New: 98,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Agent skill

Brain Connectivity Modeler

by NeuroAIHub in NeuroAIHub/BrainPilot

Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory

AGPL-3.0Auto-check passed

Install Brain Connectivity Modeler

skills CLI
$ npx skills add NeuroAIHub/BrainPilot --skill brain-connectivity-modeler -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NeuroAIHub/BrainPilot brain-connectivity-modeler --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NeuroAIHub/BrainPilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/packages/skills/skills/06_fMRI_Neuroimaging/brain-connectivity-modeler .claude/skills/brain-connectivity-modeler && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
brain-connectivity-modeler
GitHub stars
1.1k
Token cost
~6k tokens
SKILL.md length
2,941 words
Files
2 (incl. references)
Skills in repo
59
Repo updated
First seen
Licence
AGPL-3.0

At a glance

Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory

  • Works in 5 steps: State the research question — What… → Justify the method choice — Why is this… → Declare expected outcomes — What results… → …
  • SKILL.md covers Purpose, When to Use This Skill, Research Planning Protocol and ⚠️ Verification Notice, plus 10 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Brain Connectivity Modeler is an agent skill from NeuroAIHub/BrainPilot. Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory

Its SKILL.md is about 6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including reference files (for example `references/method-implementation-guide.md`).

The repository describes itself as: BrainPilot: Automating Brain Discovery with Agentic Research. The licence is AGPL-3.0.

Example prompts

  • “Use the brain-connectivity-modeler skill to advise on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory”
  • “/brain-connectivity-modeler”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. State the research question — What specific question is this analysis/paradigm addressing?
  2. Justify the method choice — Why is this approach appropriate? What alternatives were considered?
  3. Declare expected outcomes — What results would support vs. refute the hypothesis?
  4. Note assumptions and limitations — What does this method assume? Where could it mislead?
  5. Present the plan to the user and WAIT for confirmation before proceeding.

What it can do on your machine

Read from SKILL.md and the folder at commit 93f6855. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Brain Connectivity Modeler loads about 6k tokens when it runs, and up to ~8k if it reads all its reference files. Until then it costs about 31 tokens; SKILL.md has 2,941 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~31
When it runs · the whole SKILL.md, loaded when a task matches
~6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NeuroAIHub/BrainPilot at commit 93f6855, republished under its AGPL-3.0 licence (© NeuroAIHub). 2,941 words, ~6,039 tokens.

Download SKILL.mdSave it as .claude/skills/brain-connectivity-modeler/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
brain-connectivity-modeler
description
Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory
domain
cognitive-neuroscience
version
1.0.0
authors
awesome_cognitive_and_neuroscience_skills contributors
papers
Friston, 2011, Friston et al., 1997, Friston et al., 2003, Bullmore & Sporns, 2009, O'Reilly et al., 2012, McLaren et al., 2012, Watts & Strogatz, 1998
dependencies.required
research-literacy
dependencies.recommended
fmri-glm-analysis-guide, fmri-preprocessing-pipeline-guide
review_status
ai-generated

Brain Connectivity Modeler

Purpose

Brain connectivity analysis goes beyond mapping where activation occurs to ask how brain regions interact. This requires choosing among fundamentally different analytical frameworks: functional connectivity (statistical associations), effective connectivity (directed causal influences), and network topology (graph-theoretic properties). Each framework answers different questions and makes different assumptions.

A competent programmer without neuroscience training would not know the critical distinction between functional and effective connectivity, would likely confuse correlation with causation in brain networks, and would not appreciate why motion artifacts are particularly devastating for connectivity analyses. This skill encodes the domain judgment required to select and correctly implement brain connectivity methods.

When to Use This Skill

  • Choosing a connectivity method for task-based or resting-state fMRI
  • Implementing psychophysiological interaction (PPI) analysis
  • Setting up dynamic causal models (DCM)
  • Applying graph theory to brain network data
  • Evaluating whether Granger causality is appropriate for fMRI
  • Reviewing or troubleshooting existing connectivity analyses

Research Planning Protocol

Before executing the domain-specific steps below, you MUST:

  1. State the research question — What specific question is this analysis/paradigm addressing?
  2. Justify the method choice — Why is this approach appropriate? What alternatives were considered?
  3. Declare expected outcomes — What results would support vs. refute the hypothesis?
  4. Note assumptions and limitations — What does this method assume? Where could it mislead?
  5. Present the plan to the user and WAIT for confirmation before proceeding.

For detailed methodology guidance, see the research-literacy skill.

⚠️ Verification Notice

This skill was generated by AI from academic literature. All parameters, thresholds, and citations require independent verification before use in research. If you find errors, please open an issue.

Functional vs. Effective Connectivity

This is the most fundamental distinction in brain connectivity analysis (Friston, 2011):

PropertyFunctional ConnectivityEffective Connectivity
DefinitionStatistical dependency between time seriesDirected causal influence of one region on another
DirectionalityUndirectedDirected
CausalityCannot infer causalityAttempts to infer causal structure
Model requirementModel-free (correlations)Model-based (requires explicit neural model)
Hypothesis typeExploratory or confirmatoryStrictly confirmatory
Primary methodsCorrelation, partial correlation, ICADCM, PPI, structural equation modeling
SourceFriston, 2011Friston, 2011; Friston et al., 2003

Domain warning: Functional connectivity (correlation) does not imply direct anatomical or causal connection. Two regions can show high functional connectivity because they share a common input, not because they directly influence each other (Friston, 2011).

Method Selection Decision Tree

What is your connectivity question?
 |
 +-- "Do regions A and B covary in activity?"
 | --> Functional connectivity (correlation, partial correlation)
 |
 +-- "Does the coupling between A and B change with task context?"
 | --> PPI (Friston et al., 1997; O'Reilly et al., 2012)
 |
 +-- "Does region A causally influence region B, and does this
 | change with experimental manipulation?"
 | --> DCM (Friston et al., 2003)
 | Requires: strong prior hypotheses, limited model space
 |
 +-- "What is the network topology (hubs, modules, efficiency)?"
 | --> Graph theory (Bullmore & Sporns, 2009)
 |
 +-- "Does activity in A temporally precede and predict B?"
 --> Granger causality (with STRONG caveats for fMRI;
 see Granger causality section below)

Functional Connectivity Methods

Seed-Based Correlation
  1. Extract mean time series from a seed ROI
  2. Compute Pearson correlation with all other voxels (or ROIs)
  3. Apply Fisher z-transformation: z = 0.5 * ln((1+r)/(1-r)) before group statistics (Fisher, 1921)
  4. Enter z-maps into second-level group analysis

Critical preprocessing:

  • Apply stringent motion correction: FD threshold < 0.2 mm for scrubbing or censoring (Power et al., 2014)
  • Band-pass filter to 0.01-0.1 Hz for resting-state (Biswal et al., 1995; but see Niazy et al., 2011 for caveats)
  • Regress out motion parameters (24-parameter model), WM/CSF signals (aCompCor; Behzadi et al., 2007)
Partial Correlation

Partial correlation between regions A and B controls for shared variance with all other regions in the model. This reduces the influence of common inputs but requires careful regularization when the number of regions is large relative to the number of time points (Smith et al., 2011).

  • Use regularized partial correlation (e.g., L1-penalized precision matrix) when the number of ROIs exceeds ~20% of the number of time points (Varoquaux et al., 2010)
Independent Component Analysis (ICA)

ICA decomposes the whole-brain fMRI signal into spatially independent components, each representing a network (Beckmann & Smith, 2004).

  • Model order (number of components): 20-25 for major networks; 70-100 for fine-grained parcellation (Smith et al., 2009)
  • Group ICA: Use temporal concatenation or dual regression (Beckmann et al., 2009; Nickerson et al., 2017)
  • Artifact identification: Classify components as signal or noise based on spatial pattern, power spectrum, and temporal properties (Griffanti et al., 2017)

Psychophysiological Interactions (PPI)

PPI tests whether the functional coupling between a seed region and other brain areas changes as a function of psychological context (e.g., task condition; Friston et al., 1997).

PPI Model Specification

The PPI GLM includes three regressors:

  1. Physiological regressor: Deconvolved time series from the seed region (the neural signal estimate)
  2. Psychological regressor: The task regressor (e.g., condition A vs. condition B)
  3. Interaction term: Element-wise product of the physiological and psychological regressors, reconvolved with the HRF

The interaction term is the regressor of interest. A significant PPI effect means that functional coupling with the seed region differs between task conditions (O'Reilly et al., 2012).

Generalized PPI (gPPI)

Standard PPI tests one psychological contrast at a time. Generalized PPI (gPPI) includes all task conditions simultaneously (McLaren et al., 2012):

  • Create separate PPI terms for each condition
  • Include all main-effect task regressors and the seed time series
  • More sensitive than standard PPI because it properly accounts for all conditions
  • Recommended over standard PPI for designs with more than two conditions (McLaren et al., 2012)
PPI Practical Considerations
ParameterRecommendationSource
Seed ROI definitionFunctional (from activation map) or anatomical atlas; 6-10 mm sphere typicalO'Reilly et al., 2012
DeconvolutionRequired for interaction term (SPM default); extract neural signal before computing interactionGitelman et al., 2003
Minimum events per condition20+ per condition for stable PPI estimatesO'Reilly et al., 2012
Multiple seed regionsRun separate PPI analyses per seed, then correct for multiple comparisonsO'Reilly et al., 2012

Dynamic Causal Modeling (DCM)

DCM models the causal architecture of brain regions using a biophysical generative model of neural dynamics and hemodynamic coupling (Friston et al., 2003).

DCM Model Specification

A DCM is defined by three matrices:

MatrixFunctionInterpretation
A (intrinsic)Fixed connections between regions (always on)Baseline coupling strength
B (modulatory)Condition-dependent modulation of connectionsHow task context changes coupling (the key experimental question)
C (driving input)Which regions receive direct task inputWhere external stimuli enter the network
DCM Requirements and Constraints
RequirementGuidelineSource
Number of regions3-8 regions maximumStephan et al., 2010
Prior hypothesesStrong prior hypotheses about network architecture requiredStephan et al., 2010
Model space sizeKeep total models < 20-30 for reliable BMSStephan et al., 2009
Task designEvent-related or block designs with clear experimental manipulationFriston et al., 2003
Time series extractionFrom first eigenvariate of activated voxels within ROIStephan et al., 2010
Data qualityStrong task activation in all modeled regionsStephan et al., 2010
Bayesian Model Selection (BMS)

After estimating all candidate models, use BMS to identify the winning model (Stephan et al., 2009):

  • Fixed-effects BMS (FFX): Assumes all subjects have the same model. Use when sample is homogeneous
  • Random-effects BMS (RFX): Allows different subjects to have different optimal models. Use for heterogeneous populations (Stephan et al., 2009)
  • Family-level inference: Group models into families sharing a feature (e.g., all models with a particular connection), then compare families. More robust than individual model comparison (Penny et al., 2010)
  • Report exceedance probability (probability that a model/family is more likely than all others) and protected exceedance probability (corrected for chance; Rigoux et al., 2014)
DCM Pitfalls
  • Too many models: Exhaustive model comparison with > 30 models leads to unreliable BMS (Stephan et al., 2010)
  • No activation in modeled regions: DCM requires that included regions show significant task-related activation. Including silent regions produces unreliable estimates
  • Confusing DCM with Granger causality: DCM models neural dynamics, not temporal precedence in BOLD signals

Granger Causality

Granger causality tests whether past values of time series X improve prediction of time series Y beyond Y's own past (Granger, 1969).

Severe Limitations for fMRI
LimitationExplanationSource
Hemodynamic confoundThe HRF varies across brain regions by 1-2 seconds. A region with a faster HRF will appear to "Granger-cause" a region with a slower HRF, regardless of true neural timingDavid et al., 2008; Seth et al., 2013
Low temporal resolutionfMRI TR (0.5-3 seconds) is far slower than neural dynamics (milliseconds), aliasing true causal structureSeth et al., 2013
Downsampling artifactsSubsampling a continuous process can reverse the apparent causal directionSeth et al., 2013

Domain warning: Granger causality applied to fMRI BOLD signals is widely considered unreliable for inferring neural causal direction due to hemodynamic variability (David et al., 2008; Seth et al., 2013). If causal inference is needed, use DCM (which explicitly models the hemodynamic delay) or consider EEG/MEG (which has millisecond temporal resolution).

Graph Theory for Brain Networks

Graph theory characterizes the topological organization of brain networks (Bullmore & Sporns, 2009).

Node Definition
MethodDescriptionExample AtlasesSource
Anatomical parcellationPredefined atlas regionsAAL (116 regions), Harvard-OxfordTzourio-Mazoyer et al., 2002
Functional parcellationData-driven boundariesSchaefer (100-1000 parcels), Gordon (333 parcels)Schaefer et al., 2018; Gordon et al., 2016
Voxel-levelEach voxel is a node~100,000 nodesComputationally expensive; rarely used for full graph metrics

Recommendation: Use functional parcellation (Schaefer or Gordon atlases) for most analyses. Functional parcels better respect the true boundaries of functional regions than anatomical atlases (Schaefer et al., 2018).

Edge Definition
MethodDescriptionThreshold ApproachSource
CorrelationPearson r between time seriesProportional (top 10-20% of connections) or absolute (r > 0.2-0.3)Bullmore & Sporns, 2009
Partial correlationControls for indirect connectionsSame thresholding optionsVaroquaux et al., 2010
CoherenceFrequency-domain couplingMagnitude squared coherence > thresholdSun et al., 2004

Domain warning: Thresholding strategy dramatically affects graph metrics. Always report results across a range of thresholds or use proportional thresholding to control for differences in overall connectivity strength across subjects (van den Heuvel et al., 2017).

Key Graph Metrics
MetricMeaningNormalizationSource
Clustering coefficientProportion of a node's neighbors that are also connected to each otherNormalize against random networks (C/C_random)Watts & Strogatz, 1998
Characteristic path lengthAverage shortest path between all node pairsNormalize against random networks (L/L_random)Watts & Strogatz, 1998
Small-worldnesssigma = (C/C_random) / (L/L_random); sigma > 1 indicates small-worldN/A (already normalized)Watts & Strogatz, 1998; Humphries & Gurney, 2008
Modularity (Q)Strength of community structure; Q > 0.3 typically indicates strong modular organizationCompare against null distributionNewman, 2006
Hub measuresDegree centrality, betweenness centrality, participation coefficientZ-score within networkBullmore & Sporns, 2009
Global efficiencyInverse of mean shortest path lengthNormalize against random networksLatora & Marchiori, 2001

Normalization requirement: Raw graph metrics are meaningless without comparison to null models. Always normalize against random networks (preserving degree distribution) or lattice networks (Watts & Strogatz, 1998; Rubinov & Sporns, 2010). Generate at least 1,000 random networks for stable null distributions.

Show full SKILL.md (1,238 more words)Show less

Common Pitfalls

  1. Confusing functional connectivity with effective connectivity: Correlation between two regions does not imply direct or causal connection. Two regions may correlate because they receive common input from a third region (Friston, 2011)
  2. Motion artifacts in connectivity: Head motion creates spurious short-distance positive correlations and spurious long-distance negative correlations, mimicking real connectivity patterns (Power et al., 2012). Use stringent motion thresholds (FD < 0.2 mm; Power et al., 2014) and aggressive confound regression
  3. Not Fisher z-transforming correlations: Pearson r values are bounded and non-normally distributed. Always apply Fisher z-transformation before parametric group statistics (Fisher, 1921)
  4. Arbitrary graph thresholds: Reporting graph metrics at a single threshold without sensitivity analysis across thresholds can produce unreliable or threshold-dependent conclusions (van den Heuvel et al., 2017)
  5. Too many DCM models: Exhaustive model comparison across > 30 models leads to unreliable Bayesian model selection. Constrain the model space based on prior hypotheses (Stephan et al., 2010)
  6. Using Granger causality with fMRI: Regional HRF variability can reverse apparent causal direction in BOLD signals (David et al., 2008; Seth et al., 2013)
  7. Confounding connectivity with coactivation: Two regions that are both activated by a task may show correlation due to shared task-driven variance, not intrinsic neural coupling. Regress out task effects before computing resting-state-style connectivity on task data (Cole et al., 2019)
  8. Ignoring global signal effects: Global signal regression introduces artifactual anticorrelations. If using it, acknowledge this limitation (Murphy & Fox, 2017)

Minimum Reporting Checklist

  • Connectivity type (functional, effective, or graph-theoretic) and justification
  • ROI/node definition method, atlas used, number of regions
  • Time series extraction method (mean, first eigenvariate, PCA)
  • Preprocessing for connectivity (band-pass filter frequencies, confound model, motion threshold)
  • Edge definition (correlation type, thresholding method and values)
  • For PPI: seed region definition, deconvolution method, conditions in PPI model
  • For DCM: model space (A, B, C matrices for all models), BMS method (FFX/RFX), model evidence
  • For graph theory: normalization method, number of null networks, range of thresholds tested
  • Fisher z-transformation applied (yes/no)
  • Motion exclusion criteria and mean FD of included subjects
  • Software package and version

References

  • Beckmann, C. F., & Smith, S. M. (2004). Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Transactions on Medical Imaging, 23(2), 137-152.
  • Beckmann, C. F., Mackay, C. E., Filippini, N., & Smith, S. M. (2009). Group comparison of resting-state fMRI data using multi-subject ICA and dual regression. NeuroImage, 47(S1), S148.
  • Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007). A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. NeuroImage, 37(1), 90-101.
  • Biswal, B., Yetkin, F. Z., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537-541.
  • Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198.
  • Cole, M. W., Ito, T., Bassett, D. S., & Schultz, D. H. (2019). Activity flow over resting-state networks shapes cognitive task activations. Nature Neuroscience, 19, 1718-1726.
  • David, O., Guillemain, I., Saillet, S., et al. (2008). Identifying neural drivers with functional MRI: An electrophysiological validation. PLoS Biology, 6(12), e315.
  • Fisher, R. A. (1921). On the "probable error" of a coefficient of correlation deduced from a small sample. Metron, 1, 3-32.
  • Friston, K. J. (2011). Functional and effective connectivity: A review. Brain Connectivity, 1(1), 13-36.
  • Friston, K. J., Buechel, C., Fink, G. R., et al. (1997). Psychophysiological and modulatory interactions in neuroimaging. NeuroImage, 6(3), 218-229.
  • Friston, K. J., Harrison, L., & Penny, W. (2003). Dynamic causal modelling. NeuroImage, 19(4), 1273-1302.
  • Gitelman, D. R., Penny, W. D., Ashburner, J., & Friston, K. J. (2003). Modeling regional and psychophysiologic interactions in fMRI: The importance of hemodynamic deconvolution. NeuroImage, 19(1), 200-207.
  • Gordon, E. M., Laumann, T. O., Adeyemo, B., et al. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26(1), 288-303.
  • Granger, C. W. J. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica, 37(3), 424-438.
  • Griffanti, L., Douaud, G., Bijsterbosch, J., et al. (2017). Hand classification of fMRI ICA noise components. NeuroImage, 154, 188-205.
  • Humphries, M. D., & Gurney, K. (2008). Network "small-world-ness": A quantitative method for determining canonical network equivalence. PLoS ONE, 3(4), e0002051.
  • Latora, V., & Marchiori, M. (2001). Efficient behavior of small-world networks. Physical Review Letters, 87(19), 198701.
  • McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI). NeuroImage, 61(4), 1277-1286.
  • Murphy, K., & Fox, M. D. (2017). Towards a consensus regarding global signal regression for resting state functional connectivity MRI. NeuroImage, 154, 169-173.
  • Newman, M. E. J. (2006). Modularity and community structure in networks. PNAS, 103(23), 8577-8582.
  • Nickerson, L. D., Smith, S. M., Ongur, D., & Beckmann, C. F. (2017). Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Frontiers in Neuroscience, 11, 115.
  • Niazy, R. K., Xie, J., Miller, K., Beckmann, C. F., & Smith, S. M. (2011). Spectral characteristics of resting state networks. Progress in Brain Research, 193, 259-276.
  • O'Reilly, J. X., Woolrich, M. W., Behrens, T. E. J., et al. (2012). Tools of the trade: Psychophysiological interactions and functional connectivity. Social Cognitive and Affective Neuroscience, 7(5), 604-609.
  • Penny, W. D., Stephan, K. E., Daunizeau, J., et al. (2010). Comparing families of dynamic causal models. PLoS Computational Biology, 6(3), e1000709.
  • Power, J. D., Barnes, K. A., Snyder, A. Z., et al. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142-2154.
  • Power, J. D., Mitra, A., Laumann, T. O., et al. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320-341.
  • Rigoux, L., Stephan, K. E., Friston, K. J., & Daunizeau, J. (2014). Bayesian model selection for group studies revisited. NeuroImage, 84, 971-985.
  • Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059-1069.
  • Schaefer, A., Kong, R., Gordon, E. M., et al. (2018). Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cerebral Cortex, 28(9), 3095-3114.
  • Seth, A. K., Barrett, A. B., & Barnett, L. (2015). Granger causality analysis in neuroscience and neuroimaging. Journal of Neuroscience, 35(8), 3293-3297.
  • Smith, S. M., Fox, P. T., Miller, K. L., et al. (2009). Correspondence of the brain's functional architecture during activation and rest. PNAS, 106(31), 13040-13045.
  • Smith, S. M., Miller, K. L., Salimi-Khorshidi, G., et al. (2011). Network modelling methods for FMRI. NeuroImage, 54(2), 875-891.
  • Stephan, K. E., Penny, W. D., Daunizeau, J., et al. (2009). Bayesian model selection for group studies. NeuroImage, 46(4), 1004-1017.
  • Stephan, K. E., Penny, W. D., Moran, R. J., et al. (2010). Ten simple rules for dynamic causal modelling. NeuroImage, 49(4), 3099-3109.
  • Sun, F. T., Miller, L. M., & D'Esposito, M. (2004). Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage, 21(2), 647-658.
  • Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273-289.
  • van den Heuvel, M. P., de Lange, S. C., Zalesky, A., et al. (2017). Proportional thresholding in resting-state fMRI functional connectivity networks and consequences for patient-control connectome studies. NeuroImage, 159, 437-449.
  • Varoquaux, G., Gramfort, A., Poline, J. B., & Thirion, B. (2010). Brain covariance selection: Better individual functional connectivity models using population prior. NIPS, 2334-2342.
  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of "small-world" networks. Nature, 393(6684), 440-442.

See references/ for detailed method implementation guides and parameter lookup tables.

© NeuroAIHub, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file (references) in packages/skills/skills/06_fMRI_Neuroimaging/brain-connectivity-modeler of NeuroAIHub/BrainPilot.

  • SKILL.md
  • references/method-implementation-guide.md

Open the folder on GitHubat commit 93f6855

Compare with similar skills

Brain Connectivity Modeler next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Brain Connectivity Modeler compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Brain Connectivity Modeler this skillNeuroAIHub/BrainPilot1.1k—~6kAutomated safety check: PassAGPL-3.0
ConnectComposioHQ/awesome-claude-skills77k3 repos~987Automated safety check: PassNone
OmniRoute Model Catalogdiegosouzapw/OmniRoute75k—~589Automated safety check: PassMIT
Model Bank Metadatalobehub/lobehub83k—~2kAutomated safety check: PassCustom licence
Harness Threat Modelruvnet/ruflo74k—~363Automated safety check: NotesMIT
OmniRoute Model Catalog CLIdiegosouzapw/OmniRoute75k—~554Automated safety check: PassMIT

Similar skills

  • Connect

    ComposioHQ/awesome-claude-skills

    Connect Claude to any app. An agent skill from ComposioHQ/awesome-claude-skills.

    77k GitHub starsUsed in 3 repos~987 tokens
    Productivity & AutomationAuto-check passed

  • OmniRoute Model Catalog

    diegosouzapw/OmniRoute

    Looks up which AI models an OmniRoute gateway can reach, creates or updates model aliases and tests whether individual models respond.

    75k GitHub stars~589 tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Model Bank Metadata

    lobehub/lobehub

    Fills and maintains the knowledgeCutoff, family and generation fields on model cards in LobeHub's model bank, from a single new model up to repo-wide backfills.

    83k GitHub stars~2k tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Harness Threat Model

    ruvnet/ruflo

    Enterprise-review-grade threat model from harness threat-model {path}.

    74k GitHub stars~363 tokensUpdated yesterday
    SecurityAuto-check: notes

  • OmniRoute Model Catalog CLI

    diegosouzapw/OmniRoute

    Lists and manages AI models from the OmniRoute command line: browse a provider's catalog, search it, and add, edit, remove or test-add models.

    75k GitHub stars~554 tokensUpdated today
    AI & LLM EngineeringAuto-check passed

  • Model Usage

    openclaw/openclaw

    Summarize CodexBar local cost logs by model for Codex or Claude, including current or full breakdowns.

    392k GitHub starsUsed in 1 repo~637 tokens
    Auto-check passed

More from NeuroAIHub/BrainPilot

All 59 skills in this repo

  • Deeplabcut

    NeuroAIHub/BrainPilot

    Toolbox for markerless animal pose estimation with DeepLabCut.

    1.1k GitHub stars~1.7k tokensUpdated 8 days ago
    Auto-check passed

  • Fmriprep

    NeuroAIHub/BrainPilot

    Preprocess task-based or resting-state fMRI data with fMRIPrep — a robust, BIDS-App preprocessing pipeline built on FSL, ANTs, FreeSurfer, AFNI, and Nilearn.

    1.1k GitHub stars~4.1k tokensUpdated 8 days ago
    Auto-check passed

  • Mne Python Guide

    NeuroAIHub/BrainPilot

    Domain-validated pipeline guidance for EEG/MEG data analysis using MNE-Python: data loading, preprocessing (filtering, ICA, re-referencing), epoching, ERP/ERF computation, time-frequency…

    1.1k GitHub stars~2.3k tokensUpdated 8 days ago
    Auto-check passed

  • Netneurotools Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for network neuroscience analysis using netneurotools: datasets, brain network metrics, connectivity consensus, modularity, spatial statistics, null models, and cortical…

    1.1k GitHub stars~2.6k tokensUpdated 8 days ago
    Auto-check passed

  • Nature Figure

    NeuroAIHub/BrainPilot

    Submission-grade Nature/high-impact journal figure workflow for Python or R.

    1.1k GitHub starsUsed in 1 repo~1.3k tokens
    Auto-check passed

  • Pycortex Guide

    NeuroAIHub/BrainPilot

    Domain-validated guidance for cortical surface visualization and brain surface rendering of fMRI data using pycortex: data types (Volume, Vertex, Dataset), 2D cortical flatmaps, 3D WebGL brain…

    1.1k GitHub stars~1.6k tokensUpdated 8 days ago
    Auto-check passed

Questions about Brain Connectivity Modeler

What does Brain Connectivity Modeler do?

Advises on functional/effective connectivity methods: PPI, DCM, Granger causality, graph theory. Brain Connectivity Modeler is an agent skill from NeuroAIHub/BrainPilot.

How do I install Brain Connectivity Modeler in Claude Code?

Run `npx skills add NeuroAIHub/BrainPilot --skill brain-connectivity-modeler -a claude-code`. Or copy the skill folder (packages/skills/skills/06_fMRI_Neuroimaging/brain-connectivity-modeler in NeuroAIHub/BrainPilot) into .claude/skills/brain-connectivity-modeler in your project. Claude Code loads it when a task matches its description.

How do I install Brain Connectivity Modeler in Codex?

Run `npx skills add NeuroAIHub/BrainPilot --skill brain-connectivity-modeler -a codex`. Or copy the skill folder (packages/skills/skills/06_fMRI_Neuroimaging/brain-connectivity-modeler in NeuroAIHub/BrainPilot) into .agents/skills/brain-connectivity-modeler in your project. Codex loads it when a task matches its description.

Can I use Brain Connectivity Modeler in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NeuroAIHub/BrainPilot --skill brain-connectivity-modeler -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/brain-connectivity-modeler, .gemini/skills/brain-connectivity-modeler, .github/skills/brain-connectivity-modeler and .opencode/skills/brain-connectivity-modeler in your project.

What does Brain Connectivity Modeler need to run?

SKILL.md names no scripts, command-line tools or credentials: Brain Connectivity Modeler is instructions for the agent only.

Does Brain Connectivity Modeler access the network?

SKILL.md names 1 domain. As links in the text: github.com. This is read from the text; nothing was executed.

Is Brain Connectivity Modeler safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Brain Connectivity Modeler use?

Brain Connectivity Modeler is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Brain Connectivity Modeler use?

About 6k tokens (SKILL.md is roughly 24k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.9k tokens, read only when the agent opens those files.

What are the alternatives to Brain Connectivity Modeler?

Skills that share tags, products or a category with Brain Connectivity Modeler: Connect (ComposioHQ/awesome-claude-skills, 77k stars), OmniRoute Model Catalog (diegosouzapw/OmniRoute, 75k stars), Model Bank Metadata (lobehub/lobehub, 83k stars) and Harness Threat Model (ruvnet/ruflo, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Brain Connectivity Modeler?

NeuroAIHub (a GitHub organization) maintains it in NeuroAIHub/BrainPilot, which has 1,062 GitHub stars. The repository holds 59 skills in this directory. The repository was last updated on October 2, 2026.

Source: NeuroAIHub/BrainPilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.