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Janssen Daalen JM, van den Bergh R, Prins EM, Moghadam MSC, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh SKL, Evers LJW, Bloem BR. Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art. NPJ Digit Med 2024; 7:186. [PMID: 38992186 PMCID: PMC11239921 DOI: 10.1038/s41746-024-01144-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 05/22/2024] [Indexed: 07/13/2024] Open
Abstract
Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.
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Affiliation(s)
- Jules M Janssen Daalen
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands.
| | - Robin van den Bergh
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Eva M Prins
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Mahshid Sadat Chenarani Moghadam
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Rudie van den Heuvel
- HAN University of Applied Sciences, School of Engineering and Automotive, Health Concept Lab, Arnhem, The Netherlands
| | - Jeroen Veen
- HAN University of Applied Sciences, School of Engineering and Automotive, Health Concept Lab, Arnhem, The Netherlands
| | | | - Hannie Meijerink
- ParkinsonNL, Parkinson Patient Association, Bunnik, The Netherlands
| | - Anat Mirelman
- Tel Aviv University, Sagol School of Neuroscience, Department of Neurology, Faculty of Medicine, Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility (CMCM), Tel Aviv, Israel
| | - Sirwan K L Darweesh
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Luc J W Evers
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
- Radboud University, Institute for Computing and Information Sciences, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands.
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Lin J, Liu C, Hu E. Elucidating sleep disorders: a comprehensive bioinformatics analysis of functional gene sets and hub genes. Front Immunol 2024; 15:1381765. [PMID: 38919616 PMCID: PMC11196417 DOI: 10.3389/fimmu.2024.1381765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/27/2024] [Indexed: 06/27/2024] Open
Abstract
Background Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood. Methods The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses. Results The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis. Conclusion In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.
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Affiliation(s)
- Junhan Lin
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
| | - Changyuan Liu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
| | - Ende Hu
- Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University, Key Laboratory of Pediatric Anesthesiology, Ministry of Education, Wenzhou Medical University, Wenzhou, China
- Laboratory of Anesthesiology of Zhejiang Province, Wenzhou Medical University, Wenzhou, China
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Tao MX, Meng L, Xie WY, Li HX, Zhang JR, Yan JH, Cheng XY, Wang F, Mao CJ, Shen Y, Liu CF. Slow-wave sleep and REM sleep without atonia predict motor progression in Parkinson's disease. Sleep Med 2024; 115:155-161. [PMID: 38367357 DOI: 10.1016/j.sleep.2024.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/01/2024] [Accepted: 02/01/2024] [Indexed: 02/19/2024]
Abstract
BACKGROUND Growing evidence supports the potential role of sleep in the motor progression of Parkinson's disease (PD). Slow-wave sleep (SWS) and rapid eye movement (REM) sleep without atonia (RWA) are important sleep parameters. The association between SWS and RWA with PD motor progression and their predictive value have not yet been elucidated. METHODS We retro-prospectively analyzed clinical and polysomnographic data of 136 patients with PD. The motor symptoms were assessed using Unified Parkinson's Disease Rating Scale Part III (UPDRS III) at baseline and follow-up to determine its progression. Partial correlation analysis was used to explore the cross-sectional associations between slow-wave energy (SWE), RWA and clinical symptoms. Longitudinal analyses were performed using Cox regression and linear mixed-effects models. RESULTS Among 136 PD participants, cross-sectional partial correlation analysis showed SWE decreased with the prolongation of the disease course (P = 0.046), RWA density was positively correlated with Hoehn & Yahr (H-Y) stage (tonic RWA, P < 0.001; phasic RWA, P = 0.002). Cox regression analysis confirmed that low SWE (HR = 1.739, 95% CI = 1.038-2.914; P = 0.036; FDR-P = 0.036) and high tonic RWA (HR = 0.575, 95% CI = 0.343-0.963; P = 0.032; FDR-P = 0.036) were predictors of motor symptom progression. Furthermore, we found that lower SWE predicted faster rate of axial motor progression (P < 0.001; FDR-P < 0.001) while higher tonic RWA density was associated with faster rate of rigidity progression (P = 0.006; FDR-P = 0.024) using linear mixed-effects models. CONCLUSIONS These findings suggest that SWS and RWA might represent markers of different motor subtypes progression in PD.
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Affiliation(s)
- Meng-Xing Tao
- Department of Neurology, Second Hospital Affiliated of Xinjiang Medical University, Ürümqi, 830063, Xinjiang, China; Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Lin Meng
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Wei-Ye Xie
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Han-Xing Li
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Jin-Ru Zhang
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Jia-Hui Yan
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Xiao-Yu Cheng
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Fen Wang
- Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, 215123, China
| | - Cheng-Jie Mao
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China
| | - Yun Shen
- Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China.
| | - Chun-Feng Liu
- Department of Neurology, Second Hospital Affiliated of Xinjiang Medical University, Ürümqi, 830063, Xinjiang, China; Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, 215004, China; Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, 215123, China.
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Tall P, Qamar MA, Rosenzweig I, Raeder V, Sauerbier A, Heidemarie Z, Falup-Pecurariu C, Chaudhuri KR. The Park Sleep subtype in Parkinson's disease: from concept to clinic. Expert Opin Pharmacother 2023; 24:1725-1736. [PMID: 37561080 DOI: 10.1080/14656566.2023.2242786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 07/27/2023] [Indexed: 08/11/2023]
Abstract
INTRODUCTION The heterogeneity of Parkinson's disease (PD) is evident from descriptions of non-motor (NMS) subtypes and Park Sleep, originally identified by Sauerbier et al. 2016, is one such clinical subtype associated with the predominant clinical presentation of sleep dysfunctions including excessive daytime sleepiness (EDS), along with insomnia. AREAS COVERED A literature search was conducted using the PubMed, Medline, Embase, and Web of Science databases, accessed between 1 February 2023 and 28 March 2023. In this review, we describe the clinical subtype of Park Sleep and related 'tests' ranging from polysomnography to investigational neuromelanin MRI brain scans and some tissue-based biological markers. EXPERT OPINION Cholinergic, noradrenergic, and serotonergic systems are dominantly affected in PD. Park Sleep subtype is hypothesized to be associated primarily with serotonergic deficit, clinically manifesting as somnolence and narcoleptic events (sleep attacks), with or without rapid eye movement behavior disorder (RBD). In clinic, Park Sleep recognition may drive lifestyle changes (e.g. driving) along with therapy adjustments as Park Sleep patients may be sensitive to dopamine D3 active agonists, such as ropinirole and pramipexole. Specific dashboard scores based personalized management options need to be implemented and include pharmacological, non-pharmacological, and lifestyle linked advice.
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Affiliation(s)
- Phoebe Tall
- Department of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience (IoPpn), King's College London, London, UK
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, London, UK
| | - Mubasher A Qamar
- Department of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience (IoPpn), King's College London, London, UK
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, London, UK
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPpn), King's College London, London, UK
- Sleep Disorder Centre, Nuffield House, Guy's Hospital, London, UK
| | - Vanessa Raeder
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, London, UK
- Department of Neurology and Experimental Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität, Berlin, Germany
| | - Anna Sauerbier
- Department of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience (IoPpn), King's College London, London, UK
- Department of Neurology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
| | - Zach Heidemarie
- Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Cristian Falup-Pecurariu
- Faculty of Medicine, Transilvania University of Braşov, Brașov, Romania
- Department of Neurology, County Clinic Hospital, Braşov, Romania
| | - Kallol Ray Chaudhuri
- Department of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience (IoPpn), King's College London, London, UK
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, London, UK
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陈 璋, 李 桃, 唐 向. [Application of Polysomnography in Common Neurodegenerative Diseases]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:1058-1064. [PMID: 37866969 PMCID: PMC10579074 DOI: 10.12182/20230960304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Indexed: 10/24/2023]
Abstract
At present, the etiology and pathogenesis of most neurodegenerative diseases are still not fully understood, which poses challenges for the prevention, diagnosis, and treatment of these diseases. Sleep disorders are one of the common chief complaints of neurodegenerative diseases. When patients suffer from comorbid sleep disorder and neurodegenerative diseases, the severity of their condition increases, the quality of their life drops further, and the difficulty of treatment increases. A large number of studies have been conducted to monitor the sleep of patients with neurodegenerative diseases, and it has been found that there are significant changes in their polysomnography (PSG) results compared to those of healthy control populations. In addition, there are also significant differences between the PSG findings of patients with different neurodegenerative diseases and the differences are closely associated with the pathogenesis and development of the disease. Herein, we discussed the characteristics of the sleep structure of patients with Parkinson's disease, Alzheimer's disease, Huntington's disease, and dementia with Lewy bodies and provided a brief review of the sleep disorders and the PSG characteristics of these patients. The paper will help improve the understanding of the pathogenesis and pathological changes of neurodegenerative diseases, clarify the relationship between sleep disorders and these diseases, improve clinicians' further understanding of these diseases, and provide a basis for future research.
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Affiliation(s)
- 璋玥 陈
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 桃美 李
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - 向东 唐
- 四川大学华西医院 睡眠医学中心 (成都 610041)Sleep Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, China
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Chen J, Zhao D, Wang Q, Chen J, Bai C, Li Y, Guo X, Chen B, Zhang L, Yuan J. Predictors of cognitive impairment in newly diagnosed Parkinson's disease with normal cognition at baseline: A 5-year cohort study. Front Aging Neurosci 2023; 15:1142558. [PMID: 36926634 PMCID: PMC10011149 DOI: 10.3389/fnagi.2023.1142558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 02/10/2023] [Indexed: 03/08/2023] Open
Abstract
Background and objective Cognitive impairment (CI) is a substantial contributor to the disability associated with Parkinson's disease (PD). We aimed to assess the clinical features and explore the underlying biomarkers as predictors of CI in patients with newly diagnosed PD (NDPD; less than 2 years). Methods We evaluated the cognitive function status using the Montreal Cognitive Assessment (MoCA) and a battery of neuropsychological tests at baseline and subsequent annual follow-up for 5 years from the Parkinson's Progression Markers Initiative (PPMI) database. We assessed the baseline clinical features, apolipoprotein (APO) E status, β-glucocerebrosidase (GBA) mutation status, cerebrospinal fluid findings, and dopamine transporter imaging results. Using a diagnosis of CI (combined mild cognitive impairment and dementia) developed during the 5-year follow-up as outcome measures, we assessed the predictive values of baseline clinical variables and biomarkers. We also constructed a predictive model for the diagnosis of CI using logistic regression analysis. Results A total of 409 patients with NDPD with 5-year follow-up were enrolled, 232 with normal cognitive function at baseline, and 94 patients developed CI during the 5-year follow-up. In multivariate analyses, age, current diagnosis of hypertension, baseline MoCA scores, Movement disorder society Unified PD Rating Scale part III (MDS-UPDRS III) scores, and APOE status were associated with the development of CI. Predictive accuracy of CI using age alone improved by the addition of clinical variables and biomarkers (current diagnosis of hypertension, baseline MoCA scores, and MDS-UPDRS III scores, APOE status; AUC 0.80 [95% CI 0.74-0.86] vs. 0.71 [0.64-0.77], p = 0.008). Cognitive domains that had higher frequencies of impairment were found in verbal memory (12.6 vs. 16.8%) and attention/processing speed (12.7 vs. 16.9%), however, no significant difference in the prevalence of CI at annual follow-up was found during the 5-year follow-up in NDPD patients. Conclusion In NDPD, the development of CI during the 5-year follow-up can be predicted with good accuracy using a model combining age, current diagnosis of hypertension, baseline MoCA scores, MDS-UPDRS III scores, and APOE status. Our study underscores the need for the earlier identification of CI in NDPD patients in our clinical practice.
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Affiliation(s)
- Jing Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Danhua Zhao
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Qi Wang
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Junyi Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Chaobo Bai
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Yuan Li
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Xintong Guo
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Baoyu Chen
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
| | - Lin Zhang
- Department of Neurology and Neurological Surgery, UC Davis Deep Brain Stimulation (DBS), Sacramento, CA, United States
| | - Junliang Yuan
- Department of Neurology, Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Peking University, Beijing, China
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