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使用移动脑-体成像评估步行过程中的神经运动学和神经肌肉连接性。

Assessing Neurokinematic and Neuromuscular Connectivity During Walking Using Mobile Brain-Body Imaging.

作者信息

Zhao Mingqi, Bonassi Gaia, Samogin Jessica, Taberna Gaia Amaranta, Porcaro Camillo, Pelosin Elisa, Avanzino Laura, Mantini Dante

机构信息

Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium.

S.C. Medicina Fisica e Riabilitazione Ospedaliera, Azienda Sanitaria Locale Chiavarese, Genoa, Italy.

出版信息

Front Neurosci. 2022 Jun 3;16:912075. doi: 10.3389/fnins.2022.912075. eCollection 2022.

Abstract

Gait is a common but rather complex activity that supports mobility in daily life. It requires indeed sophisticated coordination of lower and upper limbs, controlled by the nervous system. The relationship between limb kinematics and muscular activity with neural activity, referred to as neurokinematic and neuromuscular connectivity (NKC/NMC) respectively, still needs to be elucidated. Recently developed analysis techniques for mobile high-density electroencephalography (hdEEG) recordings have enabled investigations of gait-related neural modulations at the brain level. To shed light on gait-related neurokinematic and neuromuscular connectivity patterns in the brain, we performed a mobile brain/body imaging (MoBI) study in young healthy participants. In each participant, we collected hdEEG signals and limb velocity/electromyography signals during treadmill walking. We reconstructed neural signals in the alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-50 Hz) frequency bands, and assessed the co-modulations of their power envelopes with myogenic/velocity envelopes. Our results showed that the myogenic signals have larger discriminative power in evaluating gait-related brain-body connectivity with respect to kinematic signals. A detailed analysis of neuromuscular connectivity patterns in the brain revealed robust responses in the alpha and beta bands over the lower limb representation in the primary sensorimotor cortex. There responses were largely contralateral with respect to the body sensor used for the analysis. By using a voxel-wise analysis of variance on the NMC images, we revealed clear modulations across body sensors; the variability across frequency bands was relatively lower, and below significance. Overall, our study demonstrates that a MoBI platform based on hdEEG can be used for the investigation of gait-related brain-body connectivity. Future studies might involve more complex walking conditions to gain a better understanding of fundamental neural processes associated with gait control, or might be conducted in individuals with neuromotor disorders to identify neural markers of abnormal gait.

摘要

步态是一种常见但相当复杂的活动,它支持日常生活中的移动性。它确实需要由神经系统控制的下肢和上肢的复杂协调。肢体运动学与肌肉活动和神经活动之间的关系,分别称为神经运动学和神经肌肉连接性(NKC/NMC),仍有待阐明。最近开发的用于移动高密度脑电图(hdEEG)记录的分析技术,使得在大脑层面研究与步态相关的神经调制成为可能。为了阐明大脑中与步态相关的神经运动学和神经肌肉连接模式,我们在年轻健康参与者中进行了一项移动脑/体成像(MoBI)研究。在每个参与者中,我们在跑步机行走过程中收集了hdEEG信号和肢体速度/肌电图信号。我们重建了α(8 - 13 Hz)、β(13 - 30 Hz)和γ(30 - 50 Hz)频段的神经信号,并评估了它们的功率包络与肌源性/速度包络的共调制。我们的结果表明,与运动学信号相比,肌源性信号在评估与步态相关的脑-体连接性方面具有更大的判别力。对大脑中神经肌肉连接模式的详细分析揭示了初级感觉运动皮层中下肢代表区域在α和β频段的强烈反应。这些反应在很大程度上与用于分析的身体传感器对侧。通过对NMC图像进行基于体素的方差分析,我们揭示了身体传感器之间的明显调制;各频段的变异性相对较低,且低于显著性水平。总体而言,我们的研究表明基于hdEEG的MoBI平台可用于研究与步态相关的脑-体连接性。未来的研究可能涉及更复杂的行走条件,以更好地理解与步态控制相关的基本神经过程,或者可能在患有神经运动障碍的个体中进行,以识别异常步态的神经标志物。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f06/9204106/f9eb06a0e91b/fnins-16-912075-g001.jpg

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