CRCNS Research Proposal: Modeling neural dynamics of naturalistic movements across contexts

日常任务，如系鞋带，扔球，伸手拿杯子，涉及复杂的表演，需要在各种行为环境中精确和协调的手和手臂运动。我们的大脑是如何控制这些丰富的运动的，也就是说，运动控制的神经基础仍然是难以捉摸的。研究这种神经基础不仅将促进我们对大脑功能的理解，而且将对开发脑机接口以恢复残疾患者的功能以及理解帕金森病等神经系统疾病和设计脑深部电刺激疗法产生广泛的翻译影响。该提案研究了大脑运动皮层中的神经元群体如何通过利用运动实验来控制运动，这些运动实验同时记录了包括数千个神经元的大型运动皮层区域，并利用计算工具来揭示复杂神经群体数据中的行为相关结构。这项研究涉及计算和实验方法之间非常密切的合作。实验部分结合了各种不同的行为条件，产生丰富的运动曲目，同时记录动物模型中大量的运动皮层神经元。该项目的主要重点是运动运动学（运动轨迹）和运动前皮质中这些大型神经元群体的时间动力学之间的关系。为了研究这种关系，我们分析了计算组件，其特征神经种群动力学使用低维状态变量，这是直接相关的行为相关的运动学。这是通过利用线性动态建模方法来实现的，该方法可以识别运动皮层人群中的低维行为相关状态动态，检查这些状态如何变化，并在行为期间对局部和输入动态进行建模。将这些计算和实验组件整合在一起，将促进我们对运动皮层群体活动如何产生丰富运动的动力学原理的理解，并有可能在恢复运动受损患者的运动功能方面取得突破。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查进行评估来支持的搜索.

Everyday tasks, such as tying shoelaces, throwing a ball, reaching for a cup involve performing complex, require precise and coordinated hand and arm movements in various behavioral contexts. How our brain controls these rich movements; that is, the neural basis of motor control, remains elusive. Investigating this neural basis not only will advance our understanding of brain function, but also will have broad translational implications for developing brain-machine interfaces to restore function in disabled patients and for understanding neurological disorders such as Parkinson’s disease and devising deep brain stimulation therapies. This proposal investigates how a population of neurons in the motor cortex of the brain controls movements by utilizing motor experiments that record simultaneously from large motor cortical areas including thousands of neurons, and by leveraging computational tools that can uncover behaviorally relevant structure in the complex neural population data. This research involves a very close collaboration between computational and experimental methods. The experimental component incorporates a variety of different behavioral conditions that produce a rich repertoire of movements, while recording from large populations of motor cortical neurons in an animal model. The primary focus of this project is on the relationship between movement kinematics (the trajectory of motion) and the temporal dynamics of these large neuronal populations in motor and premotor cortex. To study this relationship, we analyze the computational component that characterizes neural population dynamics using a low-dimensional state variable that is directly related to behaviorally relevant movement kinematics. This is achieved by leveraging linear dynamical modeling methods that can identify low-dimensional behaviorally relevant state dynamics in motor cortical populations, examining how these states change, and modeling local and input dynamics during behavior. Integrating these computational and experimental components, will advance our understanding of the dynamical principles underlying how motor cortical population activity gives rise to rich movements and has the potential to lead to breakthroughs in restoring motor function in patients with impaired movement.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.