multimodal-brain-connectivity-gnn

Integrate fMRI, DTI, and sMRI data into an interpretable graph neural network for cognitive prediction.

2|Updated Feb 12, 2026

Search Tags:#multimodal #DTI #explainability #gnn #brain-connectivity #fMRI #sMRI

Ready to run

npx skills add https://github.com/hiyenwong/ai_collection --skill multimodal-brain-connectivity-gnn

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Integrates fMRI, DTI, and sMRI data into a single, interpretable graph neural network to predict cognitive outcomes and reveal key multimodal biomarkers.

Core Features & Use Cases

Multimodal integration: fMRI functional connectivity (FC), DTI structural connectivity (SC), and sMRI morphometric features are jointly modeled.
Atlas-based parcellation: uses Glasser atlas to ensure cross-modality alignment across 360 cortical regions.
Learnable connectivity weighting: edge masks learn to weight connections based on modality differences for interpretable results.
Graph neural backbone: uses GAT/GCN layers to fuse region-level features and produce graph-level predictions.
Explainability: provides modality-wise importance and important connections for biomarker discovery and clinical insights.
Use cases: cognitive score prediction, biomarker discovery, disease profiling, and clinical prognosis in neuroimaging research.

Quick Start

Train the model on your fMRI, DTI, and sMRI data to predict cognitive outcomes and identify key multimodal biomarkers.

Frequently Asked Questions about multimodal-brain-connectivity-gnn

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

FAQPage Schema

How do I integrate fMRI, DTI, and sMRI data for cognitive prediction?▼

Multimodal brain data integration uses modality-specific encoders and graph attention layers to fuse fMRI, DTI, and sMRI into graph-level cognitive predictions. The model aligns these modalities using Glasser atlas parcellation across 360 cortical regions.

How does graph neural network explainability work for brain connectivity?▼

Explainability for brain connectivity uses learnable edge masks to weight connections and quantify modality importance. This reveals key structural and functional biomarkers by highlighting important graph connections and modality contributions.

Can I use multimodal GNN for disease profiling and biomarker discovery?▼

Multimodal GNNs support disease profiling and biomarker discovery by mapping brain connectivity to cognitive outcomes. The explainability module identifies important connections and modality weights, providing clinical insights for neurological research.

Do I need preprocessed fMRI and DTI data aligned to the Glasser atlas?▼

Yes, fMRI and DTI data need alignment using the Glasser atlas for 360 cortical regions. This atlas-based parcellation ensures structural and functional connectivity matrices match across modalities for graph neural network processing.

What is the best way to weight structural and functional connectivity in a multimodal graph?▼

The best way to weight multimodal connectivity is using learnable edge masks that adjust connection importance based on modality differences. This produces interpretable graph attention network results for biomarker discovery.

Why does the model use global pooling for brain graph predictions?▼

Global pooling aggregates region-level features from GAT and GCN layers into a single graph-level representation. This allows the multimodal graph neural network to output cognitive score estimates and disease profiles.