Graph Attention Network-Based Detection of Autism Spectrum Disorder
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. This study introduces a novel computational framework that employs an Attention-Based Graph Convolutional Network, referred to as the GATGraphClassifier, for detecting ASD. We utilize Functional Magnetic Resonance Imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) repository to construct functional connectivity matrices using Pearson correlation, which captures interactions between various brain regions. These matrices are then transformed into graph representations, where the nodes and edges ...
| Authors | Abigail Kelly, Ramchandra Rimal, Arpan Sainju |
|---|---|
| Primary category | stat.AP |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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