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

摘要 几十年的历史表明，神经元的电极化可以调节 神经元放电，这种极化可以抑制(或兴奋)尖峰活动和癫痫发作。我们有 展示了使用开环和闭环刺激策略控制癫痫发作(J NeuroPhysiol， 76：4202-4205,1996；《神经科学杂志》，21：590-600,2001)。具有过去的NIMH和CRCNS支持(R01MH50006， 1R01EB014641)-我们发现计算生物物理学中的棘波、癫痫发作和 传播性抑郁症(神经病学杂志，34：11733-11743,2014年)。这些发现表明，这些曲目 神经细胞膜的动力学包含了从正常到 致病，癫痫发作和扩散性抑郁是脑血管疾病固有特性的表现 那些膜。最近，我们完成了一项重大的实验验证，验证了来自 体内癫痫的统一预测。最近，我们实现了世界上最大的 CRCNS最近的项目“基于模型的传播抑郁控制”，通过证明神经元 极化可以抑制(或增强)、阻断或防止抑郁症的传播，生理上的 支撑着偏头痛的气息。值得注意的是，这种压制需要与之相反的极性 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 传播抑郁症的可能性。这些实验的另一个令人惊讶的发现是，抑制 弥漫性抑郁似乎不会导致癫痫发作，反之亦然，当大脑处于 癫痫发作抑制不会产生扩散性抑制。上述情况的含义是 在从大脑的不同状态控制大脑动力学时，可能存在状态依赖控制 这在质量上与其他州的要求有很大不同。此外，控制算法 保持给定稳定状态(例如，正常尖峰)所需的可能不同于引导 系统从病理状态回到稳定状态。我们提出了一个假设，即存在一个 神经元回路反馈控制的全新框架--状态依赖控制。这是一个 基于模型的框架，其中神经系统通过电或光传感器来感知，以及 这些数据被同化成可能状态的生物物理计算模型。然后是反馈控制 基于状态应用，并持续观察系统在状态空间中的轨迹。 研究大脑活动的状态依赖控制不仅对癫痫和 偏头痛，但更广泛地说是在重症监护环境中，因为蔓延的抑郁症的有害影响 创伤性脑损伤、中风和蛛网膜下腔出血中的电波。

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.