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name: ml-decoding-connectivity description: "Use Nilearn supervised decoding, searchlight, SpaceNet/FREM, connectivity, decomposition, parcellation handoffs, and mass-univariate permutation workflows safely." disable-model-invocation: true metadata: disco-role: operating license: BSD 3-Clause

ML Decoding and Connectivity

Use this sub-skill when a Nilearn task involves machine-learning models over brain images or extracted signals: supervised decoding/regression, searchlight maps, sparse/structured decoders, connectome features, decomposition maps, parcellation-to-connectome handoffs, or mass-univariate permutation tests.

Start Here

1. Identify whether the inputs are images, maskers, extracted time series, or precomputed feature matrices.
2. Route mask choice and signal extraction details to maskers-regions; route image cleaning/resampling to data-io-signal; route dataset/cache setup to datasets-interfaces; route final matrix/connectome plots to plotting-reporting.
3. Use references/api-reference.md to choose the Nilearn estimator/function and verify expected shapes.
4. Use references/workflows.md for leakage-safe recipes and handoffs between maskers, connectomes, decoders, and statistics.
5. Use references/troubleshooting.md before changing masks, CV objects, n_jobs, or expensive structured estimators.

Route by Task

• Supervised image decoding: use Decoder or DecoderRegressor for image-like samples with automatic masking, screening, CV, and model maps.
• Local information maps: use SearchLight when the question is spatially local; keep radius, process_mask_img, CV, and n_jobs intentionally small while prototyping.
• Structured sparse models: use SpaceNetClassifier, SpaceNetRegressor, FREMClassifier, or FREMRegressor only when spatial regularization, clustering, or ensembling is part of the analysis goal.
• Connectome features: use ConnectivityMeasure on one 2D time-series array per subject, then feed vectorized outputs to scikit-learn pipelines.
• Group covariance structure: use GroupSparseCovariance or GroupSparseCovarianceCV for shared sparse precision patterns across subjects; expect cubic scaling in the number of regions/features.
• Unsupervised maps/parcels: use CanICA, DictLearning, or Parcellations to learn components/parcels before extracting region signals and building connectomes.
• Voxelwise association tests: use permuted_ols for massively univariate OLS with permutation-based family-wise control; keep n_perm low for smoke checks and high only for final inference.

Bundled Smoke Check

Run the no-network smoke script to check a local Nilearn environment and shape contracts without downloading data:

python scripts/smoke_ml_connectivity.py --help
python scripts/smoke_ml_connectivity.py

The script builds tiny synthetic subject time series, computes vectorized tangent and correlation connectomes, trains a light classifier, and optionally checks a small Decoder path when nibabel image support is available.