Ramos, M. B. | bioRxiv Science

bioRxiv2026

Beyond Regional Activations: Structural Connectivity Message-Passing Shallow Neural Networks for Brain Decoding

Brain decoding from fMRI data using artificial neural networks traditionally operates at the regional level, identifying which brain areas activate during tasks but ignoring how these regions interact through structural networks. While Graph Neural Networks can capture connectivity, they require prohibitively large datasets for typical neuroscience studies. We introduce a message-passing mechanism that allows a shallow neural network to incorporate structural connectivity, enabling network-level interpretation from limited data. Using motor task data from 30 Human Connectome Project subjects, we evaluate seven structural connectivity matrices derived from deterministic and probabilistic tractography. Our approach achieves 83.0% classification accuracy while revealing functional network organization. We demonstrate that sparser, anatomy-driven connectivity matrices outperform dense alternatives, and that normalizing for network size improves model performance. Critically, our method is capable of exposing structural pathways contributing towards classification, distinguishing between complete network recruitment and selective regional activation. This approach bridges the gap between high-performance brain decoding and biological fidelity of the model, enhancing neuroscientific understanding, with implications for analyzing network dysfunctions in neurological disorders such as Alzheimers disease (AD), attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder, mild cognitive impairment (MCI), and schizophrenia.

neuroscience↗

bioRxiv2026

Structurally Restricted Message-Passing within Shallow Architectures for Explainable Network-Level Brain Decoding on Small Cohorts

The application of artificial intelligence (AI) to functional magnetic resonance imaging (fMRI) has gained increasing attention due to its ability to model complex, high-dimensional brain data and capture nonlinear patterns of neural activity. However, deep learning architectures, such as Graph Neural Networks (GNNs), typically require large sample sizes to achieve stable convergence, limiting their applicability in neuroimaging contexts where data are often scarce. This challenge highlights the need for compact, data-efficient models that maintain predictive performance and interpretability. Shallow neural networks (SNNs) have demonstrated robustness in low-sample settings but commonly rely on region-level features that treat brain areas independently, overlooking the brains intrinsically network-based organization. To address this limitation, we propose a structurally constrained message-passing framework that integrates diffusion tensor imaging (DTI)-derived structural connectivity with region-level fMRI signals within a shallow architecture. This approach enables network-level modeling while preserving the stability and data efficiency of SNNs. The method is evaluated on 30 subjects performing a Theory of Mind (ToM) task from the Human Connectome Project Young Adult dataset. A baseline SNN achieved global accuracies of 88.2% (fully connected), 80.0% (pruned), and 84.7% (retrained), while the proposed model achieved 87.1%, 77.6%, and 84.7%, respectively. Although structural constraints led to a more pronounced performance decrease after pruning, retraining restored accuracy to baseline levels, demonstrating that biological constraints can be incorporated without compromising predictive validity. Model interpretability was assessed using SHAP (Shapley Additive Explanations). While the baseline model primarily identified isolated regions as key contributors, the proposed framework revealed distributed, structurally coherent networks as the main drivers of classification. These networks showed correspondence with established ToM regions, including the temporo-parietal junction, superior temporal sulcus, and inferior frontal gyrus. Importantly, the findings suggest that groups of moderately informative regions can collectively form highly relevant subnetworks. Overall, the proposed framework achieves competitive performance in a limited dataset while incorporating graph-inspired message passing into a shallow architecture. Its explainability provides insight into how structurally constrained networks support stimulus-driven responses in ToM and demonstrates potential for investigating network dysfunction in disorders such as Alzheimers disease, ADHD, autism spectrum disorder, bipolar disorder, mild cognitive impairment, and schizophrenia.

bioinformatics↗