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gyralnet-subnetwork-partitioning

Partitions cortical subnetworks using differentiable spectral modularity on GyralNet graphs.

2|Updated Feb 12, 2026
Search Tags:#neuroscience#partitioning#brain-connectivity#gyralnet#subnetwork#spectral-modularity
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill gyralnet-subnetwork-partitioning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gyralnet-subnetwork-partitioning
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/gyralnet-subnetwork-partitioning
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill gyralnet-subnetwork-partitioning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a differentiable framework to partition cortical subnetworks by applying spectral modularity optimization to a GyralNet representation of cortical folding, enabling robust cross-subject analysis.

Core Features & Use Cases

  • Differentiable spectral modularity optimization on GyralNet graphs (3HGs as nodes, gyral crests as edges)
  • Cross-subject consistency via community-level correspondence in subnetworks
  • Applicable to MRI-derived brain graphs for connectivity analysis and hub identification

Quick Start

Run the GyralNet partitioning workflow on your structural MRI data to obtain cross-subject subnetworks.

Frequently Asked Questions about gyralnet-subnetwork-partitioning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is differentiable spectral modularity optimization in brain connectivity analysis?▼

Differentiable spectral modularity optimization enables robust subnetwork extraction from GyralNet representations by optimizing community structures. This method partitions cortical folding graphs using 3HGs as nodes and gyral crests as edges.

Does GyralNet partitioning work for cross-subject brain connectivity studies?▼

Yes, GyralNet partitioning supports cross-subject brain connectivity studies by establishing community-level correspondence in subnetworks. This ensures the extracted cortical subnetworks remain consistent across different subjects during structural MRI analysis.

What is the best way to extract consistent cortical hubs from structural MRI data?▼

The best approach is running a differentiable GyralNet partitioning workflow on your structural MRI data to obtain cross-subject subnetworks. This identifies connectivity hubs and extracts robust subnetworks via 3HG-based graph construction.

How do I construct GyralNet graphs for brain subnetwork partitioning?▼

GyralNet graphs are constructed using 3HGs as nodes and gyral crests as edges to represent cortical folding. Applying differentiable spectral modularity to these graphs enables robust subnetwork extraction and cross-subject validation.

Do I need structural MRI data to perform differentiable subnetwork partitioning?▼

Yes, you need structural MRI data to derive the cortical graphs required for this partitioning workflow. The method applies differentiable spectral modularity to these MRI-derived GyralNet graphs to achieve cross-subject consistency.

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