Stefanovski Leon, Meier Jil Mona, Pai Roopa Kalsank, Triebkorn Paul, Lett Tristram, Martin Leon, Bülau Konstantin, Hofmann-Apitius Martin, Solodkin Ana, McIntosh Anthony Randal, Ritter Petra
Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany.
Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany.
Front Neuroinform. 2021 Apr 1;15:630172. doi: 10.3389/fninf.2021.630172. eCollection 2021.
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
尽管在过去几年里神经科学领域的知识和数据积累加速，但高度流行的神经退行性疾病——阿尔茨海默病（AD）仍然是一个日益严重的问题。阿尔茨海默病是痴呆最常见的病因，也是最普遍的神经退行性疾病。目前，针对AD缺乏疾病修饰治疗方法，对疾病机制的理解仍不完整。在本综述中，我们讨论了导致AD的潜在因素，并评估了新的计算脑模拟方法，以进一步厘清它们的潜在作用。我们首先概述了现有的AD计算模型，这些模型旨在提供对该疾病的机制性理解。接下来，我们概述了利用开源、多尺度、全脑模拟神经信息学平台“虚拟大脑”（www.thevirtualbrain.org）将AD神经退行性变的分子层面与大规模脑网络建模联系起来的潜力。最后，我们讨论了这种方法如何有助于理解、改进AD的诊断和优化治疗。
