Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install hiyenwong-ai-collection-collection-skills-graph-gaussian-embedding-alzheimer-meggit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-graph-gaussian-embedding-alzheimer-meg/SKILL.md---
name: graph-gaussian-embedding-alzheimer-meg
arxiv_id: 2005.05784v2
utility: 0.88
tags: '[Alzheimer, MEG, graph embedding, disease progression, brain networks, GNN, MCI prediction]'
created: 2026-03-31
description: "Graph Gaussian Embedding Alzheimer Prediction"
---
# Graph Gaussian Embedding Alzheimer Prediction
## Activation Keywords
- 阿尔茨海默病预测
- MEG 脑网络
- 图嵌入
- MCI 转化预测
- disease progression
- graph Gaussian embedding
## Problem Statement
阿尔茨海默病(AD)早期诊断和进展预测面临挑战:
- 脑网络变化细微且异质
- 高维网络特征难以直接使用
- MCI 患者转化预测准确率低
## Method Overview
Xu et al. (2020) 提出 MG2G(Multiple Graph Gaussian embedding):
1. 高维脑网络映射到低维潜在空间
2. 潜在分布嵌入捕捉连接模式异质性
3. 支持 MCI→AD 转化预测
4. 识别显著改变的脑区
## Tools Used
- `Component` - Analysis component
- `Graph Neural Network` - Analysis component
- `Gaussian Embedding` - Analysis component
- `MEG Connectivity` - Analysis component
- `Classifier` - Analysis component
## Architecture
```
MEG Brain Networks
↓
High-dimensional Graphs
↓
┌──────────────────────┐
│ MG2G Model │
│ ┌────────────────┐ │
│ │ Graph Encoder │ │
│ └────────────────┘ │
│ ┌────────────────┐ │
│ │ Gaussian Embed │ │
│ └────────────────┘ │
└──────────────────────┘
↓
Low-dimensional Latent Space
↓
┌──────────────────────┐
│ Downstream Tasks │
│ - MCI→AD Prediction │
│ - Region Alteration │
│ - Early Diagnosis │
└──────────────────────┘
```
## Step-by-Step Instructions
### MG2G 实现流程
1. **MEG 网络构建**
```python
import numpy as np
from mne.connectivity import spectral_connectivity
def build_meg_network(meg_data, freq_band='alpha'):
"""从 MEG 数据构建功能连接网络"""
# 频带功率连接
conn = spectral_connectivity(
meg_data, method='pli',
fmin=8, fmax=12 # alpha band
)
return conn.get_data('dense')
```
2. **图高斯嵌入**
```python
import torch
import torch.nn as nn
class GraphGaussianEmbedding(nn.Module):
def __init__(self, n_regions, latent_dim=32):
super().__init__()
self.encoder = nn.Sequential(
nn.Linear(n_regions, 128),
nn.ReLU(),
nn.Linear(128, latent_dim * 2) # mean + log_var
)
def forward(self, adj_matrix):
# 编码为高斯分布参数
h = self.encoder(adj_matrix)
mean, log_var = h.chunk(2, dim=-1)
return mean, log_var
def reparameterize(self, mean, log_var):
std = torch.exp(0.5 * log_var)
eps = torch.randn_like(std)
return mean + eps * std
```
3. **多图集成**
```python
class MG2G(nn.Module):
def __init__(self, n_regions, latent_dim=32, n_views=5):
super().__init__()
self.encoders = nn.ModuleList([
GraphGaussianEmbedding(n_regions, latent_dim)
for _ in range(n_views)
])
def forward(self, graph_views):
"""多个图视图集成嵌入"""
embeddings = []
for i, encoder in enumerate(self.encoders):
mean, log_var = encoder(graph_views[i])
z = encoder.reparameterize(mean, log_var)
embeddings.append(z)
return torch.cat(embeddings, dim=-1)
```
4. **MCI 转化预测**
```python
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
def predict_mci_conversion(embeddings, labels):
"""预测 MCI 患者是否转化为 AD"""
classifier = SVC(kernel='rbf', probability=True)
classifier.fit(embeddings, labels)
return classifier
```
## Example Usage
```python
# 完整流程示例
import numpy as np
# 1. 加载 MEG 数据
meg_data = load_meg_subjects() # shape: (n_subjects, n_channels, n_times)
# 2. 构建脑网络
networks = [build_meg_network(data) for data in meg_data]
# 3. 创建多视图(不同频带)
views = ['delta', 'theta', 'alpha', 'beta', 'gamma']
multi_view_networks = create_multi_view(meg_data, views)
# 4. 训练 MG2G
model = MG2G(n_regions=68, latent_dim=32, n_views=5)
embeddings = model(multi_view_networks)
# 5. 预测
classifier = predict_mci_conversion(embeddings, mci_labels)
```
## Key Results
| Task | Performance |
|------|-------------|
| MCI→AD Prediction | ~85% accuracy |
| Early AD Detection | ~80% sensitivity |
| Region Identification | Significant in temporal lobe |
## Description
Graph Gaussian Embedding Alzheimer Prediction
**Key Concepts:**
- 阿尔茨海默病(AD)早期诊断和进展预测面临挑战:
- 脑网络变化细微且异质
- 高维网络特征难以直接使用
- MCI 患者转化预测准确率低
## Instructions for Agents
Follow these steps when applying this skill:
### Step 1: MEG 网络构建
### Step 2: 图高斯嵌入
### Step 3: 多图集成
### Step 4: MCI 转化预测
### Step 5: Understand the Request
## Examples
### Example 1: Basic Application
**User:** I need to apply Graph Gaussian Embedding Alzheimer Prediction to my analysis.
**Agent:** I'll help you apply graph-gaussian-embedding-alzheimer-meg. First, let me understand your specific use case...
**Context:** 阿尔茨海默病(AD)早期诊断和进展预测面临挑战:
- 脑网络变化细微且异质
- 高维网络特征难以直接使用
- MCI 患者转化预测准确率低
### Example 2: Advanced Scenario
**User:** Complex analysis scenario
**Agent:** Based on the methodology, I'll guide you through the advanced application...
### Example 2: Advanced Application
**User:** What are the key considerations for graph-gaussian-embedding-alzheimer-meg?
**Agent:** Let me search for the latest research and best practices...
## References
- Xu, M. et al. (2020). A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks. arXiv:2005.05784.
## Related Skills
- multimodal-brain-connectivity-gnn
- explainable-gnn-eeg-neurological
- dgcl-brain-network-construction