Nifd 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):

1. Identify input NIFD data and target modalities.
2. Generate a numbered execution plan clearly stating WHAT needs to be done and which tool skill will handle each step.
3. Present the full plan, estimated runtime, resource requirements, and risks to the user and wait for explicit confirmation ("YES" / "execute" / "proceed").
4. On confirmation, delegate every step to the appropriate skill via claw-shell.
5. After execution, save all outputs in a clean directory structure (nifd_output/).

Research use only.

Quick Reference

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

NIFD Diagnostic Groups

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)

1. Identify user target: full NIFD processing, imaging subset, phenotype extraction, or BIDS validation 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 BIDS validation using scripts/validate_nifd.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for rs-fMRI processing (functional connectivity).
7. Delegate to dwi-skill for dMRI processing (white matter integrity).
8. If phenotype extraction is requested, run scripts/extract_nifd_phenotype.py.
9. If QC summary is requested, run scripts/nifd_qc_summary.py.
10. Save outputs into nifd_output/.

Modality Processing Delegation

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 preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• dwi-skill → diffusion MRI preprocessing
• pet-skill → PET imaging (tau-PET, amyloid-PET if available)
• bids-organizer → BIDS validation and organization
• brain-visualization → visualization of derivatives
• dependency-planner → dependency resolution
• conda-env-manager → environment management
• claw-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