ica

ICA Model Doc

Overview

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

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

In NeuroClaw, this document is model-level guidance for ICA-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 ICA 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) ICA route

Representative operations:

• load subject-level or group-level rs-fMRI images
• fit ICA to estimate intrinsic connectivity components
• export component spatial maps and subject time series
• optionally compute component-level correlations

Example execution route:

# delegated through claw-shell after preprocessing is confirmed
python skills/nilearn-tool/scripts/rest_ica_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/ica

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Input / Output Contract

Required inputs

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

Optional inputs

• confounds table(s)
• mask image
• repetition time (TR)
• decomposition parameters such as number of components
• group/covariate table for downstream statistical analysis