BACKGROUND Neural oscillations represent synchronous neuronal activation and are ubiquitous throughout the brain. Oscillatory activity often includes brief high-amplitude bursts in addition to background oscillations, and burst activity may predict performance on working memory, motor, and comprehension tasks. OBJECTIVE We evaluated beta burst activity as a possible biomarker for motor symptoms in Parkinson's disease (PD). The relationship between beta amplitude dynamics and motor symptoms is critical for adaptive DBS for treatment of PD. METHODS We applied threshold-based and support vector machine (SVM) analyses of burst parameters to a defined on/off oscillator and to intraoperative recordings of local field potentials from the subthalamic nucleus of 16 awake patients with PD. RESULTS Filtering and time-frequency analysis techniques critically influenced the accuracy of identifying burst activity. Threshold-based analysis lead to biased results in the presence of changes in long-term beta amplitude and accurate quantification of bursts with thresholds required unknowable a priori knowledge of the time in bursts. We therefore implemented an SVM analysis, and we did not observe changes in burst fraction, rate, or duration with the application of cDBS in the participant data, even though SVM analysis was able to correctly identify bursts of the defined on/off oscillator. CONCLUSION Our results suggest that cDBS of the STN may not change beta burst activity. Additionally, threshold-based analysis can bias the fraction of time spent in bursts. Improved analysis strategies for continuous and adaptive DBS may achieve improved symptom control and reduce side-effects.

中文翻译：

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持续深部脑部刺激底丘脑核可能无法调节帕金森病患者的β爆发


背景技术神经振荡代表同步神经元激活并且在整个大脑中普遍存在。除了背景振荡之外，振荡活动通常还包括短暂的高振幅爆发，并且爆发活动可以预测工作记忆、运动和理解任务的表现。目的 我们评估了 β 爆发活性作为帕金森病 (PD) 运动症状的可能生物标志物。 β 振幅动态与运动症状之间的关系对于自适应 DBS 治疗 PD 至关重要。方法 我们将基于阈值和支持向量机 (SVM) 的突发参数分析应用于定义的开/关振荡器，并在术中记录 16 名清醒的 PD 患者丘脑底核的局部场电位。结果 过滤和时频分析技术严重影响识别突发活动的准确性。基于阈值的分析会在长期β振幅变化的情况下导致有偏差的结果，并且需要对突发时间的先验知识不可知的阈值来准确量化突发。因此，我们实施了 SVM 分析，尽管 SVM 分析能够正确识别定义的开/关振荡器的突发，但我们没有观察到在参与者数据中应用 cDBS 时突发分数、速率或持续时间的变化。结论 我们的结果表明 STN 的 cDBS 可能不会改变 beta 爆发活动。此外，基于阈值的分析可能会导致突发事件所花费的时间比例产生偏差。改进的连续和适应性 DBS 分析策略可以改善症状控制并减少副作用。