PPGNCG PÓS-GRADUAÇÃO EM NEUROCIÊNCIA E COGNIÇÃO FUNDAÇÃO UNIVERSIDADE FEDERAL DO ABC Phone: Not available http://propg.ufabc.edu.br/neuro

Banca de QUALIFICAÇÃO: SERGIO LEONARDO MENDES

Uma banca de QUALIFICAÇÃO de MESTRADO foi cadastrada pelo programa.
DISCENTE : SERGIO LEONARDO MENDES
DATA : 10/05/2022
HORA: 08:15
LOCAL: Remoto
TÍTULO:

Investigating infant neurodevelopment using neuroimaging and deep learning.


PÁGINAS: 20
GRANDE ÁREA: Ciências da Saúde
ÁREA: Medicina
RESUMO:

The transition between childhood and adolescence is extremely important for neurodevelopment, as this phase presents intense changes that result in the consolidation of brain connectivity networks. However, these structures, which are responsible for behavior patterns, can mature atypically, resulting in psychiatric symptoms (depression, aggression, somatization and others) or psychopathologies. In this context, structural magnetic resonance imaging (sMRI) is an important tool to provide relatively accurate characterizations of brain structures. sMRI biomarkers can provide important information about pathological mechanisms to help understand the nature of these diseases. However, most psychiatric disorders are still diagnosed exclusively by clinical interviews and little is known about the etiology of these disorders. Therefore, this project aims to investigate sMRI biomarkers for psychiatric symptoms and/or psychopathologies in children and adolescents. For this purpose, normative models based on autoregressive transformers will be used. Currently, these deep neural networks are the state of the art in computer vision and anomaly detection from medical images. The dataset that will be used in the training of the models comprises sMRI of 652 individuals, aged between 7 and 15 years, students from 57 Brazilian public schools, with different levels of psychiatric symptoms. The models will be trained from cross-sectional data, but if the data do not provide enough predictive power for learning the models, then longitudinal data will be included in the study. It is expected that the approach adopted will allow the prediction and characterization of mental health conditions, based on the sMRI of the individuals studied.


MEMBROS DA BANCA:
Presidente - Interno ao Programa - 1676329 - RAPHAEL YOKOINGAWA DE CAMARGO
Membro Titular - Examinador(a) Externo ao Programa - 1672981 - FRANCISCO JAVIER ROPERO PELAEZ
Membro Titular - Examinador(a) Externo à Instituição - ANDERSON DA SILVA SOARES - UFG
Membro Suplente - Examinador(a) Interno ao Programa - 1766041 - MARCELO BUSSOTTI REYES
Membro Suplente - Examinador(a) Externo à Instituição - RODRIGO BASILIO
Notícia cadastrada em: 29/03/2022 16:23
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