ica
Use this model doc whenever the user wants to perform resting-state network decomposition using ICA. This is a non-deep-learning unsupervised route focused on extracting intrinsic connectivity networks, component maps, and subject-level time series from resting-state fMRI.
npx skills add BioTender-max/awesome-bio-agent-skills --skill ica --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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.** --- ## 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 mani
- Overview
- Quick Start
- 1) Prepare resting-state inputs
- 2) ICA route
- Input / Output Contract
- Required inputs
- Optional inputs
- Produced outputs
- Recommended Delegation
- When to Use ICA
- Limitations and Notes
- Reference
delegated through claw-shell after preprocessing is confirmed python skills/nilearn-tool/scripts/rest_ica_reference.py \
What does the ica skill do?
Use this model doc whenever the user wants to perform resting-state network decomposition using ICA. This is a non-deep-learning unsupervised route focused on extracting intrinsic connectivity networks, component maps, and subject-level time series from resting-state fMRI.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill ica --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.