brain-stimulation-dynamics-state

Classify baseline oscillation regimes to predict focal brain stimulation effects.

2|Updated Feb 12, 2026

Search Tags:#coherence #connectome #oscillations #brain-stimulation #network-dynamics #neural-mass-model #stimulation-planning

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill brain-stimulation-dynamics-state

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: brain-stimulation-dynamics-state
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/brain-stimulation-dynamics-state
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill brain-stimulation-dynamics-state

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Brain stimulation results vary with the network's dynamical state, complicating outcome prediction and treatment planning.

Core Features & Use Cases

Classify dynamical regimes (weak vs strong oscillations)
Predict stimulation effects based on regime
Analyze network coherence and downstream effects
Use in neuromodulation planning and research interpretation

Quick Start

Classify baseline oscillation strength and predict stimulation effects based on the regime to guide neuromodulation planning.

Frequently Asked Questions about brain-stimulation-dynamics-state

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

FAQPage Schema

How does baseline network state affect brain stimulation outcomes?▼

Brain stimulation outcomes depend heavily on the baseline dynamical state of neural networks, meaning the same stimulation protocol can yield different results based on baseline oscillation strength. Analyzing this baseline state helps predict whether stimulation will produce weak or strong effects.

Can I predict TMS and tDCS effects using neural mass models and connectome data?▼

Yes, you can predict TMS, tDCS, and DBS effects by combining neural mass models with connectome-based simulations and oscillation analytics. This approach models network coherence and downstream effects to guide personalized neuromodulation planning.

How do I classify dynamical regimes to guide neuromodulation planning?▼

To guide neuromodulation planning, classify baseline oscillation strength into weak or strong dynamical regimes using dynamical-state estimation. This classification predicts stimulation effects based on the regime and helps interpret research outcomes.

What data is needed to analyze stimulation effects based on network coherence?▼

Analyzing stimulation effects based on network coherence requires accurate dynamical-state estimation, compatible neural mass models, and access to connectome-based simulations. These inputs allow the assessment of baseline oscillations and downstream network effects.

Why do focal brain stimulation results vary across identical protocols?▼

Focal brain stimulation results vary because outcomes depend on the baseline dynamical state of neural networks. Differences in baseline network coherence and oscillation regimes cause the same stimulation parameters to trigger distinct downstream responses.