Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders

Q3 Engineering
Hina Shaheen, Swadesh Pal, Roderick Melnik
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引用次数: 2

Abstract

Deep brain stimulation (DBS) has been used successfully as symptomatic treatment in several neurodegenerative disorders, including Parkinson’s disease (PD). However, the mechanisms of its activity inside the brain network are unclear. Many virtual DBS models investigate the dynamics of a subnetwork surrounding the basal ganglia (BG) as a spiking network has been attracting a growing body of research in neuroscience. Connectomic data, on the other hand, show that DBS has a wide range of impacts on many distinct cortical and subcortical sites. Notably, the nonlinear reaction–diffusion multiscale mathematical models demonstrate the effectiveness of capturing crucial disease characteristics and are used to simulate large-scale brain activity. The BG and associated nuclei comprise many subcortical cell groups in the brain, and their couplings commonly revealed MRI-based assessments of the strength of anatomical connections. We have developed a hybrid modeling formalism and a unique co-simulation technique that allows us to compute electrodiffusive ion dynamics for the cortex–BG–thalamus (BGTH) brain network model within a large-scale brain connectome. We collect data from the Human Connectome Project (HCP) and propose a closed-loop DBS approach based on the brain network model. Moreover, we select regions in the parameter space that reflect the healthy and Parkinsonian states as well as the impact of DBS on the subthalamic nucleus (STN) and globus pallidus internus (GPi) neurons. We predicted that if we apply the DBS to the system described by the temporal model, the brain maintains a healthy state until 0.05ms for STN neurons and 0.035ms for GPi neurons. A local regulatory mechanism known as feedback inhibition control (FIC) points to the existence of underlying network dynamics in the white matter of connected brain regions. The model showed unanticipated effects that in the presence of diffusion, the human brain maintained a healthy state for a long time after the DBS had been applied to STN and GPi neurons. This research helps us better understand the changes in brain activity caused by DBS and enhances this clinical therapy, thus shedding new light on the importance of DBS mechanisms in BGTH brain network models of neurodegenerative disorders.

神经退行性疾病中深部脑刺激与脑网络的多尺度联合模拟
脑深部电刺激(DBS)已成功地用于几种神经退行性疾病的对症治疗,包括帕金森病(PD)。然而,其在大脑网络中的活动机制尚不清楚。许多虚拟DBS模型研究了围绕基底神经节(BG)的子网络的动态,因为一个尖峰网络已经吸引了越来越多的神经科学研究。另一方面,连接组数据显示,DBS对许多不同的皮层和皮层下部位有广泛的影响。值得注意的是,非线性反应-扩散多尺度数学模型证明了捕获关键疾病特征的有效性,并用于模拟大规模的大脑活动。BG和相关核包括大脑中许多皮层下细胞群,它们的耦合通常显示基于mri的解剖连接强度评估。我们开发了一种混合建模形式和一种独特的联合模拟技术,使我们能够在大规模脑连接组中计算皮层-脑-丘脑(BGTH)脑网络模型的电扩散离子动力学。我们从人类连接组计划(HCP)中收集数据,提出了一种基于大脑网络模型的闭环DBS方法。此外,我们在参数空间中选择了反映健康和帕金森状态的区域,以及DBS对丘脑下核(STN)和内苍白球(GPi)神经元的影响。我们预测,如果我们将DBS应用于时间模型描述的系统,大脑在STN神经元和GPi神经元中分别保持健康状态至0.05ms和0.035ms。一种被称为反馈抑制控制(FIC)的局部调节机制指出,在脑连接区域的白质中存在潜在的网络动力学。该模型显示了意想不到的效果,在扩散存在的情况下,DBS应用于STN和GPi神经元后,人脑在很长一段时间内保持健康状态。本研究有助于我们更好地了解DBS引起的脑活动变化,并加强临床治疗,从而揭示DBS机制在神经退行性疾病BGTH脑网络模型中的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Brain multiphysics
Brain multiphysics Physics and Astronomy (General), Modelling and Simulation, Neuroscience (General), Biomedical Engineering
CiteScore
4.80
自引率
0.00%
发文量
0
审稿时长
68 days
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