fmri-glm-analysis-guide
fMRI GLM Analysis Guide
Purpose
The General Linear Model (GLM) is the standard statistical framework for task-based fMRI analysis. It models the observed BOLD time series as a linear combination of expected signal components (task regressors convolved with the hemodynamic response function) plus confound regressors plus noise (Poline & Brett, 2012; Poldrack et al., 2011, Ch. 4).
This skill encodes the domain-specific judgment needed to correctly specify a GLM for fMRI data. A competent programmer without neuroimaging training would get many of these decisions wrong -- choosing the wrong HRF model, setting an inappropriate high-pass filter cutoff, omitting critical confound regressors, or applying invalid statistical thresholds. Each decision described here requires understanding the biophysics of BOLD signal, the noise characteristics of fMRI data, and the statistical assumptions of the model.
When to Use This Skill
- Specifying a first-level (single-subject) GLM for task fMRI
- Choosing HRF models, confound regressors, and temporal filtering parameters
- Defining contrasts to test experimental hypotheses
- Setting up second-level (group) analyses
- Selecting appropriate multiple comparison correction methods
- Reviewing or troubleshooting an existing fMRI analysis pipeline
Research Planning Protocol
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