Improving the characterization of psychiatric disorders with Spatio-Temporal Graph Convolution Neural Networks using fMRI data

• Grant number: 24/00861-0
• Support Opportunities: Scholarships abroad - Research Internship - Master's degree
• Start date: July 01, 2024
• End date: December 23, 2024
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: João Ricardo Sato
• Grantee: Rodrigo da Motta Cabral de Carvalho
• Supervisor: Emma Claire Robinson
• Host Institution: Centro de Matemática, Computação e Cognição (CMCC). Universidade Federal do ABC (UFABC). Ministério da Educação (Brasil). Santo André , SP, Brazil
• Institution abroad: King's College London, England
• Associated to the scholarship: 23/02616-0 - Use of Graph convolutional neural networks in fMRI data for characterization of psychiatric disorders., BP.MS

Abstract

The period of childhood and adolescence is essential for comprehending brain function mechanisms, especially within multiple neuropsychiatric conditions. Once most psychiatric disorders begin at this phase, where the brain passes through intense changes that result in the consolidation of brain connectivity networks. In particular, the study of neurophysiology during the resting state (i.e., the state in which the brain does not consciously receive any internal or external stimulation) at the macroscopic level is of great interest, given its complex patterns that microscopic components cannot explain. Different techniques can be employed to measure brain function at the macroscopic scale, such as functional magnetic resonance imaging (fMRI). With fMRI, it is possible to study functional brain organization, which enables the search for predictive biomarkers for neurodevelopmental and neuropsychiatric disorders to elucidate their underlying mechanisms. Recently, neuroimaging studies have employed machine-learning techniques, that enable statistical inferences of neurophysiological characteristics of multiple disorders. In that context, this project aims to use a state-of-the-art machine-learning model called Spatio-Temporal Graph Convolutional Neural Networks (ST-GCN), which operates on a non-euclidian domain to capture patterns in graphs that more classical models cannot. Providing a network-level analysis that captures spatial-temporal information within brain dynamic functional connectivity from multiple neuropsychiatric disorders.