SPSGL: uncovering psychiatric network mechanisms via structural-prior guided synaptic graph learning

Functional magnetic resonance imaging (fMRI) provides a crucial window for understanding brain functional connectivity (FC) in psychiatric disorders, yet its complex spatiotemporal dynamics pose substantial challenges for modeling. Existing methods often rely on static FC, making it difficult to capture the dynamic plasticity of brain, while generally ignoring structural differences across functional networks or discarding informative weak connections due to excessive sparsification. Here, we propose SPSGL, a biologically inspired deep learning framework designed to construct novel brain connectivity patterns from fMRI signals. SPSGL transforms voxel-wise time series into frequency-domain, feature-driven functional brain graphs and employs a biologically inspired gated edge-update mechanism to capture dynamic changes in connectivity strength. On this basis, core functional networks and whole-brain patterns are mapped as structural priors to explicitly guide multi-head attention in forming complementary subspace foci that emphasize neurobiologically meaningful connections. Further combined with Orthonormal Clustering Readout (OCRead), our model achieves adaptive learning of multi-scale brain graph representations and functional parcellations. Across five psychiatry-related computational tasks, SPSGL demonstrates superior performance compared with existing approaches. Moreover, it identifies task-relevant functional connections and hub regions associated with aberrant coupling among the default mode, sensorimotor, and subcortical networks, highlighting potential neuroimaging biomarkers and uncovering shared brain network factors shared across diverse psychiatric conditions. Overall, SPSGL provides a unified, interpretable, and high-performing framework for fMRI-based brain connectivity analysis, advancing mechanistic understanding and potential clinical translation in mental health research. Our code is publicly available on https://github.com/zhaoqi106/SPSGL.

The online version contains supplementary material available at 10.1007/s13755-026-00467-6.