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

大脑是一个令人惊叹的器官，负责许多重要的功能，包括认知、注意力、情感、感知、记忆和运动控制。许多大脑功能和障碍被认为是动态起源的；例如，有人假设帕金森氏病的一些症状是由于大脑运动控制区的病理同步神经活动造成的。最近的研究表明，FDA批准的一种治疗帕金森震颤的方法，称为脑深部刺激，是有效的，因为它通过集群部分地使神经活动去同步，即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。这项研究将利用工程技术、数学原理、计算机模拟和体外实验来开发更节能的电流刺激，以促进这种聚集。此外，还将开发增强有益神经可塑性的刺激，其中神经元根据其活动模式改变其连接强度，这些工作可能对疾病的治疗以及需要可塑性的情况，如学习、记忆和中风和脊髓损伤的恢复是重要的。本研究将利用工程技术、数学原理、计算机模拟和体外实验来开发有效的电刺激，以有益的方式控制神经种群。这将包括设计能量最小化的刺激，使神经群体分裂成平衡的簇，其中每个簇包含几乎相同比例的总群体，相邻簇在相位上大致相等，这种部分去同步的状态最近的研究表明，这种状态是深部脑刺激治疗帕金森病的标准方案成功的原因。此外，Hebbian突触可塑性模型将与最优控制理论相结合，设计最佳促进可塑性的刺激，使突触连接发生有益的长期变化，预计这项工作将对帕金森氏症和其他疾病(如癫痫和抑郁症)以及可塑性是可取的情况，如学习、记忆和中风和脊髓损伤的恢复具有重要意义。可塑性研究还将包括体外脑片实验，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.