DevelopmentODE：用于十年期早期大脑发育动态的结构化神经ODE
DevelopmentODE: Structured Neural ODEs for Early Brain Development Dynamics Across a Decade
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中文总结 AI 辅助
DevelopmentODE提出结构化连续时间框架，在共享发育几何中组织群体与个体变异，通过年龄相关变形建模非平稳动态，在纵向fMRI预测中优于基线。
中文摘要 AI 辅助
理解个体大脑发育在童年时期如何展开，需要从稀疏的纵向观测中建模发育轨迹。长期神经发育预测具有挑战性，因为每个儿童通常仅在少数几个不规则的访问时间点被观测，而发育动态因个体和年龄而异。通用的连续时间模型能够适应不规则的时间间隔，但往往将这些因素吸收到一个单一的灵活转移函数中，对于群体进展、个体变异和发育年龄如何塑造动态，几乎没有提供结构。我们提出了DevelopmentODE，一个结构化的连续时间框架，在共享的发育几何结构中组织群体和受试者特定的变异，同时允许控制动态随年龄演化。该模型围绕代表群体轨迹的发育通道构建此几何结构，其局部方向为组织受试者特定变异提供参考。受试者偏离速度相对于此方向受到约束，而共享的非线性偏离场捕获个体发育运动，而不干扰群体水平的进展。DevelopmentODE进一步通过共享向量场的有序年龄相关变形来建模发育非平稳性，随着年龄变化逐步适应共同的动态结构，而经过的时间决定积分范围。这一公式利用群体水平的发育结构来指导从稀疏个体轨迹中学习，同时允许动态随年龄平滑演化。我们在纵向fMRI上评估DevelopmentODE，通过从早期观测预测同一儿童未来的功能连接性。DevelopmentODE在短期和长期预测中均持续优于竞争基线。
英文摘要
Understanding how individual brain development unfolds over childhood requires modeling developmental trajectories from sparse longitudinal observations. Long-term neurodevelopmental forecasting is challenging because each child is typically observed at only a few irregularly spaced visits, while developmental dynamics vary across individuals and age. Generic continuous-time models accommodate irregular timing but often absorb these factors into a single flexible transition function, providing little structure for how population progression, individual variability, and developmental age shape the dynamics. We propose DevelopmentODE, a structured continuous-time framework that organizes population- and subject-specific variation within a shared developmental geometry while allowing the governing dynamics to evolve with age. The model builds this geometry around a developmental canal representing the population trajectory, whose local direction provides a reference for organizing subject-specific variation. Subject deviation velocities are constrained relative to this direction, while a shared nonlinear deviation field captures individual developmental motion without disrupting population-level progression. DevelopmentODE further models developmental non-stationarity through ordered age-dependent deformations of the shared vector field, progressively adapting a common dynamical structure as age changes, while elapsed time determines the integration horizon. This formulation uses population-level developmental structure to guide learning from sparse individual trajectories while allowing dynamics to evolve smoothly with age. We evaluate DevelopmentODE on longitudinal fMRI by predicting future functional connectivity of the same child from earlier observations. DevelopmentODE consistently outperforms competing baselines across short- and long-horizon predictions.
