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Zhang T, Xu L, Wei Y, Cui H, Tang X, Hu Y, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Advancements and Future Directions in Prevention Based on Evaluation for Individuals With Clinical High Risk of Psychosis: Insights From the SHARP Study. Schizophr Bull 2024:sbae066. [PMID: 38741342 DOI: 10.1093/schbul/sbae066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
BACKGROUND AND HYPOTHESIS This review examines the evolution and future prospects of prevention based on evaluation (PBE) for individuals at clinical high risk (CHR) of psychosis, drawing insights from the SHARP (Shanghai At Risk for Psychosis) study. It aims to assess the effectiveness of non-pharmacological interventions in preventing psychosis onset among CHR individuals. STUDY DESIGN The review provides an overview of the developmental history of the SHARP study and its contributions to understanding the needs of CHR individuals. It explores the limitations of traditional antipsychotic approaches and introduces PBE as a promising framework for intervention. STUDY RESULTS Three key interventions implemented by the SHARP team are discussed: nutritional supplementation based on niacin skin response blunting, precision transcranial magnetic stimulation targeting cognitive and brain functional abnormalities, and cognitive behavioral therapy for psychotic symptoms addressing symptomatology and impaired insight characteristics. Each intervention is evaluated within the context of PBE, emphasizing the potential for tailored approaches to CHR individuals. CONCLUSIONS The review highlights the strengths and clinical applications of the discussed interventions, underscoring their potential to revolutionize preventive care for CHR individuals. It also provides insights into future directions for PBE in CHR populations, including efforts to expand evaluation techniques and enhance precision in interventions.
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Affiliation(s)
- TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - HuiRu Cui
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - ZiXuan Wang
- Department of Psychology, Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai, PR China
- Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, PR China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, PR China
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Antipsychotic prescription, assumption and conversion to psychosis: resolving missing clinical links to optimize prevention through precision. SCHIZOPHRENIA 2022; 8:48. [PMID: 35853891 PMCID: PMC9261109 DOI: 10.1038/s41537-022-00254-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/04/2022] [Indexed: 11/08/2022]
Abstract
AbstractThe current concept of clinical high-risk(CHR) of psychosis relies heavily on “below-threshold” (i.e. attenuated or limited and intermittent) psychotic positive phenomena as predictors of the risk for future progression to “above-threshold” positive symptoms (aka “transition” or “conversion”). Positive symptoms, even at attenuated levels are often treated with antipsychotics (AP) to achieve clinical stabilization and mitigate the psychopathological severity. The goal of this study is to contextually examine clinicians’ decision to prescribe AP, CHR individuals’ decision to take AP and psychosis conversion risk in relation to prodromal symptoms profiles. CHR individuals (n = 600) were recruited and followed up for 2 years between 2016 and 2021. CHR individuals were referred to the participating the naturalistic follow-up study, which research procedure was independent of the routine clinical treatment. Clinical factors from the Structured Interview for Prodromal Syndromes (SIPS) and global assessment of function (GAF) were profiled via exploratory factor analysis (EFA), then the extracted factor structure was used to investigate the relationship of prodromal psychopathology with clinicians’ decisions to AP-prescription, CHR individuals’ decisions to AP-taking and conversion to psychosis. A total of 427(71.2%) CHR individuals were prescribed AP at baseline, 532(88.7%) completed the 2-year follow-up, 377(377/532, 70.9%) were taken AP at least for 2 weeks during the follow-up. EFA identified six factors (Factor-1-Negative symptoms, Factor-2-Global functions, Factor-3-Disorganized communication & behavior, Factor-4-General symptoms, Factor-5-Odd thoughts, and Factor-6-Distorted cognition & perception). Positive symptoms (Factor-5 and 6) and global functions (Factor-2) factors were significant predictors for clinicians’ decisions to AP-prescription and CHR individuals’ decisions to assume AP, whereas negative symptoms (Factor-1) and global functions (Factor-2) factors predicted conversion. While decisions to AP-prescription, decisions to AP-taking were associated to the same factors (positive symptoms and global functions), only one of those was predictive of conversion, i.e. global functions. The other predictor of conversion, i.e. negative symptoms, did not seem to be contemplated both on the clinician and patients’ sides. Overall, the findings indicated that a realignment in the understanding of AP usage is warranted.
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Zhang D, Xu L, Xie Y, Tang X, Hu Y, Liu X, Wu G, Qian Z, Tang Y, Liu Z, Chen T, Liu H, Zhang T, Wang J. Eye movement indices as predictors of conversion to psychosis in individuals at clinical high risk. Eur Arch Psychiatry Clin Neurosci 2022; 273:553-563. [PMID: 35857090 DOI: 10.1007/s00406-022-01463-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Accepted: 06/27/2022] [Indexed: 12/17/2022]
Abstract
Eye movement abnormalities have been established as an "endophenotype" of schizophrenia. However, less is known about the possibility of these abnormalities as biomarkers for psychosis conversion among clinical high risk (CHR) populations. In the present study, 108 CHR individuals and 70 healthy controls (HC) underwent clinical assessments and eye-tracking tests, comprising fixation stability and free-viewing tasks. According to three-year follow-up outcomes, CHR participants were further stratified into CHR-converter (CHR-C; n = 21) and CHR-nonconverter (CHR-NC; n = 87) subgroups. Prediction models were constructed using Cox regression and logistic regression. The CHR-C group showed more saccades of the fixation stability test (no distractor) and a reduced saccade amplitude of the free-viewing test than HC. Moreover, the CHR-NC group exhibited excessive saccades and an increased saccade amplitude of the fixation stability test (no distractor; with distractor) compared with HC. Furthermore, two indices could effectively discriminate CHR-C from CHR-NC with an area under the receiver-operating characteristic (ROC) curve of 0.80, including the saccade number of the fixation stability test (no distractor) and the saccade amplitude of the free-viewing test. Combined with negative symptom scores of the Scale of Prodromal Symptoms, the area was 0.81. These findings support that eye movement alterations might emerge before the onset of clinically overt psychosis and could assist in predicting psychosis transition among CHR populations.
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Affiliation(s)
- Dan Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yuou Xie
- First Clinical Medical College of Nanjing Medical University, Nanjing, 211103, People's Republic of China
| | - Xiaochen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yegang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Xu Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Guisen Wu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Zhenying Qian
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Zhi Liu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, 200444, People's Republic of China.,School of Communication and Information Engineering, Shanghai University, Shanghai, 200444, People's Republic of China
| | - Tao Chen
- Senior Research Fellow, Labor and Worklife Program, Harvard University, Cambridge, MA, USA.,Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.,Niacin (Shanghai) Technology Co., Ltd., Shanghai, People's Republic of China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, People's Republic of China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China. .,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China. .,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
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Wu G, Tang X, Gan R, Zeng J, Hu Y, Xu L, Wei Y, Tang Y, Chen T, Liu H, Li C, Wang J, Zhang T. Automatic auditory processing features in distinct subtypes of patients at clinical high risk for psychosis: Forecasting remission with mismatch negativity. Hum Brain Mapp 2022; 43:5452-5464. [PMID: 35848373 PMCID: PMC9704791 DOI: 10.1002/hbm.26021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 06/21/2022] [Accepted: 07/02/2022] [Indexed: 01/15/2023] Open
Abstract
Individuals at clinical high risk (CHR) for psychosis exhibit a compromised mismatch negativity (MMN) response, which indicates dysfunction of pre-attentive deviance processing. Event-related potential and time-frequency (TF) information, in combination with clinical and cognitive profiles, may provide insight into the pathophysiology and psychopathology of the CHR stage and predict the prognosis of CHR individuals. A total of 92 individuals with CHR were recruited and followed up regularly for up to 3 years. Individuals with CHR were classified into three clinical subtypes demonstrated previously, specifically 28 from Cluster 1 (characterized by extensive negative symptoms and cognitive deficits), 31 from Cluster 2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 33 from Cluster 3 (characterized by the mildest symptoms and cognitive deficits). Auditory MMN to frequency and duration deviants was assessed. The event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were acquired using TF analysis. Predictive indices for remission were identified using logistic regression analyses. As expected, reduced frequency MMN (fMMN) and duration MMN (dMMN) responses were noted in Cluster 1 relative to the other two clusters. In the TF analysis, Cluster 1 showed decreased theta and alpha ITC in response to deviant stimuli. The regression analyses revealed that dMMN latency and alpha ERSP to duration deviants, theta ITC to frequency deviants and alpha ERSP to frequency deviants, and fMMN latency were significant MMN predictors of remission for the three clusters. MMN variables outperformed behavioral variables in predicting remission of Clusters 1 and 2. Our findings indicate relatively disrupted automatic auditory processing in a certain CHR subtype and a close affinity between these electrophysiological indexes and clinical profiles within different clusters. Furthermore, MMN indexes may serve as predictors of subsequent remission from the CHR state. These findings suggest that the auditory MMN response is a potential neurophysiological marker for distinct clinical subtypes of CHR.
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Affiliation(s)
- GuiSen Wu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - XiaoChen Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - RanPiao Gan
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - JiaHui Zeng
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - YeGang Hu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - LiHua Xu
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - YanYan Wei
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - YingYing Tang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - Tao Chen
- Big Data Research LabUniversity of WaterlooOntarioCanada,Labor and Worklife ProgramHarvard UniversityCambridgeMassachusettsUSA,Niacin (Shanghai) Technology Co., Ltd.ShanghaiPeople's Republic of China
| | - HaiChun Liu
- Department of AutomationShanghai Jiao Tong UniversityShanghaiPeople's Republic of China
| | - ChunBo Li
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
| | - JiJun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)Chinese Academy of ScienceBeijingPeople's Republic of China,Institute of Psychology and Behavioral ScienceShanghai Jiao Tong UniversityShanghaiPeople's Republic of China
| | - TianHong Zhang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of MedicineShanghai Intelligent Psychological Evaluation and Intervention Engineering Technology Research Center (20DZ2253800), Shanghai Key Laboratory of Psychotic DisordersShanghaiPeople's Republic of China
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Wu G, Tang X, Gan R, Zeng J, Hu Y, Xu L, Wei Y, Tang Y, Chen T, Li C, Wang J, Zhang T. Temporal and time-frequency features of auditory oddball response in distinct subtypes of patients at clinical high risk for psychosis. Eur Arch Psychiatry Clin Neurosci 2022; 272:449-459. [PMID: 34333669 DOI: 10.1007/s00406-021-01316-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Accepted: 07/26/2021] [Indexed: 01/10/2023]
Abstract
Individuals at clinical high risk (CHR) for psychosis exhibit a reduced P300 oddball response, which indicates deficits in attention and working memory processes. Previous studies have mainly researched these responses in the temporal domain; hence, non-phase-locked or induced neural activities may have been ignored. Event-related potential (ERP) and time-frequency (TF) information, combined with clinical and cognitive profiles, may provide an insight into the pathophysiology and psychopathology of the CHR stage. The 104 CHR individuals who completed cognitive assessments and ERP tests were recruited and followed up between 2016 and 2018. Individuals with CHR were classified by three clinical subtypes demonstrated before, specifically 32 from Cluster-1 (characterized by extensive negative symptoms and cognitive deficits, at the highest risk for conversion to psychosis), 34 from Cluster-2 (characterized by thought and behavioral disorganization, with moderate cognitive impairment), and 38 from Cluster-3 (characterized by the mildest symptoms and cognitive deficits). Electroencephalograms were recorded during the auditory oddball paradigm. The P300 ERPs were analyzed in the temporal domain. The event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) were acquired by TF analysis. A reduced P300 response to target tones was noted in Cluster-1 relative to the other two clusters. Moreover, the P300 amplitude of Cluster-1 was associated with speed of processing (SoP) scores. Furthermore, the P300 amplitude of Cluster-3 was significantly correlated with verbal and visual learning scores. In the TF analysis, decreased delta ERSP and ITC were observed in Cluster-1; delta ITC was associated with SoP scores in Cluster-3. The results indicate relatively disrupted oddball responses in a certain CHR subtype and a close affinity between these electrophysiological indexes and attention, working memory, and declarative memory within different CHR clusters. These findings suggest that the auditory oddball response is a potential neurophysiological marker for distinct clinical subtypes of CHR.
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Affiliation(s)
- GuiSen Wu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - RanPiao Gan
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - JiaHui Zeng
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada.,Senior Research Fellow, Labor and Worklife Program, Harvard University, Cambridge, MA, USA.,Niacin (Shanghai) Technology Co., Ltd., Shanghai, People's Republic of China
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China. .,Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, People's Republic of China. .,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, People's Republic of China. .,Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China.
| | - TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders (No.13dz2260500), Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai, 200030, People's Republic of China.
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