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Pouchon A, Vinckier F, Dondé C, Gueguen MC, Polosan M, Bastin J. Reward and punishment learning deficits among bipolar disorder subtypes. J Affect Disord 2023; 340:694-702. [PMID: 37591352 DOI: 10.1016/j.jad.2023.08.075] [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: 01/24/2023] [Revised: 07/24/2023] [Accepted: 08/14/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND Reward sensitivity is an essential dimension related to mood fluctuations in bipolar disorder (BD), but there is currently a debate around hypersensitivity or hyposensitivity hypotheses to reward in BD during remission, probably related to a heterogeneous population within the BD spectrum and a lack of reward bias evaluation. Here, we examine reward maximization vs. punishment avoidance learning within the BD spectrum during remission. METHODS Patients with BD-I (n = 45), BD-II (n = 34) and matched (n = 30) healthy controls (HC) were included. They performed an instrumental learning task designed to dissociate reward-based from punishment-based reinforcement learning. Computational modeling was used to identify the mechanisms underlying reinforcement learning performances. RESULTS Behavioral results showed a significant reward learning deficit across BD subtypes compared to HC, captured at the computational level by a lower sensitivity to rewards compared to punishments in both BD subtypes. Computational modeling also revealed a higher choice randomness in BD-II compared to BD-I that reflected a tendency of BD-I to perform better during punishment avoidance learning than BD-II. LIMITATIONS Our patients were not naive to antipsychotic treatment and were not euthymic (but in syndromic remission) according to the International Society for Bipolar Disorder definition. CONCLUSIONS Our results are consistent with the reward hyposensitivity theory in BD. Computational modeling suggests distinct underlying mechanisms that produce similar observable behaviors, making it a useful tool for distinguishing how symptoms interact in BD versus other disorders. In the long run, a better understanding of these processes could contribute to better prevention and management of BD.
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Affiliation(s)
- Arnaud Pouchon
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, 38000 Grenoble, France; Department of Psychiatry, CHU Grenoble Alpes, 38000 Grenoble, France.
| | - Fabien Vinckier
- Motivation, Brain & Behavior (MBB) lab, Institut du Cerveau (ICM), Hôpital Pitié-Salpêtrière, F-75013 Paris, France; Université Paris Cité, F-75006 Paris, France; Department of Psychiatry, Service Hospitalo-Universitaire, GHU Paris Psychiatrie & Neurosciences, F-75014 Paris, France
| | - Clément Dondé
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, 38000 Grenoble, France; Department of Psychiatry, CHU Grenoble Alpes, 38000 Grenoble, France; Department of Psychiatry, CH Alpes-Isère, 38000 Saint-Egrève, France
| | - Maëlle Cm Gueguen
- Department of Psychiatry, University Behavioral Health Care & the Brain Health Institute, Rutgers University-New Brunswick, Piscataway, USA; Laureate Institute for Brain Research, Tulsa, OK 74136 USA
| | - Mircea Polosan
- Univ. Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, 38000 Grenoble, France; Department of Psychiatry, CHU Grenoble Alpes, 38000 Grenoble, France
| | - Julien Bastin
- Univ. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, 38000 Grenoble, France.
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2
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Marder SR, Umbricht D. Negative symptoms in schizophrenia: Newly emerging measurements, pathways, and treatments. Schizophr Res 2023; 258:71-77. [PMID: 37517366 DOI: 10.1016/j.schres.2023.07.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 04/20/2023] [Accepted: 07/10/2023] [Indexed: 08/01/2023]
Abstract
The negative symptoms of schizophrenia, which often appear earlier than any other symptom, are prominent and clinically relevant in the majority of patients. As a result, interest in their treatment has increased. Patients who exhibit significant negative symptoms have worse functional outcomes than those without, resulting in impairments in occupational, household, and recreational functioning, as well as difficulties in relationships. Yet treatment with currently available medications does not lead to any significant improvements in this core component of schizophrenia. An increased understanding of the pathophysiology underlying negative symptoms and the discovery of novel treatments that do not directly target dopamine offer the potential to develop therapies that may reduce negative symptoms and increase quality of life for patients. The current article will discuss the impact of negative symptoms, outline current measurement tools for the assessment of negative symptoms, and examine how these measures may be improved. Insights into the neural circuitry underlying negative symptoms will be discussed, and promising targets for the development of effective treatments for these symptoms will be identified. As more prospective, large-scale, randomized studies focus on the effects of treatments on negative symptoms, progress in this area is foreseeable. However, improvements in clinical assessment instruments, a better understanding of the underlying neural mechanisms, development of novel treatments with varied targets, and a greater focus on personalized treatment are all important to produce significant benefits for patients with negative symptoms of schizophrenia.
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Affiliation(s)
- Stephen R Marder
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, United States of America; Veterans Affairs Desert Pacific Mental Illness Research, Education, and Clinical Center, Los Angeles, CA, United States of America.
| | - Daniel Umbricht
- Xperimed LLC, Basel, Switzerland; University of Zurich, Zurich, Switzerland
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3
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Montazeri M, Montazeri M, Bahaadinbeigy K, Montazeri M, Afraz A. Application of machine learning methods in predicting schizophrenia and bipolar disorders: A systematic review. Health Sci Rep 2022; 6:e962. [PMID: 36589632 PMCID: PMC9795991 DOI: 10.1002/hsr2.962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 11/10/2022] [Accepted: 11/15/2022] [Indexed: 12/29/2022] Open
Abstract
Background and Aim Schizophrenia and bipolar disorder (BD) are critical and high-risk inherited mental disorders with debilitating symptoms. Worldwide, 3% of the population suffers from these disorders. The mortality rate of these patients is higher compared to other people. Current procedures cannot effectively diagnose these disorders because it takes an average of 10 years from the onset of the first symptoms to the definitive diagnosis of the disease. Machine learning (ML) techniques are used to meet this need. This study aimed to summarize information on the use of ML techniques for predicting schizophrenia and BD to help early and timely diagnosis of the disease. Methods A systematic literature search included articles published until January 19, 2020 in 3 databases. Two reviewers independently assessed original papers to determine eligibility for inclusion in this review. PRISMA guidelines were followed to conduct the study, and the Prediction Model Risk of Bias Assessment Tool (PROBAST) to assess included papers. Results In this review, 1243 papers were retrieved through database searches, of which 15 papers were included based on full-text assessment. ML techniques were used to predict schizophrenia and BDs. The main algorithms applied were support vector machine (SVM) (10 studies), random forests (RF) (5 studies), and gradient boosting (GB) (3 studies). Input and output characteristics were very diverse and have been kept to enable future research. RFs algorithms demonstrated significantly higher accuracy and sensitivity than SVM and GB. GB demonstrated significantly higher specificity than SVM and RF. We found no significant difference between RF and SVM in terms of specificity. Conclusion ML can precisely predict results and assist in making clinical decisions-concerning schizophrenia and BD. RF often performed better than other algorithms in supervised learning tasks. This study identified gaps in the literature and opportunities for future psychological ML research.
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Affiliation(s)
- Mahdieh Montazeri
- Department of Health Information Sciences, Faculty of Management and Medical Information SciencesKerman University of Medical SciencesKermanIran,Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mitra Montazeri
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Mohadeseh Montazeri
- Department of Computer, Faculty of FatimahKerman Branch Technical and Vocational UniversityKermanIran
| | - Ali Afraz
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
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4
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Le TP, Green MF, Lee J, Clayson PE, Jimenez AM, Reavis EA, Wynn JK, Horan WP. Aberrant reward processing to positive versus negative outcomes across psychotic disorders. J Psychiatr Res 2022; 156:1-7. [PMID: 36201975 PMCID: PMC10163955 DOI: 10.1016/j.jpsychires.2022.09.045] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 09/14/2022] [Accepted: 09/23/2022] [Indexed: 01/20/2023]
Abstract
Several studies of reward processing in schizophrenia have shown reduced sensitivity to positive, but not negative, outcomes although inconsistencies have been reported. In addition, few studies have investigated whether patients show a relative deficit to social versus nonsocial rewards, whether deficits occur across the spectrum of psychosis, or whether deficits relate to negative symptoms and functioning. This study examined probabilistic implicit learning via two visually distinctive slot machines for social and nonsocial rewards in 101 outpatients with diverse psychotic disorders and 48 community controls. The task consisted of two trial types: positive (optimal to choose a positive vs. neutral machine) and negative (optimal to choose a neutral vs. negative machine), with two reward conditions: social (faces) and nonsocial (money) reward conditions. A significant group X trial type interaction indicated that controls performed better on positive than negative trials, whereas patients showed the opposite pattern of better performance on negative than positive trials. In addition, both groups performed better for social than nonsocial stimuli, despite lower overall task performance in patients. Within patients, worse performance on negative trials showed significant, small-to-moderate correlations with motivation and pleasure-related negative symptoms and social functioning. The current findings suggest reward processing disturbances, particularly decreased sensitivity to positive outcomes, extend beyond schizophrenia to a broader spectrum of psychotic disorders and relate to important clinical outcomes.
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Affiliation(s)
- Thanh P Le
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA.
| | - Michael F Green
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Junghee Lee
- Department of Psychiatry and Behavioral Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Peter E Clayson
- Department of Psychology, University of South Florida, Tampa, FL, USA
| | - Amy M Jimenez
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Eric A Reavis
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA; Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - Jonathan K Wynn
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA
| | - William P Horan
- Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, USA; WCG VeraSci, Durham, NC, USA
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5
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Liebenow B, Jones R, DiMarco E, Trattner JD, Humphries J, Sands LP, Spry KP, Johnson CK, Farkas EB, Jiang A, Kishida KT. Computational reinforcement learning, reward (and punishment), and dopamine in psychiatric disorders. Front Psychiatry 2022; 13:886297. [PMID: 36339844 PMCID: PMC9630918 DOI: 10.3389/fpsyt.2022.886297] [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] [Received: 02/28/2022] [Accepted: 09/23/2022] [Indexed: 11/13/2022] Open
Abstract
In the DSM-5, psychiatric diagnoses are made based on self-reported symptoms and clinician-identified signs. Though helpful in choosing potential interventions based on the available regimens, this conceptualization of psychiatric diseases can limit basic science investigation into their underlying causes. The reward prediction error (RPE) hypothesis of dopamine neuron function posits that phasic dopamine signals encode the difference between the rewards a person expects and experiences. The computational framework from which this hypothesis was derived, temporal difference reinforcement learning (TDRL), is largely focused on reward processing rather than punishment learning. Many psychiatric disorders are characterized by aberrant behaviors, expectations, reward processing, and hypothesized dopaminergic signaling, but also characterized by suffering and the inability to change one's behavior despite negative consequences. In this review, we provide an overview of the RPE theory of phasic dopamine neuron activity and review the gains that have been made through the use of computational reinforcement learning theory as a framework for understanding changes in reward processing. The relative dearth of explicit accounts of punishment learning in computational reinforcement learning theory and its application in neuroscience is highlighted as a significant gap in current computational psychiatric research. Four disorders comprise the main focus of this review: two disorders of traditionally hypothesized hyperdopaminergic function, addiction and schizophrenia, followed by two disorders of traditionally hypothesized hypodopaminergic function, depression and post-traumatic stress disorder (PTSD). Insights gained from a reward processing based reinforcement learning framework about underlying dopaminergic mechanisms and the role of punishment learning (when available) are explored in each disorder. Concluding remarks focus on the future directions required to characterize neuropsychiatric disorders with a hypothesized cause of underlying dopaminergic transmission.
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Affiliation(s)
- Brittany Liebenow
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Rachel Jones
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Emily DiMarco
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Jonathan D. Trattner
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Joseph Humphries
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - L. Paul Sands
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Kasey P. Spry
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Christina K. Johnson
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Evelyn B. Farkas
- Georgia State University Undergraduate Neuroscience Institute, Atlanta, GA, United States
| | - Angela Jiang
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
| | - Kenneth T. Kishida
- Neuroscience Graduate Program, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Physiology and Pharmacology, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Neurosurgery, Wake Forest University School of Medicine, Winston-Salem, NC, United States
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem, NC, United States
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6
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Barch DM, Boudewyn MA, Carter CC, Erickson M, Frank MJ, Gold JM, Luck SJ, MacDonald AW, Ragland JD, Ranganath C, Silverstein SM, Yonelinas A. Cognitive [Computational] Neuroscience Test Reliability and Clinical Applications for Serious Mental Illness (CNTRaCS) Consortium: Progress and Future Directions. Curr Top Behav Neurosci 2022; 63:19-60. [PMID: 36173600 DOI: 10.1007/7854_2022_391] [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] [Indexed: 11/25/2022]
Abstract
The development of treatments for impaired cognition in schizophrenia has been characterized as the most important challenge facing psychiatry at the beginning of the twenty-first century. The Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) project was designed to build on the potential benefits of using tasks and tools from cognitive neuroscience to better understanding and treat cognitive impairments in psychosis. These benefits include: (1) the use of fine-grained tasks that measure discrete cognitive processes; (2) the ability to design tasks that distinguish between specific cognitive domain deficits and poor performance due to generalized deficits resulting from sedation, low motivation, poor test taking skills, etc.; and (3) the ability to link cognitive deficits to specific neural systems, using animal models, neuropsychology, and functional imaging. CNTRICS convened a series of meetings to identify paradigms from cognitive neuroscience that maximize these benefits and identified the steps need for translation into use in clinical populations. The Cognitive Neuroscience Test Reliability and Clinical Applications for Schizophrenia (CNTRaCS) Consortium was developed to help carry out these steps. CNTRaCS consists of investigators at five different sites across the country with diverse expertise relevant to a wide range of the cognitive systems identified as critical as part of CNTRICs. This work reports on the progress and current directions in the evaluation and optimization carried out by CNTRaCS of the tasks identified as part of the original CNTRICs process, as well as subsequent extensions into the Positive Valence systems domain of Research Domain Criteria (RDoC). We also describe the current focus of CNTRaCS, which involves taking a computational psychiatry approach to measuring cognitive and motivational function across the spectrum of psychosis. Specifically, the current iteration of CNTRaCS is using computational modeling to isolate parameters reflecting potentially more specific cognitive and visual processes that may provide greater interpretability in understanding shared and distinct impairments across psychiatric disorders.
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Affiliation(s)
- Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA.
| | | | | | | | | | - James M Gold
- Maryland Psychiatric Research Center, Baltimore, MD, USA
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7
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Hu HX, Jiang SY, Shan HD, Chu MY, Lv QY, Yi ZH, Lui SSY, Cheung EFC, Chan RCK. Negative belief-updating bias for positive daily life events in individuals with schizophrenia and social anhedonia. Cogn Neuropsychiatry 2022; 27:237-254. [PMID: 34895073 DOI: 10.1080/13546805.2021.2014309] [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] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Low-pleasure beliefs are found in both patients with schizophrenia (SZ) and individuals with high social anhedonia (SocAnh), and are associated with anhedonia. However, little is known about the development and maintenance of these low-pleasure beliefs in the clinical and subclinical populations. We investigated whether patients with SZ and individuals with high SocAnh have deficits in updating their beliefs, which may contribute to the understanding of the formation and maintenance of low-pleasure beliefs. METHODS The Modified Belief Updating Task was administered to assess belief-updating patterns in a clinical sample (36 SZ patients and 30 matched controls) and a subclinical sample (27 individuals with high SocAnh and 30 matched controls). RESULTS We found that compared with controls, SZ patients updated their beliefs to a greater extent and more frequently when receiving bad news for positive life events, but not for negative life events. Moreover, individuals with high SocAnh also exhibited similar patterns in updating their beliefs for positive life events after controlling depressive symptoms. CONCLUSIONS Our findings suggest that negative belief-updating patterns for positive events may play an important role in the formation and maintenance of low-pleasure beliefs in patients with SZ and individuals with high SocAnh.
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Affiliation(s)
- Hui-Xin Hu
- Translational Neuropsychology and Applied Cognitive Neuroscience Laboratory, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Shu-Yao Jiang
- Translational Neuropsychology and Applied Cognitive Neuroscience Laboratory, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Hai-di Shan
- Translational Neuropsychology and Applied Cognitive Neuroscience Laboratory, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Min-Yi Chu
- Translational Neuropsychology and Applied Cognitive Neuroscience Laboratory, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Qin-Yu Lv
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Zheng-Hui Yi
- Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
| | - Simon S Y Lui
- Department of Psychiatry, The University of Hong Kong, Hong Kong, People's Republic of China
| | | | - Raymond C K Chan
- Translational Neuropsychology and Applied Cognitive Neuroscience Laboratory, Shanghai Mental Health Centre, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China.,Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, People's Republic of China.,Department of Psychology, The University of Chinese Academy of Sciences, Beijing, People's Republic of China
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8
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Wang J, Qi W, Shi H, Huang L, Ning F, Wang F, Wang K, Bai H, Wu H, Zhuang J, Hong H, Zhou H, Feng H, Zhou Y, Dong N, Liu L, Kong Y, Xie J, Zhao RC. MiR-4763-3p targeting RASD2as a Potential Biomarker and Therapeutic Target for Schizophrenia. Aging Dis 2022; 13:1278-1292. [PMID: 35855328 PMCID: PMC9286908 DOI: 10.14336/ad.2022.0103] [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: 09/14/2021] [Accepted: 01/03/2021] [Indexed: 11/06/2022] Open
Abstract
Existing diagnostic methods are limited to observing appearance and demeanor, even though genetic factors play important roles in the pathology of schizophrenia. Indeed, no molecular-level test exists to assist diagnosis, which has limited treatment strategies. To address this serious shortcoming, we used a bioinformatics approach to identify 61 genes that are differentially expressed in schizophrenia patients compared with healthy controls. In particular, competing endogenous RNA network revealed the important role of the gene RASD2, which is regulated by miR-4763-3p. Indeed, analysis of blood samples confirmed that RASD2 is downregulated in schizophrenia patients. Moreover, positron emission tomography data collected for 44 human samples identified the prefrontal and temporal lobes as potential key brain regions in schizophrenia patients. Mechanistic studies indicated that miR-4763-3p inhibits RASD2 by base-pairing with the 3’ untranslated region of RASD2 mRNA. Importantly, RASD2 has been shown to interact with β-arrestin2, which contributes to the regulation of the DRD2-dependent CREB response element-binding protein pathway in the dopamine system. Finally, results obtained with a mouse model of schizophrenia revealed that inhibition of miR-4763-3p function alleviated anxiety symptoms and improved memory. The dopamine transporters in the striatal regions were significantly reduced in schizophrenia model mice as compared with wild-type mice, suggesting that inhibition of miR-4763-3p can lessen the symptoms of schizophrenia. Our findings demonstrate that miR-4763-3p may target RASD2 mRNA and thus may serve as a potential biomarker and therapeutic target for schizophrenia, providing a theoretical foundation for further studies of the molecular basis of this disease.
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Affiliation(s)
- Jiao Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Wenxin Qi
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Hongwei Shi
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Lin Huang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Fujiang Ning
- Psychological Rehabilitation Hospital of Penglai District, Yantai, Shandong, China
| | - Fushuai Wang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Kai Wang
- Central Laboratory, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
| | - Haotian Bai
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
| | - Hao Wu
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Junyi Zhuang
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Huanle Hong
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Haicong Zhou
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Hu Feng
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Yinping Zhou
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Naijun Dong
- School of Life Sciences, Shanghai University, Shanghai, China.
| | - Li Liu
- Psychological Rehabilitation Hospital of Penglai District, Yantai, Shandong, China
| | - Yanyan Kong
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Jiang Xie
- School of Computer Engineering and Science, Shanghai University, Shanghai, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
| | - Robert Chunhua Zhao
- School of Life Sciences, Shanghai University, Shanghai, China.
- Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China.
- Centre of Excellence in Tissue Engineering, Chinese Academy of Medical Sciences, Beijing, China.
- Beijing Key Laboratory of New Drug Development and Clinical Trial of Stem Cell Therapy (BZ0381), Beijing, China.
- Correspondence should be addressed to: Dr. Jiao Wang (), School of Life Sciences, Shanghai University, Shanghai, China; Dr. Yanyan Kong (), PET Center, Huashan Hospital, Fudan University, Shanghai, China; Dr. Jiang Xie (), School of Computer Engineering and Science, Shanghai University, Shanghai, China, and Dr. Robert Chunhua Zhao (), School of Life Sciences, Shanghai University, Shanghai, China
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9
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Wong SCY, Ng MCM, Chan JKN, Luk MSK, Lui SSY, Chen EYH, Chang WC. Altered Risk-Taking Behavior in Early-Stage Bipolar Disorder With a History of Psychosis. Front Psychiatry 2021; 12:763545. [PMID: 34867547 PMCID: PMC8637446 DOI: 10.3389/fpsyt.2021.763545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 10/27/2021] [Indexed: 11/23/2022] Open
Abstract
Altered risk-taking propensity is an important determinant of functional impairment in bipolar disorder. However, prior studies primarily assessed patients with chronic illness, and risk-taking has not been evaluated in the early illness course. This study investigated risk-taking behavior in 39 euthymic early-stage bipolar disorder patients aged 16-40 years who were treated within 3 years from their first-episode mania with psychotic features and 36 demographically-matched healthy controls using the Balloon Analog Risk Task (BART), a well-validated risk-taking performance-based paradigm requiring participants to make responses for cumulative gain at increasing risk of loss. Relationships of risk-taking indices with symptoms, self-reported impulsivity, cognitive functions, and treatment characteristics were also assessed. Our results showed that patients exhibited significantly lower adjusted scores (i.e., average balloon pumps in unexploded trials) (p = 0.001), lower explosion rate (p = 0.007) and lower cumulative scores (p = 0.003) than controls on BART, indicating their suboptimal risk-taking performance with increased propensity for risk aversion. Risk-taking indices were not correlated with any symptom dimensions, self-reported impulsivity, cognitive functions or antipsychotic dose. No significant difference was observed between patients with and without antipsychotic medications on self-reported impulsivity or any of the BART performance indices. This is the first study to examine risk-taking behavior in early-stage bipolar disorder with history of psychosis and indicates that patients displayed altered risk-taking with increased risk aversion compared with controls. Further research is needed to clarify longitudinal trajectory of risk-taking propensity and its relationships with psychosis and functional outcome in the early stage of bipolar disorder.
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Affiliation(s)
- Sandra Chi Yiu Wong
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Mary Chung Mun Ng
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Joe Kwun Nam Chan
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Martha Sin Ki Luk
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Simon Sai Yu Lui
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Eric Yu Hai Chen
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
| | - Wing Chung Chang
- Department of Psychiatry, Queen Mary Hospital, The University of Hong Kong, Pokfulam, Hong Kong SAR, China.,State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Pokfulam, Hong Kong SAR, China
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Gotra MY, Hill SK, Gershon ES, Tamminga CA, Ivleva EI, Pearlson GD, Keshavan MS, Clementz BA, McDowell JE, Buckley PF, Sweeney JA, Keedy SK. Distinguishing patterns of impairment on inhibitory control and general cognitive ability among bipolar with and without psychosis, schizophrenia, and schizoaffective disorder. Schizophr Res 2020; 223:148-157. [PMID: 32674921 PMCID: PMC7704797 DOI: 10.1016/j.schres.2020.06.033] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 06/25/2020] [Accepted: 06/28/2020] [Indexed: 01/01/2023]
Abstract
BACKGROUND Deficits in inhibitory control on a Stop Signal Task (SST) were previously observed to be of similar magnitude across schizophrenia, schizoaffective, and bipolar disorder with psychosis, despite variation in general cognitive ability. Understanding different patterns of performance on the SST may elucidate different pathways to the impaired inhibitory control each group displayed. Comparing nonpsychotic bipolar disorder to the psychosis groups on SST may also expand our understanding of the shared neurobiology of this illness spectrum. METHODS We tested schizophrenia (n = 220), schizoaffective (n = 216), bipolar disorder with (n = 192) and without psychosis (n = 67), and 280 healthy comparison participants with a SST and the Brief Assessment of Cognition in Schizophrenia (BACS), a measure of general cognitive ability. RESULTS All patient groups had a similar degree of impaired inhibitory control over prepotent responses. However, bipolar groups differed from schizophrenia and schizoaffective groups in showing speeded responses and inhibition errors that were not accounted for by general cognitive ability. Schizophrenia and schizoaffective groups had a broader set of deficits on inhibition and greater general cognitive deficit, which fully accounted for the inhibition deficits. No differences were found between the clinically well-matched bipolar with and without psychosis groups, including for inhibitory control or general cognitive ability. CONCLUSIONS We conclude that 1) while impaired inhibitory control on a SST is of similar magnitude across the schizo-bipolar spectrum, including nonpsychotic bipolar, different mechanisms may underlie the impairments, and 2) history of psychosis in bipolar disorder does not differentially impact inhibitory behavioral control or general cognitive abilities.
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Affiliation(s)
- Milena Y Gotra
- Department of Psychology, Rosalind Franklin University, North Chicago, IL, United States
| | - Scot K Hill
- Department of Psychology, Rosalind Franklin University, North Chicago, IL, United States
| | - Elliot S Gershon
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States
| | - Carol A Tamminga
- Department of Psychiatry, UT-Southwestern Medical Center, Dallas, TX, United States
| | - Elena I Ivleva
- Department of Psychiatry, UT-Southwestern Medical Center, Dallas, TX, United States
| | - Godfrey D Pearlson
- Departments of Psychiatry and Neuroscience, Yale University School of Medicine, New Haven, CT, United States; Institute of Living, Hartford Hospital, Hartford, CT, United States
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconness Medical Center and Harvard Medical School, Boston, MA, United States
| | - Brett A Clementz
- Department of Psychology and Neuroscience, University of Georgia, Athens, GA, United States
| | - Jennifer E McDowell
- Department of Psychology and Neuroscience, University of Georgia, Athens, GA, United States
| | - Peter F Buckley
- School of Medicine, Virginia Commonwealth University, Richmond, VA, United States
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, United States
| | - Sarah K Keedy
- Department of Psychiatry and Behavioral Neuroscience, University of Chicago, Chicago, IL, United States.
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