bnt
Use this model doc whenever the user wants to run BrainNetworkTransformer for fMRI phenotype prediction, including data loading, training, and evaluation. BNT uses dense FC matrices (no PyG dependency) with DEC pooling + interpretable transformer encoder.
npx skills add BioTender-max/awesome-bio-agent-skills --skill bnt --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.
# BrainNetworkTransformer (BNT) Model Doc ## Overview BrainNetworkTransformer 是一种基于 Transformer 的脑网络分析模型，使用 DEC (Deep Embedded Clustering) 池化实现可解释的节点聚类。 - Paper: Kan et al., 2022, BrainNetworkTransformer - Official code: https://github.com/Wayfear/BrainNetworkTransformer - NeuroClaw reimplementation: `models/bnt/` (无 hydra/omegaconf 依赖，纯 argparse) - Primary input: ROI-level FC 矩阵（dense [N, N]，非 PyG 图格式） - Primary output: phenotype prediction (classification/regression) + attention weights + DEC assignments **Research use only.** --- ## NeuroClaw 实现要点 NeuroClaw 版本对原始 BNT 做了以下关键改动： 1. **去除 hydra/omegaconf 依赖**：原始代码使用 hydra 配置系统，NeuroClaw 改为纯 argparse，降低依赖复杂度。 2. **纯 PyTorch Dataset**：不依赖 PyG，使用标准 `torch.utils.data.Dataset` + `DataLoader`，输入为 dense FC matrix [B, N, N]。 3. **Fisher-z 反变换**：与 BrainGNN 一致，存储的 fc_matrix 是 Fisher-z，加载时用 `torch.tanh()` 还原为 Pearson r，对角线置零。 4. **nhead 自动适配**：当 forward_dim (N + pos_embed_dim) 不能被 nhead 整除时，自动降低 nhead 到最大兼容值。 5. **支持 classification + regression 双任务**：regression 时 nclass=1，输出 raw scalar，用 MSELoss。 6. **可选 T1 GM volume 融合**：`--include-t1` 将 z-scored GM volume 作为额外 1 维拼接到 FC 行末尾（node feature dim 从 N 变为 N+1）。 7. **DEC loss 集成**：训练时自动累加各 pooling 层的
- Overview
- NeuroClaw 实现要点
- Quick Start (NeuroClaw 内部)
- 前置条件
- 训练（分类）
- 训练（回归）
- Dry Run（验证数据加载）
- 核心文件
- 数据格式约定
- 输入文件 (data/braingnninput/<atlas>/sub-<id>.pt)
- BNT 内部表示
- 模型架构
- 关键训练参数
- 调试经验与注意事项
python models/bnt/scripts/train.py \ python models/bnt/scripts/train.py --atlas aal_116 --dry-run
What does the bnt skill do?
Use this model doc whenever the user wants to run BrainNetworkTransformer for fMRI phenotype prediction, including data loading, training, and evaluation. BNT uses dense FC matrices (no PyG dependency) with DEC pooling + interpretable transformer encoder.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill bnt --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.