fMRI GLM Analysis Guide
CommunityFidelity-focused fMRI GLM design guide.
Education & Research#glm#statistical-inference#neuroimaging#fMRI#design-matrix#hrf-modeling#second-level-analysis
AuthorHaoxuanLiTHUAI
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This guide provides domain-specific knowledge for designing and executing fMRI GLMs, helping researchers avoid common misspecifications and ensure robust statistical inference.
Core Features & Use Cases
- HRF model selection: canonical, derivatives, and FIR options for various designs and populations.
- Design matrix construction: block, event-related, and mixed designs with timing, covariates, and HRF convolution.
- Confound handling and autocorrelation: strategies for motion regressors, high-pass filtering, and prewhitening.
- Second-level analysis and multiple comparison corrections: planning, reporting, and ROI considerations.
- Use case: plan a first-level model for a task fMRI study and a group-level analysis with proper controls.
Quick Start
Follow the step-by-step guide to draft your first-level GLM configuration and plan the subsequent group analysis.
Dependency Matrix
Required Modules
None requiredComponents
references
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Please help me install this Skill: Name: fMRI GLM Analysis Guide Download link: https://github.com/HaoxuanLiTHUAI/awesome_cognitive_and_neuroscience_skills/archive/main.zip#fmri-glm-analysis-guide Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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