Homeostatic dynamics, hysteresis and synchronization in a low-dimensional model of burst suppression

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• 作者：Sensen Liu ; ShiNung Ching
• 关键词：Burst suppression ; Metabolism ; Mean field model ; Homeostasis ; Hysteresis ; Burst synchronization
• 刊名：Journal of Mathematical Biology
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：74
• 期：4
• 页码：1011-1035
• 全文大小：
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematical and Computational Biology; Applications of Mathematics;
• 出版者：Springer Berlin Heidelberg
• ISSN：1432-1416
• 卷排序：74

文摘

Burst suppression, a pattern of the electroencephalogram characterized by quasi-periodic alternation of high-voltage activity (burst) and isoelectric silence (suppression), is typically associated with states of unconsciousness, such as in deep general anesthesia and certain etiologies of coma. Recent computational models for burst suppression have attributed the slow (up to tens of seconds) time-scale of burst termination and re-initiation to cycling in supportive physiological process, such as cerebral metabolism. That is, activity-dependent substrate (‘energy’) depletion during bursts, followed by substrate recovery during suppression. Such a model falls into the category of a fast-slow dynamical system, commonly used to describe neuronal bursting more generally. Here, following this basic paradigm, we develop a low dimensional mean field model for burst suppression that adds several new features and capabilities to previous models. Most notably, this new model includes explicit homeostatic interactions wherein the rates of substrate recovery are tied to neuronal activity in a supply demand loop, creating a physiologically consistent, reciprocal interaction between the neural and substrate processes. We develop formal analysis of the model dynamics, showing, in particular, the capability of the model to produce burst-like activity as a consequence of neuronal downregulation only, without any direct perturbation to the substrate dynamics. Further, we use a synchronization analysis to contrast different mechanisms for spatially local versus global bursting. The analysis performed generates characterizations that are consistent with experimental observations of spatiotemporal features such as burst onset, duration, and spatial organization and, moreover, generates predictions regarding the presence of bistability and hysteresis in the underlying system. Thus, the model provides new dynamical insight into the mechanisms of burst suppression and, moreover, a tractable platform for more detailed future characterizations.