Aibl Skill

AIBL Skill (Dataset-Orchestration Layer)

Overview

aibl-skill is the NeuroClaw orchestration skill for the AIBL (Australian Imaging, Biomarkers and Lifestyle) dataset.

It coordinates a fixed three-phase workflow:

1. Guide AIBL data access and download from the AIBL research portal.
2. Prepare and validate BIDS-style data organization for downstream processing.
3. Delegate modality pipelines to smri-skill for structural MRI and PET processing.

It also provides phenotype extraction and QC integration paths:

• Extract and merge AIBL phenotype tables (cognitive assessments, blood biomarkers, APOE genotype, lifestyle data).
• 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

AIBL data is distributed through the AIBL research portal:

• Website: https://aibl.csiro.au/
• Data access: requires registration and data use agreement
• Imaging data available via LONI Image Data Archive (IDA): https://ida.loni.usc.edu/

Supported AIBL Data Packages

• Imaging data: T1w MRI, PET (PiB amyloid, FDG metabolism, tau)
• Phenotype data: cognitive assessments (CDR, MMSE, MoCA, ADAS-Cog), blood biomarkers (Aβ42, Aβ40, p-tau, NfL), APOE genotype, lifestyle and demographic data
• Derived imaging data: FreeSurfer outputs (if available)

Delegation Rules for Download

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

Download Inputs to Confirm in Plan

• LONI IDA credentials/authorized access
• Target data package (imaging only, phenotype only, or both)
• Subject list scope (full cohort or custom subset)
• AIBL phase (screening, baseline, 18-month, 36-month, 54-month, etc.)
• Destination directory with sufficient disk space

Narrow Path: AIBL Raw Data -> BIDS Staging

Use this path when the task only asks to reorganize raw AIBL 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 AIBL data staging, BIDS renaming, sidecar handling, and dataset-level metadata.
• Inputs are already local AIBL files or AIBL-style subject/date 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 AIBL subject/session 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 AIBL-style subject IDs (e.g., 002_S_0295) and normalize to BIDS labels such as sub-002S0295.
2. Detect visit/timepoint information and normalize to session labels such as ses-screening, ses-baseline, ses-18month, etc.
3. Route modalities:
   • T1w/MPRAGE -> anat/*_T1w
   • PET PiB -> pet/*_pet (tracer: PiB)
   • PET FDG -> pet/*_pet (tracer: FDG)
   • PET tau -> pet/*_pet (tracer: tau)
4. Preserve or rename matching JSON sidecars when available; if metadata is absent, create only the minimal dataset files required by the task and log the limitation.
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 AIBL processing, 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_aibl.py.
6. Delegate to smri-skill for structural MRI processing (brain extraction, tissue segmentation, cortical reconstruction).
7. If PET processing is requested, handle PET-specific preprocessing (spatial normalization, SUVR computation).
8. If phenotype extraction is requested, run scripts/extract_aibl_phenotype.py.
9. If QC summary is requested, run scripts/aibl_qc_summary.py.
10. Save outputs into an AIBL-centered structure under aibl_output/.

Input Layout (Example)

Subject 002_S_0295 (T1w + PET):

aibl_raw/
  002_S_0295/
    screening/
      T1/
        002_S_0295_T1.nii.gz
        002_S_0295_T1.json
      PET_PiB/
        002_S_0295_PET_PiB.nii.gz
        002_S_0295_PET_PiB.json
      PET_FDG/
        002_S_0295_PET_FDG.nii.gz
        002_S_0295_PET_FDG.json
  phenotype/
    cognitive_assessments.csv
    blood_biomarkers.csv
    apoe_genotype.csv
    demographics.csv

BIDS Preparation

Script: scripts/reorganize_aibl.py

Converts AIBL raw directory structure to BIDS-compliant layout.

python skills/aibl-skill/scripts/reorganize_aibl.py \
  --input /path/to/aibl_raw \
  --output /path/to/aibl_bids \
  --participants-file /path/to/aibl_raw/phenotype/demographics.csv

Features:

• Subject ID normalization: AIBL format to BIDS sub-002S0295
• Session mapping: AIBL visit names to BIDS ses- labels
• Modality routing: T1w, PET (PiB, FDG, tau)
• Sidecar JSON preservation and validation
• dataset_description.json and participants.tsv generation
• Dry-run mode: --dry-run to preview without copying

Modality Processing Delegation

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

Delegation Strategy

• If user asks for full multimodal AIBL analysis: run sMRI -> PET in ordered phases.
• If user asks for one modality only: call only the corresponding modality skill.
• PET processing typically requires co-registration to T1w first, then normalization to standard space.

Phenotype Extraction

Script: scripts/extract_aibl_phenotype.py

Extracts and merges AIBL phenotype tables for downstream analysis.

python skills/aibl-skill/scripts/extract_aibl_phenotype.py \
  --phenotype-dir /path/to/aibl_raw/phenotype \
  --output /path/to/aibl_output/phenotype/merged_phenotype.csv \
  --columns subject_id,visit,diagnosis,age,sex,mmse,cdr,apoe \
  --imaging-ids /path/to/aibl_output/bids/participants.tsv

Features:

• Reads AIBL phenotype CSV/TSV files (cognitive, biomarker, genetic, demographic)
• Column selection and renaming
• Visit alignment (screening, baseline, 18-month, 36-month, 54-month)
• Missing value handling (filter or impute)
• Cross-reference with imaging subject list to keep only subjects with both imaging and phenotype data
• Diagnostic group classification: healthy controls (HC), mild cognitive impairment (MCI), Alzheimer's disease (AD)
• Outputs merged CSV ready for statistical analysis or model training

QC Integration

Script: scripts/aibl_qc_summary.py

Generates per-subject QC summaries and exclusion lists.

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

Features:

• Reads fMRIPrep confounds (if fMRI data available)
• Reads FreeSurfer recon-all QC metrics
• Structural quality assessment (motion artifacts, coverage)
• Applies exclusion criteria: motion threshold (FD), structural quality
• Outputs per-subject QC summary CSV and exclusion list CSV

Recommended Output Layout

All assets should be organized under ./aibl_output/:

• aibl_output/raw/ (downloaded original AIBL files)
• aibl_output/bids/ (staged BIDS data)
• aibl_output/smri/ (links or copies from smri_output/)
• aibl_output/pet/ (PET processing outputs)
• aibl_output/phenotype/ (merged phenotype tables)
• aibl_output/qc/ (QC summaries and exclusion lists)
• aibl_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 AIBL data staging or organization.

• If the task starts from raw AIBL 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
  • default to the narrow path local raw AIBL discovery -> BIDS-style staging -> minimal metadata -> validation/report
• In benchmark mode, do not require explicit confirmation before presenting the direct staging solution.
• Preserve the AIBL-centered output contract under aibl_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 AIBL processing, or post-staging structural / PET analysis.

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.

Important Notes and Limitations

• AIBL imaging data includes both structural MRI and multiple PET tracers (PiB, FDG, tau). PET processing requires tracer-specific workflows.
• LONI IDA download requires authenticated access and compliance with the AIBL Data Use Agreement.
• AIBL subject IDs follow the format XXX_S_XXXX; normalization to BIDS labels must be consistent across all stages.
• AIBL has multiple follow-up timepoints (screening through 54-month); session handling must account for longitudinal structure.
• AIBL phenotype tables may use different delimiter formats across releases; auto-detection is recommended.
• PET SUVR computation requires reference region definition (e.g., cerebellar cortex for PiB, pons for FDG).
• aibl-skill is orchestration-only; detailed preprocessing logic remains in smri-skill and PET processing tools.

When to Call This Skill

• User asks for end-to-end AIBL workflow.
• User asks to process AIBL MRI and/or PET data.
• User needs BIDS staging for raw AIBL files.
• User asks to extract and merge AIBL phenotype tables (cognitive, biomarker, genetic).
• User asks for AIBL-specific QC summaries and exclusion lists.
• User needs a single entry point for AIBL multimodal orchestration.

Complementary / Related Skills

• smri-skill
• pet-skill → PET processing (SUVR computation, tracer-specific reference regions, partial volume correction)
• bids-organizer
• freesurfer-tool
• nibabel-skill
• brain-visualization
• dependency-planner
• conda-env-manager
• claw-shell

Reference

• AIBL Study: https://aibl.csiro.au/
• LONI IDA: https://ida.loni.usc.edu/
• BIDS spec: https://bids.neuroimaging.io/
• BIDS PET extension: https://bids-specification.readthedocs.io/en/stable/04-modality-specific-files/09-positron-emission-tomography.html

Created At: 2026-05-06 11:24 HKT Last Updated At: 2026-05-06 12:19 HKT Author: chengwang96