Raj Ashish, LoCastro Eve, Kuceyeski Amy, Tosun Duygu, Relkin Norman, Weiner Michael
Department of Radiology, Weill Medical College of Cornell University, 515 East 71 Street, Suite S123, New York, NY 10021, USA.
Department of Radiology, Weill Medical College of Cornell University, 515 East 71 Street, Suite S123, New York, NY 10021, USA.
Cell Rep. 2015 Jan 20;10(3):359-369. doi: 10.1016/j.celrep.2014.12.034. Epub 2015 Jan 15.
Alzheimer's disease pathology (AD) originates in the hippocampus and subsequently spreads to temporal, parietal, and prefrontal association cortices in a relatively stereotyped progression. Current evidence attributes this orderly progression to transneuronal transmission of misfolded proteins along the projection pathways of affected neurons. A network diffusion model was recently proposed to mathematically predict disease topography resulting from transneuronal transmission on the brain's connectivity network. Here, we use this model to predict future patterns of regional atrophy and metabolism from baseline regional patterns of 418 subjects. The model accurately predicts end-of-study regional atrophy and metabolism starting from baseline data, with significantly higher correlation strength than given by the baseline statistics directly. The model's rate parameter encapsulates overall atrophy progression rate; group analysis revealed this rate to depend on diagnosis as well as baseline cerebrospinal fluid (CSF) biomarker levels. This work helps validate the model as a prognostic tool for Alzheimer's disease assessment.
阿尔茨海默病病理学（AD）起源于海马体，随后以相对固定的进程扩散到颞叶、顶叶和前额叶联合皮质。目前的证据将这种有序进展归因于错误折叠蛋白沿受影响神经元的投射通路进行的跨神经元传递。最近提出了一种网络扩散模型，以数学方式预测大脑连接网络上跨神经元传递所导致的疾病地形图。在此，我们使用该模型根据418名受试者的基线区域模式来预测未来区域萎缩和代谢模式。该模型从基线数据开始就能准确预测研究结束时的区域萎缩和代谢情况，其相关强度显著高于直接由基线统计数据得出的结果。该模型的速率参数概括了整体萎缩进展速率；分组分析表明，这一速率取决于诊断以及基线脑脊液（CSF）生物标志物水平。这项工作有助于验证该模型作为阿尔茨海默病评估预后工具的有效性。
