Miremadi Soraya, Yang Kai Wai, Kalra Akshat, Malladi Sri Lasya, Scott Julia A
Brain and Memory Care Lab, Department of Neuroscience, Santa Clara University, Santa Clara, CA, United States.
Brain and Memory Care Lab, Department of Electrical and Computer Engineering, Santa Clara University, Santa Clara, CA, United States.
Front Hum Neurosci. 2025 May 19;19:1537463. doi: 10.3389/fnhum.2025.1537463. eCollection 2025.
Virtual Reality mediated virtual embodiment training (VR-VET) is designed to reduce chronic pain, yet a neuroimaging marker predictive of outcomes or associated with clinical changes in pain has not been validated. This study considers four candidate EEG metrics that are associated with cognitive states of mental imagery, chronic pain intensity, and stress states. VR-VET with EEG enables measurement of these metrics and collection of kinematic data. Kinematic data serves as an indicator of functional movement. In a healthy population, this study assessed neuroimaging markers for cognitive processes involved in VET or pain perception.
EEG was collected in 16 healthy individuals during VR-VET. Candidate EEG metrics were computed. Position data for each hand was used to calculate smoothness of movement within each activity. EEG metrics and smoothness were compared between the breathwork activity and activities with active movement of arms.
Relative global alpha was significantly different in all VET activities compared to breathwork ( < 0.001). Specifically, relative posterior alpha power ( < 0.001) and relative mu ( = 0.026) were significantly lower in all active conditions. Smoothness of the active arm varied across VET activities and was reduced compared to breathwork ( < 0.001).
Neuroimaging markers are feasible to investigate VET mechanisms during movement. Relative global alpha is sensitive to VET states and may be related to motor imagery tasks or visual attention, making it a relevant EEG metric in the study of VET.
虚拟现实介导的虚拟化身训练(VR-VET)旨在减轻慢性疼痛,但尚未验证可预测结果或与疼痛临床变化相关的神经影像标记物。本研究考虑了与心理意象的认知状态、慢性疼痛强度和应激状态相关的四个候选脑电图指标。结合脑电图的VR-VET能够测量这些指标并收集运动学数据。运动学数据可作为功能运动的指标。在健康人群中,本研究评估了参与VET或疼痛感知的认知过程的神经影像标记物。
在VR-VET期间收集了16名健康个体的脑电图。计算了候选脑电图指标。使用每只手的位置数据来计算每个活动中运动的平滑度。比较了呼吸练习活动与手臂主动运动活动之间的脑电图指标和平滑度。
与呼吸练习相比,所有VET活动中的相对全局阿尔法均有显著差异(<0.001)。具体而言,在所有活动条件下,相对后阿尔法功率(<0.001)和相对缪波(=0.026)均显著降低。主动手臂的平滑度在不同的VET活动中有所变化,与呼吸练习相比有所降低(<0.001)。
神经影像标记物对于研究运动过程中的VET机制是可行的。相对全局阿尔法对VET状态敏感,可能与运动意象任务或视觉注意力有关,使其成为VET研究中的一个相关脑电图指标。