UCLA CNP Skill (Dataset-Orchestration Layer)

Overview

ucla-cnp-skill is the NeuroClaw orchestration skill for the UCLA CNP (Consortium for Neuropsychiatric Phenomics) dataset, led by Russell Poldrack and colleagues at UCLA.

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 UCLA CNP 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 (ucla_cnp_output/).

Research use only.

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Quick Reference

Task	What needs to be done	Delegate to	Expected output
BIDS validation	Validate UCLA CNP BIDS structure	scripts/validate_ucla_cnp.py	Validation report
sMRI processing	Brain extraction, tissue segmentation	smri-skill	smri_output/ derivatives
task-fMRI processing	Task GLM, activation analysis	fmri-skill	fmri_output/ task results
dMRI processing	Diffusion preprocessing, tractography	dwi-skill	dwi_output/ metrics
Phenotype extraction	Diagnosis, cognitive, behavioral	scripts/extract_ucla_cnp_phenotype.py	Merged phenotype CSV
QC summary	Per-subject quality control	scripts/ucla_cnp_qc_summary.py	QC summary + exclusion list

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Dataset Characteristics

• Cohort: ~270 participants
  • Healthy controls: Age-matched
  • ADHD: Attention deficit hyperactivity disorder
  • Bipolar disorder: Bipolar I/II
  • Schizophrenia: Schizophrenia spectrum
• Scanner: 3T Siemens TIM Trio (UCLA)
• Modalities: T1w sMRI, task-fMRI, dMRI/DTI
• Tasks: Multiple cognitive tasks (stop-signal, spatial working memory, etc.)
• Clinical: Extensive cognitive battery, diagnostic assessments
• Access: OpenNeuro ds000030
• Format: BIDS-compliant
• Reference: Poldrack et al. (2016), Scientific Data

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Supported Modalities

Modality	Description	Tasks/Conditions
T1w	High-resolution structural MRI	1mm isotropic
task-fMRI	Task-based functional MRI	Stop-signal, spatial WM, face memory, etc.
dMRI	Diffusion-weighted imaging	DTI, white matter tractography

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UCLA CNP Task Paradigms

Task	Description	Cognitive Domain
Stop-signal	Response inhibition	Executive function
Spatial working memory	Spatial WM maintenance	Working memory
Face memory	Face encoding/retrieval	Episodic memory
Balloon analog risk	Risk taking behavior	Decision making
Monetary incentive	Reward processing	Motivation

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UCLA CNP Diagnostic Groups

Group	Description	Typical N
Control	Healthy age-matched	~130
ADHD	Attention deficit	~50
Bipolar	Bipolar disorder	~45
Schizophrenia	Schizophrenia spectrum	~45

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BIDS Preparation

Script: scripts/validate_ucla_cnp.py

Validates UCLA CNP BIDS structure and generates a compliance report.

python skills/ucla-cnp-skill/scripts/validate_ucla_cnp.py \
  --input /path/to/UCLA-CNP/bids \
  --output /path/to/ucla_cnp_output/qc/bids_validation.csv

Features:

• BIDS directory structure validation
• Diagnostic group completeness check
• Modality completeness (T1w, task-fMRI, dMRI)
• Task paradigm presence verification

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Core Workflow (Never Bypassed)

1. Identify user target: full UCLA CNP 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_ucla_cnp.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for task-fMRI processing.
7. Delegate to dwi-skill for dMRI processing.
8. If phenotype extraction is requested, run scripts/extract_ucla_cnp_phenotype.py.
9. If QC summary is requested, run scripts/ucla_cnp_qc_summary.py.
10. Save outputs into ucla_cnp_output/.

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Modality Processing Delegation

Modality	Delegated skill	Typical tasks	Main outputs
sMRI (T1w)	smri-skill	brain extraction, tissue segmentation	smri_output/ derivatives
task-fMRI	fmri-skill	task GLM, activation analysis	fmri_output/ task results
dMRI	dwi-skill	diffusion preprocessing, tensor metrics	dwi_output/ metrics

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Standard Output Layout

ucla_cnp_output/
├── bids/                   # BIDS-staged data (or validation report)
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives (task-fMRI)
├── dwi/                    # Diffusion MRI derivatives
├── phenotype/              # Merged phenotype tables (diagnosis, cognitive)
├── qc/                     # QC summaries and exclusion lists
└── logs/                   # Processing logs

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Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full orchestration when the task only asks for local UCLA CNP data validation.

• If the task starts from UCLA CNP data already present on disk and only asks for BIDS validation:
  • Skip the download stage
  • Default to the narrow path local UCLA CNP discovery -> BIDS validation -> report
• In benchmark mode, do not require explicit confirmation before presenting the validation solution.

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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.

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Important Notes and Limitations

• UCLA CNP has 4 diagnostic groups; group comparisons should account for sample size differences.
• Multiple cognitive tasks enable rich cognitive phenotyping.
• OpenNeuro ds000030 is a widely used benchmark dataset for BIDS tools.
• Extensive cognitive battery enables linking brain structure/function to behavior.
• ucla-cnp-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 UCLA CNP workflow.
• User asks to process UCLA CNP neuroimaging data.
• User needs BIDS validation for UCLA CNP data.
• User asks to extract UCLA CNP phenotype data (diagnosis, cognitive).
• User asks for multi-disorder neuroimaging analysis.

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Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing
• 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

• UCLA CNP: https://www.humanconnectome.org/
• Poldrack et al. (2016): A phenomic analysis of the UCLA Consortium for Neuropsychiatric Phenomics LA5c Study. Scientific Data.
• OpenNeuro ds000030

Created At: 2026-05-06 14:21 HKT Last Updated At: 2026-05-06 14:21 HKT Author: chengwang96