Nsd skill

NSD Skill (Dataset-Orchestration Layer)

Overview

nsd-skill is the NeuroClaw orchestration skill for the Natural Scenes Dataset (NSD).

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, stimulus extraction, and QC.

Core workflow (never bypassed):

1. Identify input NSD 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 (nsd_output/).

Research use only.

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Quick Reference

Task	What needs to be done	Delegate to	Expected output
BIDS validation	Validate NSD BIDS structure	scripts/validate_nsd.py	Validation report
sMRI processing	Brain extraction, tissue segmentation	smri-skill	smri_output/ derivatives
task-fMRI processing	Visual task GLM, voxel-wise encoding	fmri-skill	fmri_output/ task results
Stimulus extraction	COCO image metadata, annotations	scripts/extract_nsd_stimulus.py	Stimulus metadata CSV
QC summary	Per-subject quality control	scripts/nsd_qc_summary.py	QC summary + exclusion list

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Dataset Characteristics

• Cohort: 8 healthy adults (subj01-subj08)
• Scanner: 7T Siemens MAGNETOM
• Resolution: 1.8mm isotropic voxels
• Sessions: ~30-40 scanning sessions per subject
• Total fMRI: ~30 hours per subject
• Stimuli: ~73,000 natural scene images from COCO dataset
• Access: OSF (Open Science Framework) and Amazon S3
• Reference: Allen et al. (2021), Nature Neuroscience

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Supported Modalities

Modality	Description	Details
T1w	High-resolution structural MRI	7T anatomical scans
task-fMRI	Visual task fMRI	Natural scene viewing with fixation task
dMRI	Diffusion-weighted imaging	White matter tractography
Retinotopy	Retinotopic mapping	Visual area identification
Eye-tracking	Gaze position data	During image viewing

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NSD Task Paradigms

Task	Description	Duration
NSD	Natural scene viewing (COCO images)	~30-40 sessions × ~15 min each
FIXATION	Fixation task during image presentation	Continuous

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COCO Stimulus Metadata

The NSD uses images from the COCO (Common Objects in Context) dataset:

• ~73,000 unique natural scene images
• Each image has: 5 captions, 80 object categories, segmentation masks
• Images are presented for 3 seconds each
• Subjects perform a fixation task (detect image repeat)

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BIDS Preparation

Script: scripts/validate_nsd.py

Validates NSD BIDS structure and generates a compliance report.

python skills/nsd-skill/scripts/validate_nsd.py \
  --input /path/to/NSD/bids \
  --output /path/to/nsd_output/qc/bids_validation.csv

Features:

• BIDS directory structure validation
• Subject completeness check (8 subjects)
• Session count validation (~30-40 sessions per subject)
• Stimulus file presence verification
• Missing data identification

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Core Workflow (Never Bypassed)

1. Identify user target: full NSD processing, imaging subset, stimulus 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_nsd.py.
5. Delegate to smri-skill for structural MRI processing.
6. Delegate to fmri-skill for task-fMRI processing (natural scene viewing).
7. If stimulus extraction is requested, run scripts/extract_nsd_stimulus.py.
8. If QC summary is requested, run scripts/nsd_qc_summary.py.
9. Save outputs into nsd_output/.

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Modality Processing Delegation

Modality	Delegated skill	Typical tasks	Main outputs
sMRI (T1w)	smri-skill	brain extraction, tissue segmentation, cortical reconstruction	smri_output/ derivatives
task-fMRI	fmri-skill	preprocessing, denoising, voxel-wise encoding	fmri_output/ task results
dMRI	fmri-skill	diffusion preprocessing, tensor metrics	dwi_output/ metrics

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Standard Output Layout

nsd_output/
├── bids/                   # BIDS-staged data (or validation report)
├── smri/                   # Structural MRI derivatives
├── fmri/                   # Functional MRI derivatives (natural scene viewing)
├── stimulus/               # COCO stimulus metadata
├── qc/                     # QC summaries and exclusion lists
└── logs/                   # Processing logs

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Benchmark Adapter Guidance

For benchmark-style prompts, do not force the full orchestration when the task only asks for local NSD data validation.

• If the task starts from NSD data already present on disk and only asks for BIDS validation:
  • Skip the download stage
  • Default to the narrow path local NSD discovery -> BIDS validation -> report
• In benchmark mode, do not require explicit confirmation before presenting the validation solution.

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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.

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Important Notes and Limitations

• NSD is a high-resolution 7T dataset; processing requires significant compute resources.
• 8 subjects with dense repeated measures (~30 hours of fMRI each).
• Visual neuroscience focus: standard task GLM may not apply; consider voxel-wise encoding models.
• COCO stimulus metadata is essential for stimulus-response analyses.
• Cortical surface-based representations using FreeSurfer outputs.
• nsd-skill is orchestration-only; detailed preprocessing logic remains in modality skills.

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When to Call This Skill

• User asks for end-to-end NSD workflow.
• User asks to process NSD task-fMRI data.
• User needs BIDS validation for NSD data.
• User asks to extract NSD stimulus metadata (COCO images, captions, categories).
• User asks for visual cortex analysis or voxel-wise encoding.

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Complementary / Related Skills

• smri-skill → structural MRI preprocessing
• fmri-skill → functional MRI preprocessing and analysis
• nibabel-skill → NIfTI I/O and surface data
• bids-organizer → BIDS validation and organization
• brain-visualization → visualization of derivatives
• dependency-planner → dependency resolution
• conda-env-manager → environment management
• claw-shell → command execution

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Reference

• NSD: https://naturalscenesdataset.org/
• Allen et al. (2021): A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence. Nature Neuroscience.
• COCO: https://cocodataset.org/

Created At: 2026-05-06 13:31 HKT Last Updated At: 2026-05-06 13:31 HKT Author: chengwang96