Ppmi Skill

PPMI Skill (Dataset-Orchestration Layer)

Overview

ppmi-skill is the NeuroClaw orchestration skill for the Parkinson's Progression Markers Initiative (PPMI) dataset, launched by The Michael J. Fox Foundation.

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 PPMI 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 (ppmi_output/).

Research use only.

Quick Reference

Dataset Characteristics

• Cohort: ~2,000+ participants
  • PD patients: Parkinson's disease (early stage, drug-naive)
  • Prodromal: REM sleep behavior disorder, hyposmia
  • Healthy controls: Age-matched
• Scanner: 3T Siemens (multi-site)
• Modalities: T1w sMRI, rs-fMRI, dMRI/DTI, DaTscan SPECT
• Clinical: MDS-UPDRS, MoCA, UPSIT, REM sleep, DAT imaging
• Access: LONI IDA (ida.loni.usc.edu), PPMI data portal
• Format: BIDS-compliant (community conversion)
• Reference: Marek et al. (2011), Lancet Neurology

Supported Modalities

PPMI Clinical Measures

BIDS Preparation

Script: scripts/validate_ppmi.py

Validates PPMI BIDS structure and generates a compliance report.

python skills/ppmi-skill/scripts/validate_ppmi.py \
  --input /path/to/PPMI/bids \
  --output /path/to/ppmi_output/qc/bids_validation.csv

Features:

• BIDS directory structure validation
• Diagnostic group completeness (PD, prodromal, control)
• Modality completeness (T1w, rs-fMRI, dMRI)
• Clinical measure availability check

Core Workflow (Never Bypassed)

1. Identify user target: full PPMI 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_ppmi.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for rs-fMRI processing.
7. Delegate to dwi-skill for dMRI processing.
8. If phenotype extraction is requested, run scripts/extract_ppmi_phenotype.py.
9. If QC summary is requested, run scripts/ppmi_qc_summary.py.
10. Save outputs into ppmi_output/.

Modality Processing Delegation

Standard Output Layout

ppmi_output/
├── bids/                   # BIDS-staged data (or validation report)
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives (rs-fMRI connectivity)
├── dwi/                    # Diffusion MRI derivatives (DTI metrics)
├── phenotype/              # Merged phenotype tables (motor, cognitive, biomarkers)
├── 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 PPMI data validation.

• If the task starts from PPMI data already present on disk and only asks for BIDS validation:
  • Skip the download stage
  • Default to the narrow path local PPMI 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

• PPMI is a multi-site study; site effects should be modeled in group analyses.
• Early-stage PD patients are often drug-naive, which is valuable for studying untreated disease.
• DaTscan SPECT provides dopaminergic imaging but may not follow standard BIDS conventions.
• Longitudinal design enables progression modeling.
• Large sample size and rich clinical phenotyping make PPMI ideal for biomarker discovery.
• ppmi-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

When to Call This Skill

• User asks for end-to-end PPMI workflow.
• User asks to process PPMI neuroimaging data.
• User needs BIDS validation for PPMI data.
• User asks to extract PPMI phenotype data (MDS-UPDRS, MoCA, DAT).
• User asks for Parkinson's disease neuroimaging analysis.

Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing
• pet-skill → PET imaging (if available)
• 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

• PPMI: https://www.ppmi-info.org/
• Marek et al. (2011): The Parkinson Progression Marker Initiative (PPMI). Lancet Neurology.
• LONI IDA: https://ida.loni.usc.edu/

Created At: 2026-05-06 13:55 HKT Last Updated At: 2026-05-06 13:55 HKT Author: chengwang96