Neurobiology of disease · 2023
Pallidal activities during sleep and sleep decoding in dystonia, Huntington's, and Parkinson's disease.
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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
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.