lggnn
Use this model doc whenever the user wants to run LG-GNN (Local-to-Global GNN) for fMRI phenotype prediction. LG-GNN is a PyG-based GNN with SABP (Self-Attention Brain Pooling) and mutual-information regularization. NeuroClaw adapts the original population-graph version to single-subject brain graphs.
npx skills add BioTender-max/awesome-bio-agent-skills --skill lggnn --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LG-GNN Model Doc ## Overview LG-GNN (Local-to-Global GNN) 是一种针对脑疾病诊断的两阶段图神经网络。原始论文使用 Local_GNN 提取每个被试的脑图嵌入，再通过基于人口学信息构建的 Global_GNN 进行人群图分类。NeuroClaw 改造为单被试任务：保留 Local_GNN（含 SABP + 互信息正则化的创新组件），用 MLP head 替代人口图。 - Paper: Zhang et al., 2022, "Local to Global Hierarchical Graph Neural Network for Brain Disorder Diagnosis"，MICCAI - Official code: https://github.com/cnuzh/LG-GNN - NeuroClaw reimplementation: `models/lggnn/`（去除人口图依赖，单被试 PyG 流程） - Primary input: PyG Data graph（与 BrainGNN 共享数据格式） - Primary output: phenotype prediction + ROI 重要性（SABP perm）+ MI loss 辅助监督 **Research use only.** --- ## NeuroClaw 实现要点 1. **单被试改造**：原版需要非影像表型数据构建人口图，NeuroClaw 仅保留 Local_GNN，用 MLP head 输出。 2. **SABP 池化**：Self-Attention Brain Pooling，topk 选择 ROI + tanh(score) 加权，并产生互信息估计 `mi` 作为辅助 loss（论文权重 0.1，loss 取 `loss - 0.1 * mi` 鼓励高互信息）。 3. **PyG 2.7 兼容**：原 `torch_geometric.nn.pool.topk_pool` 已重构，NeuroClaw 用 `pool.select.topk` + 内联 `filter_adj`。 4. **任务统一接口**：classification (`nclass=N`) 与 regression (`nclass=1, task='regression'`) 一套代码。 5. **数据复用**：直接复用 BrainGNN 的 `NeuroClawFCDataset`，无需额外预处理。 --- ## Quick Start (NeuroClaw 内部) ### 前置条件 - conda env: `neuroclaw` (Python 3.11) - 已有 `data/braingnn_input/<atlas
- Overview
- NeuroClaw 实现要点
- Quick Start (NeuroClaw 内部)
- 前置条件
- 训练（分类，单 fold 冒烟测试）
- 训练（回归，HCP age）
- 核心文件
- 模型架构
- 关键训练参数
- 调试经验与注意事项
- NeuroClaw 委托规则
- Reference
python skills/lggnn/scripts/train_reference.py \
What does the lggnn skill do?
Use this model doc whenever the user wants to run LG-GNN (Local-to-Global GNN) for fMRI phenotype prediction. LG-GNN is a PyG-based GNN with SABP (Self-Attention Brain Pooling) and mutual-information regularization. NeuroClaw adapts the original population-graph version to single-subject brain graphs.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill lggnn --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.