Kia Maryam, Mirian Maryam S, Soori Saeed, Saedi Saeed, Arasteh Emad, Faramarzi Mohamad Hosein, Chinchani Abhijit, Lee Soojin, Luczak Artur, McKeown Martin J
Department of Neuroscience, Canadian Centre for Behavioural Neuroscience, University of Lethbridge, Lethbridge, AB, Canada.
Pacific Parkinson Research Centre, University of British Columbia, Vancouver, BC, Canada.
Front Hum Neurosci. 2025 May 14;19:1566566. doi: 10.3389/fnhum.2025.1566566. eCollection 2025.
Parkinson's disease (PD) impairs motor preparation due to basal ganglia dysfunction, contributing to motor deficits. Galvanic Vestibular Stimulation (GVS), a non-invasive neuromodulation technique, shows promise in enhancing motor function in PD, but its underlying neural mechanisms are poorly understood. This study employs a Deep Koopman model to linearize and analyze preparatory EEG dynamics in PD, hypothesizing that GVS restores cortical activity patterns critical for motor planning.
EEG data from 18 PD participants (on/off medication) and 18 healthy controls were collected during a preparatory phase of a motor task under three conditions: sham, GVS1 (50-100 Hz multi-sine), and GVS2 (100-150 Hz multi-sine). A Deep Koopman framework mapped EEG signals into a three-dimensional latent space for linear dynamical analysis. Temporal dynamics were assessed via eigenvalue analysis, spatial contributions via regression-based scalp mapping, and motor performance correlations via Pearson's coefficients. A Linear Quadratic Regulator (LQR) simulated control of PD dynamics toward healthy patterns.
The Deep Koopman model accurately captured EEG dynamics, with eigenvalue analysis showing no significant temporal dynamic differences across groups. Spatial contribution analysis revealed that PD-Off sham conditions deviated most from healthy control EEG patterns, while GVS and medication significantly reduced these deviations, aligning PD patterns closer to controls. Closer alignment correlated with improved motor performance metrics, including reduced reaction and squeeze times. LQR control effectively guided PD neural dynamics toward healthy trajectories in the latent space.
GVS enhances motor preparation in PD by restoring healthy cortical EEG patterns, with additive benefits from dopaminergic medication. The Deep Koopman framework offers a powerful approach for dissecting complex EEG dynamics and designing targeted neuromodulation strategies. These findings elucidate GVS's therapeutic mechanisms and highlight its potential for personalized PD interventions, warranting further exploration in larger cohorts and varied stimulation protocols.
帕金森病（PD）由于基底神经节功能障碍而损害运动准备，导致运动缺陷。电前庭刺激（GVS）是一种非侵入性神经调节技术，在改善帕金森病运动功能方面显示出前景，但其潜在神经机制尚不清楚。本研究采用深度库普曼模型对帕金森病准备期脑电图动态进行线性化和分析，假设GVS可恢复对运动规划至关重要的皮质活动模式。
在运动任务的准备阶段，于三种条件下收集了18名帕金森病参与者（服药/未服药）和18名健康对照者的脑电图数据：假刺激、GVS1（50 - 100赫兹多正弦波）和GVS2（100 - 150赫兹多正弦波）。深度库普曼框架将脑电图信号映射到三维潜在空间进行线性动力学分析。通过特征值分析评估时间动态，通过基于回归的头皮映射评估空间贡献，通过皮尔逊系数评估运动表现相关性。线性二次调节器（LQR）模拟对帕金森病动态向健康模式的控制。
深度库普曼模型准确捕捉了脑电图动态，特征值分析显示各组间无显著时间动态差异。空间贡献分析表明，未服药的帕金森病假刺激条件与健康对照脑电图模式偏差最大，而GVS和药物显著减少了这些偏差，使帕金森病模式更接近对照。更接近对照与改善的运动表现指标相关，包括缩短反应时间和挤压时间。LQR控制有效地将帕金森病神经动态引导至潜在空间中的健康轨迹。
GVS通过恢复健康的皮质脑电图模式增强帕金森病的运动准备，多巴胺能药物有附加益处。深度库普曼框架为剖析复杂的脑电图动态和设计靶向神经调节策略提供了有力方法。这些发现阐明了GVS的治疗机制，并突出了其在个性化帕金森病干预中的潜力，值得在更大队列和不同刺激方案中进一步探索。
