skills.rest
hiyenwong
hiyenwongCOMMUNITY
Explore all skills by this author →
𝕏@khonsu_51GitHub

brain-network-controllability

Compute average and modal controllability metrics for brain network matrices.

2|Updated Feb 12, 2026
Search Tags:#python#optimization#analysis#neuroscience#brain#controllability#network-theory
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill brain-network-controllability
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: brain-network-controllability
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/brain-network-controllability
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill brain-network-controllability

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Addresses the need to quantify and analyze how brain networks can be steered between states using network control theory, providing actionable metrics and insights for neuroscience research.

Core Features & Use Cases

  • Compute average controllability and modal controllability for network nodes to identify key control points in brain structure.
  • Evaluate minimum control energy and trajectories to understand the effort required to shift brain states in modeling experiments.
  • Modular network generation and exploration to study how modular topology affects controllability and control strategies in neural systems.
  • Use Case: A cognitive neuroscience study investigates how network topology influences the ability to transition from a resting state to a task-related state and uses AC, MC, and energy analyses to interpret results.

Quick Start

Compute AC, MC, and minimum energy for a given brain network matrix A using BrainNetworkControllability.

Frequently Asked Questions about brain-network-controllability

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

FAQPage Schema
How do I compute average and modal controllability for a brain network matrix?▼

Minimum control energy quantifies the effort required to transition brain states using linear dynamics equations. It is calculated alongside trajectory analysis to model neural modulation and cognitive control experiments.

Can I generate modular brain networks to study how topology affects controllability?▼

Modular network generation is supported to explore how modular topology influences controllability metrics and control strategies. This allows researchers to simulate and study neural system dynamics across different structural configurations.

What is brain network controllability and when is it needed for neuroscience research?▼

Brain network controllability quantifies how neural networks can be steered between cognitive states using network control theory. It is needed when investigating network topology influences on state transitions, such as resting to task-related states.

Does this Skill require external dependencies or frameworks to run controllability analysis?▼

No external dependencies are required to run controllability analysis. The Skill operates independently to compute average controllability, modal controllability, and minimum control energy from brain network data inputs.

Related Skills

Explore More Education & Research Skills→
xjtulycxjtulyc
33

connectome-analysis

Compute functional connectivity matrices and brain network graph metrics from ROI time series.

Community
Advanced
NeuroAIHubNeuroAIHub
269

cogsci-statistics

Guide mixed-effects model selection and implementation for cognitive science experimental data.

Official
Advanced
kishorkukrejakishorkukreja
56

energy-logistics

Optimize oil and gas logistics with Python for transportation and demand forecasting.

Community
Advanced
hiyenwonghiyenwong
2

seizure-suppression-hub-stimulation

Identify epileptic brain network hubs and compute optimal electrical stimulation parameters.

Community
Advanced