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Brain Connectivity Modeler

Guide fMRI connectivity analysis method selection for task or resting-state designs.

34|5|Updated Feb 28, 2026
Search Tags:#graph theory#ppi#dcm#brain connectivity#functional connectivity#effective connectivity#fmri preprocessing
One-click install
npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill brain-connectivity-modeler-neuroaihub
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Brain Connectivity Modeler
Source: https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills/tree/main/skills/brain-connectivity-modeler
Command: npx skills add https://github.com/NeuroAIHub/awesome_cognitive_and_neuroscience_skills --skill brain-connectivity-modeler-neuroaihub

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Brain connectivity studies demand precise judgment to decide whether to treat coactivation as correlation, infer directed influences, or summarize network topology, yet researchers often conflate these frameworks, overlook motion artifacts, or neglect the rigorous planning checklist that this skill enforces before any analysis.

Core Features & Use Cases

  • Method selection decision tree that distinguishes functional connectivity (correlation/partial correlation/ICA), task-modulated PPI, causal DCM, and graph-theoretic descriptions so you can choose the right lens for your task or resting-state question.
  • PPI/DCM implementation guidance covering seed definition, deconvolution, interaction construction, model matrices, Bayesian model selection, and the constraints on model space, sample homogeneity, and reporting so workflows stay hypothesis-driven.
  • Graph theory pipeline and pitfalls outlining node/edge definitions, threshold sensitivity, normalization against null models, and the checklist of key metrics plus warnings about motion and global signal effects to keep network statistics interpretable.
  • Verification protocol and reporting checklist ensures you state the research question, justify the method, declare expected outcomes, note assumptions, and document preprocessing, motion thresholds, and software versions before acting.
  • Use case: Prepare connectivity advice for an fMRI study by combining motion control, z-transformed correlations, DCM/BMS families, or graph metrics depending on whether you need exploratory patterns or causal claims.

Quick Start

Ask the Brain Connectivity Modeler to outline the most appropriate connectivity framework, including preprocessing, modeling, and validation steps, for your current fMRI study.

Frequently Asked Questions about Brain Connectivity Modeler

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

FAQPage Schema
How do I choose between functional connectivity, DCM, and graph theory for my fMRI study?▼

Functional connectivity treats coactivation as correlation, effective connectivity via DCM infers directed influences, and graph theory summarizes network topology. Selecting the right brain connectivity analysis depends on whether your fMRI study needs exploratory patterns or hypothesis-driven causal claims.

What is the best way to set up a DCM hypothesis and model space for task-based fMRI?▼

Setting up DCM requires defining hypothesis-driven model matrices, applying Bayesian model selection, and specifying constraints on model space and sample homogeneity. Effective connectivity analysis demands rigorous planning of expected outcomes and null-model normalizations before extracting directed influence estimates.

How do I implement PPI analysis for task-based fMRI preprocessing?▼

Implementing PPI analysis involves defining seeds, performing deconvolution, constructing interaction terms, and building model matrices. This task-modulated functional connectivity approach requires strict preprocessing thresholds and motion criteria documentation to ensure valid results.

What motion criteria and preprocessing thresholds are needed for valid fMRI connectivity analysis?▼

Valid fMRI connectivity analysis requires declaring motion criteria, preprocessing thresholds, and software versions before modeling. Motion artifacts and global signal effects can severely distort both functional connectivity correlations and graph theory network statistics, making strict documentation essential.

Why do my graph theory network metrics change with different thresholding methods?▼

Graph theory network metrics are highly sensitive to node and edge definitions as well as thresholding choices. Normalizing your connectivity matrices against null models is required to keep network statistics interpretable and mitigate threshold sensitivity in cognitive neuroscience research.

Can I use Granger causality instead of DCM for resting-state effective connectivity?▼

Granger causality offers an alternative to DCM for effective connectivity but carries specific caveats for resting-state fMRI. Applying directed influence inference requires justifying your method choice, declaring expected outcomes, and documenting preprocessing steps to avoid conflating causal frameworks.

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