glm

GLM Model Doc

Task-fMRI first/second-level GLM remains implemented through nilearn-tool. For tabular formula OLS, robust covariance, Cohen's d, mixed-effects, and prediction baselines, route to the statistical-ml skill.

Overview

GLM refers to the classical General Linear Model used for task-based fMRI activation analysis.

• Model family: non-deep-learning statistical model
• Typical objectives:
  • first-level GLM for subject/session-level task activation analysis
  • second-level GLM for group-level inference across subjects
• Primary input: preprocessed task fMRI, events, TR, optional confounds, optional brain mask
• Primary output: first-level contrast maps, second-level z maps, thresholded activation maps, region-level summaries

In NeuroClaw, this document is model-level guidance for statistical activation workflows rather than phenotype prediction.

Upstream preparation should usually be delegated to:

• fmri-skill for task-fMRI preprocessing and confounds preparation
• nilearn-tool for concrete GLM fitting, design matrix construction, and statistical map generation

Research use only.

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Quick Start

1) Prepare task-fMRI inputs

Expected inputs:

• preprocessed task BOLD image
• events TSV/CSV with onset, duration, trial type
• repetition time (TR)
• optional confounds TSV
• optional mask image

These should be prepared before model fitting. If not ready, delegate to fmri-skill first.

2) Typical first-level GLM flow

Representative operations:

• build design matrix from events and confounds
• fit first-level GLM per subject/session
• compute named contrasts such as task > baseline
• export z maps / effect size maps

Example execution route:

# delegated through claw-shell after preprocessing is confirmed
python skills/nilearn-tool/scripts/task_glm_reference.py \
  --bold path/to/sub-001_task-preproc_bold.nii.gz \
  --events path/to/sub-001_task-events.tsv \
  --confounds path/to/sub-001_confounds.tsv \
  --tr 2.0 \
  --contrast "task-baseline" \
  --output-dir run_models_output/glm/sub-001