Collaborative Research: Understanding and Optimizing Dynamic Stimulation for Improvement of Short- and Long-term Brain Function

大脑是一个神奇的器官，负责许多重要功能，包括认知，注意力，情感，感知，记忆和运动控制。 许多脑功能和疾病被认为具有动力学起源;例如，已经假设帕金森病的一些症状是由于大脑的运动控制区域中的病理同步神经活动。最近的研究表明，FDA批准的帕金森震颤治疗方法，称为深部脑刺激，是有效的，因为它通过聚类部分地消除了神经活动，其中一个亚群中的神经元彼此同步，但与其他亚群中的神经元去同步。这项研究将使用工程技术，数学原理，计算机模拟和体外实验，以开发更节能的电流刺激，促进这种集群。 此外，还将开发刺激物，增强有益的神经可塑性，其中神经元根据其活动模式改变其连接强度，这对治疗疾病和需要可塑性的情况（如学习，记忆和中风和脊髓损伤的恢复）可能很重要。以及体外实验，以开发有效的电刺激，用于以有益的方式控制神经群体。 这将包括设计功率最小化的刺激，使神经群体分裂成平衡的集群，其中每个集群包含几乎相同比例的总人口，相邻集群在相位上大致相等，最近的工作表明部分去极化的状态是帕金森氏病的脑深部电刺激治疗标准协议成功的原因。 此外，突触可塑性的Hebbian模型将与最优控制理论结合使用，以设计最佳促进可塑性的刺激，从而在突触连接中产生有益的长期变化，这项工作预计对帕金森病和其他疾病（如癫痫和抑郁症）以及需要可塑性的情况（如学习，记忆，以及中风和脊髓损伤的康复 可塑性研究还将包括体外脑切片实验，其中神经元将与振荡电场同步，并通过电极施加刺激以产生平衡的簇，其对突触强度的影响将被测量。

The brain is an amazing organ which is responsible for a number of important functions including cognition, attention, emotion, perception, memory, and motor control. Many brain functions and disorders are believed to have a dynamical origin; for example, it has been hypothesized that some symptoms of Parkinson's disease are due to pathologically synchronized neural activity in the motor control region of the brain. Recent research suggests that an FDA-approved treatment for Parkinsonian tremors, called deep brain stimulation, is effective because it partially desynchronizes the neural activity via clustering, in which neurons in a subpopulation are synchronized with each other, but desynchronized with neurons in other subpopulations. This research will use engineering techniques, mathematical principles, computer simulations, and in vitro experiments to develop more energy-efficient electrical current stimuli which promote such clustering. Moreover, stimuli will be developed which enhance beneficial neural plasticity in which neurons change their connection strengths based on their activity patterns, work that may be important for treatment of diseases and for situations in which plasticity is desirable such as learning, memory, and recovery from strokes and spinal cord injury.This research will use engineering techniques, mathematical principles, computer simulations, and in vitro experiments to develop efficient electrical stimuli for controlling neural populations in beneficial ways. This will include designing power-minimized stimuli which cause a neural population to split into balanced clusters, in which each cluster contains a nearly identical proportion of the overall population and neighboring clusters are roughly equally spaced in phase, a state of partial desynchronization which recent work suggests is responsible for the success of the standard protocol for deep brain stimulation treatment of Parkinson's disease. Moreover, Hebbian models for synaptic plasticity will be used in combination with optimal control theory to design stimuli which optimally promote plasticity to give beneficial long-term changes in synaptic connections, work which is expected to have important implications for Parkinson's disease and other disorders such as epilepsy and depression, and for situations in which plasticity is desirable such as learning, memory, and recovery from strokes and spinal cord injury. The plasticity studies will also include in vitro brain slice experiments in which neurons will be synchronized to an oscillating electric field and stimulation applied through an electrode to generate balanced clusters, whose effect on synaptic strengths will be measured.