Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by impairments in communication, social interaction and behavior. ASD individuals develop symptoms such as recurrent actions, atypical facial expressions and challenges in social engagement. This study proposes a Multi-Atlas Context-Aware Fusion Network (MACAFNet) for ASD classification using functional connectivity (FC) features derived from resting-state functional MRI (rs-fMRI) to enable reliable ASD classification. This framework integrates information from multiple brain atlases such as AAL, CC200, Dosenbach160, EZ, HO and TT to capture complementary neurofunctional features across diverse brain parcellations. Each atlas-specific connectivity representation is projected into a shared embedding space and fused using a Transformer-based attention mechanism that explicitly models inter-atlas contextual dependencies. Unlike traditional static fusion systems, this allows for dynamic and adaptive feature integration. The fused representation is then processed by a neural classifier for ASD classification. Experiments conducted on the ABIDE-I dataset demonstrate that the proposed approach achieves an accuracy of 88.46% and an AUC of 0.9546, indicating strong discriminative capability. The results highlight the effectiveness of context-aware multi-atlas fusion in capturing complex brain connectivity patterns for research-oriented ASD classification.
Aarthi et al. (Thu,) studied this question.