Magnet Brain Structure Function Gnn

---
name: magnet-brain-structure-function-gnn
description: Multi-Scale Adaptive Graph Network (MAGNet) for learning structural-functional brain representations. Models structure-function coupling for cognitive insight.
version: 1.0.0
author: Research Synthesis
license: MIT
metadata:
  hermes:
    tags: [brain-network, structure-function, graph-attention, cognitive-insight, gnn, neuroimaging]
    source_paper: "Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight (arXiv:2603.29967v1)"
---

# MAGNet: Multi-Scale Adaptive Graph for Brain Structure-Function

## Overview
MAGNet (Multi-scale Adaptive Graph Network) learns joint structural-functional brain representations through adaptive graph attention. By modeling how structural connectivity constrains and enables functional dynamics across multiple spatial scales, the framework provides cognitive insights into brain organization.

## Core Concepts

### Multi-Scale Architecture
- **Local Scale**: Regional connectivity patterns within brain modules
- **Mesoscale**: Inter-module connectivity and hub regions
- **Global Scale**: Whole-brain integration and small-world topology

### Adaptive Graph Attention
- Attention weights adapt based on both structural and functional features
- Learns which structural connections are most relevant for functional prediction
- Dynamic re-weighting across scales

### Structure-Function Coupling
- Structural connectome (from DTI) as graph scaffold
- Functional connectome (from fMRI) as node dynamics
- Coupling strength varies by region and cognitive state

## Implementation Pattern
```python
class MAGNet(nn.Module):
    def __init__(self, n_regions, n_scales=3, hidden_dim=128):
        super().__init__()
        self.scales = nn.ModuleList([
            ScaleAttention(n_regions, hidden_dim, scale_factor=2**i)
            for i in range(n_scales)
        ])
        self.fusion = nn.Linear(hidden_dim * n_scales, hidden_dim)
        self.decoder = nn.Linear(hidden_dim, n_regions)
    
    def forward(self, structural_conn, functional_signals):
        scale_features = []
        for scale_module in self.scales:
            feat = scale_module(structural_conn, functional_signals)
            scale_features.append(feat)
        fused = torch.cat(scale_features, dim=-1)
        fused = self.fusion(fused).relu()
        return self.decoder(fused)
```

## Applications
- Brain structure-function coupling analysis
- Cognitive trait prediction from neuroimaging
- Brain network biomarker discovery
- Personalized neuroscience

## Activation Keywords
- structure-function brain coupling, multi-scale brain network, adaptive graph attention brain, MAGNet brain model, structural connectome analysis, 脑结构功能耦合, 多尺度脑网络

## References
- Learning Structural-Functional Brain Representations through Multi-Scale Adaptive Graph Attention for Cognitive Insight
- Authors: Badhan Mazumder, Sir-Lord Wiafe, Aline Kotoski, Vince D. Calhoun, Dong Hye Ye
- Published: 2026-03-31
- arXiv: https://arxiv.org/abs/2603.29967v1