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arXiv · 2507.06136

A Linear Generative Framework for Structure-Function Coupling in the Human Brain

Sam Frank Kelemen·Joaquín Gõni·Sérgio Pequito·Arian Ashourvan
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Abstract

Brain function emerges from coordinated activity across anatomically connected regions, where structural connectivity (SC) -- the network of white matter pathways - provides the physical substrate for functional connectivity (FC) -- the correlated neural activity between brain areas. While these structural and functional networks exhibit substantial overlap, their relationship involves complex, indirect mechanisms, including the dynamic interplay of direct and indirect pathways, recurrent network interactions, and neuromodulatory influences. To systematically untangle how structural architecture shapes functional patterns, this work aims to establish a set of rules that decode how direct and indirect structural connections and motifs give rise to FC between brain regions. Specifically, using a generative linear model, we derive explicit rules that predict an individual's resting-state fMRI FC from diffusion-weighted imaging (DWI)-derived SC, validated against topological null models. Examining the rules reveals distinct classes of brain regions, with integrator hubs acting as structural linchpins promoting synchronization and mediator hubs serving as structural fulcrums orchestrating competing dynamics. Through virtual lesion experiments, we demonstrate how different cortical and subcortical systems distinctively contribute to global functional organization. Together, this framework disentangles the mechanisms by which structural architecture drives functional dynamics, enabling the prediction of how pathological or surgical disruptions to brain connectivity cascade through functional networks, potentially leading to cognitive and behavioral impairments.

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Sam Frank Kelemen, Joaquín Gõni, Sérgio Pequito, Arian Ashourvan. 2025-07-08. A Linear Generative Framework for Structure-Function Coupling in the Human Brain. https://arxiv.org/abs/2507.06136

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