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Zhang D, Xu L, Liu X, Cui H, Wei Y, Zheng W, Hong Y, Qian Z, Hu Y, Tang Y, Li C, Liu Z, Chen T, Liu H, Zhang T, Wang J. Eye Movement Characteristics for Predicting a Transition to Psychosis: Longitudinal Changes and Implications. Schizophr Bull 2024:sbae001. [PMID: 38245498 DOI: 10.1093/schbul/sbae001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/22/2024]
Abstract
BACKGROUND AND HYPOTHESIS Substantive inquiry into the predictive power of eye movement (EM) features for clinical high-risk (CHR) conversion and their longitudinal trajectories is currently sparse. This study aimed to investigate the efficiency of machine learning predictive models relying on EM indices and examine the longitudinal alterations of these indices across the temporal continuum. STUDY DESIGN EM assessments (fixation stability, free-viewing, and smooth pursuit tasks) were performed on 140 CHR and 98 healthy control participants at baseline, followed by a 1-year longitudinal observational study. We adopted Cox regression analysis and constructed random forest prediction models. We also employed linear mixed-effects models (LMMs) to analyze longitudinal changes of indices while stratifying by group and time. STUDY RESULTS Of the 123 CHR participants who underwent a 1-year clinical follow-up, 25 progressed to full-blown psychosis, while 98 remained non-converters. Compared with the non-converters, the converters exhibited prolonged fixation durations, decreased saccade amplitudes during the free-viewing task; larger saccades, and reduced velocity gain during the smooth pursuit task. Furthermore, based on 4 baseline EM measures, a random forest model classified converters and non-converters with an accuracy of 0.776 (95% CI: 0.633, 0.882). Finally, LMMs demonstrated no significant longitudinal alterations in the aforementioned indices among converters after 1 year. CONCLUSIONS Aberrant EMs may precede psychosis onset and remain stable after 1 year, and applying eye-tracking technology combined with a modeling approach could potentially aid in predicting CHRs evolution into overt psychosis.
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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, PR China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Xu Liu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Wensi Zheng
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yawen Hong
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Zhenying Qian
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yegang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Chunbo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Zhi Liu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, PR China
- School of Communication and Information Engineering, Shanghai University, Shanghai, PR China
| | - Tao Chen
- 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, PR China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai Jiaotong University School of Medicine, Shanghai, PR China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 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
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Coutts F, Koutsouleris N, McGuire P. Psychotic disorders as a framework for precision psychiatry. Nat Rev Neurol 2023. [PMID: 36879033 DOI: 10.1038/s41582-023-00779-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/24/2023] [Indexed: 03/08/2023]
Abstract
People with psychotic disorders can show marked interindividual variations in the onset of illness, responses to treatment and relapse, but they receive broadly similar clinical care. Precision psychiatry is an approach that aims to stratify people with a given disorder according to different clinical outcomes and tailor treatment to their individual needs. At present, interindividual differences in outcomes of psychotic disorders are difficult to predict on the basis of clinical assessment alone. Therefore, current research in psychosis seeks to build models that predict outcomes by integrating clinical information with a range of biological measures. Here, we review recent progress in the application of precision psychiatry to psychotic disorders and consider the challenges associated with implementing this approach in clinical practice.
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Abstract
The field of psychiatry is far from perfect in predicting which individuals will transition to a psychotic disorder. Here, we argue that visual system assessment can help in this regard. Such assessments have generated medium-to-large group differences with individuals prior to or near the first psychotic episode or have shown little influence of illness duration in larger samples of more chronic patients. For example, self-reported visual perceptual distortions-so-called visual basic symptoms-occur in up to 2/3rds of those with non-affective psychosis and have already longitudinally predicted an impending onset of schizophrenia. Possibly predictive psychophysical markers include enhanced contrast sensitivity, prolonged backward masking, muted collinear facilitation, reduced stereoscopic depth perception, impaired contour and shape integration, and spatially restricted exploratory eye movements. Promising brain-based markers include visual thalamo-cortical hyperconnectivity, decreased occipital gamma band power during visual detection (MEG), and reduced visually evoked occipital P1 amplitudes (EEG). Potentially predictive retinal markers include diminished cone a- and b-wave amplitudes and an attenuated photopic flicker response during electroretinography. The foregoing assessments are often well-described mechanistically, implying that their findings could readily shed light on the underlying pathophysiological changes that precede or accompany a transition to psychosis. The retinal and psychophysical assessments in particular are inexpensive, well-tolerated, easy to administer, and brief, with few inclusion/exclusion criteria. Therefore, across all major levels of analysis-from phenomenology to behavior to brain and retinal functioning-visual system assessment could complement and improve upon existing methods for predicting which individuals go on to develop a psychotic disorder.
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Affiliation(s)
- Alexander Diamond
- Department of Psychiatry, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA
| | - Steven M Silverstein
- Department of Psychiatry, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA
- Department of Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA
- Center for Visual Science, University of Rochester, 601 Elmwood Ave, Rochester, NY, USA
- Department of Ophthalmology, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA
| | - Brian P Keane
- Department of Psychiatry, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA.
- Department of Neuroscience, University of Rochester Medical Center, 601 Elmwood Ave, Rochester, NY, USA.
- Center for Visual Science, University of Rochester, 601 Elmwood Ave, Rochester, NY, USA.
- Department of Brain & Cognitive Sciences, University of Rochester, 358 Meliora Hall, NY, Rochester, USA.
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