| name | camcan-skill |
| description | Use this skill whenever the user wants an end-to-end workflow for the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, task-fMRI, and MEG, phenotype extraction, and QC integration. Triggers include: 'Cam-CAN', 'CamCAN', 'process Cam-CAN data', 'Cam-CAN MEG', 'Cam-CAN fMRI', or any request to run the Cam-CAN multimodal pipeline. |
| license | MIT License (NeuroClaw custom skill - freely modifiable within the project) |
| layer | subagent |
| skill_type | dataset |
| dependencies | ["smri-skill","fmri-skill","meg-skill","bids-organizer","claw-shell"] |
| complementary_skills | ["mne-eeg-tool"] |
Cam-CAN Skill (Dataset-Orchestration Layer)
Overview
camcan-skill is the NeuroClaw orchestration skill for the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset.
It strictly follows the NeuroClaw hierarchical design principles:
- This skill only describes WHAT needs to be done and which tool skill to delegate to.
- It contains no implementation code or concrete commands.
- All concrete execution is delegated to existing base/tool skills via
claw-shell.
- Companion scripts in
scripts/ provide reference implementations for BIDS validation, phenotype extraction, and QC.
Core workflow (never bypassed):
- Identify input Cam-CAN data and target modalities.
- Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
- Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
- On confirmation, delegate every step to the appropriate skill via
claw-shell.
- After execution, save all outputs in a clean directory structure (
camcan_output/).
Research use only.
Quick Reference
| Task | What needs to be done | Delegate to | Expected output |
|---|
| BIDS validation | Validate Cam-CAN BIDS structure | scripts/validate_camcan.py | Validation report |
| sMRI processing | Brain extraction, tissue segmentation, cortical reconstruction | smri-skill | smri_output/ derivatives |
| rs-fMRI processing | Preprocessing, denoising, connectivity | fmri-skill | fmri_output/ derivatives |
| task-fMRI processing | Movie-watching task GLM, activation analysis | fmri-skill | fmri_output/ task results |
| MEG processing | Source localization, time-frequency analysis | meg-skill | meg_output/ TFR and source |
| Phenotype extraction | Cognitive, sensory, health measures | scripts/extract_camcan_phenotype.py | Merged phenotype CSV |
| QC summary | Per-subject quality control | scripts/camcan_qc_summary.py | QC summary + exclusion list |
Dataset Characteristics
- Cohort: ~700 participants spanning the adult lifespan (18-88 years)
- Design: Cross-sectional population-based sample
- Site: MRC Cognition and Brain Sciences Unit, Cambridge, UK
- Scanner: Siemens 3T TIM TRIO
- MEG system: CTF 275-channel system
- Access: OpenNeuro ds003097 (CC0 license)
Supported Modalities
| Modality | Description | Tasks/Conditions |
|---|
| T1w | High-resolution structural MRI | Single acquisition |
| T2*w | Functional MRI (multi-echo EPI) | Resting-state (eyes open), Movie-watching |
| MEG | Magnetoencephalography | Resting-state (eyes open), Auditory (passive listening), Visual (passive viewing) |
| dMRI | Diffusion-weighted imaging | DTI tractography |
Cam-CAN Task Paradigms
fMRI Tasks
| Task | Description | Duration |
|---|
| REST | Resting-state (eyes open) | ~8 min |
| MOVIE | Movie-watching (feature film excerpts) | ~15 min |
MEG Tasks
| Task | Description | Duration |
|---|
| REST | Resting-state (eyes open, eyes closed) | ~8 min |
| AUDITORY | Passive listening to tones and speech | ~5 min |
| VISUAL | Passive viewing of visual stimuli | ~5 min |
BIDS Preparation
Script: scripts/validate_camcan.py
Validates Cam-CAN BIDS structure and generates a compliance report.
python skills/camcan-skill/scripts/validate_camcan.py \
--input /path/to/CamCAN/bids \
--output /path/to/camcan_output/qc/bids_validation.csv
Features:
- BIDS directory structure validation
- Modality completeness check (T1w, T2*w, MEG, dMRI)
- Sidecar JSON presence and content validation
- Participant ID consistency across modalities
- Missing data identification and reporting
Core Workflow (Never Bypassed)
- Identify user target: full Cam-CAN processing, imaging subset, phenotype extraction, or BIDS validation only.
- Generate a numbered plan with tools, outputs, runtime, storage, and risks.
- Wait for explicit confirmation (
YES / execute / proceed).
- On confirmation, run BIDS validation using
scripts/validate_camcan.py.
- Delegate to
smri-skill for structural MRI processing.
- Delegate to
fmri-skill for functional MRI processing (resting-state and movie-watching).
- Delegate to
meg-skill for MEG processing (source localization, time-frequency analysis).
- If phenotype extraction is requested, run
scripts/extract_camcan_phenotype.py.
- If QC summary is requested, run
scripts/camcan_qc_summary.py.
- Save outputs into
camcan_output/.
Modality Processing Delegation
| Modality | Delegated skill | Typical tasks | Main outputs |
|---|
| sMRI (T1w) | smri-skill | brain extraction, tissue segmentation, cortical reconstruction | smri_output/ derivatives |
| fMRI (T2*w) | fmri-skill | preprocessing, denoising, connectivity, task GLM | fmri_output/ derivatives |
| MEG | meg-skill | source localization, time-frequency, connectivity | meg_output/ TFR and source |
| dMRI | dwi-skill | diffusion preprocessing, tensor metrics | dwi_output/ metrics |
Standard Output Layout
camcan_output/
├── bids/ # BIDS-staged data (or validation report)
├── smri/ # Structural MRI derivatives
├── fmri/ # Functional MRI derivatives (rest + movie)
├── meg/ # MEG derivatives (TFR, source, connectivity)
├── dwi/ # Diffusion MRI derivatives
├── phenotype/ # Merged phenotype tables
├── qc/ # QC summaries and exclusion lists
└── logs/ # Processing logs
Benchmark Adapter Guidance
For benchmark-style prompts, do not force the full orchestration when the task only asks for local Cam-CAN data validation or staging.
- If the task starts from Cam-CAN data already present on disk and only asks for BIDS validation:
- Skip the download stage
- Default to the narrow path
local Cam-CAN discovery -> BIDS validation -> report
- In benchmark mode, do not require explicit confirmation before presenting the validation solution.
Safety and Execution Policy
- No execution before explicit plan confirmation.
- All execution must be routed via
claw-shell.
- Missing dependencies must be resolved by
dependency-planner before running.
Important Notes and Limitations
- Cam-CAN is a population-based sample spanning the full adult lifespan (18-88 years).
- MEG data uses CTF 275-channel system; MEG processing requires system-specific handling.
- Movie-watching fMRI is a unique task paradigm; standard GLM may not apply.
- Age range is a key variable; consider age-stratified analyses.
- Data is available on OpenNeuro ds003097 in BIDS format.
camcan-skill is orchestration-only; detailed preprocessing logic remains in modality skills.
When to Call This Skill
- User asks for end-to-end Cam-CAN workflow.
- User asks to process Cam-CAN MRI and/or MEG data.
- User needs BIDS validation for Cam-CAN data.
- User asks to extract Cam-CAN phenotype data (cognitive, sensory, health).
- User asks for age-related brain imaging analysis using Cam-CAN.
Complementary / Related Skills
smri-skill → structural MRI preprocessingfmri-skill → functional MRI preprocessing and analysismeg-skill → MEG processing (source localization, time-frequency)mne-eeg-tool → MNE-Python EEG/MEG processingbids-organizer → BIDS validation and organizationbrain-visualization → visualization of derivativesdependency-planner → dependency resolutionconda-env-manager → environment managementclaw-shell → command execution
Reference
Created At: 2026-05-06 13:31 HKT
Last Updated At: 2026-05-06 13:31 HKT
Author: chengwang96
