netneurotools-guide

Name: netneurotools-guide
Availability: InStock
Author: NeuroAIHub

Guide network neuroscience analysis with netneurotools, nibabel, and PyVista.

269|20|Updated Jun 13, 2026

Search Tags:#graph theory #neuroscience #fMRI #spatial statistics #connectomics #brain mapping

Ready to run

npx skills add https://github.com/NeuroAIHub/BrainPilot --skill netneurotools-guide

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: netneurotools-guide
Source: https://github.com/NeuroAIHub/BrainPilot/tree/main/packages/skills/skills/06_fMRI_Neuroimaging/netneurotools-guide
Command: npx skills add https://github.com/NeuroAIHub/BrainPilot --skill netneurotools-guide

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires netneurotools, numpy, scipy, scikit-learn, matplotlib, nibabel, nilearn, bctpy, tqdm, neuromaps, pyvista, pysurfer, numba, and includes references (resource) components.

What problem does it solve?

This skill simplifies complex network neuroscience workflows by providing a unified interface for brain connectivity analysis, spatial statistics, and cortical visualization, removing the need to manually manage disparate neuroimaging toolboxes.

Core Features & Use Cases

Connectivity Analysis: Compute network communication metrics like search information and navigation efficiency on structural or functional connectomes.
Spatial Statistics: Perform rigorous spatial autocorrelation analysis using Moran's I, Geary's C, or Lee's L on parcellated brain data.
Visualization: Generate high-quality cortical surface plots using PyVista or PySurfer with built-in support for standard templates like fsaverage and fsLR.

Quick Start

Use the netneurotools-guide skill to fetch the Schaefer 400-parcel atlas and visualize it on an inflated fsaverage surface.

Frequently Asked Questions about netneurotools-guide

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

FAQPage Schema

How do I compute graph theory metrics like navigation efficiency on a functional connectome?▼

To compute navigation efficiency on a functional connectome, you can use network neuroscience workflows that process connectivity matrices to derive network communication metrics. This provides standardized structural and functional connectome analysis.

What is the best way to perform spatial autocorrelation analysis on parcellated fMRI data?▼

Spatial autocorrelation analysis on parcellated fMRI data is performed using Moran's I, Geary's C, or Lee's L statistics. These methods measure spatial dependencies across brain parcels to validate cortical mapping distributions.

Can I visualize a Schaefer atlas on a standard cortical surface like fsaverage?▼

Yes, you can visualize a Schaefer atlas on an fsaverage cortical surface. The workflow fetches standard parcellations and renders high-quality inflated surface plots using PyVista or PySurfer.

Do I need nibabel and nilearn installed to generate null models for connectomics?▼

Yes, generating null models for connectomics requires nibabel and nilearn to handle neuroimaging data formats. These dependencies support standardized workflows for spatial statistics and atlas fetching.

How does community detection work for brain network mapping?▼

Community detection for brain network mapping works by applying graph theory algorithms to connectivity matrices. This identifies modular structures within the connectome, revealing functional networks and their spatial organization.

What are the limitations of using search information for brain connectivity analysis?▼

Search information for brain connectivity analysis is limited by its reliance on predefined topological paths within the connectome. It may not fully capture dynamic network reconfigurations or alternative routing strategies in spatial statistics.