dictlearning

DictLearning Model Doc

Overview

DictLearning is a classical non-deep-learning method for resting-state network decomposition.

• Model family: non-deep-learning unsupervised decomposition method
• Typical objectives:
  • identify sparse resting-state networks from preprocessed fMRI
  • extract dictionary component maps and subject-level time series
  • derive interpretable network summaries for downstream connectivity or clustering
• Primary input: preprocessed resting-state fMRI, optional mask, optional group subject list
• Primary output: dictionary component maps, subject time series, optional connectomes or reports

In NeuroClaw, this document is model-level guidance for DictLearning-based resting-state decomposition workflows rather than phenotype prediction.

Upstream preparation should usually be delegated to:

• fmri-skill for rs-fMRI preprocessing, nuisance regression, filtering, and standard-space alignment
• nilearn-tool for concrete DictLearning fitting and component export

Research use only.

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Quick Start

1) Prepare resting-state inputs

Expected inputs:

• preprocessed resting-state BOLD images
• optional confounds TSV files
• optional brain mask
• optional subject list or cohort manifest

If these are not ready, delegate to fmri-skill first.

2) DictLearning route

Representative operations:

• load preprocessed resting-state images
• fit sparse dictionary learning for network decomposition
• export dictionary component maps and subject time series
• optionally use outputs for connectome or clustering analysis

Example execution route:

# delegated through claw-shell after preprocessing is confirmed
python skills/nilearn-tool/scripts/rest_dictlearning_reference.py \
  --input-list path/to/rest_bold_list.txt \
  --mask path/to/group_mask.nii.gz \
  --n-components 20 \
  --output-dir run_models_output/dictlearning

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Input / Output Contract

Required inputs

• preprocessed resting-state fMRI in subject space or standard space
• subject list or image list