connectome-analysis

Compute functional connectivity matrices and brain network graph metrics from ROI time series.

33|6|Updated Mar 17, 2026

Search Tags:#functional connectivity #nilearn #connectome analysis #graph metrics #bctpy #hub detection #rich-club

Ready to run

npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill connectome-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: connectome-analysis
Source: https://github.com/xjtulyc/awesome-rosetta-skills/tree/main/skills/05-neuroscience/connectome-analysis
Command: npx skills add https://github.com/xjtulyc/awesome-rosetta-skills --skill connectome-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires bctpy, nilearn, networkx, matplotlib, numpy, pandas, scipy.

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.

Frequently Asked Questions about connectome-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I compute functional connectivity matrices from ROI time series?▼

Functional connectivity matrices are computed from ROI time series using correlation, with optional partial correlation, to produce FC matrices for downstream brain network analysis. The process uses nilearn and bctpy to transform fMRI signals into connectivity data.

What graph metrics can I derive for connectome analysis?▼

Connectome analysis graph metrics include clustering coefficient, characteristic path length, global efficiency, modularity, and rich-club structure. These network science measures are calculated from thresholded adjacency matrices derived from functional connectivity data.

How do I detect hub regions in a brain network?▼

Hub detection in a brain network uses centrality-based heuristics to identify hub regions from functional connectivity matrices. The Skill computes these centrality measures from ROI time series to pinpoint highly connected nodes in the connectome.

Can I compare functional connectivity between patients and controls?▼

Comparing functional connectivity between patients and controls is supported by computing group-level FC matrices and deriving network metrics for each group. The Skill processes ROI time series for both populations to enable statistical comparison of connectivity patterns.

Do I need bctpy and nilearn to compute rich-club organization?▼

Yes, bctpy and nilearn are required dependencies to compute rich-club organization and other graph metrics. The Skill also depends on networkx, numpy, pandas, scipy, and matplotlib to perform connectome analysis and generate visual outputs.

What's the best way to evaluate resting-state functional connectivity?▼

Evaluating resting-state functional connectivity involves computing FC matrices from ROI time series, thresholding them into adjacency matrices, and then calculating graph metrics like modularity and global efficiency. This Skill automates that pipeline using bctpy and nilearn.