| name | nifd-skill |
| description | Use this skill whenever the user wants an end-to-end workflow for the Neuroimaging in Frontotemporal Dementia (NIFD) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, and dMRI, phenotype extraction, and QC integration. Triggers include: 'NIFD', 'frontotemporal dementia', 'FTD', 'bvFTD', 'PPA', 'process NIFD data', or any request to run the NIFD 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"] |
NIFD Skill (Dataset-Orchestration Layer)
Overview
nifd-skill is the NeuroClaw orchestration skill for the Neuroimaging in Frontotemporal Dementia (NIFD) dataset, collected at the UCSF Memory and Aging Center.
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):
- Identify input NIFD data and target modalities.
- Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
- Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
- On confirmation, delegate every step to the appropriate skill via
claw-shell.
- After execution, save all outputs in a clean directory structure (
nifd_output/).
Research use only.
Quick Reference
| Task | What needs to be done | Delegate to | Expected output |
|---|
| BIDS validation | Validate NIFD BIDS structure | scripts/validate_nifd.py | Validation report |
| sMRI processing | Brain extraction, tissue segmentation, cortical thickness | smri-skill | smri_output/ derivatives |
| rs-fMRI processing | Preprocessing, denoising, connectivity | fmri-skill | fmri_output/ connectivity |
| dMRI processing | Diffusion preprocessing, tensor metrics, tractography | dwi-skill | dwi_output/ metrics |
| Phenotype extraction | Diagnosis, cognitive scores, clinical measures | scripts/extract_nifd_phenotype.py | Merged phenotype CSV |
| QC summary | Per-subject quality control | scripts/nifd_qc_summary.py | QC summary + exclusion list |
Dataset Characteristics
- Cohort: ~120 participants
- bvFTD: Behavioral variant frontotemporal dementia
- svPPA: Semantic variant primary progressive aphasia
- nfvPPA: Nonfluent variant primary progressive aphasia
- Healthy controls: Age-matched
- Scanner: 3T Siemens TIM Trio
- Modalities: T1w sMRI, rs-fMRI, dMRI/DTI
- Clinical: CDR, MMSE, neuropsychological battery
- Access: OpenNeuro ds004403 (or UCSF MAC portal)
- Format: BIDS-compliant
Supported Modalities
| Modality | Description | Details |
|---|
| T1w | High-resolution structural MRI | 1mm isotropic, cortical thickness/atrophy |
| rs-fMRI | Resting-state functional MRI | Functional connectivity, network degeneration |
| dMRI | Diffusion-weighted imaging | DTI, white matter tract integrity |
NIFD Diagnostic Groups
| Group | Description | Typical N |
|---|
| bvFTD | Behavioral variant FTD | ~40 |
| svPPA | Semantic variant PPA | ~20 |
| nfvPPA | Nonfluent variant PPA | ~15 |
| Control | Healthy age-matched controls | ~45 |
BIDS Preparation
Script: scripts/validate_nifd.py
Validates NIFD BIDS structure and generates a compliance report.
python skills/nifd-skill/scripts/validate_nifd.py \
--input /path/to/NIFD/bids \
--output /path/to/nifd_output/qc/bids_validation.csv
Features:
- BIDS directory structure validation
- Diagnostic group completeness check
- Modality completeness (T1w, rs-fMRI, dMRI)
- Missing data identification
Core Workflow (Never Bypassed)
- Identify user target: full NIFD processing, imaging subset, phenotype extraction, or BIDS validation only.
- Generate a numbered plan with tools, outputs, runtime, storage, and risks.
- Wait for explicit confirmation (
YES / execute / proceed).
- On confirmation, run BIDS validation using
scripts/validate_nifd.py.
- Delegate to
smri-skill for structural MRI processing.
- Delegate to
fmri-skill for rs-fMRI processing (functional connectivity).
- Delegate to
dwi-skill for dMRI processing (white matter integrity).
- If phenotype extraction is requested, run
scripts/extract_nifd_phenotype.py.
- If QC summary is requested, run
scripts/nifd_qc_summary.py.
- Save outputs into
nifd_output/.
Modality Processing Delegation
| Modality | Delegated skill | Typical tasks | Main outputs |
|---|
| sMRI (T1w) | smri-skill | brain extraction, tissue segmentation, cortical thickness | smri_output/ derivatives |
| rs-fMRI | fmri-skill | preprocessing, denoising, connectivity | fmri_output/ connectivity |
| dMRI | dwi-skill | diffusion preprocessing, tensor metrics, tractography | dwi_output/ metrics |
Standard Output Layout
nifd_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 (diagnosis, cognitive)
├── qc/ # QC summaries and exclusion lists
└── logs/ # Processing logs
Benchmark Adapter Guidance
For benchmark-style prompts, do not force the full orchestration when the task only asks for local NIFD data validation.
- If the task starts from NIFD data already present on disk and only asks for BIDS validation:
- Skip the download stage
- Default to the narrow path
local NIFD discovery -> BIDS validation -> report
- In benchmark mode, do not require explicit confirmation before presenting the validation 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
- NIFD is a clinical cohort; patient data requires careful handling.
- Diagnostic groups (bvFTD, svPPA, nfvPPA) have distinct atrophy patterns; group-level analyses should account for heterogeneity.
- Cortical thickness and voxel-based morphometry are commonly used structural measures.
- Network degeneration hypothesis: FTD targets specific large-scale networks.
nifd-skill is orchestration-only; detailed preprocessing logic remains in modality skills.
When to Call This Skill
- User asks for end-to-end NIFD workflow.
- User asks to process NIFD neuroimaging data.
- User needs BIDS validation for NIFD data.
- User asks to extract NIFD phenotype data (diagnosis, cognitive scores).
- User asks for frontotemporal dementia neuroimaging analysis.
Complementary / Related Skills
smri-skill → structural MRI preprocessingfmri-skill → functional MRI preprocessing and analysisdwi-skill → diffusion MRI preprocessingpet-skill → PET imaging (tau-PET, amyloid-PET if available)bids-organizer → BIDS validation and organizationbrain-visualization → visualization of derivativesdependency-planner → dependency resolutionconda-env-manager → environment managementclaw-shell → command execution
Reference
- NIFD: UCSF Memory and Aging Center
- Frontotemporal Dementia: FTDC clinical diagnostic criteria
- OpenNeuro ds004403
Created At: 2026-05-06 13:55 HKT
Last Updated At: 2026-05-06 13:55 HKT
Author: chengwang96
