Camcan Skill

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):

1. Identify input Cam-CAN data and target modalities.
2. Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
3. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
4. On confirmation, delegate every step to the appropriate skill via claw-shell.
5. After execution, save all outputs in a clean directory structure (camcan_output/).

Research use only.

Quick Reference

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

Cam-CAN Task Paradigms

fMRI Tasks

MEG Tasks

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)

1. Identify user target: full Cam-CAN processing, imaging subset, phenotype extraction, or BIDS validation only.
2. Generate a numbered plan with tools, outputs, runtime, storage, and risks.
3. Wait for explicit confirmation (YES / execute / proceed).
4. On confirmation, run BIDS validation using scripts/validate_camcan.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for functional MRI processing (resting-state and movie-watching).
7. Delegate to meg-skill for MEG processing (source localization, time-frequency analysis).
8. If phenotype extraction is requested, run scripts/extract_camcan_phenotype.py.
9. If QC summary is requested, run scripts/camcan_qc_summary.py.
10. Save outputs into camcan_output/.

Modality Processing Delegation

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 preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• meg-skill → MEG processing (source localization, time-frequency)
• mne-eeg-tool → MNE-Python EEG/MEG processing
• bids-organizer → BIDS validation and organization
• brain-visualization → visualization of derivatives
• dependency-planner → dependency resolution
• conda-env-manager → environment management
• claw-shell → command execution

Reference

• Cam-CAN: https://camcan.mrc-cbu.cam.ac.uk/
• OpenNeuro ds003097: https://openneuro.org/datasets/ds003097
• Shafto et al. (2014): The Cam-CAN study protocol. BMC Neurology.
• Taylor et al. (2017): The Cam-CAN data repository. NeuroImage.

Created At: 2026-05-06 13:31 HKT Last Updated At: 2026-05-06 13:31 HKT Author: chengwang96