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gp-cake-brain-connectivity

Infers directional brain connectivity from MEG/EEG and fMRI time-series using GP CaKe causal kernels.

2|Updated Feb 12, 2026
Search Tags:#neuroscience#time-series#gaussian-process#brain-connectivity#gp-cake#causal-kernel#nonparametric
One-click install
npx skills add https://github.com/hiyenwong/ai_collection --skill gp-cake-brain-connectivity
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: gp-cake-brain-connectivity
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/gp-cake-brain-connectivity
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill gp-cake-brain-connectivity

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Understands and infers directional, causal interactions between neural regions from time-series data using GP CaKe's causal kernels, enabling nonparametric learning of effective connectivity.

Core Features & Use Cases

  • Nonparametric learning of directional brain influence via causal kernels
  • Applicability to MEG/EEG and fMRI time-series for dynamic connectivity modeling
  • Biologically meaningful hyperparameters (time scale, delay, strength) that aid interpretation

Quick Start

Infer effective connectivity between source and target brain regions from your time-series data using GP CaKe.

Frequently Asked Questions about gp-cake-brain-connectivity

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

FAQPage Schema
How do I infer directional brain connectivity from MEG or EEG time-series data?▼

You can infer effective brain connectivity from MEG, EEG, or fMRI time-series using the GP CaKe framework. It constructs causal kernels via nonparametric Gaussian process regression to model directional influences between neural regions.

What is the GP CaKe framework for dynamic causal networks?▼

GP CaKe is a nonparametric framework that constructs causal kernels to infer effective brain connectivity. It models directional influences between neural regions from time-series data to build dynamic causal networks.

Can I use nonparametric Gaussian process regression for fMRI time-series analysis?▼

Yes, nonparametric Gaussian process regression can be applied to fMRI time-series to model dynamic connectivity. The GP CaKe framework integrates causal kernel construction with fMRI data to infer directional influences.

What hyperparameters are used to interpret causal kernels in brain connectivity modeling?▼

Causal kernels in brain connectivity modeling use biologically meaningful hyperparameters including time scale, delay, and strength. These parameters aid the interpretation of directional influences between neural regions.

Does GP CaKe require specific dependencies for causal kernel construction?▼

GP CaKe requires nonparametric Gaussian process regression and causal kernel construction to infer brain connectivity. It integrates directly with MEG, EEG, or fMRI time-series data for a convolution-like design without external dependencies.

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