Pallidal activities during sleep and sleep decoding in dystonia, Huntington's, and Parkinson's disease

• Published in: Neurobiology of disease Vol. 182; p. 106143
• Main Authors: Yin, Zixiao, Jiang, Yin, Merk, Timon, Neumann, Wolf-Julian, Ma, Ruoyu, An, Qi, Bai, Yutong, Zhao, Baotian, Xu, Yichen, Fan, Houyou, Zhang, Quan, Qin, Guofan, Zhang, Ning, Ma, Jun, Zhang, Hua, Liu, Huanguang, Shi, Lin, Yang, Anchao, Meng, Fangang, Zhu, Guanyu, Zhang, Jianguo
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15-06-2023; Elsevier
• Subjects: Basal ganglia, local field potential; Deep brain stimulation; Deep Brain Stimulation - methods; Dystonia; Dystonic Disorders; Globus Pallidus; Humans; Movement disorders; Parkinson Disease - complications; Parkinson Disease - therapy; Sleep; Sleep disorders; PSD; GPi; AASM; Deep brain stimulation; PSG; MRI; EEG; Basal ganglia, local field potential; NREM; MNI; STN; Sleep disorders; PD; DBS; VTR; RBD; LFP; REM; HD; GPe; Movement disorders
• ISSN: 0969-9961; 1095-953X
• DOI: 10.1016/j.nbd.2023.106143

Sleep disturbances are highly prevalent in movement disorders, potentially due to the malfunctioning of basal ganglia structures. Pallidal deep brain stimulation (DBS) has been widely used for multiple movement disorders and been reported to improve sleep. We aimed to investigate the oscillatory pattern of pallidum during sleep and explore whether pallidal activities can be utilized to differentiate sleep stages, which could pave the way for sleep-aware adaptive DBS.
We directly recorded over 500 h of pallidal local field potentials during sleep from 39 subjects with movement disorders (20 dystonia, 8 Huntington's disease, and 11 Parkinson's disease). Pallidal spectrum and cortical-pallidal coherence were computed and compared across sleep stages. Machine learning approaches were utilized to build sleep decoders for different diseases to classify sleep stages through pallidal oscillatory features. Decoding accuracy was further associated with the spatial localization of the pallidum.
Pallidal power spectra and cortical-pallidal coherence were significantly modulated by sleep-stage transitions in three movement disorders. Differences in sleep-related activities between diseases were identified in non-rapid eye movement (NREM) and REM sleep. Machine learning models using pallidal oscillatory features can decode sleep-wake states with over 90% accuracy. Decoding accuracies were higher in recording sites within the internus-pallidum than the external-pallidum, and can be precited using structural (P < 0.0001) and functional (P < 0.0001) whole-brain neuroimaging connectomics.
Our findings revealed strong sleep-stage dependent distinctions in pallidal oscillations in multiple movement disorders. Pallidal oscillatory features were sufficient for sleep stage decoding. These data may facilitate the development of adaptive DBS systems targeting sleep problems that have broad translational prospects.
•Pallidal activities during sleep are recorded in 39 patients with dystonia, HD, or PD.•Pallidal spectra and connectivity with cortex are modulated by sleep-wake transitions.•Differences in sleep-related activities may underly disease-specific abnormalities.•Pallidal activity-based ML decoder predicts sleep stages with accuracies of over 90%.•Decoding accuracies are influenced by the anatomical localization of the DBS lead.