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A Mechanistic Neural Field Theory for Loss of Consciousness During General Anesthesia

全身麻醉期间意识丧失的机械神经场理论

基本信息

批准号：
1708792
负责人：
Wassim Haddad
金额：
$ 34.6万
依托单位：
Georgia Tech Research Corporation
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2021-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1708792&HistoricalAwards=false
关键词：
Mechanistic Neural Field Theory Loss

项目摘要

The goal of this research is to merge dynamical systems theory and dynamic network notions to develop a framework that predicts the qualitative salient characteristics of the anesthetic transition. Specifically, we propose to develop and analyze mean field models to model the electrocortical activity of the central nervous system as a dynamical system and show that the transition to the anesthetic state exhibits multiple bifurcations (i.e., transitions) with the awake state transitioning to a stable limit cycle and subsequently to a stable unconscious equilibrium state as the concentration of the anesthetic agent increases.The proposed framework will allow for the development of models that go beyond words to dynamic equations, leading to mathematical models with greater precision and self-consistency. This research can foster the development of new frameworks that can allow us to interpret experimental and clinical results, connect biophysical findings to psychophysical phenomena, explore new hypothesis based on the cognitive neuroscience of consciousness and develop new assertions, and ultimately improve the reliability of general anesthesia.Intellectual Merit: The results will be applied to excitatory and inhibitory biological neuronal networks to explain the underlying neuronal mechanisms of action for anesthesia and unconsciousness from a macroscopic neural field electrocortical perspective, thereby providing a theoretical foundation for general anesthesia using the dynamic network properties of the brain.Broader Impacts: While the primary focus is general anesthesia, the proposed dynamical systems framework has potential applicability to understand seizure activity and schizophrenia. This also has applicability to the phenomena of multistability seen in biochemical systems, ecosystems, gene regulation and cell replications as well as numerous conditions in medical science including epilepsy, multiple sclerosis, and Alzheimer's disease. The primary impact of this research will be to allow for the development of mathematical models that advance our understanding of the wide effects of pharmacologic agents and anesthetics leading to improvements in medical care, health care, and reliability of drug development and dosing.

这项研究的目的是融合动态系统理论和动态网络概念来开发一个框架，来预测麻醉过渡的定性显著特征。具体地说，我们建议建立和分析平均场模型来将中枢神经系统的皮层活动作为一个动态系统来建模，并表明随着麻醉剂浓度的增加，从清醒状态到稳定极限环的转变以及随后到稳定的无意识平衡状态的转变呈现出多个分叉(即转变)。所提出的框架将允许发展超越文字到动态方程的模型，从而导致更高的精度和自一致性的数学模型。这项研究可以促进新框架的发展，使我们能够解释实验和临床结果，将生物物理发现与心理物理现象联系起来，基于意识的认知神经科学探索新的假说并开发新的断言，最终提高全麻的可靠性。智力价值：结果将被应用于兴奋性和抑制性生物神经元网络，从宏观神经场皮质的角度解释麻醉和无意识的潜在神经作用机制，从而为利用大脑的动态网络特性的全身麻醉提供理论基础。这也适用于生化系统、生态系统、基因调控和细胞复制中的多稳定性现象，以及医学中的许多情况，包括癫痫、多发性硬化症和阿尔茨海默病。这项研究的主要影响将是允许开发数学模型，促进我们对药理学药物和麻醉剂的广泛影响的理解，从而改善医疗保健、保健以及药物开发和剂量的可靠性。