Wimmer Michael, Kostoglou Kyriaki, Müller-Putz Gernot R
Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
BioTechMed-Graz, Graz, Austria.
Front Hum Neurosci. 2022 Mar 11;16:858873. doi: 10.3389/fnhum.2022.858873. eCollection 2022.
Electroencephalographic (EEG) correlates of movement have been studied extensively over many years. In the present work, we focus on investigating neural correlates that originate from the spine and study their connectivity to corresponding signals from the sensorimotor cortex using multivariate autoregressive (MVAR) models. To study cortico-spinal interactions, we simultaneously measured spinal cord potentials (SCPs) and somatosensory evoked potentials (SEPs) of wrist movements elicited by neuromuscular electrical stimulation. We identified directional connections between spine and cortex during both the extension and flexion of the wrist using only non-invasive recording techniques. Our connectivity estimation results are in alignment with various studies investigating correlates of movement, i.e., we found the contralateral side of the sensorimotor cortex to be the main sink of information as well as the spine to be the main source of it. Both types of movement could also be clearly identified in the time-domain signals.
多年来,运动的脑电图(EEG)相关性已得到广泛研究。在本研究中,我们专注于研究源自脊髓的神经相关性,并使用多元自回归(MVAR)模型研究它们与感觉运动皮层相应信号的连接性。为了研究皮质 - 脊髓相互作用,我们同时测量了神经肌肉电刺激引发的腕部运动的脊髓电位(SCP)和体感诱发电位(SEP)。我们仅使用非侵入性记录技术确定了腕部伸展和屈曲过程中脊髓与皮层之间的定向连接。我们的连接性估计结果与各种研究运动相关性的研究一致,即我们发现感觉运动皮层的对侧是信息的主要汇聚点,而脊髓是信息的主要来源。两种运动类型在时域信号中也都能清晰识别。