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痴呆症疾病进展的网络扩散模型。

A network diffusion model of disease progression in dementia.

机构信息

Department of Radiology, Weill Medical College of Cornell University, 515 E. 71 Street, Suite S123, New York, NY 10044, USA.

出版信息

Neuron. 2012 Mar 22;73(6):1204-15. doi: 10.1016/j.neuron.2011.12.040. Epub 2012 Mar 21.

Abstract

Patterns of dementia are known to fall into dissociated but dispersed brain networks, suggesting that the disease is transmitted along neuronal pathways rather than by proximity. This view is supported by neuropathological evidence for "prion-like" transsynaptic transmission of disease agents like misfolded tau and beta amyloid. We mathematically model this transmission by a diffusive mechanism mediated by the brain's connectivity network obtained from tractography of 14 healthy-brain MRIs. Subsequent graph theoretic analysis provides a fully quantitative, testable, predictive model of dementia. Specifically, we predict spatially distinct "persistent modes," which, we found, recapitulate known patterns of dementia and match recent reports of selectively vulnerable dissociated brain networks. Model predictions also closely match T1-weighted MRI volumetrics of 18 Alzheimer's and 18 frontotemporal dementia subjects. Prevalence rates predicted by the model strongly agree with published data. This work has many important implications, including dimensionality reduction, differential diagnosis, and especially prediction of future atrophy using baseline MRI morphometrics.

摘要

痴呆症的模式被认为是分散但离散的大脑网络,这表明疾病是通过神经元途径传播的,而不是通过邻近传播的。这种观点得到了神经病理学证据的支持,即像错误折叠的tau 和 beta 淀粉样蛋白这样的疾病因子可以通过“类朊病毒样”的突触间传递。我们通过一种扩散机制对这种传播进行数学建模,该机制由从 14 个健康大脑 MRI 的追踪获得的大脑连接网络介导。随后的图论分析提供了一个完全定量、可测试、可预测的痴呆症模型。具体来说,我们预测了空间上不同的“持续模式”,我们发现这些模式再现了已知的痴呆症模式,并与最近关于分离的易损大脑网络的报告相匹配。模型预测也与 18 名阿尔茨海默病和 18 名额颞叶痴呆患者的 T1 加权 MRI 体积学非常吻合。该模型预测的患病率与已发表的数据强烈一致。这项工作具有许多重要的意义,包括降维、鉴别诊断,特别是使用基线 MRI 形态计量学预测未来的萎缩。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bcae/3623298/233e5d92fc57/nihms357429f1.jpg

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