The dataset for the Electrospinography (ESG) for non-invasively recording spinal sensorimotor networks in humans project

Objective. Currently, few non-invasive measures exist for directly measuring spinal sensorimotor networks. Electrospinography (ESG) is one non-invasive method but is primarily used to measure evoked responses or for monitoring the spinal cord during surgery. Our objectives were to evaluate the feasibility of ESG to measure spinal sensorimotor networks by determining spatiotemporal and functional connectivity changes during single-joint movements at the spinal and cortical levels.Approach. We synchronously recorded electroencephalography (EEG), electromyography, and ESG in ten neurologically intact adults while performing one of three lower-limb tasks (no movement, plantar-flexion and knee flexion) in the prone position. A multi-pronged approach was applied for removing artifacts using H∞ filtering, artifact subspace reconstruction and independent component analysis. Next, data were segmented by task and independent components (ICs) of EEG were clustered across participants. Within-participant analysis of ICs and ESG data was conducted, and ESG was characterized in the time and frequency domains. Generalized Partial Directed Coherence (gPDC) analysis was performed within ICs and between ICs and ESG data by participant and task.Results. K-means clustering resulted in five clusters of ICs at Brodmann areas (BA) 9, BA 8, BA 39, BA 4, and BA 22. Areas associated with motor planning, working memory, visual processing, movement, and attention, respectively. Time-frequency analysis of ESG data found localized changes during movement execution when compared to no movement. Lastly, we found bi- directional changes in functional connectivity (p < 0.05, adjusted for multiple comparisons) within IC’s and between IC’s and ESG sensors during movement when compared to the no movement condition.Significance. To our knowledge this is the first report using high density ESG for characterizing lower limb movements. Our findings provide support that ESG contains information about efferent and afferent signaling in neurologically intact adults and suggests that we can utilize ESG to directly study the spinal cord.

目的。目前，可直接测量脊髓感觉运动网络的非侵入性手段较为匮乏。脊髓电图（Electrospinography，ESG）是一种非侵入性方法，但主要用于测量诱发反应或在手术期间监测脊髓。本研究的目的是通过确定脊髓和皮层水平下单关节运动期间的时空及功能连接变化，评估ESG用于测量脊髓感觉运动网络的可行性。
方法。我们在10名神经系统完整的成年人处于俯卧位时，同步记录其脑电图（electroencephalography，EEG）、肌电图和ESG，同时他们执行三种下肢任务之一（无运动、跖屈和屈膝）。采用多管齐下的方法去除伪影，包括H∞滤波、伪影子空间重构和独立成分分析。接下来，按任务对数据进行分段，并对参与者间的EEG独立成分（independent components，ICs）进行聚类。对ICs和ESG数据进行参与者内分析，并在时域和频域对ESG进行表征。按参与者和任务，在ICs内部以及ICs与ESG数据之间进行广义偏定向相干（Generalized Partial Directed Coherence，gPDC）分析。
结果。K均值聚类在布罗德曼分区（Brodmann areas，BA）9、8、39、4和22处得到5组ICs聚类，这些区域分别与运动规划、工作记忆、视觉处理、运动和注意力相关。ESG数据的时频分析发现，与无运动状态相比，运动执行期间存在局部变化。最后，与无运动条件相比，我们发现运动期间ICs内部以及ICs与ESG传感器之间的功能连接存在双向变化（p < 0.05，经多重比较校正）。
意义。据我们所知，这是首次使用高密度ESG表征下肢运动的报告。我们的研究结果支持ESG包含神经系统完整成年人的传出和传入信号信息，并表明我们可利用ESG直接研究脊髓。