Monkey-to-human transfer of trained iBCI decoders through nonlinear alignment of neural population dynamics

项目摘要 近80%的四肢瘫痪患者会同意接受脑部手术，以恢复他们的手的一些功能。然而， 尽管在过去15年中取得了进展，但皮质内脑-计算机接口(IBCI)在很大程度上仍然 仅限于控制简单的机械手动作的运动学。此外，解码器训练数据 由用户观察并试图模仿的轨迹以及伴随的神经活动组成。 但这种基于观察的解码对于对力或肌肉活动(EMG)信号进行解码的iBCI来说是不可行的 这将是控制接触力和关节刚度的更接近仿生的iBCI所需的。我们现在 提出了一种激进的新方法，基于人工视觉中使用的机器学习技术 图像处理领域，用于将从猴子收集的数据计算出的解码器传输给人类 用一只瘫痪的手。这种方法的关键是观察到伴随着 运动很大程度上存在于记录神经元的神经状态空间内的低维流形上。 流形体内的“潜在信号”承载着关于潜在的行为的非常稳定的信息。 对iBCI很有用。然而，任何给定的神经元样本都将流形嵌入到不同的坐标系中。 我们以前曾使用典型相关分析(CCA)来重新排列这些流形，以进行刻板印象的试验- 基于任务的。我们使用了固定的BCI解码器和CCA对齐的潜在信号输入来预测手臂的运动 长达两年(“跨时间对齐”)。类似地，我们用一只猴子做了肌电预测 为不同的猴子计算的解码器(“跨对象对齐”)，甚至是从记录的M1信号 有脊髓损伤的人(“跨物种配对”)。但CCA只能对以下行为有效 试验对准，这对于一个人日常生活中的大多数典型运动来说是不可能的。在此，我们建议 开发一类基于Cycle-GAN的新工具，这是一个生成性对抗网络。因为Cycle-GaN工作正常 通过最小化流形内点云之间的距离，它可以应用于不受约束的 动静。在我们最初的测试中，当用于简单时间比对时，Cycle-GaN优于CCA 行为，但它在不受约束的行为和跨主题对齐方面失败了。我们建议优化循环- GAN通过纳入有关动力学的信息(目标1)，并通过最初限制特定区域 经过对齐过程的两个部分重叠的流形(目标2)。我们将开发和验证 这些方法基于在实验室中记录的刻板印象运动行为的数据，基于收集的不受约束的数据 从被关在一个大塑料笼子里的猴子那里，从患有脊髓损伤的人类那里，通过一个 与匹兹堡大学和芝加哥大学的团队合作。最终结果将是一组 新的分析工具，既可以用来理解大脑对复杂运动行为的表征， 并将应用程序开发到更广泛适用的新类别的iBCI。

Project Summary Nearly 80% of persons with quadriplegia would consent to brain surgery to regain some use of their hands. Yet, despite advances made in the past 15 years, Intracortical Brain-Computer Interfaces (iBCIs) remain largely limited to controlling only the kinematics of simple robotic hand movements. Furthermore, decoder training data consists of a trajectory the user observes and attempts to imitate, together with the accompanying neural activity. But this “observation-based” decoding is not feasible for iBCIs that decode force or muscle activity (EMG), signals which will be needed for more nearly biomimetic iBCIs controlling contact forces and joint stiffness. We now propose a radical new approach, based on machine learning techniques used in the artificial vison and image manipulation fields, to “transfer” a decoder computed from data collected from a monkey to a human with a paralyzed hand. The key to the approach is the observation that the neural activity accompanying movement lies largely on a low-dimensional manifold within the neural state space of recorded neurons. The “latent signals” within the manifold bear remarkably stable information about behavior that is potentially useful for iBCIs. However, any given sample of neurons embeds the manifold in a different coordinate system. We have previously used Canonical Correlation Analysis (CCA) to realign these manifolds for stereotypic, trial- based tasks. We used a fixed BCI decoder with CCA-aligned latent-signal inputs to predict arm movement for as long as two years (“cross-time alignment”). Similarly, we made EMG predictions from a monkey using a decoder computed for a different monkey (“cross-subject alignment”), and even from M1 signals recorded from a person with a spinal cord injury (“cross-species alignment”). But CCA can work only on behaviors that can be trial-aligned, which is not possible for most movements typical of a person's daily life. Here, we propose to develop a new class of tools based on cycle-GAN, a Generative Adversarial Network. Because cycle-GAN works by minimizing the distance between point clouds within the manifold, it can be applied to unconstrained movements. In our initial tests, Cycle-GAN outperformed CCA when used for cross-time alignment of simple behaviors, but it failed for unconstrained behaviors and cross-subject alignment. We propose to optimize cycle- GAN by incorporating information about dynamics (Aim 1), and by initially constraining the particular regions of two partially overlapping manifolds that we subject to the alignment process (Aim 2). We will develop and validate these methods on data recorded during stereotypical motor behaviors in the lab, on unconstrained data collected wirelessly from monkeys housed in a large, plastic cage, and from humans with spinal cord injury through an on- going collaboration with groups at the Universities of Pittsburgh and Chicago. The final result will be a set of novel analytical tools that can be used both to understand the brain's representation of complex motor behaviors, and to develop applications to a new class of more broadly applicable iBCIs.