Adhd200 Skill

ADHD-200 Skill (Dataset-Orchestration Layer)

Overview

adhd200-skill is the NeuroClaw orchestration skill for the ADHD-200 dataset.

It coordinates a fixed three-phase workflow:

1. Download ADHD-200 data from the FCP/INDI repository.
2. Prepare and validate BIDS-style data organization for downstream processing.
3. Delegate modality pipelines to smri-skill and fmri-skill.

It also provides phenotype extraction and QC integration paths:

• Extract and merge ADHD-200 phenotype tables (diagnosis, ADHD measures, demographics, medication).
• Generate per-subject QC summaries with exclusion lists.

This skill follows NeuroClaw hierarchy:

• Defines WHAT to do, not low-level implementation details.
• Does not execute direct shell commands itself.
• Delegates all execution via claw-shell to base/tool skills.

Research use only.

Download Stage (Mandatory First Step)

Source

ADHD-200 data is distributed through the FCP/INDI repository:

• Website: https://fcon_1000.projects.nitrc.org/indi/adhd200/

Supported ADHD-200 Data Packages

• Imaging data: T1w, rs-fMRI (NIfTI format) from 8 imaging sites
• Phenotype data: CSV files with diagnosis, ADHD measures, demographics, medication history, QC measures
• Sites: Peking, Brown, NYU, KKI, NeuroImage, OHSU, Pitt, Washington University

Delegation Rules for Download

• Environment/setup checks: dependency-planner + conda-env-manager
• Download tool installation and execution: claw-shell
• Optional raw-data organization to BIDS-style staging: bids-organizer

Download Inputs to Confirm in Plan

• Target subset (full cohort, specific sites, or ADHD/control only)
• Subject list scope (full or custom IDs)
• Destination directory with sufficient disk space

Narrow Path: ADHD-200 Raw NIfTI -> BIDS Staging

Use this path when the task only asks to reorganize raw ADHD-200 NIfTI files into a BIDS-style dataset and does not require preprocessing, ROI extraction, phenotype merging, or downstream analysis.

When this narrow path should dominate

• The task objective is limited to ADHD-200 NIfTI staging, BIDS renaming, sidecar handling, and dataset-level metadata.
• Inputs are already local ADHD-200 NIfTI files or ADHD-200-style subject/site folders.
• The required deliverable is a direct staging script or command sequence, not a plan for fMRIPrep or downstream analysis.

Narrow-path contract

• Do not widen the solution to fMRIPrep, ROI extraction, phenotype merging, or downstream analysis unless the task explicitly requires them.
• Treat this as a direct file-organization problem: scan ADHD-200 subject/site layout, normalize subject labels, map modalities to BIDS names, copy or symlink NIfTI plus matching sidecars, and write dataset-level metadata plus staging logs.
• If the task is benchmark-style, prefer a single direct end-to-end staging script over a confirmation-first orchestration plan.

Expected narrow-path behavior

1. Detect ADHD-200-style subject IDs (numeric, e.g., 0010002) and normalize to BIDS labels such as sub-0010002.
2. Detect site information and encode in participants.tsv.
3. Route modalities:
   • T1w -> anat/*_T1w
   • rs-fMRI/BOLD -> func/*_task-rest_bold
4. Preserve or rename matching JSON sidecars when available.
5. Emit dataset-level outputs such as dataset_description.json, participants.tsv, README, and a manifest or skipped-file report.

Core Workflow (Never Bypassed)

1. Identify user target: full ADHD-200 download, 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_adhd200.py.
6. Delegate to modality skills:
   • smri-skill for structural MRI (T1w)
   • fmri-skill for resting-state fMRI (rs-fMRI)
7. If phenotype extraction is requested, run scripts/extract_adhd200_phenotype.py.
8. If QC summary is requested, run scripts/adhd200_qc_summary.py.
9. Save outputs into an ADHD-200-centered structure under adhd200_output/.

Input Layout (Example)

Subject 0010002 from site Peking:

adhd200_raw/
  Peking/
    0010002/
      anat/
        anat.nii.gz
      func/
        rest.nii.gz
  phenotype/
    ADHD200_..._phenotypic.csv

BIDS Preparation

Script: scripts/reorganize_adhd200.py

Converts ADHD-200 raw directory structure to BIDS-compliant layout.

python skills/adhd200-skill/scripts/reorganize_adhd200.py \
  --input /path/to/adhd200_raw \
  --output /path/to/adhd200_bids \
  --phenotype /path/to/adhd200_raw/phenotype/ADHD200_phenotypic.csv

Features:

• Subject ID normalization: numeric ADHD-200 IDs to BIDS sub-NNNNNNN
• Site extraction and encoding in participants.tsv
• Modality routing: T1w, rs-fMRI
• dataset_description.json and participants.tsv generation with phenotype metadata
• Dry-run mode: --dry-run to preview without copying

Multimodal Processing Delegation

Phenotype Extraction

Script: scripts/extract_adhd200_phenotype.py

python skills/adhd200-skill/scripts/extract_adhd200_phenotype.py \
  --phenotype-dir /path/to/adhd200_raw/phenotype \
  --output /path/to/adhd200_output/phenotype/merged_phenotype.csv \
  --columns subject,DX,AGE,SEX,ADHD_Index,Inatt,HyperImp \
  --imaging-ids /path/to/adhd200_output/bids/participants.tsv

QC Integration

Script: scripts/adhd200_qc_summary.py

python skills/adhd200-skill/scripts/adhd200_qc_summary.py \
  --fmriprep-dir /path/to/adhd200_output/fmriprep \
  --freesurfer-dir /path/to/adhd200_output/smri/freesurfer \
  --output /path/to/adhd200_output/qc/qc_summary.csv \
  --exclude-output /path/to/adhd200_output/qc/exclude_list.csv \
  --fd-threshold 0.3

Recommended Output Layout

All assets should be organized under ./adhd200_output/:

• adhd200_output/raw/ (downloaded original files)
• adhd200_output/bids/ (staged BIDS data)
• adhd200_output/smri/ (links or copies from smri_output/)
• adhd200_output/fmri/ (links or copies from fmri_output/)
• adhd200_output/phenotype/ (merged phenotype tables)
• adhd200_output/qc/ (QC summaries and exclusion lists)
• adhd200_output/logs/ (download + orchestration logs)

Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full download -> staging -> multimodal processing orchestration when the task is only asking for local ADHD-200 data staging or organization.

• If the task starts from raw ADHD-200 data already present on disk and only asks for BIDS-style staging / organization:
  • skip the mandatory download stage
  • default to the narrow path local raw ADHD-200 discovery -> BIDS-style staging -> minimal metadata -> validation/report
• In benchmark mode, do not require explicit confirmation before presenting the direct staging 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

• ADHD-200 has heterogeneous acquisition parameters across 8 sites; site effects must be addressed in analysis.
• ADHD-200 subject IDs are numeric and vary in length across sites.
• Diagnosis labels vary by site (ADHD-combined, ADHD-inattentive, ADHD-hyperactive, typically developing).
• ADHD-200 data does not include task-fMRI; only resting-state fMRI is available.
• adhd200-skill is orchestration-only; detailed preprocessing logic remains in smri-skill and fmri-skill.

When to Call This Skill

• User asks for end-to-end ADHD-200 workflow.
• User asks to download ADHD-200 data and then run sMRI/rs-fMRI processing.
• User needs BIDS staging for raw ADHD-200 NIfTI files.
• User asks to extract and merge ADHD-200 phenotype tables.
• User needs ADHD-200-specific QC summaries and exclusion lists.

Complementary / Related Skills

• smri-skill
• fmri-skill
• bids-organizer
• fmriprep-tool
• freesurfer-tool
• brain_gnn
• dependency-planner
• conda-env-manager
• claw-shell

Reference

• ADHD-200: https://fcon_1000.projects.nitrc.org/indi/adhd200/
• BIDS spec: https://bids.neuroimaging.io/

Created At: 2026-05-06 01:50 HKT Last Updated At: 2026-05-06 01:50 HKT Author: chengwang96