Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals.

ABSTRACT:

Introduction

Can we apply graph representation learning algorithms to identify autism spectrum disorder (ASD) patients within a large brain imaging dataset? ASD is mainly identified by brain functional connectivity patterns. Attempts to unveil the common neural patterns emerged in ASD are the essence of ASD classification. We claim that graph representation learning methods can appropriately extract the connectivity patterns of the brain, in such a way that the method can be generalized to every recording condition, and phenotypical information of subjects. These methods can capture the whole structure of the brain, both local and global properties.

Methods

The investigation is done for the worldwide brain imaging multi-site database known as ABIDE I and II (Autism Brain Ima

SUBMITTER: Yousefian A 

PROVIDER: S-EPMC9932324 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature