time-varying-brain-connectivity
CommunityInfer dynamic directed brain connectivity from neural data.
Education & Research#neuroscience#neuroimaging#time-varying#directed-connectivity#swpc#dynamic-connectivity#lti-model
Authorhiyenwong
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
None requiredComponents
Standard package💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: time-varying-brain-connectivity Download link: https://github.com/hiyenwong/ai_collection/archive/main.zip#time-varying-brain-connectivity Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
Agent Skills Search Helper
Install a tiny helper to your Agent, search and equip skill from 471,000+ vetted skills library on demand.