Song Tzu-An, Chowdhury Samadrita Roy, Yang Fan, Jacobs Heidi I L, Sepulcre Jorge, Wedeen Van J, Johnson Keith A, Dutta Joyita
University of Massachusetts Lowell, Lowell, MA, USA.
Massachusetts General Hospital & Harvard Medical School, Boston, MA, USA.
Med Image Comput Comput Assist Interv. 2020 Oct;12267:418-427. doi: 10.1007/978-3-030-59728-3_41. Epub 2020 Sep 29.
Tau tangles are a pathophysiological hallmark of Alzheimer's disease (AD) and exhibit a stereotypical pattern of spatiotemporal spread which has strong links to disease progression and cognitive decline. Preclinical evidence suggests that tau spread depends on neuronal connectivity rather than physical proximity between different brain regions. Here, we present a novel physics-informed geometric learning model for predicting tau buildup and spread that learns patterns directly from longitudinal tau imaging data while receiving guidance from governing physical principles. Implemented as a graph neural network with physics-based regularization in latent space, the model enables effective training with smaller data sizes. For training and validation of the model, we used longitudinal tau measures from positron emission tomography (PET) and structural connectivity graphs from diffusion tensor imaging (DTI) from the Harvard Aging Brain Study. The model led to higher peak signal-to-noise ratio and lower mean squared error levels than both an unregularized graph neural network and a differential equation solver. The method was validated using both two-timepoint and three-timepoint tau PET measures. The effectiveness of the approach was further confirmed by a cross-validation study.
tau缠结是阿尔茨海默病（AD）的病理生理标志，呈现出一种刻板的时空传播模式，与疾病进展和认知衰退密切相关。临床前证据表明，tau传播取决于神经元连接性，而非不同脑区之间的物理距离。在此，我们提出一种新的基于物理知识的几何学习模型，用于预测tau蛋白的积累和传播，该模型可直接从纵向tau成像数据中学习模式，同时接受物理原理的指导。该模型以潜在空间中具有基于物理正则化的图神经网络形式实现，能够以较小的数据量进行有效训练。为了对模型进行训练和验证，我们使用了来自哈佛衰老大脑研究的正电子发射断层扫描（PET）的纵向tau测量数据和扩散张量成像（DTI）的结构连接图。与未正则化的图神经网络和微分方程求解器相比，该模型具有更高的峰值信噪比和更低的均方误差水平。该方法使用双时间点和三时间点tau PET测量数据进行了验证。交叉验证研究进一步证实了该方法的有效性。
