Ucla Cnp Skill

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.

Quick Reference

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

Supported Modalities

UCLA CNP Task Paradigms

UCLA CNP Diagnostic Groups

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

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/.

Modality Processing Delegation

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

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.

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

• 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.

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.

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

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