Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor
Decoding voluntary movements and postural tremor based on thalamic LFPs as a basis for closed-loop stimulation for essential tremor
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DOI：
10.1016/j.brs.2019.02.011
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发表时间：
2019-07-01
影响因子：
7.7
通讯作者：
Brown, Peter
中科院分区：
文献类型：
--
作者：
Tan, Huiling;Debarros, Jean;Brown, Peter
背景：运动丘脑靶向高频脑深部电刺激（DBS）是治疗特发性震颤（ET）的有效方法。然而，传统的连续刺激可能会向大脑提供不必要的电流，因为震颤主要影响ET中的自主运动和持续姿势。我们的目标是从植入运动丘脑的电极记录的局部场电位（LFP）中解码自主运动和姿势性震颤的存在，以便闭合DBS的回路，从而可以按需提供刺激，方法：LFPs从运动丘脑，表面肌电图（EMG）信号和/或行为测量同时记录在七个ET患者在临时铅externalisation 3-5天后，第一次手术DBS时，他们进行不同的自愿上肢运动。在手术过程中记录了9名不同的患者，当他们被要求抬起手臂以触发姿势性震颤时。一个基于机器学习的二元分类器被用来检测自愿运动和姿势性震颤的基础上提取的特征丘脑LFPs.Results：交叉验证表明，自愿运动和姿势性震颤可以解码的平均灵敏度为0.8和错误检测率为0.2。β频段（13-23 Hz）和θ频段（4-7 Hz）中的振荡活动分别对运动和姿势性震颤的解码贡献最大，尽管使用机器学习方法将不同频段中的特征结合起来提高了解码的准确性。(C)2019作者（S）爱思唯尔公司出版
Background: High frequency Deep brain stimulation (DBS) targeting motor thalamus is an effective therapy for essential tremor (ET). However, conventional continuous stimulation may deliver unnecessary current to the brain since tremor mainly affects voluntary movements and sustained postures in ET.Objective: We aim to decode both voluntary movements and the presence of postural tremor from the Local field potentials (LFPs) recorded from the electrode implanted in motor thalamus for stimulation, in order to close the loop for DBS so that stimulation could be delivered on demand, without the need for peripheral sensors or additional invasive electrodes.Methods: LFPs from the motor thalamus, surface electromyographic (EMG) signals and/or behavioural measurements were simultaneously recorded in seven ET patients during temporary lead externalisation 3-5 days after the first surgery for DBS when they performed different voluntary upper limb movements. Nine different patients were recorded during the surgery, when they were asked to lift their arms to trigger postural tremor. A machine learning based binary classifier was used to detect voluntary movements and postural tremor based on features extracted from thalamic LFPs.Results: Cross-validation demonstrated that both voluntary movements and postural tremor can be decoded with an average sensitivity of 0.8 and false detection rate of 0.2. Oscillatory activities in the beta frequency bands (13-23 Hz) and the theta frequency bands (4-7 Hz) contributed most to the decoding of movements and postural tremor, respectively, though incorporating features in different frequency bands using a machine learning approach increased the accuracy of decoding. (C) 2019 The Author(s). Published by Elsevier Inc.