Motor neural dynamics of free behavior enabled through 3D computer vision

通过 3D 计算机视觉实现自由行为的运动神经动力学

• 批准号: 10546485
• 负责人: Paul Nuyujukian
• 金额: $ 38.2万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-15 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10546485
• 关键词: 3-Dimensional; Algorithms; Behavior; Behavioral; Brain; Computer Vision Systems; Data; Data Set; Development; Dimensions; Electrodes; Food; Implant; Individual; Limb structure; Modeling; Motor; Movement; Neurosciences; Organ; Persons; Recovery; Research; Running; Stroke; System; Techniques; Technology; Time; Utah; Walking; Work; brain machine interface; disability; experimental study; free behavior; insight; interest; kinematics; mind control; motor behavior; motor disorder; neural; neuromechanism; novel; tool

Motor systems neuroscience seeks to understand the neural mechanisms behind voluntary movement. The last two decades have witnessed a transformation in this ?eld with the use of multielectrode recordings and statistical estimation and modeling techniques. These technological advances have yielded rich, low-dimensional neural dynamics that are suggestive of the mechanisms underlying behavior. To minimize confounds, the overwhelming majority of these studies utilize behavioral constraint to isolate just the behaviors of interest for study. While effective for generating many behav- iorally similar trials, this may have the unintentional consequence of arti?cially constraining neural dynamics to a subset of its full range. This project seeks to better understand whether and how neural dynamics change with respect to the behavioral context (constrained vs unconstrained) they occur in. This type of work has historically been challenging because capturing limb kinematics in an unconstrained setting is non-trivial. However, with recent advances in computer vision technology, accurate 3D cameras have become accessible tools for research. This study will leverage these new 3D cameras to capture unconstrained behavior in a large observational enclosure. Novel algorithms for the processing of these 3D datasets will be used to estimate the subject's pose. These limb kinematics will be synchronized and correlated against neural data recorded from one or more 96-channel Utah electrode array(s) implanted in motor regions of cortex. Low-dimensional neural dynamics can be generated from this synchronized data. The dynamics will be explored in the context of two behaviors in the enclosure: walking and reaching for food on the ?oor. The dimensionality of the dynamics in these two contexts will be compared, with the null hypothesis stating that there is no difference in dimensionality of dynamics between these behavioral contexts. A subsequent experiment will be to again construct low-dimensional neural dynamics, but this time include a context of behaviorally constrained reaching. The dynamics from these three contexts will be compared to ?nd a common subspace (subset of dimensions) shared among all. This subspace, if it exists (the null hypothesis is that there is no difference in the dynamics between the behavioral contexts), represents fundamental dynamics that are invariant of the behavioral context, suggestive of causal necessity of this subspace. Taken together, these studies will further our understanding of how low-dimensional neural dynamics drive motor be- havior. This insight has implications for the development of ambulatory brain-machine interfaces and may inform the treatment of individuals with motor disorders such as stroke.

运动系统神经科学旨在了解自主运动背后的神经机制。最后两 几十年来见证了这一转变使用多电极记录和统计估计的ELD 和建模技术。这些技术进步产生了丰富的低维神经动力学， 暗示了行为背后的机制。为了尽量减少混淆，这些研究中的绝大多数 利用行为约束来隔离感兴趣的行为以供研究。虽然有效地产生了许多反病毒- 这可能会产生阿尔蒂的意外后果？将神经动力学约束为子集 其全部范围。 该项目旨在更好地了解神经动力学是否以及如何随着行为背景而变化 （受约束与不受约束），它们发生在。这种类型的工作历来具有挑战性，因为捕捉 无约束环境中的肢体运动学是重要的。然而，随着计算机视觉技术的最新进展， 精确的3D相机已经成为研究的可用工具。这项研究将利用这些新的3D相机来捕捉 在一个大的观察围栏中不受约束的行为。处理这些3D数据集的新算法将 可以用来估计对象的姿势。这些肢体运动学将与神经数据同步和相关 从植入皮层运动区的一个或多个96通道犹他州电极阵列记录。 低维神经动力学可以从这个同步的数据中产生。我们将在 两个行为的背景下，在围栏：步行和达到食物上？哦。动力学的维度 在这两种情况下将进行比较，与零假设，说明没有差异的维度 这些行为背景之间的动态。随后的实验将再次构建低维神经网络， 动态，但这一次包括行为约束的范围。这三种背景下的动态 会被比作？一个公共的子空间（维度的子集）在所有人之间共享。这个子空间，如果它存在的话（ 零假设是行为背景之间的动态没有差异），代表基本的 动态是不变的行为背景下，暗示因果必然性的这个子空间。 总之，这些研究将进一步加深我们对低维神经动力学如何驱动运动的理解- 你好这一见解对非卧床脑机接口的发展有影响，并可能为研究人员提供信息。 治疗患有运动障碍如中风的个体。