Department of Neuroscience, Weill Cornell Medical College of Cornell University, New York, NY, USA.
Department of Healthcare Policy and Research, Weill Cornell Medical College of Cornell University, New York, NY, USA.
Alzheimers Dement. 2018 Jun;14(6):797-810. doi: 10.1016/j.jalz.2017.11.014. Epub 2018 Jan 4.
The stereotypical progression of Alzheimer's disease (AD) pathology is not fully understood. The selective impact of AD on distinct regions has led the field to question if innate vulnerability exists. This study aims to determine if the causative factors of regional vulnerability are dependent on cell-autonomous or transneuronal (non-cell autonomous) processes.
Using mathematical and statistical models, we analyzed the contribution of cell-autonomous and non-cell autonomous factors to predictive linear models of AD pathology.
Results indicate gene expression as a weak contributor to predictive linear models of AD. Instead, the network diffusion model acts as a strong predictor of observed AD atrophy and hypometabolism.
We propose a convenient methodology for identifying genes and their role in determining AD topography, in comparison with network spread. Results reinforce the role of transneuronal network spread on disease progression and suggest that innate gene expression plays a secondary role in seeding and subsequent disease progression.
阿尔茨海默病（AD）病理的典型进展尚不完全清楚。AD 对不同区域的选择性影响使得该领域质疑是否存在固有脆弱性。本研究旨在确定区域脆弱性的致病因素是否依赖于细胞自主或神经传递（非细胞自主）过程。
使用数学和统计模型，我们分析了细胞自主和非细胞自主因素对 AD 病理预测线性模型的贡献。
结果表明，基因表达对 AD 预测线性模型的贡献较弱。相反，网络扩散模型是观察到的 AD 萎缩和低代谢的有力预测因子。
我们提出了一种方便的方法来识别基因及其在确定 AD 拓扑结构中的作用，与网络传播相比。结果强化了神经传递网络传播在疾病进展中的作用，并表明固有基因表达在播散和随后的疾病进展中起次要作用。
