AOMIC Skill (Dataset-Orchestration Layer)

Overview

aomic-skill is the NeuroClaw orchestration skill for the AOMIC (Amsterdam Open MRI Collection) dataset.

It coordinates a fixed three-phase workflow:

1. Guide AOMIC data access and download from OpenNeuro / the AOMIC 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 AOMIC phenotype tables (Big Five personality traits, fluid intelligence, demographics).
• 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.

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Download Stage (Mandatory First Step)

Source

AOMIC data is publicly available:

• Website: https://nilab-uva.github.io/AOMIC.github.io/
• OpenNeuro derivatives: https://openneuro.org/
• Data access: direct download, no authentication required for most components

Supported AOMIC Sub-datasets

• AOMIC-ID1000: ~1,000 participants with T1w, rs-fMRI, task-fMRI (emotion, gambling, motor, language tasks), Big Five personality, Raven's progressive matrices
• AOMIC-PIOP1: T1w, rs-fMRI, task-fMRI (emotion, working memory), personality and cognitive data
• AOMIC-PIOP2: T1w, rs-fMRI, task-fMRI (emotion, working memory), personality and cognitive data

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 sub-dataset (ID1000, PIOP1, PIOP2, or all)
• Subject list scope (full cohort or custom subset)
• Destination directory with sufficient disk space

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Narrow Path: AOMIC Raw Data -> BIDS Staging

Use this path when the task only asks to reorganize raw AOMIC 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 AOMIC data staging, BIDS renaming, sidecar handling, and dataset-level metadata.
• Inputs are already local AOMIC files or AOMIC-style subject folders.
• The required deliverable is a direct staging script or command sequence, not a plan for preprocessing or downstream analysis.

Narrow-path contract

• Do not widen the solution to preprocessing, ROI extraction, phenotype merging, or downstream analysis unless the task explicitly requires them.
• Treat this as a direct file-organization problem: scan AOMIC subject layout, normalize subject labels, map modalities to BIDS names, copy or symlink files 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 AOMIC subject IDs (e.g., sub-0001) and validate BIDS compliance.
2. Detect session/task information from directory structure and filenames.
3. Route modalities:
   • T1w -> anat/*_T1w
   • rs-fMRI -> func/*_task-rest_bold
   • task-fMRI -> func/*_task-<taskname>_bold (emotion, gambling, motor, language, workingmemory)
4. Preserve or rename matching JSON sidecars and physiological recordings when available.
5. Emit dataset-level outputs such as dataset_description.json, participants.tsv, README, and a manifest or skipped-file report.

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Core Workflow (Never Bypassed)

1. Identify user target: full AOMIC processing, specific sub-dataset, 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_aomic.py.
6. Delegate sequentially or in parallel to:
   • smri-skill for structural MRI (T1w)
   • fmri-skill for functional MRI (rs-fMRI, task-fMRI)
7. If phenotype extraction is requested, run scripts/extract_aomic_phenotype.py.
8. If QC summary is requested, run scripts/aomic_qc_summary.py.
9. Save outputs into an AOMIC-centered structure under aomic_output/.

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Input Layout (Example)

Subject sub-0001 (T1w + rs-fMRI + task-fMRI):

aomic_raw/
  sub-0001/
    anat/
      sub-0001_T1w.nii.gz
      sub-0001_T1w.json
    func/
      sub-0001_task-rest_bold.nii.gz
      sub-0001_task-rest_bold.json
      sub-0001_task-emotion_bold.nii.gz
      sub-0001_task-emotion_bold.json
      sub-0001_task-gambling_bold.nii.gz
      sub-0001_task-gambling_bold.json
      sub-0001_task-motor_bold.nii.gz
      sub-0001_task-motor_bold.json
      sub-0001_task-language_bold.nii.gz
      sub-0001_task-language_bold.json
  phenotype/
    big_five.csv
    ravens.csv
    demographics.csv

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BIDS Preparation

Script: scripts/reorganize_aomic.py

Validates and reorganizes AOMIC data into BIDS-compliant layout.

python skills/aomic-skill/scripts/reorganize_aomic.py \
  --input /path/to/aomic_raw \
  --output /path/to/aomic_bids \
  --participants-file /path/to/aomic_raw/phenotype/demographics.csv

Features:

• Subject ID validation (BIDS-compliant sub-XXXX format)
• Session/task detection from directory structure and filenames
• Modality routing: T1w, rs-fMRI, task-fMRI (emotion, gambling, motor, language, workingmemory)
• Sidecar JSON preservation and validation
• Physiological recording file handling
• dataset_description.json and participants.tsv generation
• Dry-run mode: --dry-run to preview without copying

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Modality Processing Delegation

After BIDS staging completes, aomic-skill delegates by modality:

Modality	Delegated skill	Typical tasks	Main outputs
sMRI (T1w)	smri-skill	brain extraction, tissue segmentation, cortical reconstruction, ROI morphometry	smri_output/ derivatives and stats
fMRI (rs-fMRI/task-fMRI)	fmri-skill	preprocessing, denoising, ROI time series, connectivity, task GLM	fmri_output/ derivatives, timeseries, connectivity

Delegation Strategy

• If user asks for full multimodal AOMIC analysis: run sMRI -> fMRI in ordered phases.
• If user asks for one modality only: call only the corresponding modality skill.
• Task-fMRI analysis should use task-specific event files (emotion, gambling, motor, language, workingmemory).

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Phenotype Extraction

Script: scripts/extract_aomic_phenotype.py

Extracts and merges AOMIC phenotype tables for downstream analysis.

python skills/aomic-skill/scripts/extract_aomic_phenotype.py \
  --phenotype-dir /path/to/aomic_raw/phenotype \
  --output /path/to/aomic_output/phenotype/merged_phenotype.csv \
  --columns subject_id,age,sex,openness,conscientiousness,extraversion,agreeableness,neuroticism,ravens_score \
  --imaging-ids /path/to/aomic_output/bids/participants.tsv

Features:

• Reads AOMIC phenotype CSV/TSV files (Big Five, Raven's, demographics)
• Column selection and renaming
• Missing value handling (filter or impute)
• Cross-reference with imaging subject list to keep only subjects with both imaging and phenotype data
• Outputs merged CSV ready for statistical analysis or model training

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QC Integration

Script: scripts/aomic_qc_summary.py

Generates per-subject QC summaries and exclusion lists.

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

Features:

• Reads fMRIPrep confounds (framewise displacement, DVARS)
• Reads FreeSurfer recon-all QC metrics
• Structural quality assessment
• Applies exclusion criteria: motion threshold (FD), structural quality
• Outputs per-subject QC summary CSV and exclusion list CSV

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Recommended Output Layout

All assets should be organized under ./aomic_output/:

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

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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 AOMIC data staging or organization.

• If the task starts from raw AOMIC data already present on disk and only asks for BIDS-style staging / organization:
  • skip the mandatory download stage
  • do not automatically delegate to smri-skill or fmri-skill
  • default to the narrow path local raw AOMIC discovery -> BIDS-style staging -> minimal metadata -> validation/report
• In benchmark mode, do not require explicit confirmation before presenting the direct staging solution.
• Preserve the AOMIC-centered output contract under aomic_output/bids/ when the task is specifically a staging benchmark.
• Only use the full multimodal orchestration and confirmation-heavy workflow when the prompt explicitly asks for download, end-to-end multimodal AOMIC processing, or post-staging structural / functional analysis.

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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.
• If download fails for partial subjects, continue batch with clear failure report and retry list.

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Important Notes and Limitations

• AOMIC data is already in BIDS format for many components; the reorganize script primarily validates and handles edge cases.
• AOMIC has multiple sub-datasets (ID1000, PIOP1, PIOP2) with slightly different task paradigms and phenotype measures.
• Task-fMRI event files (.tsv) must be preserved alongside BOLD data for proper task analysis.
• Some AOMIC components include physiological recordings (cardiac, respiration) that can be used for advanced denoising.
• aomic-skill is orchestration-only; detailed preprocessing logic remains in smri-skill and fmri-skill.

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When to Call This Skill

• User asks for end-to-end AOMIC workflow.
• User asks to process AOMIC MRI data (sMRI, rs-fMRI, task-fMRI).
• User needs BIDS staging for raw AOMIC files.
• User asks to extract and merge AOMIC phenotype tables (personality, cognition, demographics).
• User asks for AOMIC-specific QC summaries and exclusion lists.
• User needs a single entry point for AOMIC multimodal orchestration.

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Complementary / Related Skills

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

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Reference

• AOMIC: https://nilab-uva.github.io/AOMIC.github.io/
• OpenNeuro: https://openneuro.org/
• BIDS spec: https://bids.neuroimaging.io/

Created At: 2026-05-06 11:24 HKT Last Updated At: 2026-05-06 11:24 HKT Author: chengwang96