1
|
Zhang D, Ma C, Xu L, Liu X, Cui H, Wei Y, Zheng W, Hong Y, Xie Y, Qian Z, Hu Y, Tang Y, Li C, Liu Z, Chen T, Liu H, Zhang T, Wang J. Abnormal Scanning Patterns Based on Eye Movement Entropy in Early Psychosis. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00161-7. [PMID: 38909898 DOI: 10.1016/j.bpsc.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Restricted scan path mode is hypothesized to explain abnormal scanning patterns in patients with schizophrenia. Here, we calculated entropy scores (drawing upon gaze data to measure the statistical randomness of eye movements) to quantify how strategical and random participants were to process image stimuli. METHODS Eighty-six patients with first-episode schizophrenia (FES), 124 individuals at clinical high risk (CHR) for psychosis, and 115 healthy controls (HCs) completed an eye-tracking examination for freely viewing 35 static images (each presented 10s) and cognitive assessments. We compared the group differences in overall entropy score, as well as entropy scores under various conditions. Furthermore, we also investigated the correlation between entropy scores and symptoms along with cognitive function. RESULTS Increased overall entropy scores were noted in FES and CHR groups relative to HCs, and these differences were already apparent within 0∼2.5s. In addition, the CHR group exhibited higher entropy when viewing low-meaning images compared to HCs. Moreover, the entropy within 0∼2.5s showed significant correlations with negative symptoms in the FES group, Attention/Vigilance scores in the CHR group, as well as Speed of processing and Attention/Vigilance scores across all three groups. CONCLUSIONS The results indicate that FES and CHR individuals scan pictures more randomly and less strategically than HCs. These patterns also correlate with clinical symptoms and neurocognition. The present study highlights the potential of the eye movement entropy measure as a neurophysiological marker for early psychosis.
Collapse
Affiliation(s)
- Dan Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Chunyan Ma
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Xu Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Wensi Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yawen Hong
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yuou Xie
- First Clinical Medical College of Nanjing Medical University, Nanjing 211103, PR China
| | - Zhenying Qian
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yegang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China
| | - Zhi Liu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai 200444, PR China; School of Communication and Information Engineering, Shanghai University, Shanghai 200444, PR 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, PR China
| | - Haichun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
| | - Tianhong Zhang
- Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Jiaotong University School of Medicine, 600 Wanping Nan Road, Shanghai 200030, PR China.
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, PR China; CAS 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.
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Hamilton HK, Mathalon DH, Ford JM. P300 in schizophrenia: Then and now. Biol Psychol 2024; 187:108757. [PMID: 38316196 DOI: 10.1016/j.biopsycho.2024.108757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 01/23/2024] [Accepted: 01/23/2024] [Indexed: 02/07/2024]
Abstract
The 1965 discovery of the P300 component of the electroencephalography (EEG)-based event-related potential (ERP), along with the subsequent identification of its alteration in people with schizophrenia, initiated over 50 years of P300 research in schizophrenia. Here, we review what we now know about P300 in schizophrenia after nearly six decades of research. We describe recent efforts to expand our understanding of P300 beyond its sensitivity to schizophrenia itself to its potential role as a biomarker of risk for psychosis or a heritable endophenotype that bridges genetic risk and psychosis phenomenology. We also highlight efforts to move beyond a syndrome-based approach to understand P300 within the context of the clinical, cognitive, and presumed pathophysiological heterogeneity among people diagnosed with schizophrenia. Finally, we describe several recent approaches that extend beyond measuring the traditional P300 ERP component in people with schizophrenia, including time-frequency analyses and pharmacological challenge studies, that may help to clarify specific cognitive mechanisms that are disrupted in schizophrenia. Moreover, we discuss several promising areas for future research, including studies of animal models that can be used for treatment development.
Collapse
Affiliation(s)
- Holly K Hamilton
- University of Minnesota, Department of Psychiatry & Behavioral Sciences, Minneapolis, MN, USA; Minneapolis Veterans Affairs Health Care System, Minneapolis, MN, USA; University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA.
| | - Daniel H Mathalon
- University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| | - Judith M Ford
- University of California, San Francisco, Department of Psychiatry & Behavioral Sciences, San Francisco, CA, USA; San Francisco Veterans Affairs Health Care System, San Francisco, CA, USA
| |
Collapse
|
4
|
Zhang T, Xu L, Tang X, Wei Y, Hu Y, Cui H, Tang Y, Li C, Wang J. Comprehensive review of multidimensional biomarkers in the ShangHai At Risk for Psychosis (SHARP) program for early psychosis identification. PCN REPORTS : PSYCHIATRY AND CLINICAL NEUROSCIENCES 2023; 2:e152. [PMID: 38868725 PMCID: PMC11114265 DOI: 10.1002/pcn5.152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 09/28/2023] [Accepted: 10/20/2023] [Indexed: 06/14/2024]
Abstract
Psychosis is recognized as one of the largest contributors to nonfatal health loss, and early identification can largely improve routine clinical activity by predicting the psychotic course and guiding treatment. Clinicians have used the clinical high-risk for psychosis (CHR) paradigm to better understand the risk factors that contribute to the onset of psychotic disorders. Clinical factors have been widely applied to calculate the individualized risks for conversion to psychosis 1-2 years later. However, there is still a dearth of valid biomarkers to predict psychosis. Biomarkers, in the context of this paper, refer to measurable biological indicators that can provide valuable information about the early identification of individuals at risk for psychosis. The aim of this paper is to critically review studies assessing CHR and suggest possible biomarkers for application of prediction. We summarized the studies on biomarkers derived from the findings of the ShangHai at Risk for Psychosis (SHARP) program, including those that are considered to have the most potential. This comprehensive review was conducted based on expert opinions within the SHARP research team, and the selection of studies and results presented in this paper reflects the collective expertise of the team in the field of early psychosis identification. The three dimensions with potential candidates include neuroimaging dimension of brain structure and function, electrophysiological dimension of event-related potentials (ERPs), and immune dimension of inflammatory cytokines and complement proteins, which proved to be useful in supporting the prediction of psychosis from the CHR state. We suggest that these three dimensions could be useful as risk biomarkers for treatment optimization. In the future, when available for the integration of multiple dimensions, clinicians may be able to obtain a comprehensive report with detailed information of psychosis risk and specific indications about preferred prevention.
Collapse
Affiliation(s)
- TianHong Zhang
- Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghaiChina
- CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT)Chinese Academy of SciencesShanghaiChina
- Institute of Psychology and Behavioral ScienceShanghai Jiaotong UniversityShanghaiChina
| |
Collapse
|
5
|
Zhang Q, Luo C, Ngetich R, Zhang J, Jin Z, Li L. Visual Selective Attention P300 Source in Frontal-Parietal Lobe: ERP and fMRI Study. Brain Topogr 2022; 35:636-650. [PMID: 36178537 DOI: 10.1007/s10548-022-00916-x] [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: 12/31/2021] [Accepted: 09/03/2022] [Indexed: 11/28/2022]
Abstract
Visual selective attention can be achieved into bottom-up and top-down attention. Different selective attention tasks involve different attention control ways. The pop-out task requires more bottom-up attention, whereas the search task involves more top-down attention. P300, which is the positive potential generated by the brain in the latency of 300 ~ 600 ms after stimulus, reflects the processing of attention. There is no consensus on the P300 source. The aim of present study is to study the source of P300 elicited by different visual selective attention. We collected thirteen participants' P300 elicited by pop-out and search tasks with event-related potentials (ERP). We collected twenty-six participants' activation brain regions in pop-out and search tasks with functional magnetic resonance imaging (fMRI). And we analyzed the sources of P300 using the ERP and fMRI integration with high temporal resolution and high spatial resolution. ERP results indicated that the pop-out task induced larger P300 than the search task. P300 induced by the two tasks distributed at frontal and parietal lobes, with P300 induced by the pop-out task mainly at the parietal lobe and that induced by the search task mainly at the frontal lobe. Further ERP and fMRI integration analysis showed that neural difference sources of P300 were the right precentral gyrus, left superior frontal gyrus (medial orbital), left middle temporal gyrus, left rolandic operculum, right postcentral gyrus, and left angular gyrus. Our study suggests that the frontal and parietal lobes contribute to the P300 component of visual selective attention.
Collapse
Affiliation(s)
- Qiuzhu Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Cimei Luo
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ronald Ngetich
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Junjun Zhang
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zhenlan Jin
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Ling Li
- MOE Key Lab for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Psychiatry and Psychology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| |
Collapse
|
6
|
Perrottelli A, Giordano GM, Brando F, Giuliani L, Pezzella P, Mucci A, Galderisi S. Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12092193. [PMID: 36140594 PMCID: PMC9498272 DOI: 10.3390/diagnostics12092193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022] Open
Abstract
Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|