Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA.
Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy.
Brain Connect. 2021 Oct;11(8):624-638. doi: 10.1089/brain.2020.0841. Epub 2021 Jul 16.
Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. This combined aggregation-network-diffusion model exhibits all hallmarks of progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on imaging and fluid measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional and atrophy but also patients' cerebrospinal fluid phosphorylated profiles as a function of disease progression. This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing . Impact statement The presented aggregation-network-diffusion model exhibits all hallmarks of tau progression in human patients; it accurately captures not just the spatial distribution of empirical regional tau and atrophy but also patients' cerebrospinal fluid phosphorylated tau profiles. Thus, it serves to fill a theoretical gap between microscopic biophysical processes and empirical macroscopic measurements of pathological patterns in Alzheimer's disease. This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing .
阿尔茨海默病涉及广泛且进行性的错误折叠蛋白 tau（）沉积，首先出现在内侧颞叶皮层，在那里凝结成更长的聚合物和不溶性原纤维。越来越多的证据表明存在“类朊病毒样”的跨神经元传播，其中错误折叠的蛋白质沿着神经元通路级联，导致网络传播。然而，各种寡聚物是如何产生、聚集和传播的因果机制尚不清楚。关于蛋白质聚集和随后的传播如何导致阿尔茨海默病大脑中的刻板进展的问题仍未得到解决。我们通过对这些病理生理过程进行数学精确的简约建模来解决这些问题，并将其外推到整个大脑。我们建模了三个关键过程：单体的产生；寡聚体的聚集，然后是缠结；以及错误折叠的通过大脑连接组进入神经回路的分支扩散。我们在内侧颞叶皮层模拟单体的播种和产生，使用斯莫卢霍夫斯基方程模拟聚集；并使用我们之前的网络扩散模型模拟网络传播。这种组合的聚集-网络-扩散模型表现出了人类患者中观察到的所有进展特征。与之前关于蛋白质聚集的理论研究不同，我们在这里对来自大型数据集的成像和液体测量数据进行了实证验证。该模型不仅准确地捕捉了经验区域和萎缩的空间分布，还准确地捕捉了患者脑脊液中磷酸化 tau 作为疾病进展的函数的分布。这种统一的定量和可测试的模型具有解释观察到的现象并作为未来假设生成和测试的测试平台的潜力。
