Navegando por Autor "Peres, André Salles Cunha"

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Can somatosensory electrical stimulation relieve spasticity in post-stroke patients? A TMS pilot study
(2017-05-05) Peres, André Salles Cunha; Souza, Victor Hugo; Catunda, João Marcos Yamasaki; Mazzeto-Betti, Kelley Cristine; Santos-Pontelli, Taiza Elaine Grespan; Vargas, Claudia Domingues; Baffa, Oswaldo; Araújo, Dráulio Barros de; Pontes-Neto, Octávio Marques; Leite, João Pereira; Garcia, Marco Antonio Cavalcanti
Evidence suggests that somatosensory electrical stimulation (SES) may decrease the degree of spasticity from neural drives, although there is no agreement between corticospinal modulation and the level of spasticity. Thus, stroke patients and healthy subjects were submitted to SES (3 Hz) for 30′ on the impaired and dominant forearms, respectively. Motor evoked potentials induced by single-pulse transcranial magnetic stimulation were collected from two forearm muscles before and after SES. The passive resistance of the wrist joint was measured with an isokinetic system. We found no evidence of an acute carry-over effect of SES on the degree of spasticity.
Performance quantification of clustering algorithms for false positive removal in fMRI by ROC curves
(2017-03) Peres, André Salles Cunha; Lemos, Tenysson Will de; Barros, Allan Kardec Duailibe; Baffa Filho, Oswaldo; Araújo, Dráulio Barros de
Introduction: Functional magnetic resonance imaging (fMRI) is a non-invasive technique that allows the detection of specific cerebral functions in humans based on hemodynamic changes. The contrast changes are about 5%, making visual inspection impossible. Thus, statistic strategies are applied to infer which brain region is engaged in a task. However, the traditional methods like general linear model and cross-correlation utilize voxel-wise calculation, introducing a lot of false-positive data. So, in this work we tested post-processing cluster algorithms to diminish the false-positives. Methods: In this study, three clustering algorithms (the hierarchical cluster, k-means and self-organizing maps) were tested and compared for false-positive removal in the post-processing of cross-correlation analyses. Results: Our results showed that the hierarchical cluster presented the best performance to remove the false positives in fMRI, being 2.3 times more accurate than k-means, and 1.9 times more accurate than self-organizing maps. Conclusion: The hierarchical cluster presented the best performance in false-positive removal because it uses the inconsistency coefficient threshold, while k-means and self-organizing maps utilize a priori cluster number (centroids and neurons number); thus, the hierarchical cluster avoids clustering scattered voxels, as the inconsistency coefficient threshold allows only the voxels to be clustered that are at a minimum distance to some cluster.
A protocol for fMRI visual decoding
(2014-09) Peres, André Salles Cunha; Sato, João Ricardo; dos Santos, Antônio Carlos; Hallak, Jaime Eduardo Cecílio; Ribeiro, Sidarta Tollendal Gomes; Araújo, Dráulio Barros de
Introdução Functional magnetic resonance imaging (fMRI) is widely used to assess patterns of brain activity in response to specific tasks. Recent advances of signal processing tools opened the perspective of decoding information from different stimuli based on fMRI brain activity. Currently, the decoding of visual information is the most successful strategy. Typically, during the encoding phase the volunteers passively see a large number of images and a pattern of the fMRI signal is associated to each one of them. Based only on these BOLD signal patterns, statistical algorithms are used to infer what was the image seen by the subject. A common strategy used for visual cortex decoding is to separate the images into categories, with the intent of creating an average of BOLD distribution for each category. Thus, decoding refers to indicating the category to which an image belongs to. Objetivos Our purpose in this work is to evaluate the feasibility of implementing a visual cortex decoding protocol based on six categories: tree, car, house, food, person, and reptile. Métodos Two asymptomatic volunteers were invited to participate in the study. They were asked to passively watch a set of 1,440 images divided into these six categories, while fMRI data was continuously being acquired. Subjects participated in 13 sessions of 30 minutes each. fMRI analysis was based on the General Linear Model implemented in SPM8 (UCL UK). A threshold was set at p < 0.05 (FWE, corrected). The BOLD distribution was compared for each pair of category, doing a subtraction between them, totaling 30 comparisons. Resultados e Conclusões We found significant differences in the BOLD distribution for all pairs analyzed, which indicate the feasibility to further perform visual cortex decoding using the protocol described above.

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Navegando por Autor "Peres, André Salles Cunha"