Structure and variability of delay activity in premotor cortex
Structure and variability of delay activity in premotor cortex
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DOI：
10.1371/journal.pcbi.1006808
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发表时间：
2019-02-01
影响因子：
4.3
通讯作者：
Shenoy, Krishna V.
中科院分区：
文献类型：
--
作者：
Even-Chen, Nir;Sheffer, Blue;Shenoy, Krishna V.
人们普遍认为，自愿迁移是在执行之前就计划好的。最近的研究假设，在准备过程中，运动皮层的神经活动作为一个初始条件，种子进行神经动力学。在这里，我们详细研究了这些初始条件，通过研究1）不同河段的神经状态组织和2）这些神经状态在试验之间的方差。我们研究了猕猴运动前区皮层（PMD）的人口水平的反应，在准备阶段的延迟中心外达到任务与密集的目标配置。我们发现，目标发病后，神经活动的单次试验收敛到神经状态，有一个明确的低维结构，这是由到达端点和最大速度的以下达到。此外，我们发现，在准备过程中的神经状态的变异性类似于在没有视觉反馈的情况下达到的空间变异性：在神经状态空间中，方向的变异性小于距离。我们还使用离线解码来理解这种神经群体结构对脑机接口（BMI）的影响。我们发现，到达之间的角度解码是依赖于到达距离，而弧长解码是独立的。因此，通过使用到达端点之间的弧长而不是它们之间的角度来量化离散BMI的解码性能可能更合适。最后，我们表明，在对比的共同概念，方向可以更好地解码比距离，他们的解码能力是可比的。这些结果为强调运动控制的动态神经过程提供了新的见解，并可以为BMI的设计提供信息。
Voluntary movements are widely considered to be planned before they are executed. Recent studies have hypothesized that neural activity in motor cortex during preparation acts as an initial condition' which seeds the proceeding neural dynamics. Here, we studied these initial conditions in detail by investigating 1) the organization of neural states for different reaches and 2) the variance of these neural states from trial to trial. We examined population-level responses in macaque premotor cortex (PMd) during the preparatory stage of an instructed-delay center-out reaching task with dense target configurations. We found that after target onset the neural activity on single trials converges to neural states that have a clear low-dimensional structure which is organized by both the reach endpoint and maximum speed of the following reach. Further, we found that variability of the neural states during preparation resembles the spatial variability of reaches made in the absence of visual feedback: there is less variability in direction than distance in neural state space. We also used offline decoding to understand the implications of this neural population structure for brain-machine interfaces (BMIs). We found that decoding of angle between reaches is dependent on reach distance, while decoding of arc-length is independent. Thus, it might be more appropriate to quantify decoding performance for discrete BMIs by using arc-length between reach end-points rather than the angle between them. Lastly, we show that in contrast to the common notion that direction can better be decoded than distance, their decoding capabilities are comparable. These results provide new insights into the dynamical neural processes that underline motor control and can inform the design of BMIs.