Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease.
Average beta burst duration profiles provide a signature of dynamical changes between the ON and OFF medication states in Parkinson's disease.
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DOI：
10.1371/journal.pcbi.1009116
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发表时间：
2021-07
影响因子：
4.3
通讯作者：
Bogacz R
中科院分区：
文献类型：
--
作者：
Duchet B;Ghezzi F;Weerasinghe G;Tinkhauser G;Kühn AA;Brown P;Bick C;Bogacz R
帕金森病运动症状与丘脑底核β带振荡功率增加相关然而，这些振荡是阶段性的，并且有越来越多的证据表明β爆发持续时间可能对运动症状至关重要。这使得对β爆发产生的动力学的见解很有价值，特别是在帕金森病中改进闭环深部脑刺激。在这项研究中，我们提出了一个问题：“平均爆发持续时间能否揭示开药和停药状态之间的动态变化？"。我们的局部场电位从丘脑底核的分析表明，使用线性替代品，系统产生β振荡更可能在非线性制度关闭药物和非线性措施的变化与运动障碍。此外，我们通过拟合数据生物启发模型和更简单的β包络模型，确定了最简单的动态变化，这些变化可能是药物状态之间β振荡时间模式变化的原因。最后，我们表明，非线性可以直接提取的平均突发持续时间的包络模型的假设下，恒定的噪声配置文件。这表明，平均爆发持续时间曲线提供了爆发动态的窗口，这可能是爆发持续时间作为生物标志物成功的基础。总之，我们证明了平均爆发持续时间曲线、产生β振荡的系统动力学和运动障碍之间的关系，这使我们能够更好地理解病理学并改进诸如深部脑刺激等疗法。在帕金森氏病中，运动障碍与大脑深层运动区神经元的异常振荡活动有关。这些振荡以爆发的形式出现，并且这些爆发的持续时间最近被证明对运动症状很重要。为了更好地了解疾病和改进治疗方法，我们将这些爆发的持续时间与在病理状态和健康状态的代理中产生它们的系统的属性相关联。这些数据表明，系统产生的突发涉及更多的复杂性，在病理状态，我们表明，这种复杂性的措施与运动障碍。我们提出了生物启发的模型和更简单的模型，可以生成在病理和健康状态下观察到的突发模式。这些模型证实了在数据中观察到的情况，并告诉我们爆发产生机制在疾病中如何不同。最后，我们确定了一个数学链接，使我们能够推断出属性的突发产生系统的突发持续时间测量患者的记录。这揭示了爆发持续时间作为病理学标志的意义。
Parkinson’s disease motor symptoms are associated with an increase in subthalamic nucleus beta band oscillatory power. However, these oscillations are phasic, and there is a growing body of evidence suggesting that beta burst duration may be of critical importance to motor symptoms. This makes insights into the dynamics of beta bursting generation valuable, in particular to refine closed-loop deep brain stimulation in Parkinson’s disease. In this study, we ask the question “Can average burst duration reveal how dynamics change between the ON and OFF medication states?”. Our analysis of local field potentials from the subthalamic nucleus demonstrates using linear surrogates that the system generating beta oscillations is more likely to act in a non-linear regime OFF medication and that the change in a non-linearity measure is correlated with motor impairment. In addition, we pinpoint the simplest dynamical changes that could be responsible for changes in the temporal patterning of beta oscillations between medication states by fitting to data biologically inspired models, and simpler beta envelope models. Finally, we show that the non-linearity can be directly extracted from average burst duration profiles under the assumption of constant noise in envelope models. This reveals that average burst duration profiles provide a window into burst dynamics, which may underlie the success of burst duration as a biomarker. In summary, we demonstrate a relationship between average burst duration profiles, dynamics of the system generating beta oscillations, and motor impairment, which puts us in a better position to understand the pathology and improve therapies such as deep brain stimulation. In Parkinson’s disease, motor impairment is associated with abnormal oscillatory activity of neurons in deep motor regions of the brain. These oscillations come in the shape of bursts, and the duration of these bursts has recently been shown to be of importance to motor symptoms. To better understand the disease and refine therapies, we relate the duration of these bursts to properties of the system generating them in the pathological state and in a proxy of the healthy state. The data suggest that the system generating bursts involves more complexity in the pathological state, and we show that a measure of this complexity is associated with motor impairment. We propose biologically inspired models and simpler models that can generate the burst patterns observed in the pathological and healthy state. The models confirm what was observed in data, and tell us how burst generation mechanisms could differ in the disease. Finally, we identify a mathematical link allowing us to infer properties of the burst generating system from burst duration measurements in patient recordings. This sheds some light on the significance of burst duration as a marker of pathology.
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影响因子：
2.7
作者：
Benjamini, Yoav;Krieger, Abba M.;Yekutieli, Daniel
通讯作者：
Yekutieli, Daniel
DOI：
10.1073/pnas.1517629112
发表时间：
2015-11-03
影响因子：
11.1
作者：
Feingold, Joseph;Gibson, Daniel J.;Graybiel, Ann M.
通讯作者：
Graybiel, Ann M.
影响因子：
3.1
作者：
Audet, C;Dennis, JE
通讯作者：
Dennis, JE
影响因子：
56.9
作者：
Fernandez-Ruiz, Antonio;Oliva, Azahara;Buzsaki, Gyorgy
通讯作者：
Buzsaki, Gyorgy
影响因子：
5.3
作者：
Brown, P;Oliviero, A;Di Lazzaro, V
通讯作者：
Di Lazzaro, V