Georgiadis Konstantinos, Wray Selina, Ourselin Sébastien, Warren Jason D, Modat Marc
Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London NW1 2HE, United Kingdom.
Department of Molecular Neuroscience, Institute of Neurology, University College London, London WC1N 3BG, United Kingdom.
PLoS One. 2018 Feb 5;13(2):e0192518. doi: 10.1371/journal.pone.0192518. eCollection 2018.
Pathogenic protein accumulation and spread are fundamental principles of neurodegenerative diseases and ultimately account for the atrophy patterns that distinguish these diseases clinically. However, the biological mechanisms that link pathogenic proteins to specific neural network damage patterns have not been defined. We developed computational models for mechanisms of pathogenic protein accumulation, spread and toxic effects in an artificial neural network of cortical columns. By varying simulation parameters we assessed the effects of modelled mechanisms on network breakdown patterns. Our findings suggest that patterns of network breakdown and the convergence of patterns follow rules determined by particular protein parameters. These rules can account for empirical data on pathogenic protein spread in neural networks. This work provides a basis for understanding the effects of pathogenic proteins on neural circuits and predicting progression of neurodegeneration.
致病性蛋白质的积累和扩散是神经退行性疾病的基本原理，最终导致了在临床上区分这些疾病的萎缩模式。然而，将致病性蛋白质与特定神经网络损伤模式联系起来的生物学机制尚未明确。我们在皮质柱人工神经网络中开发了致病性蛋白质积累、扩散和毒性作用机制的计算模型。通过改变模拟参数，我们评估了模拟机制对网络崩溃模式的影响。我们的研究结果表明，网络崩溃模式以及模式的趋同遵循由特定蛋白质参数决定的规则。这些规则可以解释关于致病性蛋白质在神经网络中扩散的实验数据。这项工作为理解致病性蛋白质对神经回路的影响以及预测神经退行性变的进展提供了基础。
