connectome-analysis
CommunityTurn ROI signals into brain network metrics
Education & Research#functional connectivity#nilearn#connectome analysis#graph metrics#bctpy#hub detection#rich-club
Authorxjtulyc
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill solves the challenge of transforming ROI-level fMRI time series into functional connectivity matrices and then computing interpretable graph-theoretic brain network metrics for neuroscience research.
Core Features & Use Cases
- Functional connectivity computation: Build connectivity (FC) matrices from ROI time series using correlation (and optional partial correlation).
- Network science metrics: Quantify clustering, characteristic path length, global efficiency, modularity, and rich-club structure.
- Hub detection & group comparison: Identify hub regions using centrality-based heuristics and compare connectivity patterns across groups (e.g., patients vs. controls).
Quick Start
Ask the model to compute a functional connectivity matrix from your ROI time series and then return key graph metrics (clustering, path length, modularity, rich-club, and hubs) for downstream analysis.
Dependency Matrix
Required Modules
bctpynilearnnetworkxmatplotlibnumpypandasscipy
Components
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: connectome-analysis Download link: https://github.com/xjtulyc/awesome-rosetta-skills/archive/main.zip#connectome-analysis Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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