Azgomi Hamid Fekri, Louie Kenneth H, Bath Jessica E, Presbrey Kara N, Balakid Jannine P, Marks Jacob H, Wozny Thomas A, Galifianakis Nicholas B, Luciano Marta San, Little Simon, Starr Philip A, Wang Doris D
Department of Neurological Surgery.
Department of Physical Therapy and Rehabilitation Science.
medRxiv. 2024 Nov 1:2024.10.30.24316305. doi: 10.1101/2024.10.30.24316305.
Although high-frequency deep brain stimulation (DBS) is effective at relieving many motor symptoms of Parkinson's disease (PD), its effects on gait can be variable and unpredictable. This is due to 1) a lack of standardized and robust metrics for gait assessment in PD patients, 2) the challenges of performing a thorough evaluation of all the stimulation parameters space that can alter gait, and 3) a lack of understanding for impacts of stimulation on the neurophysiological signatures of walking. In this study, our goal was to develop a data-driven approach to identify optimal, personalized DBS stimulation parameters to improve gait in PD patients and identify the neurophysiological signature of improved gait. Local field potentials from the globus pallidus and electrocorticography from the motor cortex of three PD patients were recorded using an implanted bidirectional neural stimulator during overground walking. A walking performance index (WPI) was developed to assess gait metrics with high reliability. DBS frequency, amplitude, and pulse width on the "clinically-optimized" stimulation contact were then systemically changed to study their impacts on gait metrics and underlying neural dynamics. We developed a Gaussian Process Regressor (GPR) model to map the relationship between DBS settings and the WPI. Using this model, we identified and validated personalized DBS settings that significantly improved gait metrics. Linear mixed models were employed to identify neural spectral features associated with enhanced walking performance. We demonstrated that improved walking performance was linked to the modulation of neural activity in specific frequency bands, with reduced beta band power in the pallidum and increased alpha band pallidal-motor cortex coherence synchronization during key moments of the gait cycle. Integrating WPI and GPR to optimize DBS parameters underscores the importance of developing and understanding personalized, data-driven interventions for gait improvement in PD.
尽管高频深部脑刺激（DBS）在缓解帕金森病（PD）的许多运动症状方面有效，但其对步态的影响可能是多变且不可预测的。这是由于：1）缺乏用于评估PD患者步态的标准化且可靠的指标；2）全面评估所有可能改变步态的刺激参数空间存在挑战；3）对刺激对行走神经生理特征的影响缺乏了解。在本研究中，我们的目标是开发一种数据驱动的方法，以确定优化的、个性化的DBS刺激参数来改善PD患者的步态，并确定改善步态的神经生理特征。在三名PD患者地面行走期间，使用植入式双向神经刺激器记录苍白球的局部场电位和运动皮层的皮层脑电图。开发了一种行走性能指数（WPI）来高度可靠地评估步态指标。然后系统地改变“临床优化”刺激触点上的DBS频率、幅度和脉宽，以研究它们对步态指标和潜在神经动力学的影响。我们开发了一个高斯过程回归（GPR）模型来映射DBS设置与WPI之间的关系。使用该模型，我们识别并验证了能显著改善步态指标的个性化DBS设置。采用线性混合模型来识别与增强行走性能相关的神经频谱特征。我们证明，改善的行走性能与特定频段神经活动的调制有关，在步态周期的关键时刻，苍白球的β频段功率降低，苍白球 - 运动皮层的α频段相干同步增加。将WPI和GPR整合以优化DBS参数强调了开发和理解用于改善PD患者步态的个性化、数据驱动干预措施的重要性。
