ppmi-skill

name	ppmi-skill
description	Use this skill whenever the user wants an end-to-end workflow for the Parkinson's Progression Markers Initiative (PPMI) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'PPMI', 'Parkinson', 'Parkinson disease', 'process PPMI data', 'PPMI fMRI', or any request to run the PPMI multimodal pipeline.
license	MIT License (NeuroClaw custom skill - freely modifiable within the project)
layer	subagent
skill_type	dataset
dependencies	["smri-skill","fmri-skill","dwi-skill","bids-organizer","claw-shell"]
complementary_skills	["brain-visualization","pet-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.

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Quick Reference

Task	What needs to be done	Delegate to	Expected output
BIDS validation	Validate PPMI BIDS structure	scripts/validate_ppmi.py	Validation report
sMRI processing	Brain extraction, tissue segmentation	smri-skill	smri_output/ derivatives
rs-fMRI processing	Preprocessing, denoising, connectivity	fmri-skill	fmri_output/ connectivity
dMRI processing	Diffusion preprocessing, tensor metrics	dwi-skill	dwi_output/ metrics
Phenotype extraction	Motor scores, cognitive, biomarkers	scripts/extract_ppmi_phenotype.py	Merged phenotype CSV
QC summary	Per-subject quality control	scripts/ppmi_qc_summary.py	QC summary + exclusion list

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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

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Supported Modalities

Modality	Description	Details
T1w	High-resolution structural MRI	1mm isotropic, substantia nigra volumetry
rs-fMRI	Resting-state functional MRI	Basal ganglia connectivity
dMRI	Diffusion-weighted imaging	DTI, nigrostriatal tract integrity
DaTscan	SPECT dopamine transporter	Striatal binding ratios

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PPMI Clinical Measures

Measure	Description	Domain
MDS-UPDRS	Movement Disorder Society Unified PD Rating Scale	Motor function
MoCA	Montreal Cognitive Assessment	Global cognition
UPSIT	University of Pennsylvania Smell Identification Test	Olfaction
RBD	REM Sleep Behavior Disorder screening	Sleep
H&Y	Hoehn and Yahr staging	Disease stage
DAT	Dopamine transporter binding (SPECT)	Dopaminergic function

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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

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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/.

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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
rs-fMRI	fmri-skill	preprocessing, denoising, connectivity	fmri_output/ connectivity
dMRI	dwi-skill	diffusion preprocessing, tensor metrics	dwi_output/ metrics

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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

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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.

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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

• 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.

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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.

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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

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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