SENSORIMOTOR TRANSFORMATIONS IN CORTICAL MOTOR AREAS

这项研究的目标是了解大脑皮质运动场 (CMF)在选择和选择中使用外部感知和上下文信息 规划视觉指导的肢体运动。神经细胞的研究 皮质运动区的活动表明有秩序地招募 感觉相关，其次是联想，然后是运动相关 在这种行为中的活动。这些和其他观察结果表明 感觉-运动转换(SMT)的CMF模型 视觉指导肢体运动所需的信息分布在 时间，在每个马达领域内，在空间中，跨越CMF网络。 该模型的预测之一是早期招聘从事CMF的人 视觉信息的感觉和联想处理，以及后来的 招聘地区对其加工的机动要求较为严格。这 提案有4个具体目标：SA1：检验以下假设 其中视觉刺激(IS)的位置指示的空间SMT 有针对性的肢体运动指令有一个共同的序列 神经元招募(与感觉有关-大于运动- 相关活动)。神经元活动 将从运动皮质(MC)、背侧运动前区(PMD)、 以及执行分离感觉任务的猴子的前PMD， 关联的、与电机相关的处理。多元回归将是 在连续时间进程分析中用于确定比例 每个细胞的活动可归因于三种类型的处理。 人口分析将评估招募是否遵循 每个区域内的预测模式。SA2：为了检验假设 空间SMT需要一连串的神经元募集 通过CMF网络分布，及早招募CMF 从事感官加工，然后依次是那些专门为 联想，最后是马达加工。从MC、PMD、 将跨地区比较SA1中的Pre-PMD，以确定是否 神经元募集的模式分布在 CMF符合模型的预测。SA3：检验假设 在每个参与的CMF中，相同的神经元序列 预测空间SMT(SA1)的招募适用于视觉SMT 都是非空间的。神经元活动将从MC，即 辅助运动区(SMA)及其前运动区(Pre-SMA) 由颜色和形状指示的有针对性的肢体运动(不 位置)的视觉刺激。数据将以相同的方式进行分析 与SA1相同。SA4：为了检验非空间SMT需要 跨CMF网络的神经元募集模式 与预测的空间SMT(SA2)相当。收集的数据 SA3中的MC、SMA和Pre-SMA将跨CMF进行比较，以评估 招聘模式是否分布在整个网络上 与模型的预测相符。

The goal of this research is to learn how the cortical motor fields (CMFs) use exteroceptive and contextual information in the selection and planning of visually instructed limb movements. Studies of neuronal activity in cortical motor areas suggest the orderly recruitment of sensory-related, followed by associative, followed by motor-related activity during such behaviors. These and other observations suggest a model of the CMFs in which sensory-to-motor transformations (SMTs) required for visual instructed limb movements are distributed both in time, within each motor field, and in space, across the network of CMFs. Among the model's predictions are early recruitment of CMFs engaged in sensory and associative processing of visual information, and later recruitment of areas whose processing is more strictly motor. This proposal has 4 specific aims: SA1: To test the hypothesis that for spatial SMTs in which the location of a visual stimulus (IS) instructs a targeted limb movements instructed there is a common sequence of neuronal recruitment (sensory-related-greater than-greater than motor- related activity) within each participating CMF. Neuronal activity will be sampled from motor cortex (MC), the dorsaal premotor area (PMd), and pre-PMd in monkeys performing tasks that dissociate sensory, associative, and motor-related processing. Multiple regression will be used in a continuous time course analysis to determine the proportions of each cell's activity attributable to the 3 types of processing. Population analyses will assess whether recruitment follows the predicted pattern within each region. SA2: To test the hypothesis that spatial SMTs entail a cascading sequence of neuronal recruitment distributed across the network of CMFs, with early recruitment of CMFs engaged in sensory processing, followed in turn by those specialized for associative and finally motor processing. Data collected from MC, PMd, and pre-PMd in SA1 will be compared across regions, to determine whether the pattern of neuronal recruitment distributed across the network of CMFs conforms to the model's predictions. SA3: To test the hypothesis that within each participating CMF the same sequence of neuronal recruitment predicted for spatial SMTs (SA1) holds for visual SMTs that are non-spatial. Neuronal activity will be sampled from MC, the supplementary motor area (SMA), and pre-SMA in monkeys performing non- targeted limb movements instructed by the colors and shapes (not locations) of visual stimuli. Data will be analyzed in the same manner as in SA1. SA4: To test the hypothesis that non-spatial SMTs entail a pattern of neuronal recruitment distributed across the network of CMFs comparable to that predicted for spatial SMTs (SA2). Data collected from MC, SMA, and pre-SMA in SA3 will be compared across CMFs to assess whether the pattern of recruitment distributed across the network conforms to the model's predictions.