep states,
we subjected animals to the object recognition task,
injected either haloperidol or vehicle, and performed sub-
sequent electrophysiological recordings across the sleep–
wake cycle. Two-Way ANOVA was used to compare exposed
animals (vehicle and haloperidol 0.3 mg/kg I.A.E.) to con-
trols (vehicle and haloperidol 0.3 mg/kg I.A.E.). Compar-
isons were independently made for the duration of each
state (WK, SWS, REM) versus the independent variables tre-
atment (haloperidol/vehicle) and task (exposure and con-
trol). For WK and SWS, the two-way ANOVA detected no
difference regarding treatment (F (1, 14)=1.76 p=0.21 for
WK; F (1, 14)=0.07 p=0.80 for SWS) or task (F (1, 14)=3.63
p=0.07 for WK; F (1, 14)=0.50 p=0.49 for SWS). For REM
sleep, however, significant differences were detected for
both treatment (F (1, 14)=7.69 p=0.014) and task: (F (1,
14)=32.85 Po0.0001). Bonferroni post hoc tests pointed to
decreased REM sleep duration in both haloperidol and
vehicle control groups, in comparison with corresponding
object-exposed groups (Figure 6A). We also detected a
significant reduction of the preference ratio in
haloperidol-injected I.A.E. mice (Figure 6B).
Finally, to investigate the relationship between object
recognition and REM sleep duration, we calculated the
Spearman correlation between normalized preference ratio
and normalized REM sleep duration (Haloperidol or Vehicle
divided by [Haloperidol+Vehicle]). We found that lower
preference indexes in haloperidol-injected animals were
significantly correlated with lower REM sleep durations, just
like the higher preference and higher REM sleep durations
verified in control animals were also correlated (Figure 6C;
Rho=0.56 p=0.0125).
4. Discussion
In the present work we aimed at a better understanding of the
biological mechanisms that link sleep, memory formation and
dopamine receptor regulation. The behavioral and electro-
physiological data showed that haloperidol impairs novel
object recognition when injected immediately after training,
but has no effect when injected 6 h later. Haloperidol also
decreases total REM sleep duration for up to 4 h after training.
Importantly, for injections immediately after training, learning
and REM sleep duration showed strong positive correlation,
suggesting a possible common cause linking REM sleep reduc-
tion and learning deficits. Mechanistic insight was sought thr-
ough an assessment of calcium-dependent plasticity factors.
We found that haloperidol injection immediately after training
decreased the hippocampal levels of these factors for several
hours after treatment, starting with pCaMKII at 3 h, Zif-268 at
6 h and then BDNF at 12 h. Our data point to the calcium-
dependent plasticity pathway as a candidate to mediate the
effects of haloperidol on both sleep and learning.
Figure 3 Haloperidol injection decreases the hippocampal levels of pCaMKII, Zif-268 and BDNF according to a temporal gradient.
The columns exemplify representative data of different groups. Arrows indicate hippocampal regions (DG, CA3, CA1) with significant
labeling differences in the haloperidol group, in comparison with naive and vehicle groups (empty and filled arrows, respectively).
Non-exposed, not injected naive animals (NV), and animals injected with haloperidol (HALO) or vehicle (VH) immediately after
training were euthanized at 3 h, 6 h and 12 h post-exploration. The first three rows represent frontal hippocampal sections at 40

labeled for pCaMKII (A), Zif-268 (B) or BDNF (C). The last row at 200
 focuses on regions where significant differences were
detected (D). Panels show differential labeling for pCaMKII (naïve and 3 h), Zif-268 labeling at 6 h (vehicle), and BDNF at 12 h
(vehicle). Please note that haloperidol decreased the hippocampal levels of pCaMKII at all time points. Decreased Zif-268 levels in
the haloperidol group were detected only after 6 h, while decreased BDNF levels occurred only after 12 h.
499Dopaminergic regulation of learning, sleep and plasticity
The possible involvement of the dopaminergic system in the
consolidation of memories has been previously reported in the
literature. While haloperidol impairs water maze learning
(Morice et al., 2007), dopamine D2 agonists decr-
ease memory consolidation in fear conditioning (Nader and
LeDoux, 1999), and impair the extinction of conditioned fear
memory (Ponnusamy et al., 2005). Both antagonists and
agonists of D1 receptors reduce memory consolidation in a
fear-conditioning task (Rossato et al., 2009). Haloperidol works
as an inverse agonist of D2 receptors, disinhibiting adenylyl
cyclase activity and therefore leading to elevated cyclic AMP
levels (Konradi and Heckers, 1995). Zif-268 and BDNF are
thought to be directly influenced by prior activation of
pCaMKII-mediated signaling pathways (Hasbi et al., 2009). It
is therefore expected that the levels of cAMP-influenced
targets such as pCaMKII (Blitzer et al., 1998), Zif-268 (Kang
et al., 2007) and BDNF (Ji et al., 2005; Bekinschtein et al.,
2007) are decreased after haloperidol treatment. The putative
mechanism to explain the dopaminergic influence on memory-
related plasticity factors is the regulation of non-NMDA
glutamatergic receptor activation by D2 dopamine receptor
(Hakansson et al., 2006). Since these factors show experience-
dependent up-regulation during REM sleep (Ribeiro et al.,
1999, 2007; Ulloor and Datta, 2005), the decrease in REM sleep
promoted by haloperidol is likely to contribute further to the
decrease in the levels of plasticity factors.
One important aspect to consider is the possible motor
effect of haloperidol. At high doses, haloperidol impairs
motility for several hours after injection. However, no
motility differences were detected when total travelled
distance was assessed one day after drug injection (data not
shown). Furthermore, haloperidol-injected animals injected
immediately after training exhibited no object preference
24 h later, indicating that these learning deficits are not
related to a possible decrease in motor activity during expl-
oration of the novel objects, but rather reflect an impair-
ment in post-exploration memory consolidation.
A time course analysis of the molecular data indicates
that haloperidol impairs calcium signaling through a broad
cascade of events distributed over time (Figure 4). At 3 h
post-exploration, haloperidol decreased pCaMKII levels in
the CA3 field; at 6 h and 12 h post-exploration, the decrease
in pCaMKII levels occurred in all hippocampal regions (Sup-
plementary Figure 1, left panels). Zif-268 levels were sign-
ificantly reduced by haloperidol 6 h after training (Supp-
lementary Figure 1, middle panels), and BDNF levels were
decreased by haloperidol 12 h after training in the DG field
(Supplementary Figure 1, right panels).
Although several studies have reported increased pCaMKII
activity after task training or LTP induction, the exact time
window for the activation of this kinase after stimulus remains
controversial. A study using in vitro LTP recordings found a
significant increase in pCaMKII in the CA1 field 30 min after
tetanic stimulation (Ouyang et al., 1997), whereas another
study revealed that CaMKII remained in a specific, but enh-
anced phosphorylated state during the induction, and through-
out early- and late-LTP, maintaining high levels for 8 h (Ahmed
and Frey, 2005). The use of different tetanization paradigms in
those studies possibly resulted in large calcium influx changes,
affecting other signaling mechanisms in a dose-dependent
manner. Additionally, in vitro CaMKII phosphorylation in the
hippocampus was increased 30 min after inhibitory avoidance,
but not after 120 min (Cammarota et al., 1998). CaMKII plays a
role in plasticity-related synaptic tagging, a mechanism whe-
reby suitable synapses are sorted for subsequent strengthening
(Hernandez and Abel, 2011).
Zif-268 levels were lower in animals treated with haloper-
idol at 6 h (Figures 4, 5 and Supplementary 1). Since Zif-268
levels in the hippocampus are increased 1–2 h after a novel
stimulus (Barbosa et al., 2013), we believe that a 6 h increase
in vehicle-injected animals could be related to a second wave
of Zif-268 transcription and translation. This re-induction is
probably related to the occurrence of REM sleep in vehicle-
injected animals, but not in haloperidol-injected animals,
during the 4 h interval following object exposure.
Our results corroborate the notion of a late phase of BDNF
synthesis in the rat hippocampus 12 h after training, related
to the long-term persistence of memories (Bekinschtein
et al., 2007, 2014). A significant decrease of BDNF levels
Figure 4 Densitometry quantification of pCaMKII, Zif-268 and
BDNF staining at three distinct time points. The three graphs
represent pCaMKII, Zif-268 and BDNF staining measures at
different time points (3 h, 6 h and 12 h as indicated). Vehicle
and haloperidol were injected immediately after exploration.
Naïve animals did not receive any injection, nor were presented
to objects. Two-way ANOVAs and Bonferroni post-hoc tests were
performed for comparisons among these groups for each
molecule, considering time and treatment as independent
variables. *po0.05, ***po0.001 related to vehicle groups;
po0.05, po0.001 related to control groups.
A.S.C. França et al.500
in the hippocampus of animals treated with haloperidol was
detected in the pooled data (Figure 4). Injections of anti-
BDNF antibodies into the DG demonstrated BDNF's essential
role in the spontaneous location recognition task, impairing
task execution when animals had to disambiguate two
similar locations within an open field, but not when these
locations were made less similar (Bekinschtein et al., 2014).
Injection of recombinant BDNF into the DG enhanced patt-
ern separation. Furthermore, exposure of the animals to
either similar or dissimilar objects led to a 4-fold increase in
BDNF levels in the DG when rats explored two similar loc-
ations, but not when dissimilar locations were explored
(Katche et al., 2010; Bekinschtein et al., 2014).
Various studies have described the relation of BDNF with
other molecules involved in synaptic and cellular plasticity.
Activity-dependent BDNF upregulation may depend on
interaction with the NMDA receptor, which in turn interacts
with CaMKII (Sanhueza et al., 2011). The blockade of NMDA
and pCaMKII fully abolishes exercise-induced increases in
synapsin I and TrkB mRNA levels, promoting a decrease
of CREB and BDNF mRNA levels (Vaynman et al., 2003).
Complementarily, BDNF blockade 12 h after a behavioral
task prevents a late increase in c-Fos and Zif-268 proteins,
24 h after the initial stimulus (Bekinschtein et al., 2007).
The cyclic reactivation of plasticity-related proteins during
the post-training time may be essential to consolidate certain
kinds of memory Ribeiro and Nicolelis 2004; Hernandez and
Abel, 2011). For instance, the long-term persistence of some
memories depends on the circadian reactivation of the cAMP/
MAPK/CREB transcriptional pathway in the hippocampus
(Eckel-Mahan et al., 2008). This pathway can be modulated
by sleep deprivation, circadian rhythms, and neurotransmitter
systems involved in sleep–wake regulation (Hernandez and
Abel, 2011). The dopaminergic regulation of calcium-
dependent signaling pathways is a likely candidate mechanism
to causally connect sleep deficits and learning impairment.
Conflicts of interest
We declare no conflicts of interest with regard to the study
“D2 dopamine receptor regulation of learning, sleep and
plasticity”.
Figure 5 Cell counting quantification of Zif-268 staining at three distinct time points. The first three plots from left to right
illustrate Zif-268 individual cell staining of naïve (NV), vehicle (VH) and haloperidol (Halo) groups at different time points, set at 3 h
(A), 6 h (B) and 12 h (C) after injection, using Bonferroni-corrected ANOVAs followed by Bonferroni post-hoc tests. The two last plots
on the right illustrate inter-time ANOVA followed by Bonferroni post-hoc comparisons in vehicle groups (D) and Halo groups
(E) performed to evaluate the time course of Zif-268 protein levels in CA1 and CA3. Bars in black and white represent CA3 and CA1,
respectively. In A, B, C, *po0.05, **po0.01, ***po0.001 related to vehicle groups; po0.05, po0.01, po0.001 related to control
groups. In D and E, the differences are indicated by linked lines.
501Dopaminergic regulation of learning, sleep and plasticity
List of grants
Here follows a list of grants received by the authors during
preparation of this work:
Grant (1) FAPERN- PPP (Dr. Lobão-Soares: 2013-2014).
Grant (2) CNPQ- Universal; Grant number: 484408/2013-
5 (Dr. Lobão-Soares).
Grant (3) CNPq Universal; Grant number: 481351/2011-6
(Dr. Ribeiro).
Grant (4) FINEP Grant number 01.06.1092.00 (Dr.
Ribeiro).
Grant (5) FAPERN/CNPq Pronem. Grant Number: 003/
2011 (Dr. Ribeiro).
Grant (6) FAPESP Center for Neuromathematics Grant
number: 2013/ 07699-08 (Dr. Ribeiro).
Grant (7) UFRN. Grant number 00001/2010 (Dr. Ribeiro).
Only Grant 1 was used for salary support. Nobody except the
authors had a role in study design; in the collection, analysis
and interpretation of data; in the writing of the report; and in
the decision to submit the paper for publication.
Contributors
Arthur S. C. França helped design the experimental procedures,
participated in the acquisition of the electrophysiological and beha-
vioral data, performed data analysis, prepared figures and helped to
edit the manuscript. Bruno Lobão-Soares supervised the experimen-
tal design and data acquisition, helped in the analysis and helped
write the last version of the manuscript. Larissa Muratori collected and
analyzed histochemical and immunohistochemical data, prepa-
red figures and helped to edit the manuscript. George Nascimento
participated in surgical procedures and manufactured the head-stage
used in the electrophysiological recordings. Jessica Winne helped to
collect immunohistochemical data, Catia Pereira conducted behavioral
and immunohistochemical experiments, Selma Jeronimo supervised
histochemical and immunohistochemical procedures and contributed
to the experimental design. Sidarta Ribeiro coordinated the experi-
mental design, data analysis, figure preparation, and manuscript
writing.
Acknowledgement
Support was obtained from the Pew Latin American Fellows Program
in the Biomedical Sciences, Financiadora de Estudos e Projetos
(FINEP) Grant 01.06.1092.00, Ministério da Ciência, Tecnologia e
Inovação (MCTI), CNPq Universal 481351/2011-6, PQ 306604/2012-
4, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
SWS
Exposure Control
Waking 
Control
REM 
Exposure Control
P
re
fe
re
nc
e 
R
at
io
Vehicle Halo 0.3mg/Kg
Novelty Preference 
N
or
m
al
iz
ed
 R
E
M
 
Normalized Preference Ratio
Vehicle
Haloperidol
Exposure 
Vehicle 
Haloperidol
 D
ur
at
io
n 
(m
in
)
150 150 40
50
50
100
10
20
30
50
0.0
0.5
1.5
1.0
2.0
2.5
0.0
0.2
0.4
0.6
0.8
0.0 0.2 0.4 0.6 0.8
Figure 6 Haloperidol reduces REM sleep and proportionally impairs novelty preference. (A) Duration (minutes over a 4 h interval)
of WK, SWS and REM sleep in control versus exposed animals, injected with either vehicle or haloperidol immediately after
exploration. Haloperidol treatment or novelty exposure had no effect on the duration of WK or SWS (two-way ANOVA). In contrast,
REM sleep was significantly affected by both factors, with increased REM sleep duration after novelty exposure, and decreased REM
duration after haloperidol. (B) Animals injected with haloperidol showed decreased novelty preference ratio in comparison with
vehicle-injected controls group. (C) Normalized REM sleep duration and normalized preference ratio (Halo or Vehicle/Halo+Vehicle)
are positively correlated (Spearman's regression R=0.56, p=0.0127). *po0.05 and ***po0.001.
A.S.C. França et al.502
(CAPES), FAPERN/CNPq Pronem 003/2011, Capes SticAmSud, FAPESP
Center for Neuromathematics (Grant no. 2013/07699-0, São Paulo
Research Foundation), Associação Alberto Santos Dumont para
Apoio à Pesquisa (AASDAP), and NIMBIOS working group “Multi-scale
analysis of cortical networks.” We thank V. Arboes for veterin-
ary care, A. Ragoni and A. Karla for administrative help, G.L. Borba
Filho for electrode manufacture, and D. Koshiyama for library
support.
Appendix. Supporting information
Supplementary data associated with this article can be
found in the online version at http://dx.doi.org/10.1016/
j.euroneuro.2015.01.011.
References
Ahmed, T., Frey, J.U., 2005. Plasticity-specific phosphorylation of
CaMKII, MAP-kinases and CREB during late-LTP in rat hippocam-
pal slices in vitro. Neuropharmacology 49, 477–492.
Barbosa, F.F., Santos, J.R., Meurer, Y.S.R., Macêdo, P.T., Ferreira, L.
M.S., Pontes, I.M.O., Ribeiro, A.M., Silva, R.H., 2013. Differ-
ential cortical c-Fos and Zif-268 expression after object and
spatial memory processing in a standard or episodic-like object
recognition task. Front. Behav. Neurosci. 7, 112.
Bekinschtein, P., Cammarota, M., Igaz, L.M., Bevilaqua, L.R.M.,
Izquierdo, I., Medina, J.H., 2007. Persistence of long-term
memory storage requires a late protein synthesis- and bdnf-
dependent phase in the hippocampus. Neuron 53, 261–277.
Bekinschtein, P., Cammarota, M., Medina, J.H.,, 2014. BDNF and
memory processing. Neuropharmacology. BDNF Regulation of
Synaptic Structure, Function, and Plasticity 76 (Part C),
677–683.
Binder, S., Baier, P.C., Mölle, M., Inostroza, M., Born, J., Marshall,
L., 2012. Sleep enhances memory consolidation in the
hippocampus-dependent object-place recognition task in rats.
Neurobiol. Learn. Mem. 97, 213–219.
Blitzer, R.D., Connor, J.H., Brown, G.P., Wong, T., Shenolikar, S.,
Iyengar, R., Landau, E.M., 1998. Gating of CaMKII by cAMP-
regulated protein phosphatase activity during LTP. Science 280,
1940–1942.
Bozon, B., Davis, S., Laroche, S., 2003. A requirement for the
immediate early gene zif268 in reconsolidation of recognition
memory after retrieval. Neuron 40, 695–701.
Cammarota, M., Bernabeu, R., Levi De Stein, M., Izquierdo, I.,
Medina, J.H., 1998. Learning-specific, time-dependent increases
in hippocampal Ca2+/calmodulin-dependent protein kinase II
activity and AMPA GluR1 subunit immunoreactivity. Eur. J.
Neurosci. 10, 2669–2676.
De Lima, M.N.M., Presti-Torres, J., Dornelles, A., Scalco, F.S.,
Roesler, R., Garcia, V.A., Schröder, N., 2011. Modulatory
influence of dopamine receptors on consolidation of object
recognition memory. Neurobiol. Learn. Mem. 95, 305–310.
Dzirasa, K., Ribeiro, S., Costa, R., Santos, L.M., Lin, S.-C., Grosmark,
A., Sotnikova, T.D., Gainetdinov, R.R., Caron, M.G., Nicolelis, M.A.
L., 2006. Dopaminergic control of sleep–wake states. J. Neurosci.
26, 10577–10589.
Eckel-Mahan, K.L., Phan, T., Han, S., Wang, H., Chan, G.C.K.,
Scheiner, Z.S., Storm, D.R., 2008. Circadian oscillation of
hippocampal MAPK activity and cAmp: implications for memory
persistence. Nat. Neurosci. 11, 1074–1082.
França, A.S.C., do Nascimento, G.C., Lopes-dos-Santos, V., Mur-
atori, L., Ribeiro, S., Lobão-Soares, B., Tort, A.B.L., 2014. Beta2
oscillations (23–30 Hz) in the mouse hippocampus during novel
object recognition. Eur. J. Neurosci 40, 3693–3703.
Gervasoni, D., Lin, S.C., Ribeiro, S., Soares, E.S., Pantoja, J.,
Nicolelis, M.A.L., 2004. Global forebrain dynamics predict rat
behavioral states and their transitions. J. Neurosci. 24,
11137–11147.
Guzowski, J.F., 2002. Insights into immediate-early gene function in
hippocampal memory consolidation using antisense oligonucleo-
tide and fluorescent imaging approaches. Hippocampus 12,
86–104.
Hakansson, K., Galdi, S., Hendrick, J., Snyder, G., Greengard, P.,
Fisone, G., 2006. Regulation of phosphorylation of the GluR1
AMPA receptor by dopamine D 2 receptors. J. Neurochem. 96,
482–488.
Hasbi, A., Fan, T., Alijaniaram, M., Nguyen, T., Perreault, M.L.,
O'Dowd, B.F., George, S.R., 2009. Calcium signaling cascade
links dopamine D1–D2 receptor heteromer to striatal BDNF
production and neuronal growth. Proc. Natl. Acad. Sci. USA
106, 21377–21382.
Hernandez, P.J., Abel, T., 2011. A molecular basis for interactions
between sleep and memory. Sleep Med. Clin. 6, 71–84.
Hughes, R.N., 2007. Neotic preferences in laboratory rodents:
issues, assessment and substrates. Neurosci. Biobehav. Rev. 31,
441–464.
Ji, Y., Pang, P.T., Feng, L., Lu, B., 2005. Cyclic AMP controls BDNF-
induced TrkB phosphorylation and dendritic spine formation in
mature hippocampal neurons. Nat. Neurosci. 8, 164–172.
Kandel, E.R., Dudai, Y., Mayford, M.R., 2014. The molecular and
systems biology of memory. Cell 157, 163–186.
Kang, J.H., Kim, M.J., Jang, H.I., Koh, K.H., Yum, K.S., Rhie, D.J.,
Yoon, S.H., Hahn, S.J., Kim, M.S., Jo, Y.H., 2007. Proximal cyclic
AMP response element is essential for exendin-4 induction of rat
EGR-1 gene. Am. J. Physiol. Endocrinol. Metab. 292, E215–E222.
Katche, C., Bekinschtein, P., Slipczuk, L., Goldin, A., Izquierdo, I.
A., Cammarota, M., Medina, J.H., 2010. Delayed wave of c-Fos
expression in the dorsal hippocampus involved specifically in
persistence of long-term memory storage. Proc. Natl. Acad. Sci.
USA 107, 349–354.
Konradi, C., Heckers, S., 1995. Haloperidol-induced Fos expression
in striatum is dependent upon transcription factor cyclic amp
response element binding protein. Neuroscience 65, 1051–1061.
Lemon, N., Manahan-Vaughan, D., 2006. Dopamine D1/D5 receptors
gate the acquisition of novel information through hippocampal
long-term potentiation and long-term depression. J. Neurosci.
26, 7723–7729.
Lima, M.M.S., Andersen, M.L., Reksidler, A.B., Vital, M.A.B.F.,
Tufik, S., 2007. The role of the substantia nigra pars compacta
in regulating sleep patterns in rats. PLoS One 2 (6), e513.
Lima, M.M.S., Andersen, M.L., Reksidler, A.B., Silva, A., Zager, A.,
Zanata, S.M., Vital, M.A.B.F., Tufik, S., 2008. Blockage of
dopaminergic D(2) receptors produces decrease of REM but not
of slow wave sleep in rats after REM sleep deprivation. Behav.
Brain Res. 188, 406–411.
Lisman, J., Schulman, H., Cline, H., 2002. The molecular basis of
CaMKII function in synaptic and behavioural memory. Nat. Rev.
Neurosci. 3, 175–190.
Lisman, J.E., Grace, A.A., 2005. The hippocampal-VTA loop: con-
trolling the entry of information into long-term memory. Neuron
46, 703–713.
Manahan-Vaughan, D., Kulla, A., 2003. Regulation of depotentiation
and long-term potentiation in the dentate gyrus of freely moving
rats by dopamine D2-like receptors. Cereb. Cortex N.Y. 1991
(13), 123–135.
Medina, J.H., Bekinschtein, P., Cammarota, M., Izquierdo, I., 2008.
Do memories consolidate to persist or do they persist to
consolidate? Behav. Brain Res. 192, 61–69.
Monti, J.M., Monti, D., 2007. The involvement of dopamine in the
modulation of sleep and waking. Sleep Med. Rev. 11, 113–133.
Morice, E., Billard, J.M., Denis, C., Mathieu, F., Betancur, C.,
Epelbaum, J., Giros, B., Nosten-Bertrand, M., 2007. Parallel loss
503Dopaminergic regulation of learning, sleep and plasticity
of hippocampal LTD and cognitive flexibility in a genetic model
of hyperdopaminergia. Neuropsychopharmacol.: Off. Publ. Am.
Coll. Neuropsychopharmacol. 32, 2108–2116.
Nader, K., LeDoux, J.E., 1999. Inhibition of the mesoamygdala
dopaminergic pathway impairs the retrieval of conditioned fear
associations. Behav. Neurosci. 113, 891–901.
Ouyang, Y., Kantor, D., Harris, K.M., Schuman, E.M., Kennedy, M.
B., 1997. Visualization of the distribution of autophosphorylated
calcium/calmodulin-dependent protein kinase II after tetanic
stimulation in the CA1 area of the hippocampus. J. Neurosci. 17,
5416–5427.
Panja, D., Bramham, C.R., 2014. BDNF mechanisms in late LTP
formation: a synthesis and breakdown. Neuropharmacology 76
(Part C), 664–676.
Piterkin, P., Cole, E., Cossette, M.P., Gaskin, S., Mumby, D.G., 2008.
A limited role for the hippocampus in the modulation of novel-
object preference by contextual cues. Learn. Mem. 15, 785–791.
Ponnusamy, R., Nissim, H.A., Barad, M., 2005. Systemic blockade of
D2-like dopamine receptors facilitates extinction of conditioned
fear in mice. Learn. Mem. 12, 399–406.
Proença, M.B., Dombrowski, P.A., Da Cunha, C., Fischer, L., Ferraz,
A.C., Lima, M.M.S., 2014. Dopaminergic D2 receptor is a key
player in the substantia nigra pars compacta neuronal activation
mediated by REM sleep deprivation. Neuropharmacology 76
(Part A), 118–126.
Ribeiro, S., Goyal, V., Mello, C.V., Pavlides, C., 1999. Brain gene
expression during REM sleep depends on prior waking experi-
ence. Learn. Mem. 6, 500–508.
Ribeiro, S., Mello, C.V., Velho, T., Gardner, T.J., Jarvis, E.D.,
Pavlides, C., 2002. Induction of hippocampal long-term poten-
tiation during waking leads to increased extrahippocampal zif-
268 expression during ensuing rapid-eye-movement sleep. J.
Neurosci. 22, 10914–10923.
Ribeiro, S., Nicolelis, M.A.L., 2004. Reverberation, storage, and
postsynaptic propagation of memories during sleep. Learn.
Mem. 11, 686–696.
Ribeiro, S., Shi, X., Engelhard, M., Zhou, Y., Zhang, H., Gervasoni,
D., Lin, S.C., Wada, K., Lemos, N.A.M., Nicolelis, M.A.L., 2007.
Novel experience induces persistent sleep-dependent plasticity
in the cortex but not in the hippocampus. Front. Neurosci. 1,
43–55.
Jones, M.W., Errington, M.L., French, P.J., Fine, A., Bliss, T.V.,
Garel, S., Charnay, P., Bozon, B., Laroche, S., Davis, S., 2001. A
requirement for the immediate early gene Zif268 in the expres-
sion of late LTP and long-term memories. Nat. Neurosci. 4,
289–296.
Rossato, J.I., Bevilaqua, L.R.M., Izquierdo, I., Medina, J.H., Cam-
marota, M., 2009. Dopamine controls persistence of long-term
memory storage. Science 325, 1017–1020.
Sanhueza, M., Fernandez-Villalobos, G., Stein, I.S., Kasumova, G.,
Zhang, P., Bayer, K.U., Otmakhov, N., Hell, J.W., Lisman, J.,
2011. Role of the CaMKII/NMDA receptor complex in the main-
tenance of synaptic strength. J. Neurosci. 31, 9170–9178.
Ulloor, J., Datta, S., 2005. Spatio-temporal activation of cyclic AMP
response element-binding protein, activity-regulated cytoskele-
tal-associated protein and brain-derived nerve growth factor: a
mechanism for pontine-wave generator activation-dependent
two-way active-avoidance memor. J. Neurochem. 95, 418–428.
Vaynman, S., Ying, Z., Gomez-Pinilla, F., 2003. Interplay between
brain-derived neurotrophic factor and signal transduction mod-
ulators in the regulation of the effects of exercise on synaptic-
plasticity. Neuroscience 122, 647–657.
Vecsey, C.G., Baillie, G.S., Jaganath, D., Havekes, R., Daniels, A.,
Wimmer, M., Huang, T., Brown, K.M., Li, X.Y., Descalzi, G., Kim,
S.S., Chen, T., Shang, Y.Z., Zhuo, M., Houslay, M.D., Abel, T.,
2009. Sleep deprivation impairs cAMP signalling in the hippo-
campus. Nature 461, 1122–1125.
Whitlock, J.R., Heynen, A.J., Shuler, M.G., Bear, M.F., 2006.
Learning induces long-term potentiation in the hippocampus.
Science 313, 1093–1097.
Xia, Z., Storm, D.R., 2005. The role of calmodulin as a signal
integrator for synaptic plasticity. Nat. Rev. Neurosci. 6, 267–276.
A.S.C. França et al.504
All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
N
V
VH
H
A
LO N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
N
V
VH
H
A
LO N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
st
N
V
VH
 3
H
VH
 6
H
VH
 1
2H N
V
VH
 3
H
VH
 6
H
VH
 1
2H N
V
VH
 3
H
VH
 6
H
VH
 1
2H
0.0
0.5
1.0
1.5
N
V
VH
 3
H
VH
 6
H
VH
 1
2H N
V
VH
 3
H
VH
 6
H
VH
 1
2H
0.0
0.5
1.0
1.5
N
V
V
H
 3
H
V
H
 6
H
V
H
 1
2H N
V
V
H
 3
H
V
H
 6
H
V
H
 1
2H N
V
V
H
 3
H
V
H
 6
H
V
H
 1
2H
0.0
0.5
1.0
1.5
DG
CA1
CA3
* *** *** ******
N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H
0.0
0.5
1.0
1.5 DG
CA1
CA3
**
***
*** **
***
**
***
Ce
ll 
st
ai
ni
ng
 ra
tio
N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H
0.0
0.5
1.0
1.5
***
*** ***
***
*** *
N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H N
V
H
A
LO
 3
H
H
A
LO
 6
H
H
A
LO
 1
2H
0.0
0.5
1.0
1.5
CaMKII-P 
N
V
VH
H
A
LO N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
#
N
V
VH
H
A
LO N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
##
**
***
### ***
###
N
V
VH
H
A
LO N
V
VH
H
A
LO N
V
VH
H
A
LO
DG
CA1
CA3
0.0
0.5
1.0
1.5
*** ** ***
##
ZIF-268
N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
N
V
VH
H
A
LO N
V
VH
H
A
LO
0.0
0.5
1.0
1.5
###
**
##
*
NV VH
HA
LO NV VH
HA
LO
0.0
0.5
1.0
1.5
 BDNF
NV VH
HA
LO NV VH
HA
LO NV VH
HA
LO
0.0
0.5
1.0
1.5
A B C
3h
6h
12h
VH
Inter
time
Halo
Inter
time
 
Ce
ll 
st
ai
ni
ng
 ra
tio
Ce
ll 
st
ai
ni
ng
 ra
tio
Ce
ll 
st
ai
ni
ng
 ra
tio DG
CA1
CA3
DG
CA1
CA3
Ce
ll 
st
ai
ni
ng
 ra
tio
Figura relacionada as tabelas suplementares do anexo1. (Figura não publicada). A figura mostra
a comparação entre as regiões CA1, CA3 e giro denteado de animais submetidos aos tratamentos 
de haloperidol 0.3 mg/Kg, veículo e naive.
 pCaMKII 
Source of 
Variation 
MANOVA 
F P Value DF 
Interaction 7.87 0.0001 (4,54) 
Treatment 42.88 0.0001 (2,54) 
Time 1.67 0.19 (2,54) 
Time 
Bonferroni 
Groups Mean ± SEM; N P Value 
3 hours VH x Halo 
0.92 ± 0.04; N = 5 
0.83 ± 0.05; N = 6 
P > 0.05  
6 hours NV X VH 
1.05 ± 0.05; N = 7 
1.17 ± 0.01; N = 6 
P > 0.05 
12 hours 
NV x VH 
1.01 ± 0.03; N = 7 
1.14 ± 0.04; N = 7 
P > 0.05 
NV x Halo 
1.01 ± 0.03; N = 7 
0.87 ± 0.03; N = 7 
P > 0.05 
Zif-268 
Source of 
Variation 
MANOVA 
F P Value DF 
Interaction 5.58 0.0007 (4,45) 
Treatment 7.82 0.0012 (2,45) 
Time 0.14 0.86 (2,45) 
Time 
Bonferroni 
Groups Mean ± SEM; N P Value 
3 hours 
NV x Halo 
0.98 ± 0.06; N = 6 
0.98 ± 0.07; N = 5 
P > 0.05 
VH x Halo 
1.03 ± 0.05; N = 5 
0.98 ± 0.07; N = 5 
P > 0.05 
NV x VH 
0.98 ± 0.06; N = 6 
1.03 ± 0.05; N = 5 
P > 0.05 
6 hours NV x VH 
1.17 ± 0.06; N = 6 
1.08 ± 0.07; N = 6 
P > 0.05 
12 hours 
NV x Halo 
1.07 ± 0.06; N = 6 
0.98 ± 0.04; N = 7 
P > 0.05 
VH x Halo 
0.94 ± 0.05; N = 7 
0.98 ± 0.04; N = 7 
P > 0.05 
NV x VH 
1.07 ± 0.06; N = 6 
0.94 ± 0.05; N = 7 
P > 0.05 
BDNF 
Source of 
Variation 
MANOVA 
F P Value DF 
Interaction 0.24 0.91 (4,43) 
Treatment 5.18 0.009 (2,43) 
Time 0.22 0.80 (2,43) 
Time Bonferroni 
Groups Mean ± SEM; N P Value 
3 hours 
NV x Halo 
1.02 ± 0.11; N = 6 
0.89 ± 0.06; N = 5 
P > 0.05 
VH x Halo 
1.06 ± 0.05; N = 5 
0.89 ± 0.06; N = 5 
P > 0.05 
NV x VH 
1.02 ± 0.11; N = 6 
1.06 ± 0.05; N = 5 
P > 0.05 
6 hours 
NV x Halo 
1.01 ± 0.03; N = 5 
0.96 ± 0.08; N = 5 
P > 0.05 
VH x Halo 
1.11 ± 0.03; N = 5 
0.96 ± 0.08; N = 5 
P > 0.05 
NV x VH 
1.01 ± 0.03; N = 5 
1.11 ± 0.03; N = 5 
P > 0.05 
12 hours 
NV x Halo 
1.07 ± 0.06; N = 6 
0.98 ± 0.04; N = 7 
P > 0.05 
NV x VH 
1.07 ± 0.06; N = 6 
0.94 ± 0.06; N = 7 
P > 0.05 
 
 
Supplementary Table 1: Statistical summary of the non-significant results in the 
densitometry analysis for p-CAMKII, Zif-268 and BDNF staining. MANOVA 
comparisons of labeling measurements in the hippocampus were followed by 
Bonferroni post-hoc tests. Comparisons were performed considering two independent 
variables: treatment and time of injection. 
 
 
   
3h Group 
Molecule Region 
ANOVA  Bonferroni 
F P value* DF  Groups Mean ± SEM; N P Value 
pCaMKII 
CA1 1.98 0.51 2;18 
VH X halo 
0.95 ± 0.07; N = 5  
0.81 ± 0.08; N = 6 
P > 0.05 
NV x halo 
1.03 ± 0.08; N = 7  
0.81 ± 0.08; N = 5 
P > 0.05 
VH x NV 
0.95 ± 0.07; N = 5  
1.03 ± 0.08; N = 7  
P > 0.05 
CA3 5.58 0.049 2;17 
VH X halo 
0.98 ± 0.10; N = 5  
0.86 ± 0.06; N = 5 
P > 0.05 
NV X halo 
1.18 ± 0.10; N = 7 
0.86 ± 0.06; N = 5 
P < 0.05 
VH x NV 
0.98 ± 0.10; N = 5  
1.18 ± 0.10; N = 7 
P > 0.05 
DG 3.02 0.25 2;15 
VH x halo 
0.83 ± 0.05; N = 5  
0.91 ± 0.08; N = 5 
P > 0.05 
NV x halo 
1.05 ± 0.05; N = 6 
0.91 ± 0.08; N = 5 
P > 0.05 
VH x NV 
0.83 ± 0.05; N = 5  
1.05 ± 0.05; N = 6 
P > 0.05 
Zif-268 
CA1 0.43 0.65 2;15 
VH X halo 
1.05 ± 0.05; N = 5  
0.95 ± 0.05; N = 5 
P > 0.05 
NV x halo 
0.98 ± 0.09; N = 6  
0.95 ± 0.05; N = 5 
P > 0.05 
VH x NV 
1.05 ± 0.05; N = 5  
0.98 ± 0.09; N = 6   
P > 0.05 
CA3 0.05 0.94 2;15 
VH X halo 
1.00 ± 0.07; N = 5  
1.01 ± 0.09; N = 5 
P > 0.05 
NV X halo 
0.98 ± 0.05; N = 6 
1.01 ± 0.09; N = 5 
P > 0.05 
VH x NV 
1.00 ± 0.07; N = 5  
0.98 ± 0.05; N = 6 
P > 0.05 
BDNF CA1 2.03 0.49 2;15 
VH X halo 
1.07 ± 0.08; N = 5  
0.86 ± 0.08; N = 5 
P > 0.05 
NV x halo 
1.08 ± 0.08; N = 6  
0.86 ± 0.08; N = 5 
P > 0.05 
VH x NV 
1.07 ± 0.08; N = 5 
1.08 ± 0.08; N = 6  
P > 0.05 
CA3 1.74 0.63 2;15 
VH X halo 
1.06 ± 0.09; N = 5  
0.86 ± 0.07; N = 5 
P > 0.05 
NV X halo 
1.09 ± 0.09; N = 6 
0.86 ± 0.07; N = 5 
P > 0.05 
VH x NV 
1.09 ± 0.09; N = 6 
1.18 ± 0.10; N = 7 
P > 0.05 
DG 1.04 0.99 2;15 
VH x halo 
1.10 ± 0.03; N = 5  
0.96 ± 0.05; N = 5 
P > 0.05 
NV x halo 
1.03 ± 0.08; N = 6 
0.96 ± 0.05; N = 5 
P > 0.05 
VH x NV 
1.10 ± 0.03; N = 5  
1.08 ± 0.05; N = 6 
P > 0.05 
 
Supplementary Table 2: Statistical summary of the densitometry analysis 
at 3h post-exploration for different hippocampal regions (CA1, CA3, DG). 
Bonferroni-corrected ANOVA comparisons of labeling measurements for 
pCAMKII, Zif-268 and BDNF staining were followed by Bonferroni post-hoc 
tests. The significant difference is indicated in red. 
 
6h Group 
Molecule Region 
ANOVA  Bonferroni 
F P value* DF  Groups Mean ± SEM; N P Value 
pCaMKII 
CA1 33.22 0.0003 2;16 
VH x halo 
1.25 ± 0.02; N = 6 
0.63 ± 0.05; N = 6 
P < 0.001  
NV x halo 
1.06 ± 0.06; N = 7 
0.63 ± 0.05; N = 6 
P < 0.001  
VH x NV 
1.25 ± 0.02; N = 6 
1.06 ± 0.06; N = 7 
P > 0.05 
CA3 28.28 0.0003 2;16 
VH x halo 
1.17 ± 0.02; N = 6 
0.71 ± 0.05; N = 6 
P < 0.001  
NV x halo 
1.05 ± 0.04; N = 7 
0.71 ± 0.05; N = 6 
P < 0.001 
VH x NV 
1.17 ± 0.02; N = 6 
1.05 ± 0.04; N = 7 
P > 0.05 
DG 11.56 0.0027 2,15 
VH x halo 
1.10 ± 0.05; N = 6 
0.77 ± 0.03; N = 6 
P < 0.01 
NV x halo 
1.05 ± 0.05; N = 6 
0.77 ± 0.03; N = 6 
P < 0.01 
VH x NV 
1.10 ± 0.05; N = 6 
1.05 ± 0.05; N = 6 
P > 0.05 
Zif-268 
CA1 14.27 0.0009 2;15 
NV x halo 
1.18 ± 0.07; N = 6 
0.61 ± 0.09; N = 6 
P < 0.001  
VH x halo 
1.11 ± 0.06; N = 6 
0.61 ± 0.09; N = 6 
P < 0.01 
VH x NV 
1.18 ± 0.07; N = 6 
1.11 ± 0.06; N = 6 
P > 0.05 
CA3 9.92 0.0054 2;15 
NV x halo 
1.18 ± 0.07; N = 6 
0.70 ± 0.06; N = 6 
P < 0.01 
VH x halo 
1.07 ± 0.09; N = 6 
0.70 ± 0.06; N = 6 
P < 0.05 
VH x NV 
1.18 ± 0.07; N = 6 
1.07 ± 0.09; N = 6 
P > 0.05 
BDNF CA1 2.83 0.27 2;15 
VH X halo 
1.19 ± 0.06; N = 5  
0.99 ± 0.06; N = 5 
P > 0.05 
NV x halo 
1.04 ± 0.05; N = 6  
0.99 ± 0.06; N = 5 
P > 0.05 
VH x NV 
1.19 ± 0.06; N = 5  
1.04 ± 0.05; N = 6  
P > 0.05 
CA3 0.61 0.99 2;15 
VH X halo 
1.05 ± 0.07; N = 5  
0.94 ± 0.07; N = 5 
P > 0.05 
NV X halo 
1.05 ± 0.05; N = 6 
0.94 ± 0.07; N = 5 
P > 0.05 
VH x NV 
1.05 ± 0.07; N = 5 
1.05 ± 0.05; N = 6 
P > 0.05 
DG 1.24 0.96 2;15 
VH x halo 
1.10 ± 0.08; N = 5  
0.95 ± 0.02; N = 5 
P > 0.05 
NV x halo 
1.00 ± 0.08; N = 6 
0.95 ± 0.02; N = 5 
P > 0.05 
VH x NV 
1.10 ± 0.08; N = 5  
1.00 ± 0.08; N = 6 
P > 0.05 
 
Supplementary Table 3: Statistical summary of the densitometry analysis 
at 6h post-exploration for different hippocampal regions (CA1, CA3, DG). 
Bonferroni-corrected ANOVA comparisons of labeling measurements for 
pCaMKII, Zif-268 and BDNF staining were followed by Bonferroni post-hoc 
tests. The significant differences are indicated in red. 
 
12h Group 
Molecule Region 
ANOVA  Bonferroni 
F P value* DF  Groups Mean ± SEM; N P Value 
pCaMKII 
CA1 9.23 0.0051 2;21 
VH x halo 
1.13 ± 0.01; N = 7 
0.87 ± 0.04; N = 7 
P < 0.01  
NV x halo 
0.97 ± 0.05; N = 7 
0.87 ± 0.04; N = 7 
P > 0.05 
VH x NV 
1.13 ± 0.01; N = 7 
0.97 ± 0.05; N = 7 
P > 0.05 
CA3 14.24 0.0003 2;21 
VH x halo 
1.17 ± 0.02; N = 7 
0.84 ± 0.04; N = 7 
P < 0.001  
NV x halo 
1.04 ± 0.03; N = 7 
0.84 ± 0.04; N = 7 
P < 0.01  
VH x NV 
1.17 ± 0.02; N = 7 
1.04 ± 0.03; N = 7 
P > 0.05 
DG 11.12 0.0024 2;21 
VH x halo 
1.14 ± 0.02; N = 7 
0.90 ± 0.04; N = 7 
P < 0.001 
NV x halo 
1.01 ± 0.04; N = 7 
0.90 ± 0.04; N = 7 
P > 0.05 
VH x NV 
1.14 ± 0.02; N = 7 
1.01 ± 0.04; N = 7 
P > 0.05 
Zif-268 
CA1 0.56 0.99 2;20 
VH x halo 
0.93 ± 0.08; N = 7 
0.99 ± 0.06; N = 7 
P > 0.05 
NV x halo 
1.05 ± 0.08; N = 6 
0.99 ± 0.06; N = 7 
P > 0.05 
VH x NV 
0.93 ± 0.08; N = 7 
1.05 ± 0.08; N = 6 
P > 0.05 
CA3 1.27 0.90 2;15 
VH x halo 
0.95 ± 0.07; N = 7 
0.96 ± 0.06; N = 7 
P > 0.05 
NV x halo 
1.09 ± 0.06; N = 6 
0.96 ± 0.06; N = 7 
P > 0.05 
VH x NV 
0.95 ± 0.07; N = 7 
1.09 ± 0.06; N = 6 
P > 0.05 
BDNF CA1 1.18 0.96 2;20 
VH X halo 
1.12 ± 0.05; N = 7  
1.01 ± 0.05; N = 7 
P > 0.05 
NV x halo 
0.98 ± 0.08; N = 6  
1.01 ± 0.05; N = 7 
P > 0.05 
VH x NV 
1.12 ± 0.05; N = 7  
0.98 ± 0.08; N =6 
P > 0.05 
CA3 2.99 0.21 2;19 
VH X halo 
1.11 ± 0.06; N = 7  
0.87 ± 0.05; N = 7 
P > 0.05 
NV X halo 
0.95 ± 0.09; N = 6 
0.87 ± 0.05; N = 7 
P > 0.05 
VH x NV 
1.11 ± 0.06; N = 7  
0.95 ± 0.09; N = 6 
P > 0.05 
DG 5.06 0.0560 2;20 
VH x halo 
1.19 ± 0.08; N = 7 
0.81 ± 0.08; N = 7 
P > 0.05 
NV x halo 
1.01 ± 0.09; N = 6 
0.81 ± 0.08; N = 7 
P > 0.05 
VH x NV 
1.19 ± 0.08; N = 7 
1.01 ± 0.09; N = 6 
P > 0.05 
 
Supplementary Table 4: Statistical summary of the densitometry analysis 
at 12h post-exploration for different hippocampal regions (CA1, CA3, 
DG). Bonferroni-corrected ANOVA comparisons of labeling measurements for 
pCaMKII, Zif-268 and BDNF staining were followed by Bonferroni post-hoc 
tests. The significant differences are indicated in red. Note the statistical trend 
for a difference between VH and Halo for BDNF in the DG (p=0.056). 
 
Behavioural Brain Research 308 (2016) 211–216
Contents lists available at ScienceDirect
Behavioural  Brain  Research
jou rn al hom epage: www.elsev ier .com/ locate /bbr
Short  communication
Object  recognition  impairment  and  rescue  by  a  dopamine  D2
antagonist  in  hyperdopaminergic  mice
Arthur  S.C.  Franc¸ aa, Larissa  Muratorib, George  Carlos  Nascimentoc,
Catia  Mendes  Pereirad, Sidarta  Ribeiroa,  Bruno  Lobão-Soarese,∗
a Brain Institute, Federal University of Rio Grande do Norte, RN 59056-450, Brazil
b Biochemistry Department, Federal University of Rio Grande do Norte, Brazil
c Medical Engineering Department, Federal University of Rio Grande do Norte, Brazil
d Edmond & Lily Safra International Institute of Neuroscience of Natal, Brazil
e Biophysics and Pharmacology Department, Federal University of Rio Grande do Norte, RN 59078-970, Brazil
h  i g  h  l  i  g  h  t  s
• Heterozygous  mice  for  dopamine  transporter  (DAT+/−)  exhibit  higher  levels  of  synaptic  dopamine.
• Here  we  confirmed  that  D2  antagonism  can  interfere  in  object  recognition.
• We  observed  in  DAT+/−  a natural  phenotype  of  impaired  novel  object  memory  recognition.
• The  injection  of haloperidol  at  0.05 mg  before  object  exposition  restored  object  recognition.
• This  effect  could  be explained  by restoring  D2  activity  to  optimal  levels,  acting  on memory  acquisition.
a  r  t i  c  l  e  i  n  f  o
Article history:
Received 4 September 2015
Received in revised form 29 March 2016
Accepted 4 April 2016
Available online 6 April 2016
Keywords:
DAT-KO
Heterozygous
Haloperidol
Dopamine
Object recognition
a  b  s  t  r  a  c  t
Genetically-modified  mice  without  the  dopamine  transporter  (DAT)  are  hyperdopaminergic,  and  serve
as models  for  studies  of  addiction,  mania  and  hyperactive  disorders.  Here  we investigated  the  capacity
for  object  recognition  in mildly  hyperdopaminergic  mice  heterozygous  for  DAT  (DAT  +/−),  with  synap-
tic  dopaminergic  levels  situated  between  those  shown  by DAT  −/−  homozygous  and  wild-type  (WT)
mice.  We  used  a classical  dopamine  D2 antagonist,  haloperidol,  to modulate  the  levels  of  dopaminergic
transmission  in  a dose-dependent  manner,  before  or after  exploring  novel  objects.  In  comparison  with
WT  mice,  DAT +/−  mice showed  a deficit  in  object  recognition  upon  subsequent  testing  24  h  later.  This
deficit  was  compensated  by  a single  0.05  mg/kg  haloperidol  injection  30 min  before  training.  In  all  mice,
a  0.3  mg/kg  haloperidol  injected  immediately  after  training  impaired  object  recognition.  The  results  indi-
cate  that  a  mild  enhancement  of dopaminergic  levels  can be  detrimental  to object  recognition,  and  that
this deficit  can  be  rescued  by a low  dose  of a D2  dopamine  receptor  antagonist.  This  suggests  that  novel
object  recognition  is  optimal  at intermediate  levels  of D2  receptor  activity.
© 2016  Elsevier  B.V.  All  rights  reserved.
Dopamine (DA) is a neurotransmitter related to complex behav-
iors, such as: reward perception, social interaction [1,2], and is also
linked to memory consolidation both in humans and rodents [3].
Alterations in DA synaptic regulation are related to a large vari-
ety of mental diseases, such as schizophrenia, hyperactivity, mood
disorders, and Parkinson disease [4,5].
∗ Corresponding author.
E-mail addresses: brunolobaosoares@gmail.com,
brunolobaosoares@hotmail.com (B. Lobão-Soares).
DA has many receptor subtypes, but they are basically divided
in D1 and D2 families [3]. DA, mainly through D1 receptors, elicits
the onset of the late phase of long term potentiation in the hip-
pocampus [6], control plasticity-induced protein synthesis [6], and
enhance the persistence of hippocampus-dependent memories [7].
The involvement of both dopamine receptors families with
learning and memory is widely reported for working memory [3],
spatial learning [3,6], aversive memory [7], reward-related learn-
ing [8] and cognitive flexibility [9]. In particular, impairment in
object recognition has been induced by D2 activity suppression due
to haloperidol IP injection [10], by D1 activity suppression trough
http://dx.doi.org/10.1016/j.bbr.2016.04.009
0166-4328/© 2016 Elsevier B.V. All rights reserved.
212 A.S.C. Franc¸ a et al. / Behavioural Brain Research 308 (2016) 211–216
IP injection of SCH-23390 [11], or by D1 activity facilitation via
SKF81297 microinfusion in the prefrontal cortex [12].
More specifically, the lack of D2 receptors was  associated to
odor discrimination in mice [13]. The impairment of D2 activity
was related to sleep regulation and memory consolidation, through
the down-regulation of plasticity factors and reduction of rapid
eye movement (REM) sleep amount [10]. The antagonism of D2
receptors also led to electrophysiological changes during object
exploration [14]. Furthermore, mice lacking D2 receptors do show
memory consolidation deficits [15]. On the other hand, animals
with high levels of synaptic DA, namely knockout mice for the DA
transporter (DAT-KO), show impairment in the Morris water maze
task [9], and also impairment of spatial memory in the Y-maze
[16]. These animals presented, however, less immobility time in
the forced swimming task [17], and exhibited an increase in loco-
motion, reversed by D2 receptor blocking [17].
DAT-KO mice were initially generated to study the influence
of hyperdopaminergia in physiological and behavioral parame-
ters, and in response to dopaminergic drug administration [13].
DAT—heterozygous (defined here as DAT +/−)  mice, expressing
only one copy of the DAT gene, were also investigated [18]. Clear-
ance of dopamine released in the synapse takes thrice more time
in DAT +/−  mice than in wild-type (WT) mice [18].
Yet, in addition to neurochemical alterations leading to a mild
DA increase at the synaptic level, only a handful of studies have
described memory alterations in DAT +/−  mice [19,20]. Previous
studies revealed that DAT +/−  mice show impairment in pattern
completion in a partial cue environment [19], and exhibit decreased
anxiety-related behaviors [20]. The mild hyperdopaminergic DAT
+/− mice present biochemical changes [18] related to memory
impairment that could be reversed by D2 down regulation [19].
The present work aimed to investigate the relation between D2
activity and novel object recognition in mild hyperdopaminergic
DAT +/−  mice. To that end, we investigated DAT +/−  mice trained
in the object recognition (OR) task, with assessment at basal levels
of dopamine transmission as well as under the influence of differ-
ent doses of haloperidol, (0.05 and 0.3 mg/Kg) before or after the
training session of the task.
A total of 75 adult (2–5 months) male mice were used, com-
prising 39 WT  (C57Bl-6 strain) and 36 heterozygous (DAT +/−
strain, on C57Bl/6J background). The animals were housed in
cages (2–4 animals/cage), under a 12 h/12 h light/dark cycle,
with lights on at 07:00, and food and water ad libitum. Ani-
mals were daily handled for 5 min  for 10 sessions prior to the
experiments, in order to decrease stress responses WT  and KO
littermates were generated form C57BL/6J-129/SvJ hybrid DAT het-
erozygotes as previously described [18]. Mice were genotyped
by PCR using sense WT  (5′-CCCGTCTACCCATGAGTAAAA-3′), sense
KO (5′-TGACCGCTTCCTCGTGC-3′) and a common antisense primer
(5′-CTCCACCTTCCTAGCACTAAC-3′). The procedures applied in the
study followed guidelines of the National Institutes of Health and
were approved by the Edmond and Lily Safra International Insti-
tute of Neuroscience of Natal Ethics Committee (protocol number
08/2010).
Animals were submitted to an OR task, based on the novelty
exploration tendency of rodents. The task was performed in a cir-
cular arena (50 cm diameter and 30 cm high) in a room with dim
and well-spread light, so as to avoid producing shadows in the
apparatus. Animals were naïve to the apparatus when exposed.
We employed 6 different objects presented over 2 consecutive
days (two sessions); 4 objects (A–D) were presented during the
initial exploration session (First session, 10 min); and 2 unfamiliar
objects (E and F) replaced 2 familiar objects (C and D) during the
second session, 24 h later (testing session, 10 min). In order to eval-
uate memory recognition, an object preference ratio was calculated
(time spent with E and F/A and B objects). Notice that if the animal
follows the natural behavior, they will spend more time exploring
novel objects [14,15] (ratio E and F/A and B > 1), if they had impair-
ment in OR they will explore similarly the familiar and the novel
objects (ratio E and F/A and B = 1).
Based on the D2 influence on learning and memory [8–10],
we used haloperidol to induce OR impairment. At the time point
of 30 min  before exploration (B.E.) of the object, as well as
immediately after exploration (I.A.E.), animals were injected with
haloperidol (0.3 mg/Kg and 0.05 mg/Kg) or vehicle, and were then
allowed to behave freely in their home cages until the second explo-
ration session. In order to differentiate whether a possible memory
deficit could be due to memory acquisition, or specifically related
to the memory consolidation phase, we performed injections of
haloperidol at low dose both before and after object exploration.
The injection of 0.3 mg/Kg haloperidol before the exploration phase
results in partial deficit of movement that impairs object explo-
ration [17,21]. Therefore, we  could not determine whether the
decrease in object recognition was due to impaired consolida-
tion, or to a deficit in locomotor activity during the exploration
phase. For this reason, we  did not investigate animals injected
with 0.3 mg/Kg haloperidol before the exploration. In contrast, the
use of the 0.3 mg/Kg dose after the exploration could not affect
the mobility neither during the acquisition phase nor during the
test/evocation phase, and therefore we set out to investigate this
condition.
The parameter of object exploration considered the time ani-
mals spent with the whiskers or front paws in contact with one
of the objects for at least 0.5 s, with a 0.5 s as a minimum inter-
val bout. On test session, by presenting half of objects as novelty,
animals should spend more time with novel objects, giving rise to
unequal exploration time between novel and familiar objects [22].
By recording videos with a Panasonic camera and AMcap 9.21 free
software, we  defined the preference ratio as the exploration dura-
tion of novel objects (E and F) divided by the time spent exploring
the objects that were the same as in training in sessions (objects A
and B).
First we  measured the influence of haloperidol (0.3 and
0.05 mg/Kg) in the WT mice strain during OR task (Fig. 1). We  exe-
cuted two  different statistical analyses; one-way ANOVA followed
by Bonferroni correction was  performed to analyze the difference
among groups submitted to vehicle or haloperidol. We  also per-
formed the difference between the group (vehicle or haloperidol)
and the ratio = 1 (described above), t test against 1, to find out if the
preference ratio presented by WT  groups was significant different
of the hypothesis of non-recognition of objects. To verify possible
effects of haloperidol in the motor and motivation system [8,17]
we measured the total distance traveled and the total time spent
in object exploration.
2 We applied Shapiro-Wilk normality tests to assess the dis-
tribution of the data. For the comparison within strains, in which
treatment is the only independent variable, we used one-way
ANOVAs of three dependent variables (Preference Ratio, Total
Exploration Time and Total Distance Traveled). For the comparison
between strains we  used a Two-way ANOVA with strain and treat-
ment as independent variables, followed by Bonferroni post-hoc
tests. The comparison against one was performed by a paired t-test
(alpha = 95%, all data with Bonferroni correction). The descriptive
statistics comprise normality test values (W), mean ± SEM, F, p val-
ues and degrees of freedom (DF)
First we  applied the Shapiro-Wilk normality tests with all data
set groups to verify if the data followed the normal distribu-
tion in order to choose correctly parametric or non-parametric
tests. W values for the Wild Type groups were: Vehicle 30 min
B.E., W = 0.94; Halo0,05 mg/Kg 30 min  B.E. I.A.E. W = 0.95; Halo
0,3 mg/Kg I.A.E, W = 0.93; Vehicle I.A.E., W = 0.97; Halo 0,05 mg/Kg
I.A.E, W = 0.95 (Shapiro-Wilk p summary values p > 0.05). For the
A.S.C. Franc¸ a et al. / Behavioural Brain Research 308 (2016) 211–216 213
Fig. 1. Behavioral and pharmacological procedures. Handled C57BL-6 and DAT +/−  mice were presented to 4 novel objects at light periods (training session). Haloperidol
or  vehicle was injected 30 min  before of the exploration (B.E.) or immediately after exploration (I.A.E.) of novel objects exploration. One day after training, animals were
exposed to 2 novel objects and 2 familiar objects (test session).
DAT +/−  animals, Shapiro-Wilk normality test W values were:
Vehicle 30 min  B.E., W = 0.89; Halo0,05 mg/Kg 30 min  B.E. I.A.E.
W = 0.81; Halo 0,3 mg/Kg I.A.E, W = 0.90; Vehicle I.A.E., W = 0.86;
Halo 0,05 mg/Kg I.A.E, W = 0.77. (Shapiro-Wilk p summary values
p > 0.05). Therefore, in this we used only parametric tests to analyze
all comparisons Fig. 1.
We  applied ANOVA to compare the preference ratio among WT
groups. ANOVA comparison of preference ratio revealed a signif-
icant difference among groups (F = 11.74, df = 4,38 and p < 0.0001)
(Fig. 2A). The Bonferroni’s post-hoc showed significant preference
ratio decreases of haloperidol 0.3 mg/Kg in comparison to the other
groups (see details in Table 1). The higher dose of haloperidol was
the only treatment that led to impairment in OR, supported by the
t-test against 1, t = 1.74, p = 0.6. The other groups showed signif-
icant preference ratio higher than 1: Vehicle I.A.E. (t = 4.57 df = 6,
p = 0.019); Haloperidol 0.05 mg/Kg I.A.E. (t = 4.14 df = 7, p = 0.021);
Vehicle 30 min  B.E. (t = 11.13 df = 7, p < 0.0001); and Haloperidol
0.05 mg/Kg 30 min  B.E. (t = 11.05 df = 7, p < 0.0001).
The second part of the study tested if the mild disbalance of the
DA levels in DAT +/−  mice strain had influence in OR task. Therefore,
we analyzed the effect of haloperidol (0.3 and 0.05 mg/Kg) during
OR task.
We applied one-way ANOVA to compare all the groups of DAT
+/− (Fig. 2B). The ANOVA comparison of preference ratio revealed
a significant difference among groups (F = 12.44, df = 4,35 and
p < 0.0001). Bonferroni post hoc revealed that the treatment with
haloperidol (0.05 mg/Kg) injected 30 min  before exploration led to
a higher preference ratio in comparison to the other groups (see
details in Table 1). When we performed the comparison to 1, DAT
+/− with haloperidol 0.05 mg/Kg 30 min  B.E. was the only group to
present difference (t = 5.621, df = 6, p = 0.007), demonstrating to be
the only group with a preference ratio compatible with novel OR.
The other comparisons between 1 and DAT +/− groups revealed
no difference: Vehicle I.A.E. (t = 0.73 df = 6, p > 0.99); Haloperidol
0.05 mg/Kg I.A.E. (t = 0.39, df = 7, p > 0.99); Haloperidol 0.3 mg/Kg
I.A.E. (t = 0.26 df = 6, p > 0.99); and Vehicle 30 min  B.E. (t = 2.095,
df = 6, p = 0.4).
We  also measured the influence of haloperidol in WT  or DAT +/−
by using the strains and haloperidol dosage as independent vari-
ables. The Two-way ANOVA (Strain: F (1, 65) = 21.78, p < 0.0001;
Treatment: F (4, 65) = 41.65, p < 0.0001; Interaction: F (4, 65) = 4.39,
p = 0.072) revealed that both treatment and strain has influence in
the preference ratio of the animals (Fig. 2C). Finally, we applied
the Bonferroni post hoc to be able to compare where in each
treatment both strains have different preference ratios. Bonfer-
roni post hoc showed in haloperidol 0.3 mg/Kg I.A.E. group similar
results considering WT  and DAT +/−  preference ratios (p > 0.05).
Both strains presented ratios similar to 1 indicating that both
strains have impairment in OR. Haloperidol at 0.05 mg/Kg 30 min
B.E. also induced a similar preference ratio considering the two
strains (p > 0.5). Nevertheless, in this case both strains revealed a
preference ratio higher than 1 indicating that they are able to rec-
ognize novel objects. Regarding the comparison of WT and DAT
P
re
fe
re
nc
e 
R
at
io
P
re
fe
re
nc
e 
R
at
io
P
re
fe
re
nc
e 
R
at
io
A - Haloperidol Effects on WT Mice
B - Haloperidol Effects on DAT +/- Mice
C - WT vs DAT +/- Mice
Fig. 2. Haloperidol differentially modulates memory consolidation in WT and
DAT+/−  mice in a novel object recognition task. A—Effect of two different haloperidol
(halo) doses (0.05 and 0.3 mg/Kg) when injected immediately after exposition (I.A.E.)
and 30 min  before exposition (B.E.) in wild type (WT) mice novel object preference
ratio (unfamiliar/familiar object exploration duration), used as memory consolida-
tion  (M.C.) parameter. Comparison through ANOVA revealed that injection of halo
0.3  mg/Kg immediately after exploration led to a decrease on M.C  when compared
to  control group (p < 0.01) and other groups. Also, the dose of 0.05 mg/Kg injected
30  min  before the exploration (B.E.) revealed an association with increased novel
object exploration when compared to other groups excepting its control vehicle
group (p < 0.001). B—Effect of different halo doses when injected I.A.E. and 30 min
B.E. in heterozygous mice in novel object preference ratio. ANOVA revealed that
injection of halo 0.05 mg/Kg B.E. increased preference ratio when compared to all
other groups (p < 0.01). C—DAT +/− and WT mice disclose different preference ratios
and specific responses to halo injection. WT and DAT +/− group pairs were compared
in  each treatment using Student’s T test. DAT +/− groups revealed no novelty recog-
nition in vehicle and halo 0.3 and 0.05 I.A.E., as well as vehicle B.E. (ratios close to
1.0),  and decreased ratios compared to WT in both vehicle treatments (I.A.E and B.E.)
and in halo 0.05 mg/kg I.A.E. There was no difference concerning WT and DAT +/−
in  halo 0.05 mg/kg B.I. * p < 0.05; ** p < 0.01; *** p < 0.001.
+/−  groups: vehicle I.A.E., haloperidol 0.05 mg/Kg I.A.E. and vehicle
30 min  B.E. revealed a difference in the preference ratio (see details
in Table 1). Among those three treatments, WT  animals presented
a preference ratio higher than 1 and DAT +/−  similar to 1 (Fig. 2C).
214 A.S.C. Franc¸ a et al. / Behavioural Brain Research 308 (2016) 211–216
Table 1
Statistical summary of the significant results in the comparisons among WT and DAT
+/−.  ANOVA comparisons of preference ratio were applied among WT and DAT +/−
separately. Two-way ANOVA comparisons were applied to compare the WT and DAT
+/−  groups followed by Bonferroni post-hoc tests. Comparisons were performed
considering two independent variables: treatment and strain line.
Wild-Type Mice
ANOVA
F P Value DF
11.74 0.0001 (4.38)
Bonferroni
Groups Mean ± SEM; N P Value
Vehicle I.A.E. x Halo 0.3 mg/Kg I.A.E. 2.28 ± 0.28; N = 7 P < 0.01
1.23 ± 0.13; N = 8
Halo 0.05 mg/Kg B.E. x Halo 0.05 mg/Kg I.A.E 2.73 ± 0.15; N = 8 P < 0.001
1.66 ± 0.16; N = 8
Vehicle B.E. x Halo0.3 mg/Kg I.A.E. 2.20 ± 0.10; N = 8 P < 0.01
1.23 ± 0.13; N = 8
Halo0.05 mg/Kg B.E. x Halo0.3 mg/Kg I.A.E 2.73 ± 0.15; N = 8 P < 0.001
1.23 ± 0.13; N = 8
DAT+/-Mice
ANOVA
F  P Value DF
12.44 0.0001 (4.35)
Bonferroni
Groups Mean ± SEM; N P Value
Halo 0.05 mg/Kg B.E. x Vehicle I.A.E 2.25 ± 0.22; N = 7 P  < 0.001
1.07 ± 0.10; N = 7
Halo 0.05 mg/Kg B.E. x Halo
0.05 mg/Kg I.A.E
2.25 ± 0.22; N = 7 P  < 0.001
0.96 ± 0.10; N = 8
Halo 0.05 mg/Kg B.E. x Halo
0.3 mg/Kg I.A.E
2.25 ± 0.22; N = 7 P  < 0.001
0.97 ± 0.12; N = 7
Halo 0.05 mg/Kg B.E. x Vehicle B.E 2.25 ± 0.22; N = 7 P  < 0.01
1.38 ± 0.18; N = 7
Wild-Type vs DAT+/-Mice
Two-Way ANOVA
Source of variation F P Value DF
Interaction 4.39 0.0720 (4.65)
Strain 21.78 0.0001 (1.65)
Treatment 41.65 0.0001 (4.65)
Bonferroni
Groups Mean ± SEM; N P Value
Vehicle I.A.E (WT  vs Hz) 2.28 ± 0.28; N = 7 P < 0.01
1.07 ± 0.10; N = 7
Halo0.05mg/Kg I.A.E. (WT  vs Hz) 1.66 ± 0.16; N = 8 P < 0.05
0.96 ± 0.10; N = 8
Vehicle B.E 2.20 ± 0.10; N = 8 P < 0.01
(WT  vs Hz) 1.38 ± 0.18; N = 7
To test whether the involvement of D2 receptors in the motiva-
tional and motor systems played a role during the exploration of
objects in the test session, we measured the total exploration time
and the total distance traveled. We  applied ANOVA to compare
the total exploration time among WT  groups. ANOVA compari-
son of total exploration revealed a significant difference among
groups (F = 17.59, df = 4, 39 and p < 0.0001), but the comparison of
the total distance traveled in the WT  groups revealed no signifi-
cant difference (F = 2.372, df = 4, 39 and p = 0.0712). We  applied the
same comparison to the DAT +/−  groups. The comparison of total
exploration time revealed no difference (F = 1.805, df = 4, 35 and
p = 0.153). When we compared the total distance traveled, a signif-
icant difference was detected (F = 4.684, df = 4, 35 and p = 0.0045).
Finally, we applied the Two-Way ANOVA to compare both strains.
We found a significant interaction regarding total exploration
(Strain: F (1.65) = 11.27, p = 0.0013; Treatment: F (4, 65) = 8.398,
p <0.0001; Interaction: F (4, 65) = 9.301, p <0.0001), while the com-
parison of total distance traveled did not reveal any significant
interaction (Strain: F (1, 65) = 27.30, p <0.0001; Treatment: F (4,
65) = 6.746, p <0.0001; Interaction: F (4, 65) = 1.236, p = 0.304).
The present study shows that DA D2 antagonist haloperidol was
able to both decrease and increase memory formation, depend-
ing on the mouse strain, and on the time of injection. Specifically,
we replicated WT  mice data, in which haloperidol 0.3 mg/Kg after
exploration led to an impairment in a memory task related to OR
[10,14]. We  also showed that mild hyperdopaminergic DAT +/−
mice have a basal impairment in this parameter. Thereafter, in DAT
+/− mice, we demonstrated that halo injection before (0.05 mg/kg),
but not after exploration (0.05 or 0.3 mg/kg), was  able to recover
OR capacity to similar levels of their WT  correspondents.
Dopamine D1 and D2 receptors are linked to attention and
memory consolidation and to both mesolimbical and mesocorti-
cal dopaminergic pathways [3,6]. Rossato et al. reported that D1
family receptor modulates the long-term memory storage persis-
tence in the inhibitory avoidance task [7], as well D1 participate in
the spatial memory process [23]. Similarly, the systemic injection
and intra-accumbens of D2 antagonist impair spatial memory. Both
D1 and D2 suppression results in impairment to recognize changes
in the environment [24].
The use of DAT-KO mice as a model to study the behaviors asso-
ciated with dopamine is widely described. DAT-KO mice exhibit
hyper locomotion [18] and this phenotype can be reversed by
haloperidol injection [17,20]. DAT-KO animals also exhibit a pref-
erence for the borders of an open field [20], and a decrease of
immobility time in the forced swimming task [17]. When main-
tained in isolation, DAT-KO mice exhibit increase in reactivity and
aggression when submitted to social interactions [2]. DAT-KO mice
also present deficits in odor recognition [13] and impairment in the
normal wake-sleep cycle [25]. Using DAT-KO, Morice et al. showed
that these animals had impairment in the water-maze paradigm,
and long-term depression [9]. In contrast, only a few studies have
been published on DAT +/− mice, a mild model of hyperdopaminer-
gia. In this model, a mild disbalance of dopamine results in several
cognitive changes [17,19,20]. DAT +/− mice exhibit less immobility
time during the forced swimming test [17], higher time spent in the
center of an open field [20], and more time spent in the open arms
in comparison to WT  mice in a plus maze [19] or zero maze [20]. In
addition, these heterozygous mice showed an increase in the explo-
ration of objects placed in an open field [17], and in the frequency
of object exploration [20]. DAT +/− mice showed impaired pattern
completion in an environment with few spatial cues, which could
be reversed by haloperidol [19]. However, no impairment of OR has
been described [19,20].
In the present work we  found basal impairment in OR  in the
DAT +/− mice (without pharmacological interference), differently
from previous studies [19,20], but our OR protocol was quite dif-
ferent from the protocol used by Li et al. We  used 50% less time of
object exploration sessions and twice the number of novel objects
than the protocol of that study [19]. The increase in the amount of
novel objects, combined with less time to explore them, likely led
to the differences between the previously published results and
our own. In the Pogorolev protocol, animals were submitted to
7 consecutive days of novel exploration. The animals had 3 daily
sessions to explore the same objects. In the fourth day the place-
ment of the objects was  changed, and in the sixth day one of the
objects was  replaced [20]. Most likely, any mild impairment of OR
in DAT+/− mice should be reversed by several consecutive contacts
with the same objects. Similarly to our results, pattern completion
impairment was  reversed by haloperidol injection in DAT +/− mice.
[19].
Secondly, regarding exploration and overall mobility, note that
both behaviors are modulated by dopamine. Yet the results in this
A.S.C. Franc¸ a et al. / Behavioural Brain Research 308 (2016) 211–216 215
study, related to memory in DAT +/− or WT  mice, are probably
not influenced by an alteration of these two variables. As shown
in the results above, the WT  0.3 mg/Kg I.A.E. group exhibited the
same levels of total exploration seen in the vehicle I.A.E group, but
they explored differently the new and familiar objects (see Fig. 2A).
Furthermore, we observed no difference among groups comparing
0.3 mg/kg and their respective controls in the mobility parameter
(distance traveled). The same basic explanation can be applied to
the DAT +/−:  the statistics indicate no difference in total exploration
time when we  compare DAT with vehicle and with halo 0.05 mg/kg
or 0.5 mg/kg, (and only differences between I.A.E and B.E. treat-
ments, treated in sequence) but these groups exhibited differential
exploration of the new and familiar objects (see Fig. 2B).
A useful rationale in this case is to explain the results as an
increase in potentially stressful stimuli presented to the DAT+/−
animals, since more objects were presented and the animals had
less time to be familiarized with the environment. This increase in
potentially stressful conditions may  be subtle for wild type animals,
but not for DAT+/− animals with a mild dopamine excess.
In agreement with this hypothesis, it has been demonstrated
that the enhanced activity regulation of DAT receptor in WT mice
occurs as a synaptic response to restraint stress, and this effect
occurs even in haloperidol-treated animals [26]. Since DAT+/−
mice have less DAT receptors, they might be more susceptible
to this stress-associated phenomenon. Despite of the scarcity of
pharmacological and neuroanatomical data in DAT+/− mice, DAT-
KO littermate mice exhibit a different neurocircuitry [27] and a
non-dopaminergic acute response to psychostimulant drugs [28].
Future investigation of the neurocircuitry of DAT+/− animals shall
clarify what are the neural correlates of the differential modulation
of its reward system implied by our results.
Interestingly, with regard to motor parameters, we  observed
that WT  mice with injection before exploration and DAT+/− mice
with B.E. injection at 0.05 mg/Kg of haloperidol, presented locomo-
tion alterations when compared to both control and to 0.3 mg/kg
after exploration. The differences are more evident in DAT+/−
mice, which presented, antagonically, a decrease of total explo-
ration in injections B.E. as well as an increase in distance traveled
(0.05 mg/kg B.E.). These findings, together with an increase in DAT
+/− (distance traveled) at 0.05 mg/kg after exposure, also suggest,
additionally, an increased susceptibility of motor modulation in
DAT-+/− animals due to previous injection stress, and to haloperi-
dol action at lower doses.
Object recognition deficits in DAT+/− were, in fact, the main
findings in this study: this finding is supported by related research.
There is involvement of D2 receptor activity in neuroplasticity
markers impairment associated with OR deficits [10,14]. A high
dose of haloperidol (0.3 mg/Kg) lead to a decrease in CAMKII, ZIF-
268 and BDNF [10] after object exploration. In the same study
above, the haloperidol injected animals that presented impairment
in OR had significant decrease in the REM sleep duration [10]; they
also revealed a strong correlation between REM sleep decrease and
OR impairment [10].
Besides corroborating previous studies in the field, data from
WT mice wereimportant for the comparison with DAT +/− results
on memory impairment. DAT +/− mice did not receive major scien-
tific attention as did their knockout counterparts, since they only
exhibit a mild hyperdopaminergia and exhibit less phenotypic dif-
ferences [18]. Nevertheless, in the present study these mice showed
a clear impairment in OR. These data are relevant since they indi-
cate that a mild hyperdopaminergia can be detrimental enough to
cause attention or memory deficits related to novelty recognition.
It is important to note that the two strains investigated here,
WT and DAT+/−,  present different basal levels of dopamine [18].
Because of these different basal synaptic dopamine levels, we
expected different sensibility to dopamine antagonists in these two
mice strains. A higher sensibility was observed in DAT+/−  mice at a
low haloperidol dose (0.05 mg/Kg, BE injection), since haloperidol
reverted the natural OR deficit phenotype, and notably enhanced
object memory formation in this strain. In contrast, in WT we
did not detect any difference between haloperidol and vehicle BE,
using the ANOVA test. In fact, in this case a trend was revealed
by Student’s T test (t = 2.69, df = 14 and p = 0.0176), indicating that
haloperidol BE at a low 0.05 mg/kg dose could possibly enhance
object recognition in WT mice. We  interpret these specific strain
variations at low haloperidol doses as different susceptibilities to a
minor modulation of dopamine receptors. However, when injected
after exploration, haloperidol at the same low dose did not increase
OR in DAT +/− mice during the test session. Together, both results
suggest that this impairment could most probably be due to DA
imbalance during memory acquisition in DAT +/− mice. Conse-
quently, since the acquisition phase seems to be directly affected,
no further pharmacological intervention after memory acquisition
could exert any effect on OR in DAT +/− mice, as we observed with
haloperidol injections after object exploration.
Interestingly, the same drug that caused memory deficits after
exploration in WT  mice also caused an increase in this parameter in
DAT +/− mice, when used before exploration and at a lower dose. An
inverted U-shape activity, in which optimal activity levels enhance
performance, is proposed for DA D1 receptor activation, and is
related to goal-oriented tasks, memory consolidation and atten-
tion [29,30]. In this approach model, both low or high DA receptor
activation reduces the performance in these activities, and medium
optimal activation produces the best performances. An interesting
study conducted in humans indicates the inverted U-shaped, D-2
receptor- based neuroplasticity is also observed [31].
Since we used a drug that is mainly a D2 receptor antagonist,
the results can indicate that D2 activation levels may also induce
an inverted U-shape performance in mice related to OR. The D2
receptor action may  modulate OR both at acquisition and consoli-
dation phases. We  infer that in the present study, both high and low
D2 DA receptor activation levels led to object memory impairment.
The high D2 activation could be relative to DAT +/− at basal perfor-
mance, which was  reverted by haloperidol in pre-exploration (at
0.05 mg\kg). The low activation case could be that of WT,  impaired
by haloperidol injection after exploration (at 0.3 mg\kg).
These results indicate that optimal D2 activity levels can be
pharmacologically induced, and can potentially be considered a
therapeutic target for dopamine-related dysfunctions. Indeed, the
use of D2/D3 family antagonists has been described for reducing the
impairment of memory deficits induced by amnesic compounds
in rodents [32]. This is also supported by the trend for OR facili-
tation in WT injected with 0.05 mg/kg haloperidol. Altogether, the
results indicate that D2 family antagonists in low doses can produce
memory enhancement.
In summary, we report here an OR deficit in DAT +/− mice.
Importantly, injection of the D2 DA antagonist before object explo-
ration reverted this phenotype. These results are complemented by
an haloperidol-induced decrease of OR in WT  mice when injected
after the training session. This indicates that D2 DA receptors can
modulate OR tasks by acting both in attentional processes during
acquisition, and during subsequent memory consolidation. More
studies could be performed examining other phenotypic charac-
teristics of DAT +/− mice, in order to associate these behavioral
deficits to molecular and electrophysiological parameters. Further-
more, additional pharmacological studies involving DA agonists
and antagonists for different receptors are in order to build a more
comprehensive understanding of D2 regulation of attention and
memory.
216 A.S.C. Franc¸ a et al. / Behavioural Brain Research 308 (2016) 211–216
Conflict of interest
None. The authors declare no competing interests.
Acknowledgments
We thank Marc Caron and Miguel Nicolelis for making the
DAT+/− mice available; Valéria Arboes for animal care. We
thank to the listo f grants: Grant (1) FAPERN-PPP (Dr. Lobão-
Soares: 2013–2014). Grant (2) CNPQ- Universal; Grant number:
484408/2013-5 (Dr. Lobão-Soares).Grant (3) CNPq Universal; Grant
number: 481351/2011-6 (Dr. Ribeiro). Grant (4) FINEP Grant num-
ber 01.06.1092.00 (Dr. Ribeiro). Grant (5) FAPERN/CNPq Pronem.
Grant Number: 003/2011 (Dr. Ribeiro). Grant (6) FAPESP Cen-
ter for Neuromathematics Grant number: #2013/07699-08 (Dr.
Ribeiro).Grant (7) UFRN. Grant number 00001/2010 (Dr. Ribeiro).
(8) Conselho Nacional de Desenvolvimento Científico e Tecnológico
(CNPQ) Research productivity 306604/2012-4.
References
[1] M.  Le Moal, H. Simon, Mesocorticolimbic dopaminergic network: functional
and  regulatory roles, Physiol. Rev. 71 (1991) 155–234.
[2] R.M. Rodriguiz, R. Chu, M.G. Caron, W.C. Wetsel, Aberrant responses in social
interaction of dopamine transporter knockout mice, Behav. Brain Res. 148
(2004) 185–198.
[3] M.  El-Ghundi, B.F. O’Dowd, S.R. George, Insights into the role of dopamine
receptor systems in learning and memory, Rev. Neurosci. 18 (2007) 37–66.
[4] I. Creese, S.D. Iversen, The role of forebrain dopamine systems in
amphetamine induced stereotyped behavior in the rat, Psychopharmacologia
39 (1974) 345–357.
[5] J.M. Ryan, Pharmacologic approach to aggression in neuropsychiatric
disorders, Semin. Clin. Neuropsychiatry 5 (2000) 238–249.
[6] N. Hansen, D. Manahan-Vaughan, Dopamine D1/D5 receptors mediate
informational saliency that promotes persistent hippocampal long-term
plasticity, Cereb. Cortex 362 (2012).
[7] J.I. Rossato, L.R.M. Bevilaqua, I. Izquierdo, J.H. Medina, M. Cammarota,
Dopamine controls persistence of long-term memory storage, Science 325
(2009) 1017–1020.
[8] R.A. Wise, Dopamine, learning and motivation, Nat. Rev. Neurosci. 5 (2004)
483–494.
[9] E. Morice, J.-M. Billard, C. Denis, F. Mathieu, C. Betancur, J. Epelbaum, et al.,
Parallel loss of hippocampal LTD and cognitive flexibility in a genetic model of
hyperdopaminergia, Neuropsychopharmacology 32 (2007) 2108–2116.
[10] A.S.C. Franc¸ a, B. Lobão-Soares, L. Muratori, G. Nascimento, J. Winne, C.M.
Pereira, et al., D2 dopamine receptor regulation of learning, sleep and
plasticity, Eur. Neuropsychopharmacol. 25 (2015) 493–504.
[11] J. Besheer, H.C. Jensen, R.A. Bevins, Dopamine antagonism in a novel-object
recognition and a novel-object place conditioning preparation with rats,
Behav. Brain Res. 103 (1999) 35–44.
[12] M.A. Pezze, H.J. Marshall, K.C.F. Fone, H.J. Cassaday, Dopamine D1 receptor
stimulation modulates the formation and retrieval of novel object recognition
memory: role of the prelimbic cortex, Eur. Neuropsychopharmacol. 25 (2015)
2145–2156.
[13] J.L. Tillerson, W.M. Caudle, J.M. Parent, C. Gong, T. Schallert, G.W. Miller,
Olfactory discrimination deficits in mice lacking the dopamine transporter or
the D2 dopamine receptor, Behav. Brain Res. 172 (2006) 97–105.
[14] A.S.C. Franc¸ a, G.C. do Nascimento, V. Lopes-dos-Santos, L. Muratori, S. Ribeiro,
B.  Lobão-Soares, et al., Beta2 oscillations (23–30 Hz) in the mouse
hippocampus during novel object recognition, Eur. J. Neurosci. 40 (2014)
3693–3703.
[15] S.B. Glickstein, P.R. Hof, C. Schmauss, Mice lacking dopamine D2 and D3
receptors have spatial working memory deficits, J. Neurosci. 22 (2002)
5619–5629.
[16] B. Li, Y. Arime, F.S. Hall, G.R. Uhl, I. Sora, Impaired spatial working memory
and decreased frontal cortex BDNF protein level in dopamine transporter
knockout mice, Eur. J. Pharmacol. 628 (2010) 104–107.
[17] C. Spielewoy, C. Roubert, M.  Hamon, M.  Nosten-Bertrand, C. Betancur, B.
Giros, Behavioural disturbances associated with hyperdopaminergia in
dopamine-transporter knockout mice, Behav. Pharmacol. 11 (2000) 279–290.
[18] B. Giros, M.  Jaber, S.R. Jones, R.M. Wightman, M.G. Caron, Hyperlocomotion
and indifference to cocaine and amphetamine in mice lacking the dopamine
transporter, Nature 379 (1996) 606–612.
[19] F. Li, L.P. Wang, X. Shen, J.Z. Tsien, Balanced dopamine is critical for pattern
completion during associative memory recall, PLoS One 5 (2010) e15401.
[20] V.M. Pogorelov, R.M. Rodriguiz, M.L. Insco, M.G. Caron, W.C. Wetsel, Novelty
seeking and stereotypic activation of behavior in mice with disruption of the
Dat1 gene, Neuropsychopharmacology 30 (2005) 1818–1831.
[21] S. Mithani, M.T. Martin-Iverson, A.G. Phillips, H.C. Fibiger, The effects of
haloperidol on amphetamine- and methylphenidate-induced conditioned
place preferences and locomotor activity, Psychopharmacology (Berl.) 90
(1986) 247–252.
[22] E. Dere, J.P. Huston, M.A. De Souza Silva, The pharmacology, neuroanatomy
and neurogenetics of one-trial object recognition in rodents, Neurosci.
Biobehav. Rev. 31 (2007) 673–704.
[23] W.C.N. da Silva, C.C. Köhler, A. Radiske, M.  Cammarota, D1/D5 dopamine
receptors modulate spatial memory formation, Neurobiol. Learn. Mem.  97
(2012) 271–275.
[24] A. Mele, M.  Avena, P. Roullet, E. De Leonibus, S. Mandillo, F. Sargolini, et al.,
Nucleus accumbens dopamine receptors in the consolidation of spatial
memory, Behav. Pharmacol. 15 (2004) 423–431.
[25] K. Dzirasa, S. Ribeiro, R. Costa, L.M. Santos, S.-C. Lin, A. Grosmark, et al.,
Dopaminergic control of sleep-wake states, J. Neurosci 26 (2006)
10577–10589.
[26] B.J. Copeland, N.H. Neff, M.  Hadjiconstantinou, Enhanced dopamine uptake in
the  striatum following repeatedrestraint stress, Synapse 57 (2005) 167–174.
[27] X. Zhang, E.L. Bearer, B. Boulat, F.S. Hall, G.R. Uhl, R.E. Jacobs, Altered
neurocircuitry in the dopamine transporter knockout mouse brain, PLoS One
5  (2010) e11506.
[28] J.V. Trinh, D.L. Nehrenberg, J.P.R. Jacobsen, M.G. Caron, W.C. Wetsel,
Differential psychostimulant-induced activation of neural circuits in
dopamine transporter knockout and wild type mice, Neuroscience 118 (2003)
297–310.
[29] G.V. Williams, S.A. Castner, Under the curve: critical issues for elucidating D1
receptor function in working memory, Neuroscience 139 (2006) 263–276.
[30] S. Vijayraghavan, M.  Wang, S.G. Birnbaum, G.V. Williams, A.F.T. Arnsten,
Inverted-u dopamine D1 receptor actions on prefrontal neurons engaged in
working memory, Nat. Neurosci. 10 (2007) 376–384.
[31] S. Fresnoza, E. Stiksrud, F. Klinker, D. Liebetanz, W.  Paulus, M.-F. Kuo, et al.,
Dosage-dependent effect of dopamine D2 receptor activation on motor cortex
plasticity in humans, J. Neurosci. 34 (2014) 10701–10709.
[32] J. Laszy, I. Laszlovszky, I. Gyertyán, Dopamine D3 receptor antagonists
improve the learning performance in memory-impaired rats,
Psychopharmacology (Berl.) 179 (2005) 567–575.
In Preparation for Journal of Psychopharmacology 
High Dose of Nicotine Shifts the Object Exploration 
Preference 
Arthur S. C. França1,2, Amilcar Reis, Thiago Moulin, Adriano B. L. Tort1, Klas 
Kullander2*.  
Running title: High Dose of Nicotine Shifts the Object Preference 
1  –Brain Institute, Federal University of Rio Grande do Norte, Brazil. 
2 –Developmental Genetics, Department of Neuroscience, Uppsala University, Uppsala, Sweden. 
 
Keywords: Nicotine; Object Recognition; Object Preference; Memory Impairment.  
Introduction  
The nicotine, originated of Tabaco, is 
one of the most widely abused drug 
worldwide (Benowitz, 2008; Kawazoe 
and Shinkai, 2015; Picciotto, 2003), 
probably due to the relation of 
acetylcholine nicotinic receptors with 
the reward system (Benowitz, 2008; 
Kawazoe and Shinkai, 2015). The 
acetylcholine nicotinic receptors are 
commonly related to modulatory events 
of synaptic transmission (Dajas-Bailador 
and Wonnacott, 2004), as like long term 
potentiation (LTP) events in the 
hippocampus (Alzoubi et al., 2007; 
Matsuyama et al., 2000) and calcium 
permeability change (Dajas-Bailador 
and Wonnacott, 2004).  
The usage of pharmacological approach 
targeting nicotinic receptors subtypes 
has been related to different roles in the 
nervous system, to the control of 
movement (Morrison and Armitage, 
1967; Rezvani and Levin, 2004), until 
the modulation for different cognitive 
changes (Levin, 2002). More 
specifically, nicotine has been largely 
related to improvement of several 
aspects of spatial memory: encoding 
(Aren- et al., 1995; Puma et al., 1999; 
Socci et al., 1995; Sultana et al., 2012), 
consolidation (Puma et al., 1999; 
Rezvani and Levin, 2001), 
reconsolidation (Tian et al., 2015), 
working memory performance (Levin, 
2002), as well pointed as a possible 
therapeutic drug (Arendash et al., 1995; 
Decker et al., 1992; Rezvani and Levin, 
2001) and also has been related to fear 
and anxiety behaviors (Gould and 
Lommock, 2003; Kutlu and Gould, 
2015). However, the effect of high-dose 
of nicotine, common to the addicted 
In Preparation for Journal of Psychopharmacology 
motivated users, is one of the aspects that 
has not been so well studied.   
The objective in the present study is to 
investigate the effect of high dose of 
nicotine in two spatial related tasks, an 
item recognition task and a spatial 
recognition task. We choose two 
different doses, the first dose (0.5 
mg/Kg) is slight higher than doses 
common used in similar tasks that variate 
among 0.1 to 0.4 mg/Kg  (Rezvani and 
Levin, 2001(Aren- et al., 1995; Puma et 
al., 1999; Socci et al., 1995; Sultana et 
al., 2012) and the second dose 
exponential higher 1.5 mg/Kg (some of 
mice at 2 mg/Kg dose had seizure and we 
decrease to 1.5 to avoid this aspect). 
Using object recognition (OR) task we 
verified the mice sublimated to treatment 
of 1.5 mg/Kg before the encoding phase 
shift the preference of object exploration, 
but the shift was reverse when we inject 
the same dose immediately after 
encoding phase. Both of nicotine 1.5 
mg/Kg (before and after encoding) 
impairs the spatial recognition in the 
modified T-maze task, indicating that the 
difference of nicotine interference is 
related to the tasks. Our results suggest 
that the nicotine in high dose can 
interfere spatial related tasks. 
Method 
Animals 
 
104 Chrna2-cre(-/-) mice  were used in 
this study. Animals were housed 
individually, with a 12-h cycle of light 
and dark (lights on at 06.00 h), and no 
food or water restriction. All procedures 
followed guidelines of the National 
Institutes of Health and approved by All 
procedures were approved by Uppsala 
Animal Ethics Committee, 
Jordbruksverket (C135/14,  
C132/13) 
 
Nicotine Dosage 
To able to test the interference of 
nicotine high dose in the object and 
spatial recognition capacity we use in the 
present study two doses of nicotine, the 
dose of 0.5 mg/Kg and the high dose (1.5 
mg/Kg).   
Behavior procedures 
Object Recognition 
The OR task is based on the natural 
behavior of rodents to explore novelty, 
therefore when the novelty is presented 
to the animal he will explore more the 
novelty rather familiarity. In the present 
study we performed the object 
recognition task in two sessions. 20 
minutes before the training session we 
submitted animals to an injection of 
Saline, nicotine 0.5 mg/Kg or nicotine 
In Preparation for Journal of Psychopharmacology 
1.5 mg/Kg.  During the training session 
animals were allowed to explore two 
similar objects in a round open field. One 
group, nicotine 1.5 mg/Kg, was 
submitted to injection immediately after 
exploration (I.A.E). 24hours latter 
animals were submitted to the test 
session, where we replaced one of the 
objects by a novel object (Figure 1A).      
Modified T maze 
To explore other aspect of the natural 
behavior of rodents to explore novelty, 
instead of item recognition capacity we 
submitted animals to recognize novelty 
spatial environment. The modified T 
(MT) maze is composed by three arms. 
The arms were identified by spatial clues 
(figures with different shapes and 
contrast colors) in the end of wall and 
also each arm present different textures 
in the ground to help animals to 
differentiate each arm. Similarly to the 
OR test, the MT maze task was divided 
in two session and had same 
experimental groups. During the training 
session two of the arms were presented 
to the animals, 24hours latter a third arm 
(new arm) was presented, therefore we 
account the exploration time expend in 
each arm (Figure 2A).   
Y maze 
The Y-maze is composed by 3 arms with 
the same size and together form an Y 
shape maze (Figure 3A). The y-maze 
task is based on the natural behavior of 
exploring novel environments, assuming 
this, in the y-maze, rodents typically 
prefer to investigate new arm rather than 
returning to the previous explored arm. 
Therefore, we account the number of 
correct alternation sequence, (i.e. A-B-
C, A-C-B, B-A-C), but not the incorrect 
alternation (i.e. A-B-A; A-C-A, C-B-C). 
The protocol consists in a single session, 
10 minutes, where the animal is freely to 
explore the maze. It is worth noting that 
in opposite of the previous behavior 
tasks (Object Recognition and MT maze) 
the y-maze have only one session, 
therefore the group 1.5 mg/Kg I.A.E. is 
absent.  
Behavior data 
During all behavior procedures animals 
were video recorded and the data 
collected was analyzed by the Ethovision 
program. We used as measure of object 
recognition the percentage of 
exploration of new and familiar objects. 
To verify if the nicotine doses interfere 
in the discrimination capacity of the 
objects we compare the discrimination 
index (absolute value of (new obj – 
familiar obj)/( new obj + familiar obj) 
among groups, the values of 
In Preparation for Journal of Psychopharmacology 
discrimination index variate between 0 
(when the animal explore equally the 
objects) until 1 (when the animal explore 
only one of the objects), meaning that 
values near to 1 animals discriminate 
more the objects. To be able to verify the 
reward component related to nicotine, 
we verify the object total exploration 
time as a measure of interest in the 
objects. Other nicotine related hole is the 
relation to the motor activity, therefore 
we measured the total distance traveled 
during the object recognition task. In the 
MT maze we used the discrimination 
index (new arm / (new arm + Arm A + 
Arm B). Because the MT maze is 
composed by 3 arms, the discrimination 
index that determine the equal time 
exploring the arms is 0.33 (100/3 ≈ 
33%), meaning that animals recognize 
the new arm if they present 
discrimination index values higher than 
0.33. If they present values equal to 0.33 
is an indication that they don’t recognize 
the new arm.   
Statistics 
Firstly, we test all group data set in the 
Shapiro-Wilk normality test to verify if 
the data set would match normal 
distribution characteristics. We verified 
that all data set of percentage of 
exploration, discrimination index and 
total distance traveled passed in the 
normality test, therefore we used 
parametric student t-test and we also 
performed ANOVA comparison, and 
post hoc of Bonferroni, though the 
groups. We performed ANOVA to 
compare the new objects percentage 
values in the NOR test groups control, 
nicotine 0.5mg/Kg 30min B.E., 
1.5mg/Kg 30min B.E. and 1.5mg/Kg 
I.A.E. For the total exploration time the 
data set did not pass in the Shapiro-Wilk 
normality test, therefore we used 
Kruskal-Wallis test, and post hoc of 
Dunn's Multiple Comparison Test, to 
compare different groups. The ANOVA 
was performed to compare the 
discrimination values in the MT maze 
and we also performed a paired t test to 
the hypothetical value of 0.33 to be able 
to verify if the animals recognize or not 
the new arm. We provide mean ± SEM, 
statistics test value t and F, degree of 
freedom and p value as descriptive 
statistics.  
 
Results 
 
High dose of nicotine shift the object 
preference 
Aiming to study what would be 
interference of the nicotine high dose to 
investigate the nicotine effect in the 
object recognition task we use the 
In Preparation for Journal of Psychopharmacology 
percentage of object exploration and 
discrimination index as parameter of 
object recognition, the total exploration 
time as parameter of interest in the 
animal to explore objects and total 
distance traveled as parameter of 
locomotor activity. 
To verify the nicotine effect on the object 
recognition capacity we compare all 
percentage exploration groups values 
using ANOVA, with Bonferroni’s post 
hoc. The ANOVA indicate difference 
among groups (F (3,35) = 15.21, p ≈ 
0.0001). Using the Bonferroni post hoc 
we could verify the control group 
preferred significantly more the new 
object in comparison to familiar object 
(60,06 ± 4.9% and 39,94 ± 4.9%, 
respectively.  t = 3.106, p ≈ 0.05, Figure 
1B). The group of 0.5 mg/Kg presented 
even more difference between the 
exploration of the new object and 
familiar object (62.17 ± 5.8% and 37.83 
± 5.8% respectively.  t = 3.56, p ≈ 0.01, 
Figure 1B). Surprisingly, the group 1.5 
mg/Kg demonstrate a completely shift in 
the object preference. The difference saw 
in the new object and familiar object was 
significantly (23.71 ± 4.2% and 76.29 ± 
4.2% respectively.  t = 8.11, p ≈ 0.001, 
Figure 1B), but in the opposite direction 
saw in the other groups. Therefore, we 
tried to verify if the result of the nicotine 
high dose was due to the action during 
the object memory acquisition, then we 
submitted other group to the same dose, 
but injecting IAE. The Bonferroni post 
hoc revealed no difference between the 
exploration of new object and familiar 
object (56.75 ± 3% and 43.25 ± 3% 
respectively.  t = 1.7, p > 0.05, Figure 
1B), but we could verify that the shift in 
the object exploration was not detected.  
Clearly the high dose of nicotine injected 
before the encoding phase shift the 
preference of object exploration to the 
familiar object. However, apparently the 
difference of exploration between 
objects was bigger in this group. Aiming 
to verify this aspect we performed 
comparison among the discrimination 
index of all groups. The ANOVA 
revealed difference among groups (F 
(3,35) = 4.88, p = 0.006, Figure 2C), 
showing that 1.5 mg/Kg has a higher 
index. The post hoc of Bonferroni 
multiple comparison test revealed 
difference between the 1.5 mg/Kg and 
1.5 mg/Kg IAE (t = 3.69, p ≈ 0.01, Figure 
2C) but revealed no difference in 
comparison to the other groups (although 
the unpaired t test shows difference 
between 1.5 mg/Kg and the Control 
group t (18) = 2.4, p = 0.027.). 
 
As showed previously, the nicotine 
affects the locomotor activity (Itzhak and 
Martin, 1999). Therefore, we verify the 
In Preparation for Journal of Psychopharmacology 
total distance traveled to see if the 
nicotine was affecting the locomotor 
activity during the test session. The 
ANOVA revealed no difference among 
groups (F (3,35) = 0.52, p = 0.66, Figure 
2D). The nicotine also interfere in the 
reward system (Tapper et al., 2004), 
therefore we verify the object total 
exploration time to see if the nicotine 
was increasing the interest to explore the 
objects. The Kruskal-Wallis test 
revealed difference among groups (K (4) 
= 7.87, p = 0.048, Figure 2E), but the 
Dunn's Multiple Comparison Test post 
hoc did not revealed where was the 
difference (although the Mann-Whitney 
test revealed difference between 1.5 
mg/Kg and 0.5 mg/Kg (U = 11, p = 
Figura 1 – High dose of nicotine shifts object preference and increase object 
discrimination. A) object recognition schematic. First panel shows the training phase 
with two similar objects. The lower panel shows the test phase 24 hours apart of training 
phase two objects, one familiar presented previously and one new object. B) Percentage 
of object exploration. The inset shows the color separated groups. In yellow the control 
group; the group subject to injection 20 minutes before the training phase to 0.5 mg/Kg 
of nicotine is represented in green; light blue represents the group subject to 1.5 mg/Kg 
20 minutes before the training phase; In dark blue the group subject to nicotine at dose 
1.5mg/Kg immediately after exploration of training phase. The colors indicate the groups 
in the next sections. C) Object discrimination index. D) Total distance traveled during 
object recognition task. E) Total exploration of objects. The data is presented by mean ± 
SEM. ANOVA comparisons were performed in B, C and D. Kruskal-Wallis test was 
performed in E. *p < 0.05, *** p < 0.001. 
 
In Preparation for Journal of Psychopharmacology 
0.004), 1.5 mg/Kg IAE (U = 10, p = 
0.013) but not with Control group (U = 
34, p = 0.24).  
 
 
High dose of nicotine impairs the spatial 
recognition  
 
To verify the if the nicotine would have 
the same effect in spatial memory we 
used a modified T-Maze applying the 
same novelty seek preference as used in 
the previous task. As described before 
we use a T-maze with spatial cues in the 
wall and the ground, presenting two arms 
in the training phase, then during the test 
phase we present the third arm, thereafter 
we account the time exploring each arm 
(Figure 2A). We found that both 
treatments of 1.5 mg/Kg (B.E. and 
I.A.E.) lead to the impairment of spatial 
recognition. The ANOVA comparison 
among discrimination index of groups 
revealed no difference (F (3,39) = 1.88, 
p = 0.15, Figure 2B). Therefore, we 
compared the discrimination index with 
the theoretical mean value of 0.33 
(described in the method section) to be 
able to verify if the animals expend more 
time in the new arm than the chance to 
equally explore the three arms. Only the 
Control group (t(12) = 2.37, p = 0.034) 
and the 0.5 mg/Kg group (t(9) = 2.25, p 
= 0.048) revealed difference in 
comparison to 0.33. The group 1.5 
mg/Kg (t(9) = 0.64, p = 0.53) and 1.5 
mg/Kg IAE (t(6) = 0.02, p = 0.98) 
revealed no difference, indicating that 
the high dose of nicotine impairs the 
spatial recognition (Figure 2B). 
Accordingly, we found when we 
compare the percentage of arm 
exploration that within groups, only 
control group had the percentage of 
exploration in the new arm higher than 
that found in the mean of the two other 
familiar arms (39.75 ± 2.8% and 30.13 ± 
1.4%, new arm and familiar arms, 
respectively, t = 2.26, df = 12, p = 0.043)  
but the 0.5 mg/Kg treatment revealed a 
strong trend to new arm higher 
percentage of exploration   (39.71 ± 
2.9% and 30.15 ± 1.5%, new arm and 
familiar arms, respectively, t = 2.13, df = 
9, p = 0.06). Both 1.5 mg/Kg groups 
exhibit no difference between % of 
exploration between the new and 
familiar arms, as we could verify in the 
paired comparison between new arm and 
the mean of familiar arm of 1.5 mg/Kg 
B.E. group (31.75 ± 2.4% and 34.13 ± 
1.2%, new arm and familiar arms, 
respectively, t = 0.77, df = 9, p = 0.45) 
and 1.5 mg/Kg I.A.E. (32.87 ± 5.2% and 
33.56 ± 2.6%, new arm and familiar 
arms, respectively, t = 0.087, df = 6, p = 
0.93).. 
In Preparation for Journal of Psychopharmacology 
 
Figure 2 – High dose of nicotine 
impairs spatial recognition in the 
Modified T-maze. A) MT Maze 
schematic task, the three arms are 
identified with different geometric 
shapes and contrasts in the end of the 
arm, and also with different texture in the 
flor as cues. First panel shows the 
training phase with two different arms 
presented (open arms) and one arm not 
allowed to explore (closed arm). The 
lower panel shows the test phase, 24 
hours apart of training phase, with three 
different arms, two familiar presented 
previously and one new arm. B) Arm 
Discrimination index. The inset shows 
the color labels of the groups. The 
dashed line represents the 0.33 value (if 
animals explore the arms equally). The 
data is presented by mean ± SEM. 
*comparison of group discrimination 
index against 0.33, p < 0.05. 
 
3.3 – High dose of nicotine impairs 
working memory and locomotor 
activity 
 
Assuming that nicotine affect working 
memory performance, either to 
enhancing or decreasing performance 
(Levin and Torry, 1996; Rezvani and 
Levin, 2001), we investigate the usage of 
high dose of nicotine in the Y-maze, as a 
tool to evaluate working memory. 
Animals were exposed to the Y-maze, 
composed by 3 arms with same size 
(Figure 3A), for 10 minutes and the 
alternation sequences were accounted. 
We verified that both nicotine acute 
treatments (0.5 and 1.5 mg/Kg) had 
lower number of correct sequence in the 
Y maze, the ANOVA comparison among 
control (41.36 ± 2.8), 0.5 mg/Kg (22 ± 
In Preparation for Journal of Psychopharmacology 
2.2) and 1.5 mg/Kg (16 ± 2.3) revealed 
decrease in the number of correct 
sequence for both treated groups 
(F(2,27) = 28.53, p ≈ 0.0001, Figure 3B). 
We also verified if the locomotor activity 
was altered by the nicotine effect, the 
ANOVA comparison among control 
(3.41 ± 0.20), 0.5 mg/Kg (1.60 ± 0.15) 
and 1.5 mg/Kg (1.12 ± 0.18) revealed 
decrease in the total distance traveled for 
both treated groups (F(2,27) = 41.58, p ≈ 
0.0001, Figure 3C). Next we verified the 
relation between the correct alternation 
sequence and total distance traveled 
applying the Person correlation between 
those metrics. The Person correlation 
revealed a positive correlation between 
groups (r = 0.83 and p ≈ 0.0001, Figure 
3D). 
 
 
 
Figure 3 – The decrease of correct 
alternation sequence because of high 
nicotine injection correlates with the 
decrease of locomotor activity in the 
Y-maze working memory task. A) Y-
In Preparation for Journal of Psychopharmacology 
Maze schematic task, the maze in 
composed by three arms with same size. 
B) Correct alternation sequence. Panel 
show the result of ANOVA comparisons 
among the number of correct alternation 
sequence in the control and both doses of 
nicotine, 0.5 and 1.5 mg/Kg. C) Total 
distance traveled. Panel show the result 
of ANOVA comparisons among the 
mean of total distance traveled with the 
same groups of above. D) Correlation 
between correct alternation sequence and 
total distance traveled. *** p < 0.001. 
 
4.0 – Discussion 
Aiming to investigate the interference of 
high doses of nicotine in the 
hippocampal related types of memory, 
object and spatial recognition, we 
submitted animals to two different doses 
(0.5 mg/Kg and 1.5 mg/Kg) in different 
time points (before and after training 
session). Both behavioral tasks used here 
were divided in two different sessions, 
training and test session. Regarding 
NOR task, during the training session 
animals were allowed to explore two 
objects in the NOR task, 24 hours later 
one object previous explored was 
replaced by a new object and other was 
maintained in the arena, then animals 
were allowed to explore object for 10 
minutes. Using the same principle of 
recognition of novelty, we used the MT 
task to evaluate the spatial recognition 
capacity in mice under high dose 
nicotine effect. Two arms identified with 
geometric clues and different ground 
texture were presented in the training 
session, 24 hours later a third arm was 
presented. We found that nicotine high 
dose caused a shift on the object 
preference during the test session, while 
resulted in impairment on spatial 
recognition in the MT task. 
Nicotine is one of the most widely 
abused drug worldwide (Benowitz, 
2008; Kawazoe and Shinkai, 2015; 
Picciotto, 2003). The direct consume, as 
well the indirect exposure, of nicotine 
trough smoked Tabaco is linked to 
several diseases (Law et al., 1997; Oberg 
et al., 2011). Apparently nicotine has an 
key role in the dependence development 
in the smoke consumer because of action 
on the reward system (Benowitz, 2008; 
Kawazoe and Shinkai, 2015). 
Meanwhile, the nicotine is not only 
related to the reward system, but has a 
very spread action on the brain (Levin, 
2013) and it roles are linked with 
different subtypes of receptors (Levin, 
2013; Rezvani and Levin, 2001) and 
different subareas in the brain, including 
limbic system (Berg et al., 2014; 
Pidoplichko et al., 2013),  thalamus 
(Cannady et al., 2009), and cortical areas 
In Preparation for Journal of Psychopharmacology 
(Levin, 2013; Wallace and Bertrand, 
2013). The molecular action of nicotine 
is also very diverse, it been related to 
both pre synaptic and post synaptic 
mechanism of action (Berg and Conroy, 
2002), also are linked to  modulatory 
events of synaptic transmission (Dajas-
Bailador and Wonnacott, 2004), as like 
long term potentiation (LTP) events in 
the hippocampus (Alzoubi et al., 2007; 
Matsuyama et al., 2000), calcium 
permeability change (Dajas-Bailador 
and Wonnacott, 2004) and gene 
expression (Berg and Conroy, 2002).  
Despite the negative aspect found with 
the link between nicotine and the drug 
seek behavior, the major of works 
appoint increase in the performance of 
several hippocampal dependent task 
under effect of moderate doses 
(Arendash et al., 1995; Gould and 
Lommock, 2003; Puma et al., 1999; 
Rezvani and Levin, 2001; Tian et al., 
2015). Showing the participation of 
nicotine acetylcholine network in 
diverse aspects of memory formation 
process, to the encoding (Aren- et al., 
1995; Puma et al., 1999; Socci et al., 
1995; Sultana et al., 2012), passing by 
the consolidation (Puma et al., 1999; 
Rezvani and Levin, 2001), 
reconsolidation (Tian et al., 2015) and 
retrieval (Cp et al., 1992; Martí Barros et 
al., 2004). However, the all spectrum of 
nicotine doses should be investigated to 
try to mimetic all spectrum of nicotine 
consume behavior. Therefore, we 
investigate what was the implication of 
nicotine high doses in the object and 
spatial recognition memory.  
Assuming that nicotine doses between 
0.1 to 0.4 mg/Kg were related to the 
increase of object discrimination 
memory in the NOR task (Puma et al., 
1999; Tian et al., 2015), we tried to 
verify if the high doses 0.5 mg/Kg, slight 
higher than doses above, and 1.5 mg/Kg 
would replicate the same result pattern 
found in the previous works. First in the 
NOR task, we verified that our control 
group, as well the 0.5 mg/Kg group 
exhibit the pattern to explore more the 
novel object in the test session, although 
they did not differ between each other, 
the 0.5 mg/Kg group exhibit a bigger 
difference between the exploration of the 
novel and familiar object (Figure 2), 
despite that they revealed no difference 
in the discrimination ratio. Surprisingly, 
the injection of 1.5 mg/Kg B.E. resulted 
in the shift of object preference in the 
NOR task. Animals of this group exhibit 
lower percentage of new object 
exploration in comparison to the other 
groups, but higher discrimination index 
in comparison to the other groups. We 
In Preparation for Journal of Psychopharmacology 
hypothesized that the nicotine was acting 
in the encoding phase pairing the 
pleasure of the nicotine with the objects 
of training phase, adding the reward 
system to the formation of the memory 
trace. Aiming to investigate this, we 
submitted animals to the injection of 1.5 
mg/Kg I.A.E. and we verified that 
animals reversed the preference to the 
novel object. Although, as showed with 
place preference task (Grabus et al., 
2006; Vastola et al., 2002; Walters et al., 
2006), it would be expected that animals 
that associate pleasure with the object, 
would spent more time exploring 
objects, but the comparison with total 
time exploration revealed that they had 
the similar time of exploration found in 
the control group and even lower than 
the 1.5 mg/Kg I.A.E. group (Figure 2). 
Nonetheless the participation of reward 
system in promoting the shift in object 
explorations seems to be the most 
probable in this case.  
In the spatial recognition task, using MT 
maze, animals of control and 0.5mg/Kg 
groups exhibit similar recognition 
capacity found in the NOR task, 
preferring the novel arm instead the 
familiar arms. The recognition index 
comparison revealed that despite no 
difference among groups was found, 
animals of control and 0.5 mg/Kg group 
explored more the new arm than chance 
(0.33) revealing that they preferred the 
new arm. But both 1.5 mg/Kg treated 
groups exhibit no difference in 
comparison to 0.33. The same trend 
could be verified in the comparisons 
within groups that revealed difference, in 
the control group, between the 
percentage of exploration in the new arm 
and the percentage of exploration mean 
of familiar arm, as well as the trend of 
0.5 mg/Kg group (p = 0.06), but not in 
the groups treated with 1.3 mg/Kg.  
More studies most be realized to 
investigate why high doses of nicotine 
provoked such shift in the object 
preference, why this not happened with 
the injection after exploration, why was 
only found in the object recognition, 
what is the molecular cascade involved, 
there is involvement with the reward 
system?    
 
 
Figures Legends  
 
Figure 1 – High dose of nicotine shifts 
object preference and increase object 
discrimination. A) object recognition 
schematic. First panel shows the training 
phase with two similar objects. The 
lower panel shows the test phase 24 
hours apart of training phase two objects, 
one familiar presented previously and 
one new object. B) Percentage of object 
In Preparation for Journal of Psychopharmacology 
exploration. The inset shows the color 
separated groups. In yellow the control 
group; the group subject to injection 20 
minutes before the training phase to 0.5 
mg/Kg of nicotine is represented in 
green; light blue represents the group 
subject to 1.5 mg/Kg 20 minutes before 
the training phase; In dark blue the group 
subject to nicotine at dose 1.5mg/Kg 
immediately after exploration of training 
phase. The colors indicate the groups in 
the next sections. C) Object 
discrimination index. D) Total distance 
traveled during object recognition task. 
E) Total exploration of objects. The data 
is presented by mean ± SEM. ANOVA 
comparisons were performed in B, C and 
D. Kruskal-Wallis test was performed in 
E. *p < 0.05, *** p < 0.001. 
 
Figure 2 – High dose of nicotine 
impairs spatial recognition in the 
Modified T-maze. A) MT Maze 
schematic task, the three arms are 
identified with different geometric 
shapes and contrasts in the end of the 
arm, and also with different texture in the 
flor as cues. First panel shows the 
training phase with two different arms 
presented (open arms) and one arm not 
allowed to explore (closed arm). The 
lower panel shows the test phase, 24 
hours apart of training phase, with three 
different arms, two familiar presented 
previously and one new arm. B) Arm 
Discrimination index. The inset shows 
the color labels of the groups. The 
dashed line represents the 0.33 value (if 
animals explore the arms equally). C) 
Percentage of exploration. Figure shows 
the paired comparisons within groups of 
the new arms percentage of exploration 
against the mean of familiar arms 
percentage of exploration. The data is 
presented by mean ± SEM. *comparison 
of group discrimination index against 
0.33, p < 0.05. 
Figure 3 – High dose of nicotine 
impairs working memory in the Y-
maze. A) Y-Maze schematic task, the 
maze in composed by three arms with 
same size. B) Correct alternation 
sequence. Panel show the result of 
ANOVA comparisons among the 
number of correct alternation sequence 
in the control and both doses of nicotine, 
0.5 and 1.5 mg/Kg. The inset shows the 
color labels of the groups. 
 
5.0 – Reference 
Alzoubi K h., Aleisa A m. and Alkadhi 
K a. (2007) Nicotine prevents 
disruption of the late phase LTP-
related molecular cascade in 
adult-onset hypothyroidism. 
Hippocampus 17(8): 654–664. 
Aren- GW, Sanberg PR and Sengstock 
GJ (1995) Nicotine enhances the 
learning and memory of aged 
rats. Pharmacology 
Biochemistry and Behavior 
52(3): 517–523. 
Arendash GW, Sengstock GJ, Sanberg 
PR, et al. (1995) Improved 
learning and memory in aged 
rats with chronic administration 
of the nicotinic receptor agonist 
GTS-21. Brain Research 674(2): 
252–259. 
Benowitz NL (2008) Neurobiology of 
Nicotine Addiction: Implications 
for Smoking Cessation 
Treatment. The American 
Journal of Medicine, Update on 
Smoking Cessation 
Interventions for the Primary 
Care Physician 121(4, 
Supplement): S3–S10. 
In Preparation for Journal of Psychopharmacology 
Berg DK and Conroy WG (2002) 
Nicotinic α7 receptors: Synaptic 
options and downstream 
signaling in neurons. Journal of 
Neurobiology 53(4): 512–523. 
Berg SA, Sentir AM, Bell RL, et al. 
(2014) Nicotine effects in 
adolescence and adulthood on 
cognition and α4β2-nicotinic 
receptors in the neonatal ventral 
hippocampal lesion rat model of 
schizophrenia. 
Psychopharmacology 232(10): 
1681–1692. 
Cannady R, Weir R, Wee B, et al. 
(2009) Nicotinic antagonist 
effects in the mediodorsal 
thalamic nucleus: regional 
heterogeneity of nicotinic 
receptor involvement in 
cognitive function. Biochemical 
Pharmacology 78(7): 788–794. 
Cp F, Ga  de E and Cm B (1992) 
Modulation of memory retrieval 
by pre-testing vasopressin: 
involvement of a central 
cholinergic nicotinic 
mechanism. Methods and 
findings in experimental and 
clinical pharmacology 14(8): 
607–613. 
Dajas-Bailador F and Wonnacott S 
(2004) Nicotinic acetylcholine 
receptors and the regulation of 
neuronal signalling. Trends in 
Pharmacological Sciences 
25(6): 317–324. 
Decker MW, Majchrzak MJ and 
Anderson DJ (1992) Effects of 
nicotine on spatial memory 
deficits in rats with septal 
lesions. Brain Research 572(1–
2): 281–285. 
Gould TJ and Lommock JA (2003) 
Nicotine Enhances Contextual 
Fear Conditioning and 
Ameliorates Ethanol-Induced 
Deficits in Contextual Fear 
Conditioning. Behavioral 
Neuroscience 117(6): 1276–
1282. 
Grabus SD, Martin BR, Brown SE, et 
al. (2006) Nicotine place 
preference in the mouse: 
influences of prior handling, 
dose and strain and attenuation 
by nicotinic receptor 
antagonists. 
Psychopharmacology 184(3–4): 
456–463. 
Itzhak Y and Martin JL (1999) Effects 
of cocaine, nicotine, dizocipline 
and alcohol on mice locomotor 
activity: cocaine-alcohol cross-
sensitization involves 
upregulation of striatal 
dopamine transporter binding 
sites. Brain Research 818(2): 
204–211. 
Kawazoe S and Shinkai T (2015) 
[Nicotine dependence]. Nihon 
Rinsho. Japanese Journal of 
Clinical Medicine 73(9): 1516–
1521. 
Kutlu MG and Gould TJ (2015) 
Nicotine modulation of fear 
memories and anxiety: 
Implications for learning and 
anxiety disorders. Biochemical 
Pharmacology 97(4): 498–511. 
Law MR, Morris JK and Wald NJ 
(1997) Environmental tobacco 
smoke exposure and ischaemic 
heart disease: an evaluation of 
the evidence. BMJ 315(7114): 
973–980. 
Levin ED (2002) Nicotinic receptor 
subtypes and cognitive function. 
Journal of Neurobiology 53(4): 
633–640. 
In Preparation for Journal of Psychopharmacology 
Levin ED (2013) Complex relationships 
of nicotinic receptor actions and 
cognitive functions. Biochemical 
Pharmacology, Nicotinic 
Acetylcholine Receptors as 
Therapeutic Targets: Emerging 
Frontiers in Basic Research and 
Clinical Science (Satellite to the 
2013 Meeting of the Society for 
Neuroscience) 86(8): 1145–
1152. 
Levin ED and Torry D (1996) Acute 
and chronic nicotine effects on 
working memory in aged rats. 
Psychopharmacology 123(1): 
88–97. 
Martí Barros D, Ramirez MR, dos Reis 
EA, et al. (2004) Participation of 
hippocampal nicotinic receptors 
in acquisition, consolidation and 
retrieval of memory for one trial 
inhibitory avoidance in rats. 
Neuroscience 126(3): 651–656. 
Matsuyama S, Matsumoto A, Enomoto 
T, et al. (2000) Activation of 
nicotinic acetylcholine receptors 
induces long-term potentiation 
in vivo in the intact mouse 
dentate gyrus. European Journal 
of Neuroscience 12(10): 3741–
3747. 
Morrison CF and Armitage AK (1967) 
Effects of Nicotine Upon the 
Free Operant Behavior of Rats 
and Spontaneous Motor Activity 
of Mice. Annals of the New York 
Academy of Sciences 142(1): 
268–276. 
Oberg M, Jaakkola MS, Woodward A, 
et al. (2011) Worldwide burden 
of disease from exposure to 
second-hand smoke: a 
retrospective analysis of data 
from 192 countries. Lancet 
(London, England) 377(9760): 
139–146. 
Picciotto MR (2003) Nicotine as a 
modulator of behavior: beyond 
the inverted U. Trends in 
Pharmacological Sciences 
24(9): 493–499. 
Pidoplichko VI, Prager EM, Aroniadou-
Anderjaska V, et al. (2013) α7-
Containing nicotinic 
acetylcholine receptors on 
interneurons of the basolateral 
amygdala and their role in the 
regulation of the network 
excitability. Journal of 
Neurophysiology 110(10): 
2358–2369. 
Puma C, Deschaux O, Molimard R, et 
al. (1999) Nicotine improves 
memory in an object recognition 
task in rats. European 
Neuropsychopharmacology: The 
Journal of the European College 
of Neuropsychopharmacology 
9(4): 323–327. 
Rezvani AH and Levin ED (2001) 
Cognitive effects of nicotine. 
Biological Psychiatry, Nicotine 
Mechanisms in Alzheimer’s 
Disease 49(3): 258–267. 
Rezvani AH and Levin ED (2004) 
Adolescent and adult rats 
respond differently to nicotine 
and alcohol: motor activity and 
body temperature. International 
Journal of Developmental 
Neuroscience, Developmental 
Aspects of Addiction 22(5–6): 
349–354. 
Socci DJ, Sanberg PR and Arendash 
GW (1995) Nicotine enhances 
morris water maze performance 
of young and aged rats. 
Neurobiology of Aging 16(5): 
857–860. 
Sultana R, Ameno K, Jamal M, et al. 
(2012) Low-dose nicotine 
In Preparation for Journal of Psychopharmacology 
facilitates spatial memory in 
ApoE-knockout mice in the 
radial arm maze. Neurological 
Sciences 34(6): 891–897. 
Tapper AR, McKinney SL, Nashmi R, 
et al. (2004) Nicotine Activation 
of α4* Receptors: Sufficient for 
Reward, Tolerance, and 
Sensitization. Science 
306(5698): 1029–1032. 
Tian S, Pan S and You Y (2015) 
Nicotine enhances the 
reconsolidation of novel object 
recognition memory in rats. 
Pharmacology, Biochemistry, 
and Behavior 129: 14–18. 
Vastola BJ, Douglas LA, Varlinskaya 
EI, et al. (2002) Nicotine-
induced conditioned place 
preference in adolescent and 
adult rats. Physiology & 
Behavior 77(1): 107–114. 
Wallace TL and Bertrand D (2013) 
Importance of the nicotinic 
acetylcholine receptor system in 
the prefrontal cortex. 
Biochemical Pharmacology 
85(12): 1713–1720. 
Walters CL, Brown S, Changeux J-P, et 
al. (2006) The β2 but not α7 
subunit of the nicotinic 
acetylcholine receptor is 
required for nicotine-
conditioned place preference in 
mice. Psychopharmacology 
184(3–4): 339–344.