linear-structure-function-coupling
CommunityPredict FC from SC with a linear model.
Education & Research#brain connectivity#structure-function coupling#structural connectivity#functional connectivity#motifs#hub classification#virtual lesion
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This tool provides a linear generative framework to model how structural connectivity shapes functional connectivity in the brain, enabling interpretable SC-FC coupling analysis and virtual perturbations.
Core Features & Use Cases
- Linear Generative Model: Predict FC from SC using motif-based transformations (direct, indirect, triadic) with a ridge-regularized fit.
- Hub Classification & Virtual Lesions: Identify integrator and mediator hubs and simulate cascades to study disruption effects.
- End-to-End Workflow: From SC/FC extraction, to fitting, prediction, evaluation, and exploratory analyses with Python implementations.
Quick Start
Provide SC and FC matrices and run model.fit(SC, FC) to train the predictor, then use model.predict(SC) to obtain FC predictions.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: linear-structure-function-coupling Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#linear-structure-function-coupling Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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