Kunert James M, Maia Pedro D, Kutz J Nathan
Department of Physics, University of Washington, Seattle, Washington, United States of America.
Department of Applied Mathematics, University of Washington, Seattle, Washington, United States of America.
PLoS Comput Biol. 2017 Jan 5;13(1):e1005261. doi: 10.1371/journal.pcbi.1005261. eCollection 2017 Jan.
Using a model for the dynamics of the full somatic nervous system of the nematode C. elegans, we address how biological network architectures and their functionality are degraded in the presence of focal axonal swellings (FAS) arising from neurodegenerative disease and/or traumatic brain injury. Using biophysically measured FAS distributions and swelling sizes, we are able to simulate the effects of injuries on the neural dynamics of C. elegans, showing how damaging the network degrades its low-dimensional dynamical responses. We visualize these injured neural dynamics by mapping them onto the worm's low-dimensional postures, i.e. eigenworm modes. We show that a diversity of functional deficits arise from the same level of injury on a connectomic network. Functional deficits are quantified using a statistical shape analysis, a procrustes analysis, for deformations of the limit cycles that characterize key behaviors such as forward crawling. This procrustes metric carries information on the functional outcome of injuries in the model. Furthermore, we apply classification trees to relate injury structure to the behavioral outcome. This makes testable predictions for the structure of an injury given a defined functional deficit. More critically, this study demonstrates the potential role of computational simulation studies in understanding how neuronal networks process biological signals, and how this processing is impacted by network injury.
利用线虫秀丽隐杆线虫全躯体神经系统动力学模型,我们探讨了在神经退行性疾病和/或创伤性脑损伤引发的局灶性轴突肿胀(FAS)存在的情况下,生物网络架构及其功能是如何退化的。利用生物物理测量得到的FAS分布和肿胀大小,我们能够模拟损伤对秀丽隐杆线虫神经动力学的影响,展示网络损伤如何降低其低维动力学响应。我们通过将这些受损的神经动力学映射到线虫的低维姿势,即本征蠕虫模式,来对其进行可视化。我们表明,在连接组网络上相同程度的损伤会导致多种功能缺陷。使用统计形状分析(一种用于分析表征诸如向前爬行等关键行为的极限环变形的正交最小二乘法分析)对功能缺陷进行量化。这种正交最小二乘法度量携带了模型中损伤功能结果的信息。此外,我们应用分类树将损伤结构与行为结果联系起来。这使得在给定定义的功能缺陷的情况下,能够对损伤结构进行可测试的预测。更关键的是,这项研究证明了计算模拟研究在理解神经元网络如何处理生物信号以及这种处理如何受到网络损伤影响方面的潜在作用。