linear-structure-function-coupling

A linear generative model predicts functional connectivity from structural connectivity using motif-based transformations. It applies direct, indirect, and triadic motifs with a ridge-regularized fit to model how structural connections shape functional networks.

Structure-function coupling analyzes how structural connectivity shapes functional connectivity across brain networks. Network motifs like direct, indirect, and triadic patterns act as transformation mechanisms within a linear model to map structural pathways into functional interactions.

You classify integrator and mediator hubs from SC-FC data, then simulate virtual lesions to study disruption cascades. The framework applies virtual perturbation analyses to evaluate structural damage effects on functional connectivity predictions.

Yes, ridge regression serves as the core approach for fitting the linear SC-FC coupling model. This ridge-regularized method processes structural connectivity inputs to generate and evaluate functional connectivity predictions across brain networks.

The model requires structural and functional connectivity matrices as direct input arrays. You provide SC and FC matrices to execute the fitting and prediction workflow, delivering model evaluation and hub classification insights.