Beyond Anatomy: Explainable ASD Classification from rs-fMRI via Functional Parcellation and Graph Attention Networks

Abstract / Extracted Text

Anatomical brain parcellations dominate rs-fMRI-based Autism Spectrum Disorder (ASD) classification, yet their rigid boundaries may fail to capture the idiosyncratic connectivity patterns that characterise ASD. We present a graph-based deep learning framework comparing anatomical (AAL, 116 ROIs) and functionally-derived (MSDL, 39 ROIs) parcellation strategies on the ABIDE I dataset. Our FSL preprocessing pipeline handles multi-site heterogeneity across 400 balanced subjects, with site-stratified 70/15/15 splits to prevent data leakage. Gaussian noise augmentation within training folds expands samples from 280 to 1,680. A three phase pipeline progresses from a baseline GCN with AAL (73.3% accuracy, AUC=0.74), to an optimised GCN with MSDL (84.0%, AUC=0.84), to a Graph Attention Network ensemble achieving 95.0% accuracy (AUC=0.98), outperforming all recent GNN-based benchmarks on ABIDE I. The 10.7-point gain from atlas substitution alone demonstrates that functional parcellation is the most impactful modelling decision. Gradient-based saliency and GNNExplainer analyses converge on the Posterior Cingulate Cortex and Precuneus as core Default Mode Network hubs, validating that model decisions reflect ASD neuropathology rather than acquisition artefacts. All code and datasets will be publicly released upon acceptance.

Latest Summary

Key Findings

• Functional parcellation (MSDL atlas) in resting-state fMRI (rs-fMRI) data provides a 10.7% accuracy gain (from 73.3% to 84.0%) for Autism Spectrum Disorder (ASD) classification compared to traditional anatomical (AAL) atlases, highlighting atlas choice as the single most impactful modeling decision.
• A Graph Attention Network (GAT) ensemble, combined with functional parcellation and rigorous data augmentation, achieved state-of-the-art performance: 95.0% test accuracy, AUC ≈ 0.98 on the ABIDE I dataset, outperforming recent GNN-based methods.
• Extensive site-stratified partitioning (70/15/15 splits) and multi-site balanced cohorts prevent data leakage and ensure robust model evaluation.
• Data augmentation with Gaussian noise increased training data from 280 to 1,680 samples, effectively mitigating overfitting and addressing neuroimaging study sample scarcity.
• Explainability analyses (gradient saliency and GNNExplainer) consistently identified the Posterior Cingulate Cortex and Precuneus—core hubs of the Default Mode Network (DMN)—as most salient for ASD classification, validating biological plausibility and correspondence with established ASD neuropathology.
• The framework’s reproducibility is ensured via open release of all code, preprocessing scripts, and processed graph datasets.

Practical Takeaways

• Prefer functionally derived parcellation atlases (like MSDL) over traditional anatomical ones (like AAL) for capturing the heterogeneous, distributed connectivity patterns in ASD neuroimaging studies.
• Employ graph neural network models, specifically GAT ensembles, as these architectures can learn from functional connectivity with adaptive importance weights, delivering higher accuracy and interpretability.
• Augment sparse neuroimaging datasets with Gaussian noise exclusively within training folds to boost model robustness and prevent overfitting without compromising evaluation integrity.
• Site-stratified data partitioning is crucial to prevent site-specific biases and data leakage, especially with multi-site datasets.
• Integrate explainable AI techniques such as saliency mapping and GNNExplainer to confirm that predictive models reflect established neurobiological mechanisms rather than artifacts or noise.
• The presented pipeline and methods are generalizable to other neurodevelopmental disorder classification tasks, supporting both rigorous evaluation and biological validation.

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