fMRI GLM Analysis Guide

To construct a design matrix for fMRI GLM, define your block or event-related timing, add covariates, and model the hemodynamic response function. This ensures robust statistical inference by properly separating neural signals from noise.

HRF model selection for fMRI depends on your design and population, choosing among canonical, derivative, and FIR options. This guides you in specifying the correct hemodynamic response to avoid common model misspecifications.

Handling motion regressors and high-pass filtering in fMRI GLM involves adding nuisance regressors and applying temporal filtering to confounds. This strategy mitigates autocorrelation and physiological noise during prewhitening.

The best way to plan second-level fMRI analysis is to define group-level contrasts and apply multiple comparison corrections. This ensures valid statistical inference across participants and experiments for reproducible workflows.

Yes, the fMRI GLM guide supports mixed designs with both block and event-related timing. It helps you document timing, convolve with the HRF, and configure covariates within a single robust statistical model.