NetNeuroTools Guide (Claude Skill)

Guides Claude through connectome analysis with the Network Neuroscience Lab’s netneurotools package — building consensus networks, computing communication and assortativity metrics, and testing them against null models that preserve degree, distance or spatial autocorrelation.

   
Type Claude Skill
Supplier Awesome Cognitive and Neuroscience Skills (community OSS, MIT)
Availability GA — one of ~40 research skills in the collection
Pricing Free / OSS (MIT) — netneurotools itself is BSD-3-Clause
Capabilities Read/Write — methodology guidance; Claude writes and runs the analysis code locally
Verified works · 2026-08-13
Security cleared · 2026-08-13 — genuine org transfer to NeuroAIHub confirmed, MIT, no external credentials

How to install

  • Claude Code — plugin marketplace (installs all skills in the collection): 
    /plugin marketplace add HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills
/plugin install awesome-cognitive-and-neuroscience-skills@awesome-cognitive-and-neuroscience-skills

    Restart Claude Code afterwards. The skills are description-activated — there is no slash command; ask a connectome or brain-network question and Claude loads the skill.

  • Claude Code — single-skill alternative: 
    git clone https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills
cp -r awesome_cognitive_and_neuroscience_skills/skills/netneurotools-guide ~/.claude/skills/

    (Project-scoped alternative: copy into .claude/skills/ instead. The repo’s default branch is master, not main. Unlike most skills in this collection, this one declares no research-literacy dependency — copy that directory too if you want the collection’s shared literature conventions.)

  • Underlying software — install the package the skill drives: 
    pip install "netneurotools[pyvista]"

    The extra pulls PyVista for surface plotting; drop it if you only need metrics. Core dependencies are numpy ≥ 1.16, scipy ≥ 1.4.0, scikit-learn, matplotlib, nibabel ≥ 3.0.0, nilearn, bctpy and neuromaps.

What it does

Walks the eight netneurotools submodules as a pipeline:

  1. datasets — fetch templates, parcellations and reference connectomes.
  2. networks — build group consensus connectivity and apply thresholding.
  3. metrics — communication measures, assortativity and related graph statistics.
  4. spatial — spatial autocorrelation statistics: Moran’s I, Geary’s C, Lee’s L.
  5. modularity — consensus community detection.
  6. stats — permutation tests and dominance analysis.
  7. Null models — randomizations and surrogates that preserve degree and/or distance.
  8. plotting — cortical surface renderings and heatmaps.

Parameter rules the skill states: consensus modularity requires non-negative input, so zero out negative weights with A[A < 0] = 0 before running Louvain; convert weights to distances for communication metrics via D = -np.log(W / (np.max(W) + 1)); and remember that a permutation p-value cannot go below 1 / (n_perm + 1) — 1,000 permutations floors you at ~0.001, so report the permutation count alongside any p-value.

Primary use cases: structural and functional connectome analysis, brain-map correspondence testing against spatial nulls, community/module detection in parcellated networks.

Notes

AI-generated content — verify before use. All skills in this collection carry review_status: ai-generated, and the README states the content “has not been individually verified by human domain experts.” This skill’s front-matter lists no cited papers — check its parameter guidance against the netneurotools documentation before relying on it.

The null-model layer is the reason to reach for this package over a generic graph library: brain-network statistics compared against a plain random-graph null routinely produce significance that reflects spatial embedding rather than biology.

Related catalogued tools: BrainNetworkTransformer and Com-BrainTF for deep-learning approaches to the same connectome matrices, Nilearn for constructing them from fMRI, Pycortex Guide for surface rendering, and Lesion-Symptom Mapping Guide for the lesion-network case.

Sources

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