Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings.
Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings.
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DOI：
10.1016/j.nbd.2022.105963
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发表时间：
2023-01
影响因子：
6.1
通讯作者：
Johnson, Luke A.
中科院分区：
文献类型：
--
作者：
Verma, Ajay K.;Yu, Ying;Acosta-Lenis, Sergio F.;Havel, Tyler;Sanabria, David Escobar;Molnar, Gregory F.;MacKinnon, Colum D.;Howell, Michael J.;Vitek, Jerrold L.;Johnson, Luke A.
关键词：
白天过度嗜睡是一种公认的非运动症状，对帕金森病（PD）患者的生活质量产生不利影响，但有效的治疗方案仍然有限。丘脑底核深部脑刺激（DBS）是治疗PD运动体征的有效方法。可靠的白天睡眠-觉醒分类使用从植入在脑内的DBS电极导线记录的局部场电位（LFP）可以告知闭环DBS方法的发展，以及时检测和中断睡眠相关的神经振荡。我们在三只非人灵长类动物中进行了DBS导联记录，这些灵长类动物通过给予神经毒素1-甲基-4-苯基-1，2，3，6-四氢吡啶（MPTP）而患帕金森病。通过眼睛的视频监测（睁眼、清醒和闭眼、睡眠），逐秒确定参考睡眠-清醒状态。从每个觉醒和睡眠时期提取δ（1-4 Hz）、θ（4-8 Hz）、低β（8-20 Hz）、高β（20-35 Hz）、γ（35-90）和高频（200-400 Hz）频带中的谱功率，用于独立地训练（70%数据）和测试（30%数据）每个受试者的支持向量机分类器。光谱特征产生合理的日间睡眠-觉醒分类（灵敏度：90.68±1.28;特异度：88.16±1.08;准确度：89.42±0.68;阳性预测值：88.70±0.89，n=3）。我们的研究结果支持使用DBS导联记录监测白天睡眠-觉醒状态的可行性。这些结果可能具有未来的临床意义，为开发闭环DBS方法提供信息，用于自动检测和中断PD患者的睡眠相关神经振荡，以促进觉醒。
Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson’s disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1–4 Hz), theta (4–8 Hz), low-beta (8–20 Hz), high-beta (20–35 Hz), gamma (35–90), and high-frequency (200–400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68±1.28; specificity: 88.16±1.08; accuracy: 89.42±0.68; positive predictive value; 88.70±0.89, n=3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.
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DOI：
10.1038/s41531-021-00229-z
发表时间：
2021-09-28
期刊：
NPJ Parkinson's disease
影响因子：
--
作者：
Bocci T;Prenassi M;Arlotti M;Cogiamanian FM;Borellini L;Moro E;Lozano AM;Volkmann J;Barbieri S;Priori A;Marceglia S
通讯作者：
Marceglia S
影响因子：
2.9
作者：
Cagle JN;Wong JK;Johnson KA;Foote KD;Okun MS;de Hemptinne C
通讯作者：
de Hemptinne C
影响因子：
6.1
作者：
Aman, Joshua E.;Johnson, Luke A.;Vitek, Jerrold L.
通讯作者：
Vitek, Jerrold L.
影响因子：
4
作者：
Amara, Amy W.;Walker, Harrison C.;Standaert, David G.
通讯作者：
Standaert, David G.
影响因子：
11
作者：
ASKENASY, JJM;YAHR, MD
通讯作者：
YAHR, MD