Data-driven and equation-free methods for neurological disorders: analysis and control of the striatum network.

Frontiers in network physiology Pub Date : 2024-08-07 eCollection Date: 2024-01-01 DOI:10.3389/fnetp.2024.1399347
Konstantinos Spiliotis, Rüdiger Köhling, Wolfram Just, Jens Starke
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引用次数: 0

Abstract

The striatum as part of the basal ganglia is central to both motor, and cognitive functions. Here, we propose a large-scale biophysical network for this part of the brain, using modified Hodgkin-Huxley dynamics to model neurons, and a connectivity informed by a detailed human atlas. The model shows different spatio-temporal activity patterns corresponding to lower (presumably normal) and increased cortico-striatal activation (as found in, e.g., obsessive-compulsive disorder), depending on the intensity of the cortical inputs. By applying equation-free methods, we are able to perform a macroscopic network analysis directly from microscale simulations. We identify the mean synaptic activity as the macroscopic variable of the system, which shows similarity with local field potentials. The equation-free approach results in a numerical bifurcation and stability analysis of the macroscopic dynamics of the striatal network. The different macroscopic states can be assigned to normal/healthy and pathological conditions, as known from neurological disorders. Finally, guided by the equation-free bifurcation analysis, we propose a therapeutic close loop control scheme for the striatal network.

神经系统疾病的数据驱动和无方程方法:纹状体网络的分析与控制。
纹状体是基底神经节的一部分,是运动和认知功能的核心。在这里,我们提出了大脑这一部分的大规模生物物理网络,使用改进的霍奇金-赫胥黎动力学来模拟神经元,并根据详细的人体图谱建立连接。该模型显示了不同的时空活动模式,这些模式与皮质纹状体激活较低(推测为正常)和较高(如强迫症)相对应,取决于皮质输入的强度。通过应用无方程方法,我们能够直接从微观模拟中进行宏观网络分析。我们将平均突触活动确定为系统的宏观变量,它与局部场电位显示出相似性。无方程方法可对纹状体网络的宏观动力学进行数值分岔和稳定性分析。不同的宏观状态可被归类为正常/健康状态和病理状态,正如神经系统疾病中已知的那样。最后,在无方程分岔分析的指导下,我们提出了纹状体网络的治疗性闭环控制方案。
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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CiteScore
2.70
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