Nilearn tool

Nilearn Tool (Base/Tool Layer)

Overview

nilearn-tool is the NeuroClaw base/tool skill that implements concrete Nilearn workflows for turning preprocessed BOLD into features (ROI time series, connectivity matrices, seed maps) and optional statistical modeling (GLM).

It is never called directly by the user. It is delegated to by fmri-skill (or other interface/modality skills) and executed via claw-shell.

Research use only.

Agent Reference Rule

When the agent needs Nilearn-based implementation code, it should first consult the curated snippets in skills/nilearn-tool/scripts/ instead of copying directly from long tutorial scripts with hard-coded paths.

Reference snippets available:

• scripts/preprocess_bold_reference.py -> dummy removal, smoothing, band-pass filtering, MNI resampling
• scripts/connectome_reference.py -> atlas ROI extraction and ROI-to-ROI connectivity export
• scripts/zalff_summary_reference.py -> MNI resampling, zALFF summary, atlas-level regional export
• scripts/task_glm_reference.py -> first-level task GLM with design matrix and contrast maps
• scripts/second_level_glm_reference.py -> group-level GLM from subject contrast maps
• scripts/rest_ica_reference.py -> resting-state CanICA component extraction
• scripts/rest_dictlearning_reference.py -> resting-state DictLearning component extraction
• scripts/svm_classifier_reference.py -> ROI/tabular disease classification with SVM
• scripts/spacenet_classifier_reference.py -> voxel-wise disease classification with SpaceNet
• scripts/kmeans_parcellation_reference.py -> mask-based K-means brain parcellation
• scripts/hierarchical_parcellation_reference.py -> mask-based hierarchical brain parcellation
• scripts/denoise_timeseries_reference.py -> confound regression and detrending with clean_img

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Scope (What this tool does / does not do)

✅ This tool does

• Load BOLD NIfTI and (optional) brain mask.
• Load fMRIPrep confounds TSV and apply common denoising regressors.
• Extract ROI time series from an atlas/parcellation.
• Compute ROI-to-ROI functional connectivity matrices.
• Compute seed-to-voxel connectivity maps.
• (Optional) Run first-/second-level GLM when events/maps are provided.

❌ This tool does NOT do

• Raw fMRI preprocessing (slice timing, motion correction, susceptibility distortion correction, eddy/topup, etc.). Those belong to fmriprep-tool, hcppipeline-tool, fsl-tool.

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Core Outputs (Typical)

• roi_timeseries.csv (T × R)
• connectome.npy / connectome.csv (R × R)
• seed_zmap.nii.gz
• (Optional) first_level_zmap.nii.gz, second_level_zmap.nii.gz
• Optional figures: connectome matrix PNG, connectome graph PNG, stat map PNG

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Minimal Nilearn Usage Patterns (Short Snippets)

1) fMRIPrep confounds (recommended)

from nilearn.interfaces.fmriprep import load_confounds
confounds, sample_mask = load_confounds(confounds_tsv, strategy=["motion", "wm_csf"])

2) ROI time series (atlas/parcellation)

from nilearn.maskers import NiftiLabelsMasker
masker = NiftiLabelsMasker(labels_img=atlas_img, t_r=tr, standardize=True, detrend=True)
roi_ts = masker.fit_transform(bold_img, confounds=confounds, sample_mask=sample_mask)  # (T, R)

3) ROI-to-ROI connectivity

from nilearn.connectome import ConnectivityMeasure
conn = ConnectivityMeasure(kind="correlation").fit_transform([roi_ts])[0]  # (R, R)

4) Seed-to-voxel connectivity (concept)

• Use NiftiSpheresMasker for seed TS, NiftiMasker for voxel TS, then correlate and Fisher-z.

Curated Reference Snippets

These scripts are distilled from rs-fMRI-Pipeline-Tutorial/ and should be the preferred starting point for new code in this skill:

scripts/preprocess_bold_reference.py

• Covers the Nilearn-centric part of resting-state preprocessing shown in multimodal_brain_connectivity_pipeline.py
• Includes dummy-scan removal, spatial smoothing, temporal band-pass filtering, and MNI152 resampling

Example:

python skills/nilearn-tool/scripts/preprocess_bold_reference.py \
  --bold path/to/rest_bold.nii.gz \
  --output fmri_output/sub-001/nilearn/preprocessed_bold_mni.nii.gz

scripts/connectome_reference.py

• Extracts atlas ROI time series with NiftiLabelsMasker
• Computes ROI-to-ROI connectivity with ConnectivityMeasure
• Exports roi_timeseries.csv, connectome.npy, and connectome.csv

Example:

python skills/nilearn-tool/scripts/connectome_reference.py \
  --bold path/to/preprocessed_bold_mni.nii.gz \
  --atlas path/to/AAL3v1.nii \
  --labels path/to/AAL3v1.nii.txt \
  --output-dir fmri_output/sub-001/nilearn/connectome

scripts/zalff_summary_reference.py

• Adapts the regional zALFF summarization logic from MNI152_zALFF_Brain_Region_Activation_Analysis.py
• Uses Nilearn resampling, cleaning, and NiftiLabelsMasker for atlas-level reporting

Example:

python skills/nilearn-tool/scripts/zalff_summary_reference.py \
  --bold path/to/rest_bold.nii.gz \
  --atlas path/to/AAL3v1.nii \
  --labels path/to/AAL3v1.nii.txt \
  --mask path/to/mni_mask.nii.gz \
  --output-dir fmri_output/sub-001/nilearn/zalff

Additional model-routing snippets

• scripts/task_glm_reference.py -> first-level task GLM
• scripts/second_level_glm_reference.py -> second-level / group GLM
• scripts/rest_ica_reference.py -> resting-state ICA decomposition
• scripts/rest_dictlearning_reference.py -> resting-state DictLearning decomposition
• scripts/svm_classifier_reference.py -> tabular / ROI SVM classifier
• scripts/spacenet_classifier_reference.py -> voxel-wise SpaceNet classifier
• scripts/kmeans_parcellation_reference.py -> K-means parcellation from masked image features
• scripts/hierarchical_parcellation_reference.py -> Hierarchical parcellation from masked image features
• scripts/denoise_timeseries_reference.py -> confound-aware detrending and time-series cleaning

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Wrapper Entry (Recommended)

This tool should expose a small CLI wrapper (implementation kept in a separate file, not embedded here):

• File: skills/nilearn-tool/nilearn_pipeline.py
• Subcommands (recommended):
  • roi-ts → extract ROI time series
  • connectome → compute connectivity matrix from ROI TS
  • seed-corr → seed connectivity z-map
  • first-glm / second-glm (optional)

All execution must be routed through claw-shell.

Example calls:

conda run -n neuroclaw-nilearn python skills/nilearn-tool/nilearn_pipeline.py roi-ts \
  --bold <preproc_bold.nii.gz> --confounds <confounds.tsv> --tr 2.0 --atlas schaefer_2018_200_7 \
  --outdir fmri_output/sub-001/nilearn/roi_ts

conda run -n neuroclaw-nilearn python skills/nilearn-tool/nilearn_pipeline.py connectome \
  --roi-timeseries fmri_output/sub-001/nilearn/roi_ts/roi_timeseries.csv --kind correlation \
  --outdir fmri_output/sub-001/nilearn/connectome

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Installation (Handled by dependency-planner)

Recommended isolated environment:

conda create -n neuroclaw-nilearn python=3.11 -y
conda install -n neuroclaw-nilearn -c conda-forge nilearn nibabel numpy scipy pandas scikit-learn matplotlib -y

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Safety / Execution Rules (NeuroClaw)

• No direct subprocess.run() for long operations in this skill.
• All shell commands go through claw-shell.
• Always produce outputs under fmri_output/.../nilearn/... with deterministic filenames.

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Complementary / Related Skills

• dependency-planner + conda-env-manager → install/manage neuroclaw-nilearn
• claw-shell → mandatory execution layer

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Reference

• Nilearn documentation: https://nilearn.github.io/
• fMRIPrep confounds interface: Nilearn nilearn.interfaces.fmriprep
• Curated code snippets in this skill:
  • skills/nilearn-tool/scripts/preprocess_bold_reference.py
  • skills/nilearn-tool/scripts/connectome_reference.py
  • skills/nilearn-tool/scripts/zalff_summary_reference.py
  • skills/nilearn-tool/scripts/task_glm_reference.py
  • skills/nilearn-tool/scripts/second_level_glm_reference.py
  • skills/nilearn-tool/scripts/rest_ica_reference.py
  • skills/nilearn-tool/scripts/rest_dictlearning_reference.py
  • skills/nilearn-tool/scripts/svm_classifier_reference.py
  • skills/nilearn-tool/scripts/spacenet_classifier_reference.py
  • skills/nilearn-tool/scripts/kmeans_parcellation_reference.py
  • skills/nilearn-tool/scripts/hierarchical_parcellation_reference.py
  • skills/nilearn-tool/scripts/denoise_timeseries_reference.py

Post-Execution Verification (Harness Integration)

After Nilearn processing completes, this skill automatically invokes harness-core's VerificationRunner to validate output integrity:

Integrated verification checks:

from skills.harness_core import VerificationRunner, AuditLogger

verifier = VerificationRunner(task_type="nilearn_processing")

# 1. ROI time series shape and completeness
verifier.add_check("roi_timeseries",
    checker=lambda: verify_roi_timeseries(output_dir),
    severity="error"
)

# 2. Confounds loading and application
verifier.add_check("confounds_handling",
    checker=lambda: verify_confounds_applied(output_dir),
    severity="warning"
)

# 3. Connectivity matrix dimensionality (N_ROI × N_ROI)
verifier.add_check("connectivity_shape",
    checker=lambda: verify_connectome_shape(output_dir),
    severity="error"
)

# 4. Correlation bounds (-1 to +1)
verifier.add_check("correlation_bounds",
    checker=lambda: verify_correlation_bounds(output_dir),
    severity="warning"
)

# 5. Data integrity (NaN/Inf checks)
verifier.add_check("data_integrity",
    checker=lambda: verify_no_nan_inf(output_dir),
    severity="error"
)

report = verifier.run(output_dir)

# Log verification results
logger = AuditLogger(log_file=f"{output_dir}/nilearn_verification.jsonl")
logger.log_validation(
    task_name="nilearn_processing",
    checks_passed=len([r for r in report.results if r.passed]),
    total_checks=len(report.results),
    output_path=output_dir
)

Output: fmri_output/nilearn_verification.jsonl (structured audit log with JSONL format)

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Created At: 2026-03-26 00:54 HKT Last Updated At: 2026-04-14 00:26 HKT Author: chengwang96