netneurotools-guide

name	netneurotools-guide
description	Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical surface visualization. Use this skill whenever the user works with brain connectivity matrices, connectomes, graph theory on brain networks, parcellated brain data (Schaefer, Cammoun, Desikan-Killiany), cortical surface templates (fsaverage, fsLR, CIVET, Conte69), network communication metrics, null model generation, spatial autocorrelation on brain maps, community detection in connectomes, or surface-based visualization with PyVista/PySurfer. Also trigger when the user mentions netneurotools, netneurolab, structure-function coupling, network neuroscience, brain graph analysis, or needs to fetch neuroimaging atlases and templates.
version	1.0.0
authors	["Claude (AI-assisted)"]
review_status	ai-generated

NetNeuroTools Guide

Purpose

This skill encodes the complete API and recommended workflows for netneurotools, a Python toolbox for network neuroscience developed by the Network Neuroscience Lab (netneurolab). It covers dataset fetching, brain connectivity metrics, network randomization and null models, community detection, spatial autocorrelation statistics, parcellation interface utilities, and cortical/subcortical surface visualization.

When to Use This Skill

Activate when the user:

• Works with brain connectivity matrices (structural or functional)
• Needs network communication metrics (shortest path, navigation, communicability, search information, diffusion efficiency)
• Wants to generate null/surrogate networks preserving degree, strength, or distance distributions
• Performs community detection or consensus clustering on brain networks
• Computes spatial autocorrelation (Moran's I, Geary's C, Lee's L) on parcellated brain data
• Fetches standard neuroimaging templates (fsaverage, fsLR, CIVET, Conte69) or atlases (Schaefer, Cammoun, Pauli)
• Needs to convert between vertex-level and parcel-level brain data
• Visualizes data on cortical surfaces using PyVista or PySurfer
• Mentions netneurotools, netneurolab, or any function from this toolbox
• Performs structure-function coupling, assortativity analysis, or dominance analysis
• Simulates atrophy spreading on brain networks (SIR model)

Reference Files (Progressive Disclosure)

Topic	File	When to Read
Datasets	references/datasets.md	User fetches templates, atlases, or project datasets
Network Metrics	references/metrics.md	User computes communication, assortativity, spreading, or statistical network metrics
Networks	references/networks.md	User builds consensus connectivity, randomizes networks, or thresholds graphs
Statistics	references/stats.md	User runs permutation tests, correlations, residualization, or dominance analysis
Spatial & Modularity	references/spatial-modularity.md	User computes spatial autocorrelation or performs community detection
Interface & Plotting	references/interface-plotting.md	User converts parcels/vertices, handles CIFTI/GIFTI files, or plots on cortical surfaces

Installation

pip install netneurotools

# For PyVista surface plotting (recommended)
pip install netneurotools[pyvista]

# For PySurfer surface plotting (legacy)
pip install netneurotools[pysurfer]

# For numba acceleration
pip install netneurotools[numba]

Core dependencies: numpy>=1.16, scipy>=1.4.0, scikit-learn, matplotlib, nibabel>=3.0.0, nilearn, bctpy, tqdm, neuromaps

Overview Pipeline

1. Fetch data       --> netneurotools.datasets (templates, atlases, connectomes)
2. Build networks   --> netneurotools.networks (consensus, thresholding)
3. Analyze metrics  --> netneurotools.metrics (communication, assortativity)
4. Null models      --> netneurotools.networks (randomization, surrogates)
5. Statistics       --> netneurotools.stats (permutation tests, dominance)
6. Spatial stats    --> netneurotools.spatial (Moran's I, Geary's C, Lee's L)
7. Modularity       --> netneurotools.modularity (consensus clustering)
8. Visualize        --> netneurotools.plotting (cortical surfaces, heatmaps)

Quick Start

Fetch Atlas and Template

from netneurotools.datasets import fetch_schaefer2018, fetch_fsaverage_curated

# Fetch Schaefer 400-parcel atlas in fsaverage space
parc = fetch_schaefer2018('fsaverage')['400Parcels7Networks']
# parc is a SURFACE namedtuple with fields .L and .R

# Fetch curated fsaverage surfaces
surfaces = fetch_fsaverage_curated('fsaverage5')
# surfaces has keys: 'white', 'pial', 'inflated', 'sphere', 'medial', 'sulc', 'vaavg'
# Each value is a SURFACE namedtuple with fields .L and .R

Consensus Functional Connectivity

from netneurotools.networks import func_consensus
import numpy as np

# data: (N_nodes, T_timepoints, S_subjects) array
consensus = func_consensus(data, n_boot=1000, ci=95, seed=42)

Community Detection

from netneurotools.modularity import consensus_modularity
import numpy as np

# adjacency: (N, N) non-negative connectivity matrix
consensus, Q_all, zrand_all = consensus_modularity(
    adjacency, gamma=1.5, repeats=100, seed=1234
)

Generate Distance-Preserving Surrogates

from netneurotools.networks import match_length_degree_distribution

newB, newW, nr = match_length_degree_distribution(
    W, D, nbins=10, nswap=1000, seed=42
)

Permutation Test for Correlation

from netneurotools.stats import permtest_pearsonr, make_correlated_xy

x, y = make_correlated_xy(corr=0.3, size=100, seed=42)
r, p = permtest_pearsonr(x, y, n_perm=5000, seed=42)

Spatial Autocorrelation

from netneurotools.spatial import morans_i

I = morans_i(annotation_vector, spatial_weight_matrix)

Plot on Cortical Surface (PyVista)

from netneurotools.plotting import pv_plot_surface
import numpy as np

data_L = np.random.random((10242,))
data_R = np.random.random((10242,))
pl = pv_plot_surface(
    (data_L, data_R),
    template="fsaverage5",
    surf="inflated",
    cmap="viridis",
    lighting_style="plastic",
    jupyter_backend="static",
)

Plot Parcellated Data (Shortcut)

from netneurotools.plotting import pv_plot_parcellated_data
import numpy as np

data = np.random.rand(400)
pl = pv_plot_parcellated_data(data, 'schaefer400x7', template='fsaverage')

Key Data Structures

Structure	Description	Fields
SURFACE	namedtuple for hemisphere file pairs	.L, .R (left/right hemisphere paths)
sklearn.utils.Bunch	Dict-like object returned by fetch functions	Varies per function
FREESURFER_IGNORE	Labels to ignore in FreeSurfer parcellations	["unknown", "corpuscallosum", "Background+FreeSurfer_Defined_Medial_Wall"]
PARCIGNORE	Labels to ignore in parcellation operations	["unknown", "corpuscallosum", "Background+FreeSurfer_Defined_Medial_Wall", "???", "Unknown", "Medial_wall", "Medial wall", "medial_wall"]

Core Modules Quick Reference

Module	Key Functions	Purpose
datasets	fetch_fsaverage, fetch_schaefer2018, fetch_cammoun2012, fetch_conte69, fetch_famous_gmat	Fetch templates, atlases, connectomes
metrics	distance_wei_floyd, navigation_wu, communicability_wei, search_information, mean_first_passage_time, assortativity_und, simulate_atrophy	Network communication and properties
networks	func_consensus, struct_consensus, match_length_degree_distribution, strength_preserving_rand_sa	Build consensus, generate null models
stats	permtest_pearsonr, efficient_pearsonr, residualize, get_dominance_stats	Statistical testing and regression
spatial	morans_i, gearys_c, lees_l, local_morans_i, local_gearys_c, local_lees_l	Spatial autocorrelation
modularity	consensus_modularity, find_consensus, zrand, get_modularity	Community detection and evaluation
interface	vertices_to_parcels, parcels_to_vertices, load_surf_parc_file, deconstruct_cifti	Format conversion
plotting	pv_plot_surface, pv_plot_parcellated_data, pv_plot_subcortex, plot_mod_heatmap	Visualization

Common Pitfalls

1. SURFACE fields are .L and .R, not .lh and .rh. Use surface.L and surface.R to access hemisphere paths.

2. Weight-to-distance conversion must be done before calling distance_wei_floyd or search_information. Common transform: D = -np.log(W / (np.max(W) + 1)).

3. Minimum permutation p-value is 1 / (n_perm + 1). With n_perm=1000, the smallest p-value is ~0.001.

4. consensus_modularity requires non-negative input. Louvain cannot handle negative weights. Set negatives to zero: A[A < 0] = 0.

5. struct_consensus hemiid encoding: 0 = right hemisphere, 1 = left hemisphere.

6. pv_plot_surface data format: When hemi='both', vertex_data can be a tuple (left, right) or a single concatenated array. Data length must match template vertex count.

7. Data directory: All fetch functions default to ~/nnt-data. Override with data_dir= parameter or set NNT_DATA environment variable.

8. match_length_degree_distribution recommended nswap: Use nswap = nnodes * 20 for adequate randomization.

9. strength_preserving_rand_sa frac parameter must be between 0 and 1. It controls temperature decrease per annealing stage.

10. parcels_to_vertices and vertices_to_parcels support .annot, .gii, and .dlabel.nii parcellation files. For .dlabel.nii, pass a single file path; for .annot or .gii, pass a tuple of (left, right) paths.

11. Numba acceleration is available for spatial stats, weighted correlation, and some metrics. Install numba for significant speedups on large datasets.

12. Headless rendering with PyVista: set os.environ["VTK_DEFAULT_OPENGL_WINDOW"] = "vtkOSOpenGLRenderWindow" before importing pyvista.