Análise estrutural de redes funcionais de indivíduos com transtorno do espectro autista

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by deficits in social communication and restricted, repetitive behavioral patterns. Despite advances in clinical diagnosis, there are still no objective biomarkers for its identification. This study employed Network Science to analyze brain connectivity patterns in individuals with ASD, aiming to identify potential neural signatures of the condition. We analyzed resting-state functional magnetic resonance imaging (fMRI) data from 1,025 individuals (488 with ASD and 537 neurotypical controls - NT) from the Autism Brain Imaging Data Exchange (ABIDE) database. The brain was parcellated into 116 regions of interest (ROIs) using the AAL116 atlas, and connectivity matrices were constructed using Pearson correlation coeffici- ents between time series. Networks were analyzed using Graph Theory metrics, including clustering coefficient, assortativity, diameter, and degree distribution. Additionally, a Machine Learning model (Linear Discriminant Analysis) was applied for group classification.