CRCNS: Collaborative Research: State-Dependent Control for Brain Modulation

摘要 几十年的历史证明，神经元的电极化可以调节 这种极化可以抑制（或激发）尖峰活动和癫痫发作。我们有 使用开环和闭环刺激策略证明了癫痫发作控制（J Neurophysiol， 76：4202- 4205，1996; J Neurosci，21：590-600，2001）。在过去的NIMH和CRCNS支持下（R 01 MH 50006， 1 R 01 EB 014641）-我们发现了一个统一的计算生物物理学的尖峰，癫痫发作， 扩散性抑郁症（J Neurosci，34：11733-11743，2014）。这些发现表明， 神经元膜的动力学包括广泛的动力学范围，从正常的 癫痫发作和扩散性抑郁症是精神分裂症固有特性的表现， 这些膜。最近，我们实现了一个主要的实验验证的关键预测， 体内癫痫的统一预测。最近，我们实现了实验目标， 最近的CRCNS项目，“基于模型的控制扩散性抑郁症”，通过证明神经元， 极化可以抑制（或增强）、阻断或防止扩散性抑制，即生理性抑制。 偏头痛先兆的基础。值得注意的是，这种抑制需要相反的极性， 需要抑制尖峰和癫痫发作，并与计算生物物理模型完全一致 扩散性抑郁症这些实验进一步令人惊讶的发现是， 扩散性抑郁症似乎不会引起癫痫发作，反之亦然，当大脑处于 癫痫发作活动抑制不产生扩散性抑郁。以上所述的含义是， 从大脑的不同状态来控制大脑动力学， 这在质量上与其他国家所要求的非常不同。此外，控制算法 维持给定稳定状态（例如，正常尖峰）所需的参数可能不同于引导 系统从病态状态回到稳定状态。我们提出假设，有一个 神经元回路反馈控制的全新框架-状态相关控制。这是一 - 基于模型的框架，其中通过电或光传感器感测神经元系统，以及 这些数据被同化到可能状态的生物物理计算模型中。反馈控制是 基于状态来应用，并且连续地观察系统通过状态空间的轨迹。 研究出大脑活动的状态依赖控制不仅对癫痫和 偏头痛，但更广泛的重症监护设置，因为扩散性抑郁症的有害影响， 创伤性脑损伤、中风和蛛网膜下腔出血的波形。

Abstract There is a several decade history demonstrating that electrical polarization of neurons can modulate neuronal firing, and that such polarization can suppress (or excite) spiking activity and seizures. We have demonstrated seizure control using both open- and closed-loop stimulation strategies (J Neurophysiol, 76:4202-4205,1996; J Neurosci, 21:590-600, 2001). With past NIMH and CRCNS support (R01MH50006, 1R01EB014641) – we discovered a unification in the computational biophysics of spikes, seizures, and spreading depression (J Neurosci, 34:11733-11743, 2014). These findings demonstrate that the repertoire of the dynamics of the neuronal membrane encompasses a broad range of dynamics ranging from normal to pathological, and that seizures and spreading depression are manifestations of the inherent properties of those membranes. Recently we achieved a major experimental verification of key predictions from the unification predictions in in vivo epilepsy. Most recently, we achieved the experimental goal of the most recent CRCNS project, “Model-Based Control of Spreading Depression”, by demonstrating that neuronal polarization can suppress (or enhance), block, or prevent spreading depression, the physiological underpinning of migraine auras. Remarkably, this suppression requires the opposite polarity as that required to suppress spikes and seizures, and is fully consistent with the computational biophysical models of spreading depression. Further surprising findings from these experiments was that suppression of spreading depression does not appear to generate seizures, and vice versa, that when the brain is in seizure activity suppression does not generate spreading depression. The implications of the above is that in controlling brain dynamics from different states of the brain, that there can be state dependent control which is qualitatively very different from that required in other states. Furthermore, the control algorithms required to maintain a given steady state (e.g. normal spiking) may differ from that required to guide a system from a pathological state back into a steady state. We propose the hypothesis that there is an entirely new framework for feedback control of neuronal circuitry – State Dependent Control. This is a model-based framework, wherein neuronal systems are sensed through electrical or optical sensors, and the data assimilated into a biophysical computational model of the possible states. Feedback control is then applied based upon the state, and the trajectory of the system through state space is continually observed. Working out state dependent control for brain activity has health implications for not only epilepsy and migraine, but more broadly in intensive care settings because of the harmful effects of spreading depression waves in traumatic brain injury, stroke, and subarachnoid hemorrhage.