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，并且这种极化可以抑制（或刺激）尖峰活动和癫痫发作。我们有 证明使用开环和闭环刺激策略可以控制癫痫发作（J Neurophysicalol， 76：4202-4205，1996；神经科学杂志，21：590-600，2001）。凭借过去的 NIMH 和 CRCNS 支持（R01MH50006， 1R01EB014641) – 我们发现了尖峰、癫痫和癫痫发作的计算生物物理学的统一 抑郁症的蔓延（J Neurosci，34：11733-11743，2014）。这些发现表明曲目 神经元膜动力学的研究涵盖了广泛的动力学范围，从正常的 病理性的，癫痫发作和蔓延的抑郁症是其固有特性的表现 那些膜。最近，我们对关键预测进行了重大实验验证 体内癫痫的统一预测。最近，我们实现了最多的实验目标 最近的 CRCNS 项目“基于模型的抑郁症扩散控制”通过证明神经元 极化可以抑制（或增强）、阻止或防止抑郁症的蔓延，这是一种生理现象 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 抑制尖峰和癫痫发作所需的，并且与计算生物物理模型完全一致 抑郁症的蔓延。这些实验的进一步令人惊讶的发现是抑制 抑郁症的蔓延似乎不会引起癫痫发作，反之亦然，当大脑处于 抑制癫痫发作不会产生抑郁症的蔓延。上述的含义是 在从大脑的不同状态控制大脑动力学时，可以存在状态依赖控制 这在质量上与其他州的要求有很大不同。此外，控制算法 维持给定稳定状态（例如正常尖峰）所需的可能与引导稳定状态所需的不同 系统从病理状态回到稳态。我们提出这样的假设：存在一个 神经回路反馈控制的全新框架——状态相关控制。这是一个 基于模型的框架，其中神经元系统通过电或光学传感器进行感测，以及 数据被吸收到可能状态的生物物理计算模型中。那么反馈控制就是 基于状态应用，并且持续观察系统在状态空间中的轨迹。 研究出大脑活动的状态依赖性控制不仅对癫痫和 偏头痛，但更广泛地在重症监护环境中，因为传播抑郁症的有害影响 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.