[论文解读] Charting the velocity of brain growth and development

Bayer, Johanna M. M., Augustijn A. A. de Boer+31 more|Research Portal (King's College London)|Jan 12, 2026

Dementia and Cognitive Impairment Research被引用 1

一句话总结

本文提出速度百分位数以从大规模多站点成像数据建模纵向脑变化率，从而检测个体偏离并改善认知下降轨迹的预测。

ABSTRACT

Brain charts have emerged as a highly useful approach for understanding brain development and aging on the basis of brain imaging and have shown substantial utility in describing typical and atypical brain development with respect to a given reference model. However, all existing models are fundamentally cross-sectional and cannot capture change over time at the individual level. We address this using velocity centiles, which directly map change over time and can be overlaid onto cross-sectionally derived population centiles. We demonstrate this by modelling rates of change for 24062 scans from 10795 healthy individuals with up to 8 longitudinal measurements across the lifespan. We provide a method to detect individual deviations from a stable trajectory, generalising the notion of thrive lines, which are used in pediatric medicine to declare failure to thrive. Using this approach, we predict transition from mild cognitive impairment to dementia more accurately than by using either time point alone, replicated across two datasets. Last, by taking into account multiple time points, we improve the sensitivity of velocity models for predicting the future trajectory of brain change. This highlights the value of predicting change over time and makes a fundamental step towards precision medicine.

研究动机与目标

• 在脑发育的时间变化领先指标需求的动机，超越横截面模型。
• 提出速度百分位数以映射随时间的变化并将其叠加在横截人口百分位数上。
• 在大规模纵向成像数据集上演示该方法，以建模整个生命周期的脑变化速率。
• 展示速度建模如何检测偏离稳定轨迹的个体，并提高临床转归预测的准确性。

提出的方法

• 开发量化脑随时间变化速率的速度百分位模型。
• 将纵向测量（每个受试者最多8次）与横截面百分位数相结合。
• 将 Thrive-line 概念推广用于检测偏离稳定轨迹的个体。
• 将该方法应用于预测从轻度认知障碍向痴呆的转归。
• 在两个独立数据集上验证预测性能。

实验结果

研究问题

• RQ1 速度百分位数能否比横截面百分位数更准确绘制个体脑变化速率？
• RQ2 基于速度的对稳定轨迹的偏离是否能提高对异常发育或衰退的检测？
• RQ3 该方法是否比单一时间点模型更准确地预测如轻度认知障碍向痴呆的转归？
• RQ4 纳入多个时间点是否提升对未来脑变化轨迹的预测敏感度？

主要发现

• 对10,795名健康个体的24,062次扫描在最多八次纵向测量的条件下建模，提供了以轨迹为驱动的脑发育视角。
• 可以将速度百分位数叠加到横截面派生的/population 百分位数上，以绘制随时间的变化。
• 该方法能够检测偏离稳定轨迹的个体（延展出 thriving 与 fail to thrive 的概念）。
• 基于速度的轻度认知障碍向痴呆的预测比仅基于时间点的方法更为准确，在两个数据集中得到复制。
• 使用多时间点可提高对未来脑变化轨迹的预测灵敏度。