Jacob Deborah, Guerrini Lorena, Pescaglia Federica, Pierucci Simona, Gelormini Carmine, Minutolo Vincenzo, Fratini Antonio, Di Lorenzo Giorgio, Petersen Hannes, Gargiulo Paolo
Institute of Biomedical and Neural Engineering, Reykjavik University, Reykjavik, Iceland.
Department of Engineering, University of Campania L. Vanvitelli, Aversa, Italy.
Front Hum Neurosci. 2023 Jul 17;17:1197142. doi: 10.3389/fnhum.2023.1197142. eCollection 2023.
There is accumulating evidence that many pathological conditions affecting human balance are consequence of postural control (PC) failure or overstimulation such as in motion sickness. Our research shows the potential of using the response to a complex postural control task to assess patients with early-stage Parkinson's Disease (PD).
We developed a unique measurement model, where the PC task is triggered by a moving platform in a virtual reality environment while simultaneously recording EEG, EMG and CoP signals. This novel paradigm of assessment is called BioVRSea. We studied the interplay between biosignals and their differences in healthy subjects and with early-stage PD.
Despite the limited number of subjects (29 healthy and nine PD) the results of our work show significant differences in several biosignals features, demonstrating that the combined output of posturography, muscle activation and cortical response is capable of distinguishing healthy from pathological.
The differences measured following the end of the platform movement are remarkable, as the induced sway is different between the two groups and triggers statistically relevant cortical activities in α and θ bands. This is a first important step to develop a multi-metric signature able to quantify PC and distinguish healthy from pathological response.
越来越多的证据表明,许多影响人体平衡的病理状况是姿势控制(PC)失败或过度刺激的结果,如晕动病。我们的研究显示了利用对复杂姿势控制任务的反应来评估早期帕金森病(PD)患者的潜力。
我们开发了一种独特的测量模型,其中姿势控制任务由虚拟现实环境中的移动平台触发,同时记录脑电图(EEG)、肌电图(EMG)和压力中心(CoP)信号。这种新颖的评估范式称为BioVRSea。我们研究了生物信号之间的相互作用以及它们在健康受试者和早期帕金森病患者中的差异。
尽管受试者数量有限(29名健康受试者和9名帕金森病患者),但我们的研究结果显示,在几个生物信号特征方面存在显著差异,表明姿势描记法、肌肉激活和皮层反应的综合输出能够区分健康人和病人。
平台运动结束后测量到的差异非常显著,因为两组之间诱发的摇摆不同,并触发了α和θ波段中具有统计学意义的皮层活动。这是开发一种能够量化姿势控制并区分健康和病理反应的多指标特征的重要的第一步。