HCP-YA Skill (Dataset-Orchestration Layer)

Overview

hcpya-skill is the NeuroClaw orchestration skill for the HCP Young Adult (HCP-YA / HCP1200) 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 data reorganization, phenotype extraction, and QC.

Core workflow (never bypassed):

1. Identify input HCP-YA 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 (hcpya_output/).

Research use only.

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Quick Reference

Task	What needs to be done	Delegate to	Expected output
Data download	Select HCP-YA packages in ConnectomeDB powered by BALSA after registration and terms acceptance	claw-shell	Raw or preprocessed HCP-YA packages
BIDS staging	Reorganize HCP-YA native layout to BIDS	scripts/reorganize_hcpya.py	BIDS-compliant dataset
sMRI processing	Brain extraction, tissue segmentation, cortical reconstruction	smri-skill	smri_output/ derivatives
fMRI processing	Preprocessing, denoising, connectivity, task GLM	fmri-skill	fmri_output/ derivatives
dMRI processing	Eddy correction, tensor metrics, tractography	dwi-skill	dwi_output/ metrics
Phenotype extraction	Cognitive, behavioral, demographic data	scripts/extract_hcpya_phenotype.py	Merged phenotype CSV
QC summary	Per-subject quality control	scripts/hcpya_qc_summary.py	QC summary + exclusion list

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Download Stage (Mandatory First Step)

Source

The current HCP-YA release is distributed through ConnectomeDB powered by BALSA:

• Release: https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-young-adult-2025-release
• Platform: https://balsa.wustl.edu/ (open the ConnectomeDB tab)
• Register and accept the applicable HCP data-use terms before downloading.
• Restricted non-imaging variables require the corresponding restricted-data approval.

Release Selection

• Prefer the HCP-YA 2025 Release for new work. It contains updated processing and package organization.
• Do not mix 2025 processed packages with the legacy 2017 S1200 processed release in one analysis.
• Use the legacy controlled-access S3 route only when reproducing an S1200-era workflow and after satisfying HCP access terms.
• Download a modality-specific package or explicit subject subset instead of assuming the entire cohort is needed.

Download Inputs to Confirm in Plan

• BALSA/ConnectomeDB account and accepted data-use terms
• Release (2025 by default or legacy S1200 for reproduction)
• Target package and modalities (structural, resting fMRI, task fMRI, diffusion, or non-imaging)
• Subject list scope and restricted-variable requirements
• Destination directory with capacity calculated from the selected packages

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HCP-YA Task Paradigms

Task	Description	Duration
MOTOR	Finger tapping, toe movement, tongue movement	~3 min
EMOTION	Faces and shapes matching	~2 min
GAMBLING	Card guessing with reward/loss	~3 min
LANGUAGE	Story comprehension and math	~4 min
RELATIONAL	Relational reasoning matching	~3 min
SOCIAL	Social cognition (mentalizing) movie clips	~3 min
WM	Working memory (faces, places, tools, body parts)	~5 min
REST	Resting-state (eyes open)	~15 min × 4 runs

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BIDS Preparation

Script: scripts/reorganize_hcpya.py

Converts HCP-YA native directory structure to BIDS-compliant layout.

python skills/hcpya-skill/scripts/reorganize_hcpya.py \
  --input /path/to/HCPYA/raw \
  --output /path/to/HCPYA/bids \
  --participants /path/to/subject_list.txt

Features:

• Subject ID normalization: HCP format (100307) to BIDS (sub-100307)
• Modality routing: T1w, T2w, dMRI, rs-fMRI, task-fMRI (7 tasks)
• Sidecar JSON generation from HCP metadata
• dataset_description.json and participants.tsv generation
• Dry-run mode: --dry-run to preview without copying

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Core Workflow (Never Bypassed)

1. Identify user target: full HCP-YA processing, imaging subset, phenotype extraction, or BIDS staging 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 download stage first (if needed).
5. After download success, run BIDS preparation using scripts/reorganize_hcpya.py.
6. Delegate to smri-skill for structural MRI processing.
7. Delegate to fmri-skill for functional MRI processing.
8. Delegate to dwi-skill for diffusion MRI processing.
9. If phenotype extraction is requested, run scripts/extract_hcpya_phenotype.py.
10. If QC summary is requested, run scripts/hcpya_qc_summary.py.
11. Save outputs into hcpya_output/.

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Modality Processing Delegation

Modality	Delegated skill	Typical tasks	Main outputs
sMRI (T1w/T2w)	smri-skill	brain extraction, tissue segmentation, cortical reconstruction, ROI morphometry	smri_output/ derivatives
fMRI (rs-fMRI/task-fMRI)	fmri-skill	preprocessing, denoising, ROI time series, connectivity, task GLM	fmri_output/ derivatives
dMRI (DWI)	dwi-skill	eddy correction, tensor metrics, tractography, connectome	dwi_output/ metrics

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Standard Output Layout

hcpya_output/
├── raw/                    # Downloaded original HCP-YA files
├── bids/                   # BIDS-staged data
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives
├── dwi/                    # Diffusion MRI derivatives
├── phenotype/              # Merged phenotype tables
├── qc/                     # QC summaries and exclusion lists
└── logs/                   # Download + orchestration logs

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Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full download -> staging -> multimodal processing orchestration when the task only asks for local HCP-YA data staging or organization.

• If the task starts from raw HCP-YA data already present on disk and only asks for BIDS-style staging:
  • Skip the mandatory download stage
  • Default to the narrow path local raw HCP-YA discovery -> BIDS-style staging -> minimal metadata -> validation/report
• In benchmark mode, do not require explicit confirmation before presenting the direct staging solution.
• Only use the full multimodal orchestration when the prompt explicitly asks for download or end-to-end processing.

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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.
• If download fails for partial subjects, continue batch with clear failure report and retry list.

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Important Notes and Limitations

• HCP-YA processing is resource intensive (CPU, RAM, and storage).
• HCP-YA releases span many large packages; estimate storage from the selected BALSA packages before transfer.
• The 2025 release reports processed data for 1,071 subjects and unprocessed imaging for 1,113 subjects; "HCP1200" is the cohort/release name, not a complete-case count.
• Age range: 22-35 years.
• For HCP-native preprocessing (minimal preprocessing pipelines), optionally delegate to hcppipeline-tool.
• hcpya-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

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When to Call This Skill

• User asks for end-to-end HCP Young Adult workflow.
• User asks to download HCP1200 and run sMRI/fMRI/DTI processing.
• User needs BIDS staging for HCP-YA data.
• User asks to extract HCP-YA phenotype data (cognitive, behavioral, demographic).

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Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing and analysis
• hcppipeline-tool → HCP-native minimal preprocessing pipelines
• bids-organizer → BIDS validation and organization
• brain-visualization → visualization of derivatives
• dependency-planner → dependency resolution
• conda-env-manager → environment management
• claw-shell → command execution

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Reference

• HCP-YA: https://www.humanconnectome.org/study/hcp-young-adult
• HCP-YA 2025 release: https://www.humanconnectome.org/study/hcp-young-adult/document/hcp-young-adult-2025-release
• HCP data-use terms: https://www.humanconnectome.org/study/hcp-young-adult/data-use-terms
• Glasser et al. (2013): The Human Connectome Project minimally preprocessed pipelines

Created At: 2026-05-06 13:02 HKT Last Updated At: 2026-08-11 HKT Author: chengwang96