Detection of Autism Spectrum Disorder Using Attention Based Graph Convolutional Network

Music City Center 

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition marked by atypical brain connectivity. This study presents a novel computational framework that utilizes an Attention-Based Graph Convolutional Network (GCN) to detect ASD. We use functional Magnetic Resonance Imaging data from the Autism Brain Imaging Data Exchange repository to construct functional connectivity matrices based on Pearson correlation, which captures the interactions among various brain regions given by the AAL atlas. Connectivity matrices are transformed into graph representations, where the nodes represent brain regions, and the edges encode functional connections. The Attention-Based GCN employs attention mechanisms to identify crucial connectivity patterns, enhancing both interpretability and diagnostic accuracy. The proposed framework achieves an accuracy of 90.57%, precision of 85.90%, and recall of 95.53%, outperforming existing results. This study not only advances the detection of ASD but also underscores the broader potential of Attention-Based Graph GCNs in analyzing complex relational data across various other applications.

ASD

Attention-Based Graph Convolutional Network

fMRI

Machine Learning

functional connectivity 

Section on Statistical Learning and Data Science