--- name: aibl-skill description: "Use this skill whenever the user wants an end-to-end workflow for the AIBL (Australian Imaging, Biomarkers and Lifestyle) dataset, including data access guidance, BIDS organization, and multimodal processing of sMRI and PET (PiB, FDG, tau). Triggers include: 'AIBL', 'AIBL data', 'process AIBL', 'AIBL PET', 'AIBL MRI', or any request to run the AIBL multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for AIBL." license: MIT License (NeuroClaw custom skill - freely modifiable within the project) layer: subagent skill_type: dataset dependencies: - smri-skill - bids-organizer - claw-shell complementary_skills: - pet-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. ```bash 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: | Modality | Delegated skill | Typical tasks | Main outputs | |---|---|---|---| | sMRI (T1w) | `smri-skill` | brain extraction, tissue segmentation, cortical reconstruction, ROI morphometry | `smri_output/` derivatives and stats | | PET (PiB/FDG/tau) | `pet-skill` | spatial normalization to template, SUVR computation, reference region quantification | `pet_output/` SUVR maps and ROI values | ### 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. ```bash 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. ```bash 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