Sharma Rashi, Thirugnanasambandam Nivethida
Human Motor Neurophysiology and Neuromodulation Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, India.
Human Motor Neurophysiology and Neuromodulation Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, India.
Brain Res. 2025 Mar 1;1850:149406. doi: 10.1016/j.brainres.2024.149406. Epub 2024 Dec 19.
Individuals with Parkinson's disease (PD) exhibit altered reward processing, reflected by a decreased amplitude of an event-related potential (ERP) marker called reward positivity (RewP). Most studies have used RewP to investigate reward behavior due to the high temporal resolution of EEG and its high sensitivity. However, traditional single-electrode ERP analyses often overlook the intricate dynamics of non-phase-locked oscillatory activity and the complex interactions within these neural oscillatory patterns. Studying oscillatory activity is crucial as it provides mechanistic insights into the functional, spatial, and temporal aspects of neuronal processing. To address this gap, we employed a data-driven approach to identify EEG-based markers associated with PD reward processing deficits. Using an openly available 64-channel EEG dataset of 28 age- and sex-matched PD and control participants during a reinforcement learning task, we conducted a comprehensive secondary analysis. First, we employed a cluster-based permutation method to extract ERP markers, finding a consistent decrease in reward positivity in PD, regardless of medication status. Additionally, through region of interest (ROI) analysis on time-frequency data, we identified specific oscillatory patterns during reward processing. PD patients exhibited attenuated theta power and increased gamma power compared to healthy controls (HC). Notably, within the PD group, those off medication showed anterior localization of high gamma power, while those on medication displayed higher posterior gamma power. Building upon these findings, we explored phase-amplitude coupling between theta phase and gamma amplitude measured by the modulation index. We observed a trend of decreased theta-gamma coupling in PD patients, with statistically significant differences between on and off medication conditions. These results highlight the potential role of theta-gamma coupling as a neuromodulatory target for improving goal-oriented behavior in PD. Our correlation analyses suggest that high gamma power is linked to longer disease duration, while reduced reward positivity and low theta-gamma coupling may serve as markers of the dopaminergic impact on reward processing. Thus, our study unveils the intricate time-frequency dynamics underlying reward processing deficits in PD, emphasizing the utility of a data-driven approach to elucidate neural mechanisms and to identify potential therapeutic targets.
帕金森病（PD）患者表现出奖赏处理的改变，这通过一种称为奖赏正性波（RewP）的事件相关电位（ERP）标记物的振幅降低得以体现。由于脑电图（EEG）具有高时间分辨率及其高灵敏度，大多数研究都使用RewP来研究奖赏行为。然而，传统的单电极ERP分析往往忽略了非锁相振荡活动的复杂动态以及这些神经振荡模式内的复杂相互作用。研究振荡活动至关重要，因为它能为神经元处理的功能、空间和时间方面提供机制性见解。为了弥补这一差距，我们采用了一种数据驱动的方法来识别与PD奖赏处理缺陷相关的基于EEG的标记物。利用一个公开可用的64通道EEG数据集，该数据集包含28名年龄和性别匹配的PD患者及对照参与者在强化学习任务期间的数据，我们进行了全面的二次分析。首先，我们采用基于聚类的置换方法来提取ERP标记物，发现无论用药状态如何，PD患者的奖赏正性波均持续降低。此外，通过对时频数据进行感兴趣区域（ROI）分析，我们确定了奖赏处理过程中的特定振荡模式。与健康对照（HC）相比，PD患者表现出θ波功率减弱和γ波功率增加。值得注意的是，在PD组中，未用药者的高γ波功率位于前部，而用药者的后部γ波功率更高。基于这些发现，我们通过调制指数探索了θ相位与γ振幅之间的相位 - 振幅耦合。我们观察到PD患者中θ - γ耦合有降低的趋势，在用药和未用药状态之间存在统计学显著差异。这些结果突出了θ - γ耦合作为改善PD患者目标导向行为的神经调节靶点的潜在作用。我们的相关性分析表明，高γ波功率与疾病持续时间较长有关，而奖赏正性波降低和低θ - γ耦合可能作为多巴胺能对奖赏处理影响的标记物。因此，我们的研究揭示了PD奖赏处理缺陷背后复杂的时频动态，强调了数据驱动方法在阐明神经机制和识别潜在治疗靶点方面的实用性。
