bridge-neurodivergence-detection

Quantify neural divergence from typical developmental trajectories using graph-regularized modeling and deep generative networks.

2|Updated Feb 12, 2026

Search Tags:#diagnostics #neuroimaging #connectome #graph-modeling #neurodivergence #brain-age #deep-generative-models

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill bridge-neurodivergence-detection

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: bridge-neurodivergence-detection
Source: https://github.com/hiyenwong/ai_collection/tree/main/collection/skills/bridge-neurodivergence-detection
Command: npx skills add https://github.com/hiyenwong/ai_collection --skill bridge-neurodivergence-detection

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

BRIDGE Neurodivergence Detection provides a quantitative framework to assess neural divergence from typical developmental trajectories using graph-regularized modeling and deep generative networks.

Core Features & Use Cases

Graph-regularized modeling to establish typical developmental trajectories
Deep generative models to learn connectivity changes
Neurodivergence scoring and brain-age deviation assessment
Use Case: evaluate brain maturation and connectome deviations in neurodevelopmental conditions

Quick Start

Input a connectivity dataset and obtain a neurodivergence score along with the estimated brain-age trajectory.

Frequently Asked Questions about bridge-neurodivergence-detection

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

FAQPage Schema

How do I quantify neurodivergence from typical developmental trajectories using MRI data?▼

Connectome analysis through graph-regularized modeling establishes typical developmental trajectories, while deep generative networks learn connectivity changes to detect deviations in neurodevelopmental conditions.

What is graph-based brain-age estimation for neurodivergence?▼

Graph-based brain-age estimation uses graph-regularized modeling on brain connectivity inputs to assess neural divergence, producing a neurodivergence score that indicates deviation from typical developmental trajectories.

How do I calculate a neurodivergence score from a connectome dataset?▼

Input your MRI-derived connectivity dataset into the deep generative model to obtain a neurodivergence score along with the estimated brain-age trajectory.

Can I use deep generative models for neurodevelopmental diagnostics on MRI connectivity data?▼

Yes, deep generative networks combined with graph-regularization can be applied to MRI-derived connectivity datasets for neurodevelopmental diagnostics to evaluate connectome deviations and brain maturation.

Do I need graph-regularization to assess brain-age deviation?▼

Yes, graph-regularization is required alongside deep generative modeling on brain connectivity inputs to establish typical developmental trajectories and compute the neurodivergence score.

What are the limitations of using graph modeling for brain-age deviation assessment?▼

Brain-age deviation assessment requires graph-regularization and deep generative modeling specifically on MRI-derived connectivity datasets, limiting its application to connectome analysis for neurodevelopmental conditions.