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Neural dynamics and adaption for brain machine interface control

脑机接口控制的神经动力学和适应

基本信息

批准号：
9765066
负责人：
Saurabh Vyas
金额：
$ 3.46万
依托单位：
STANFORD UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-09-30 至 2020-09-29
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9765066
关键词：
Address Area Artificial Arm Automobile Driving Behavior Brain Clinical Clinical Trials Complex Computers Coupled Data Dimensions Electrodes Freedom Generations Goals Implanted Electrodes Individual Intention Joint Prosthesis Joints Learning Limb Prosthesis Limb structure Link Literature Macaca Macaca mulatta Machine Learning Maps Measures Methods Modeling Monkeys Motor Motor Cortex Movement Nervous System Trauma Neurodegenerative Disorders Neurons Paralysed Pattern Performance Population Property Prosthesis Quality of life Rehabilitation device Robotics Spinal Cord Statistical Data Interpretation Statistical Methods Statistical Models Structure System Systems Theory Techniques Technology Time Training Visual Volition Work arm arm movement brain machine interface clinical application dynamic system experimental study falls high dimensionality improved insight motor impairment neural model neural patterning neurophysiology neuroregulation nonhuman primate pre-clinical prosthesis control rehearsal relating to nervous system

项目摘要

Project Summary/Abstract Millions of people suffer from some form of paralysis. In most of these cases the connection between the brain and the spinal cord is damaged, however, the motor cortex is healthy and intact. Thus, for these individuals, brain-machine interfaces (BMIs) hold significant promise for improving quality of life. BMIs decode an individual's intention to move by utilizing statistical models of neural activity patterns recorded from the motor cortex using implanted electrode arrays. While these methods have been encouraging in preclinical experiments and clinical trials for controlling thought-driven 2D computer cursors, they suffer from poor performance when applied to higher degrees-of-freedom (e.g., robotic limbs), and are not robust to the inevitable degradation of the electrode array. In order to address these clinical needs, this project starts from the recent observation that just as some behaviors are easier to learn, some patterns of neural activity, termed neural states, are also easier to generate. The overarching goal of this project is to elucidate if these “easy to generate” neural states can be used to robustly control a prosthetic arm. This is a significant departure from current decoding methods, which incorporate little to no information about the motor system, especially its ability to learn and adapt. The first major aim of this work is to develop experiments and analysis methods in order to find these “easy to generate” neural states in the non- human primate (i.e., rhesus monkey) motor system. Here “easy to generate” can be understood as the monkey's ability to volitionally generate that particular neural state. The second major aim of this work is to characterize the properties of the motor system that enable some states to be more easily generated than others. Prior work in our lab has shown that motor cortical population activity has well-defined structure, as predicted by dynamical system theory. These dynamics cause neural states to evolve in lawful ways through time. The work here will extend these findings by characterizing the dynamics associated with a monkey learning to generate a neural state. Finally, the third major aim of this work is to determine if neural states that monkeys can volitionally generate can be utilized for robust control of a prosthetic arm. The central hypothesis of this work is that building a model that only utilizes firing patterns that can be easily generated (as determined experimentally) will enable robust and high-performance control of a prosthetic arm. If successful, this study could have significant clinical impact by presenting a new paradigm to enable robust control of a prosthesis.

项目总结/摘要数百万人患有某种形式的瘫痪。在大多数情况下，大脑和大脑之间的联系脊髓受损，但运动皮层健康完好。因此，对于这些人来说，脑机接口（BMI）对改善生活质量具有重要意义。BMI解码个人的移动意图利用使用植入的电极阵列从运动皮层记录的神经活动模式的统计模型。而这些方法在临床前实验和临床试验中用于控制思维驱动的2D 计算机光标，它们在应用于更高自由度时性能较差（例如，机器人肢体），以及对于电极阵列的不可避免的劣化并不稳健。为了满足这些临床需求，该项目开始从最近的观察，正如一些行为更容易学习，一些神经活动的模式，称为神经国家，也更容易产生。这个项目的首要目标是阐明这些“容易产生”的神经状态可以用来鲁棒地控制假肢。这与当前的解码方法有很大的不同，几乎没有关于运动系统的信息，特别是它的学习和适应能力。第一个主要目标这项工作是发展实验和分析方法，以便在非- 人灵长类动物（即，恒河猴）运动系统。这里的“容易生成”可以理解为猴子的能力，产生特定的神经状态。这项工作的第二个主要目的是表征使某些状态比其他状态更容易产生的电动机系统。我们实验室的先前工作表明，如动力系统理论所预测的，皮层群体活动具有明确的结构。这些动态导致神经状态以合法的方式进化这里的工作将通过表征动力学来扩展这些发现与猴子学习产生神经状态有关。最后，这项工作的第三个主要目标是确定，猴子可以自愿产生的神经状态可以用于假肢的鲁棒控制。这项工作的假设是，建立一个模型，只利用发射模式，可以很容易地产生（如确定实验）将使假肢的鲁棒性和高性能控制成为可能。如果成功，这项研究可以通过提出一种新的范例来实现对假体的鲁棒控制，从而产生显著的临床影响。