Breault Macauley Smith, Fitzgerald Zachary B, Sacré Pierre, Gale John T, Sarma Sridevi V, González-Martínez Jorge A
Neuromedical Control Systems Laboratory, Department of Biomedical Engineering, Institute of Computational Medicine, Johns Hopkins University, Baltimore, MD, United States.
Department of Neurosurgery, Cleveland Clinic, Epilepsy Center, Neurological Institute, Cleveland, OH, United States.
Front Neurosci. 2019 Jul 16;13:715. doi: 10.3389/fnins.2019.00715. eCollection 2019.
Sensorimotor control studies have predominantly focused on how motor regions of the brain relay basic movement-related information such as position and velocity. However, motor control is often complex, involving the integration of sensory information, planning, visuomotor tracking, spatial mapping, retrieval and storage of memories, and may even be emotionally driven. This suggests that many more regions in the brain are involved beyond premotor and motor cortices. In this study, we exploited an experimental setup wherein activity from over 87 non-motor structures of the brain were recorded in eight human subjects executing a center-out motor task. The subjects were implanted with depth electrodes for clinical purposes. Using training data, we constructed subject-specific models that related spectral power of neural activity in six different frequency bands as well as a combined model containing the aggregation of multiple frequency bands to movement speed. We then tested the models by evaluating their ability to decode movement speed from neural activity in the test data set. The best models achieved a correlation of 0.38 ± 0.03 (mean ± standard deviation). Further, the decoded speeds matched the categorical representation of the test trials as correct or incorrect with an accuracy of 70 ± 2.75% across subjects. These models included features from regions such as the right hippocampus, left and right middle temporal gyrus, intraparietal sulcus, and left fusiform gyrus across multiple frequency bands. Perhaps more interestingly, we observed that the non-dominant hemisphere (ipsilateral to dominant hand) was most influential in decoding movement speed.
感觉运动控制研究主要集中在大脑的运动区域如何传递与基本运动相关的信息，如位置和速度。然而，运动控制通常很复杂，涉及感觉信息的整合、计划、视觉运动跟踪、空间映射、记忆的检索和存储，甚至可能受情绪驱动。这表明大脑中涉及的区域远不止运动前区和运动皮层。在本研究中，我们利用了一种实验装置，在执行中心向外运动任务的八名人类受试者中记录了大脑87个以上非运动结构的活动。受试者因临床目的植入了深度电极。利用训练数据，我们构建了特定于受试者的模型，将六个不同频段的神经活动频谱功率以及包含多个频段聚合的组合模型与运动速度相关联。然后，我们通过评估模型从测试数据集中的神经活动解码运动速度的能力来测试这些模型。最佳模型的相关性为0.38±0.03（平均值±标准差）。此外，解码速度与测试试验的分类表示匹配，受试者的准确率为70±2.75%。这些模型包括来自多个频段的右侧海马体、左右颞中回、顶内沟和左侧梭状回等区域的特征。也许更有趣的是，我们观察到非优势半球（与优势手同侧）在解码运动速度方面最具影响力。
