用时序图神经网络解析蠕虫神经发育过程，揭示连接稳定性与关键神经元角色演变。
DevoTG: Temporal Graph Neural Networks for Modeling C. elegans Developmental Connectomics
- 构建连续与离散时间动态图，捕捉细胞分裂和突触连接的时序演化。
- 在细胞谱系预测中达0.839测试AUC，比静态图模型高26点，证明时序记忆关键作用。
- 识别出三种连接稳定性类型，适合神经发育研究者与开发新模型的工程师。
理解神经系统从出生到成年如何自我连接是发育神经科学的核心挑战。我们提出DevoTG，一种时序图神经网络框架，应用于秀丽隐杆线虫神经发育的两种互补表示：基于细胞谱系数据的连续时间动态图（CTDG）和跨越八个重构电子显微镜数据集的离散时间动态突触连接组图（DTDG）。在谱系预测任务中，该TGN取得0.839±0.007的平均测试AUC（5次随机种子；验证AUC 0.937±0.001），相比同架构静态GNN提升26 AUC点（0.577±0.080），证明时序记忆为决定性因素。应用于连接组DTDG，DevoTG识别出225个神经元、858至2,496个连接在发育过程中的三类连接稳定性（稳定、发育中、可变），提供对Witvliet等人个体变异分类的时序图理论补充。对枢纽命令间神经元AVA、AVB和AVE的分析显示其持续中心性，并揭示其整合角色随幼虫阶段逐步强化。配套的交互式可视化（3D动画网络、中心性热图、时空谱系图）助力生物假说生成。DevoTG开源，设计可扩展至其他发育神经系统，代码已公开于https://github.com/DevoLearn/DevoGraph/tree/main/DevoTG。
原文摘要 · Abstract (English)
Understanding how a nervous system wires itself from birth to adulthood is a fundamental challenge in developmental neuroscience. We present DevoTG, a temporal graph framework that applies Temporal Graph Neural Networks (TGNs) to two complementary representations of C. elegans neural development: a Continuous-Time Dynamic Graph (CTDG) of cell division events derived from cell lineage data, and a Discrete-Time Dynamic Graph (DTDG) of the developing synaptic connectome spanning eight reconstructed electron-microscopy datasets. On the lineage prediction task, our TGN achieves a mean test AUC of 0.839 +/- 0.007 (5 seeds; validation AUC 0.937 +/- 0.001), outperforming a static GNN with the identical architecture by 26 AUC points (0.577 +/- 0.080), demonstrating that temporal memory is the decisive factor. Applied to the connectome DTDG, DevoTG identifies three connection stability classes (stable, developmental, and variable) across 225 neurons and 858 to 2,496 connections over development (L1 birth to adult), providing a temporal-graph-theoretic complement to the individual-variability classification of Witvliet et al. Analysis of hub command interneurons AVA, AVB, and AVE reveals their persistent centrality and how their integration roles are progressively reinforced across larval stages. Accompanying interactive visualizations (3D animated networks, centrality heatmaps, and a spatiotemporal lineage graph) make developmental dynamics accessible for biological hypothesis generation. DevoTG is open-source and designed for extension to other developing nervous systems. Code is publicly available at https://github.com/DevoLearn/DevoGraph/tree/main/DevoTG.
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