GNN Analysis of Fronto-Temporal Deficits in Autism-Related Verb Morphology

Autism spectrum disorder (ASD) frequently manifests with profound language impairments, particularly in verb morphology processing, which hinges on fronto-temporal connectivity for grammatical rule application. This study pioneers the use of graph neural networks (GNNs) to map these deficits, analysing task-based fMRI data from 72 children (36 ASD, 36 controls). Fronto-temporal graphs were constructed with nodes representing key regions (e.g., inferior frontal gyrus, superior temporal gyrus) and edges capturing dynamic Pearson correlations during an auditory verb tense judgment task. A three-layer GraphSAGE model, incorporating message passing and temporal embeddings, achieved 91.7% classification accuracy (AUC=0.95), outperforming traditional classifiers by 14%. Attention maps revealed hypo-connectivity in the arcuate fasciculus pathway (p<0.001), correlating with ADOS language scores (r=-0.62), alongside compensatory frontal hyperconnectivity. Ablation studies confirmed the model’s reliance on task-evoked dynamics. These findings elucidate the neural substrates of morphology impairments, offering interpretable biomarkers for early ASD diagnosis and personalized interventions. By bridging graph theory with cognitive neuroscience, this work advances precision psychiatry, with implications for neurofeedback therapies targeting syntactic networks. Future extensions to multi-modal data promise enhanced generalizability across ASD heterogeneity.