time-varying-brain-connectivity

Estimate time-varying directional brain connectivity from neural data using SWpC.

2|Updated Feb 12, 2026

Search Tags:#neuroscience #neuroimaging #time-varying #directed-connectivity #swpc #dynamic-connectivity #lti-model

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill time-varying-brain-connectivity

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This methodology estimates time-varying directed interactions in brain networks from neural data, enabling detection of directional information flow over time.

Core Features & Use Cases

Sliding-window prediction correlation (SWpC) to infer directional connectivity within moving windows.
In-window embedded linear time-invariant (LTI) modeling to quantify directionality and transfer duration.
Use cases span resting-state and task-based neuroimaging (fMRI, EEG, LFP) for clinical stratification, cognitive neuroscience, and multimodal validation.

Quick Start

Apply SWpC-based directed connectivity analysis to your neuroimaging dataset to estimate time-varying information flow.

Frequently Asked Questions about time-varying-brain-connectivity

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

FAQPage Schema

How do I estimate time-varying directed brain connectivity from fMRI or EEG data?▼

Time-varying directed brain connectivity is estimated using a sliding-window prediction correlation (SWpC) workflow with an embedded linear time-invariant model. This approach derives directional strength and window-wise duration from multi-modal neuroimaging or neural recordings.

What is sliding-window prediction correlation (SWpC) and how does it track dynamic connectivity?▼

Sliding-window prediction correlation (SWpC) is a technique that infers directional connectivity within moving windows over neural data. It embeds a linear time-invariant model to quantify information flow directionality and transfer duration across each window.

Can I use this dynamic directed connectivity analysis for both resting-state and task-driven neuroimaging?▼

Dynamic directed connectivity analysis applies to both resting-state and task-based neuroimaging. It supports multi-modal recordings including fMRI, EEG, and LFP for clinical stratification, cognitive neuroscience, and multimodal validation.

What is the best way to infer directional information flow in brain networks over time?▼

Inferring directional information flow over time is best achieved by applying SWpC-based directed connectivity analysis. This workflow estimates time-varying directional interactions in brain networks and provides guidance for interpretation and validation.

Do I need specific dependencies to run the SWpC-based directed connectivity workflow?▼

The SWpC-based directed connectivity workflow operates without specific external dependencies. You apply the analysis directly to your neuroimaging dataset to estimate time-varying information flow and derive directional strength.

Why does time-varying directed connectivity require an embedded linear time-invariant model?▼

Time-varying directed connectivity requires an embedded linear time-invariant model to quantify directionality and transfer duration accurately. The LTI model operates within each sliding window to derive directional strength for dynamic information flow.