Nilearn fMRI Denoising Starter

Use this skill to build a tiny toy fMRI-like timeseries matrix, regress out confounds with nilearn.signal.clean, and summarize the denoising effect.

What it does

• Creates deterministic toy voxel signals with known nuisance-confound structure.
• Uses nilearn.signal.clean to detrend, regress confounds, and standardize the cleaned output.
• Returns pre/post confound-correlation summaries and cleaned-signal statistics in JSON.

When to use it

• You need a runnable starter for fMRI preprocessing and denoising.
• You want a verified local denoising example without requiring a full BIDS or fMRIPrep runtime.

Example

slurm/envs/neuro/bin/python skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter/scripts/run_nilearn_fmri_denoising.py \
  --out scratch/neuro/nilearn_denoising_summary.json

Verification

• Skill-local tests: python3 -m unittest discover -s skills/neuroscience-and-neuroimaging/nilearn-fmri-denoising-starter/tests -p 'test_*.py'
• Repository smoke: python3 -m unittest tests.smoke.test_phase31_frontier_leaf_conversion_skills -v