针对阿尔茨海默氏症患者个体的基于网络的 tau 传播的数据驱动模拟

IF 8.7 2区 工程技术 Q1 Mathematics Engineering with Computers Pub Date : 2024-05-13 DOI:10.1007/s00366-024-01988-y
Sung-Woo Kim, Hanna Cho, Yeonjeong Lee, Chul Hyoung Lyoo, Joon-Kyung Seong
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摘要

阿尔茨海默氏症患者大脑皮层中的 Tau 结沿着大脑网络以不同的模式扩散。我们的目标是模拟它们在大脑皮层中基于网络的扩散,为阿尔茨海默氏症连续体中的每个个体量身定制,而不对网络结构做任何假设。通过优化生物物理模型,构建了一个群体级的固有扩散网络,以78名淀粉样蛋白阳性患者的纵向tau正电子发射断层扫描图像发现数据集为基础,模拟tau缠结的近端和远端扩散途径。此外,还获得了群体水平的扩散参数,并随后进行了调整,以生成个体化的 tau 轨迹。通过为发现数据集中的每个个体模拟这些个体化的 tau 扩散模型,我们成功地捕捉到了 tau 的近端和远端扩散,从而可靠地推断出了 tau 扩散的基本机制。模拟这些模型还能高度准确地预测发现数据集和独立验证数据集的未来头尾拓扑结构。
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Data-driven simulation of network-based tau spreading tailored to individual Alzheimer's patients

Tau tangles in the brain cortex spread along the brain network in distinct patterns among Alzheimer's patients. We aim to simulate their network-based spreading within the cortex, tailored to each individual along the Alzheimer's continuum, without assuming any assumptions about the network architecture. A group-level intrinsic spreading network was constructed to model the pathways for the proximal and distal spreading of tau tangles by optimizing the biophysical model based on a discovery dataset of longitudinal tau positron emission tomography images for 78 amyloid-positive individuals. Group-level spreading parameters were also obtained and subsequently adjusted to produce individuated tau trajectories. By simulating these individuated tau spreading models for every individual in the discovery dataset, we successfully captured proximal and distal tau spreading, allowing reliable inferences about the underlying mechanism of tau spreading. Simulating the models also allowed highly accurate prediction of future tau topography for both discovery and independent validation datasets.

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来源期刊
Engineering with Computers
Engineering with Computers 工程技术-工程:机械
CiteScore
16.50
自引率
2.30%
发文量
203
审稿时长
9 months
期刊介绍: Engineering with Computers is an international journal dedicated to simulation-based engineering. It features original papers and comprehensive reviews on technologies supporting simulation-based engineering, along with demonstrations of operational simulation-based engineering systems. The journal covers various technical areas such as adaptive simulation techniques, engineering databases, CAD geometry integration, mesh generation, parallel simulation methods, simulation frameworks, user interface technologies, and visualization techniques. It also encompasses a wide range of application areas where engineering technologies are applied, spanning from automotive industry applications to medical device design.
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