Hierarchical Model Doc

Overview

Hierarchical clustering is a classical non-deep-learning method for data-driven brain parcellation.

• Model family: non-deep-learning unsupervised clustering method
• Typical objectives:
  • partition voxels, vertices, or ROI features into data-driven brain parcels
  • build subject-level or group-level parcellations from functional or structural similarity
  • export parcel labels and merge summaries across scales
• Primary input: preprocessed neuroimaging features, optional mask, optional similarity or connectivity representation
• Primary output: parcel label map, cluster summaries, optional dendrogram outputs

In NeuroClaw, this document is model-level guidance for Hierarchical-clustering-based brain parcellation workflows rather than supervised prediction.

Upstream preparation should usually be delegated to:

• fmri-skill for rs-fMRI or task-fMRI feature preparation when parcellation is function-driven
• smri-skill for structural feature preparation when parcellation is anatomy-driven
• nilearn-tool for concrete masking, feature matrix preparation, and hierarchical parcel export

Research use only.

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Quick Start

1) Prepare parcellation inputs

Expected inputs:

• preprocessed feature matrix or image list
• optional brain mask
• optional subject list or cohort manifest
• target parcel number or clustering granularity

If these are not ready, delegate preprocessing to fmri-skill or smri-skill first.

2) Hierarchical route

Representative operations:

• prepare aligned feature representation
• compute similarity or distance structure across spatial units
• fit agglomerative / Ward-style hierarchical clustering
• export parcel labels and optional dendrogram or merge summaries

Example execution route:

# delegated through claw-shell after features are prepared
python skills/nilearn-tool/scripts/hierarchical_parcellation_reference.py \
  --input-list path/to/image_list.txt \
  --mask path/to/group_mask.nii.gz \
  --n-clusters 200 \
  --output-dir run_models_output/hierarchical

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Input / Output Contract

Required inputs

• feature matrix or aligned neuroimaging image list
• requested clustering target such as parcel count

Optional inputs

• mask image
• connectivity or similarity matrix
• spatial adjacency constraints
• subject grouping or cohort definition
• linkage parameters

Produced outputs

• parcel label image or table
• cluster size summary
• optional hierarchical merge information or dendrogram summary

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Recommended Delegation

• imaging preprocessing and feature preparation -> fmri-skill and/or smri-skill
• concrete implementation of Hierarchical clustering -> nilearn-tool
• shell execution and logging -> claw-shell

No execution before explicit plan confirmation.

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When to Use Hierarchical Clustering

• The user wants data-driven brain region partitioning rather than using a predefined atlas.
• The goal is to derive parcel labels for downstream connectivity, decoding, or visualization.
• A classical unsupervised clustering baseline is preferred over deep learning.
• The user wants multi-scale organization or merge structure.

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Limitations and Notes

• Clustering quality depends strongly on preprocessing, feature definition, and spatial normalization.
• Hierarchical clustering can be computationally expensive for large voxel spaces.
• Data-driven parcellations may vary across cohorts and may not align directly with standard atlases.

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Reference

• Bellec P et al. Multi-level bootstrap analysis of stable clusters in resting-state fMRI.
• Nilearn regions and parcellations documentation: https://nilearn.github.io/stable/connectivity/region_extraction.html

Created At: 2026-04-14 00:37 HKT Last Updated At: 2026-04-14 00:45 HKT Author: chengwang96