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Minichino A, Davies C, Karpenko O, Christodoulou N, Ramalho R, Nandha S, Damiani S, Provenzani U, Esposito CM, Mensi MM, Borgatti R, Stefana A, McGuire P, Fusar-Poli P. Preventing psychosis in people at clinical high risk: an updated meta-analysis by the World Psychiatric Association Preventive Psychiatry section. Mol Psychiatry 2025; 30:2773-2782. [PMID: 39953286 DOI: 10.1038/s41380-025-02902-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 09/19/2024] [Accepted: 01/17/2025] [Indexed: 02/17/2025]
Abstract
Recently published large-scale randomised controlled trials (RCTs) have questioned the efficacy of preventive interventions in individuals at clinical high risk for psychosis (CHR-P). We conducted a systematic review and meta-analysis to include this new evidence and provide future directions for the field. We followed the PRISMA guidelines and a pre-registered protocol, with a literature search conducted from inception to November 2023. We included RCTs that collected data on psychosis transition (the primary outcome) in CHR-P. Secondary outcomes were symptoms severity and functioning. Investigated time points were 6,12,24,36, and +36 months. We used odd ratios (ORs) and standardised mean differences (SMD) as summary outcomes. Heterogeneity was estimated with the Higgins I2. Twenty-four RCTs, involving 3236 CHR-P individuals, were included. Active interventions were Cognitive Behavioural Therapy (CBT), family-focused therapy, Integrated Psychological Therapy, antipsychotics, omega-3 fatty acids, CBT plus risperidone, minocycline, and other non-pharmacological approaches (cognitive remediation, sleep-targeted therapy, brain stimulation). Results showed no evidence that any of the investigated active interventions had a sustained and robust effect on any of the investigated outcomes in CHR-P, when compared to control interventions, including CBT on transition to psychosis at 12 months (9 RCTs; OR: 0.64; 95% CI: 0.39-1.06; I2: 21%; P = 0.08). These results highlight the need for novel treatment approaches in CHR-P. Future studies should consider the heterogeneity of this clinical population and prioritise stratification strategies and bespoke treatments.
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Affiliation(s)
| | - Cathy Davies
- EPIC Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Olga Karpenko
- Mental-health Clinic No. 1 named after N.A. Alexeev, Moscow, Russia
| | - Nikos Christodoulou
- Department of Psychiatry, Faculty of Medicine, University of Thessaly, Larisa, Greece
| | - Rodrigo Ramalho
- Department of Social and Community Health, School of Population Health, University of Auckland, Auckland, New Zealand
| | - Sunil Nandha
- Outreach And Support in South London (OASIS) Service, South London & Maudsley NHS Foundation Trust, London, UK
| | - Stefano Damiani
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
| | - Umberto Provenzani
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
| | - Cecilia Maria Esposito
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
| | - Martina Maria Mensi
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
- Child and Adolescent Neuropsichiatry Unit, IRCCS F. Mondino, Pavia, Italia
| | - Renato Borgatti
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
- Child and Adolescent Neuropsichiatry Unit, IRCCS F. Mondino, Pavia, Italia
| | - Alberto Stefana
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy
| | - Philip McGuire
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Paolo Fusar-Poli
- EPIC Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Outreach And Support in South London (OASIS) Service, South London & Maudsley NHS Foundation Trust, London, UK.
- Dipartimento di Scienze del Sistema Nervoso e del Comportamento, Università di Pavia, Pavia, Italy.
- Department of Psychiatry and Psychotherapy, University Hospital, Ludwig-Maximilian-University (LMU), Munich, Germany.
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Ju M, Long B, Wei Y, Tang X, Xu L, Gan R, Cui H, Tang Y, Yi Z, Liu H, Wang Z, Chen T, Gao J, Hu Q, Zeng L, Li C, Wang J, Liu H, Zhang T. Cognitive impairments in first-episode psychosis patients with attenuated niacin response. Schizophr Res Cogn 2025; 40:100346. [PMID: 39925786 PMCID: PMC11803152 DOI: 10.1016/j.scog.2025.100346] [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: 12/18/2024] [Revised: 01/24/2025] [Accepted: 01/24/2025] [Indexed: 02/11/2025]
Abstract
Background Psychosis is a complex brain disorder with diverse biological subtypes influenced by various pathogenic mechanisms, which can affect treatment efficacy. The ANR(Attenuated Niacin Response) subtype is characterized by pronounced negative symptoms and functional impairments, suggesting a distinct clinical profile. However, research on the cognitive characteristics associated with the ANR subtype in drug-naïve first-episode psychosis(FEP) patients remains limited. Methods This observational study involved 54 FEP patients and 52 healthy controls(HC). Clinical psychopathology was assessed using the Positive and Negative Syndrome Scale(PANSS), while cognitive performance was evaluated through the Chinese version of the MATRICS Consensus Cognitive Battery(MCCB). Additionally, niacin response was measured using aqueous methylnicotinate patches, with responses quantified to classify participants into ANR or normal niacin response (NNR) groups. Results Among the FEP patients, 25.9 % were classified as having ANR, significantly higher than the 7.7 % in the HC group (χ 2 = 6.247, p = 0.012). The ANR group exhibited more severe negative symptoms and higher total PANSS scores compared to the NNR group, with significant differences in cognitive performance on the Trail Making test and the Brief Visuospatial Memory Test-Revised. Correlation analyses revealed a significant positive relationship between overall symptom severity and niacin response, as well as between cognitive performance and niacin response, particularly for the Trail Making and Symbol coding tests. Conclusions This study demonstrates that the ANR subtype in first-episode psychosis is linked to more severe negative symptoms and cognitive impairments. Targeted assessments and interventions for patients with ANR may improve treatment outcomes and enhance understanding of cognitive dysfunction in psychotic disorders.
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Affiliation(s)
- MingLiang Ju
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei 238000, China
- 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 200030, China
| | - Bin Long
- 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 200030, 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 200030, 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 200030, 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 200030, China
| | - RanPiao Gan
- 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 200030, 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 200030, 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 200030, China
| | - ZhengHui Yi
- 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 200030, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Center, Shanghai, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Ontario, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, United States
| | - Jin Gao
- Department of Clinical Psychology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, Shandong, China
| | - Qiang Hu
- Department of Psychiatry, ZhenJiang Mental Health Center, Zhenjiang, China
| | - LingYun Zeng
- Department of Psychiatric Rehabilitation, Shenzhen Kangning Hospital, ShenZhen, GuangDong, China
| | - 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 200030, 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 200030, China
- Department of Psychiatry, Nantong Fourth People's Hospital & Nantong Brain Hospital, Suzhou 226000, China
| | - HuanZhong Liu
- Department of Psychiatry, Chaohu Hospital of Anhui Medical University, Hefei 238000, China
| | - 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 200030, China
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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 2025; 51:343-351. [PMID: 38741342 PMCID: PMC11908854 DOI: 10.1093/schbul/sbae066] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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Su W, Wang J, Tang Y. A narrative literature review of white matter microstructure in individuals at clinical high risk for psychosis. PSYCHORADIOLOGY 2024; 5:kkae031. [PMID: 40406529 PMCID: PMC12097486 DOI: 10.1093/psyrad/kkae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 12/08/2024] [Accepted: 12/22/2024] [Indexed: 05/26/2025]
Abstract
Schizophrenia is a severe psychiatric disorder characterized by widespread white matter (WM) alterations, manifesting as neurodevelopmental deficits and dysconnectivity abnormalities. Over the past two decades, studies have focused on the clinical high-risk (CHR) stage of psychosis and have yielded fruitful information on WM abnormalities that exist prior to the full onset of psychosis, shedding light on biological mechanisms underlying psychosis development. This review presents a summary of current findings on cross-sectional and longitudinal WM alterations in individuals with CHR and their links to clinical symptoms and neurocognitive dysfunction. Next, we review the utilization of WM characterization in predicting clinical outcomes. Taken together, the literature suggests the clinical significance of WM characteristics and their great potential in predicting the conversion to psychosis, despite some methodological and conceptual challenges that remain to be addressed in future studies. Future CHR research would greatly benefit from utilizing WM to guide pharmacological and non-pharmacological targeted treatments, optimize clinical prediction models, and enable more accurate clinical care.
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Affiliation(s)
- Wenjun Su
- Department of Psychiatry, NYU Langone Health, New York, NY 10016, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai 200031, China
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, China
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Ding Y, Hou W, Wang C, Sha S, Dong F, Li X, Wang N, Lam ST, Zhou F, Wang C. Longitudinal changes in cognitive function in early psychosis: a meta-analysis with the MATRICS consensus cognitive battery (MCCB). Schizophr Res 2024; 270:349-357. [PMID: 38968806 DOI: 10.1016/j.schres.2024.06.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 05/14/2024] [Accepted: 06/25/2024] [Indexed: 07/07/2024]
Abstract
INTRODUCTION A previous meta-analysis indicated stable progress in cognitive functions in early psychosis, assessed through various tools. To avoid assessment-related heterogeneity, this study aims to examine the longitudinal cognitive function changes in early psychosis utilizing the MATRICS Consensus Cognitive Battery (MCCB). METHODS Embase, PubMed, and Scopus were systematically searched from their inception to September 26th 2023. The inclusion criteria were longitudinal studies that presented follow-up MCCB data for individuals experiencing first-episode psychosis (FEP) and those with ultra-high risk for psychosis (UHR). RESULTS Twelve studies with 791 participants (566 FEP patients and 225 healthy controls) were subjected to analysis. Suitable UHR studies were absent. Over time, both FEP patients and healthy controls showed significant improvements in MCCB total scores. Furthermore, FEP patients demonstrated improvements across all MCCB domains, while healthy controls only showed augmentations in specific domains such as speed of processing, attention, working memory, and reasoning and problem-solving. Visuospatial learning improvements were significantly greater in FEP patients compared to healthy controls. Subgroup analyses suggested that neither diagnostic type nor follow-up duration influenced the magnitude of cognitive improvement in FEP patients. CONCLUSION The magnitude of cognitive improvement for MCCB domains was not significantly different between FEP and healthy controls other than visuospatial learning. This underscores visuospatial learning as a potentially sensitive cognitive marker for early pathologic state changes in psychotic disorders.
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Affiliation(s)
- Yushen Ding
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Wenpeng Hou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Chenxi Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Sha Sha
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Fang Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Xianbin Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Nan Wang
- Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore.
| | - Sze Tung Lam
- Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore; Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, 12 Science Drive 2, Singapore 117549, Singapore.
| | - Fuchun Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
| | - Chuanyue Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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Hou W, Zhou F, Wang Q, Li H, Qin X, Ding Y, Dong F, Bo Q, Li A, Zhang L, Chen Z, Wang Z, Li X, Lee J, Wang C. Effect of transcranial direct current stimulation with concurrent cognitive performance targeting posterior parietal cortex vs prefrontal cortex on working memory in schizophrenia: a randomized clinical trial. Transl Psychiatry 2024; 14:279. [PMID: 38977683 PMCID: PMC11231223 DOI: 10.1038/s41398-024-02994-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024] Open
Abstract
Working memory deficits are linked to irregularities in the dorsolateral prefrontal cortex (DLPFC) and the posterior parietal cortex (PPC) in schizophrenia, effective intervention strategies are lacking. We evaluated the differential efficacy and underlying neuromechanisms of targeting transcranial direct current stimulation (tDCS) at the DLPFC and the PPC with concurrent cognitive performance for working memory in schizophrenia. In a randomized and double-blind clinical trial, sixty clinically stable schizophrenic patients with below-average working memory were randomly assigned to active DLPFC, active PPC, and sham tDCS groups. Two sessions of tDCS during N-back task were delivered daily for five days. The primary outcome was changes in spatial span test scores from baseline to week 1. The secondary outcomes included changes in scores of color delay-estimation task, other cognitive tasks, and mismatch negativity (biomarker of N-methyl-d-aspartate receptor functioning). Compared with the active DLPFC group, the active PPC group demonstrated significantly greater improvement in spatial span test scores (p = 0.008, d = 0.94) and an augmentation in color delay-estimation task capacity at week 1; the latter sustained to week 2. Compared with the sham tDCS group, the active PPC group did not show a significant improvement in spatial span test scores at week 1 and 2; however, significant enhancement was observed in their color delay-estimation task capacity at week 2. Additionally, mismatch negativity amplitude was enhanced, and changes in theta band measures were positively correlated with working memory improvement in the active PPC group, while no such correlations were observed in the active DLPFC group or the sham tDCS group. Our results suggest that tDCS targeting the PPC relative to the DLPFC during concurrent cognitive performance may improve working memory in schizophrenia, meriting further investigation. The improvement in working memory appears to be linked to enhanced N-methyl-d-aspartate receptor functioning.
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Affiliation(s)
- Wenpeng Hou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Fuchun Zhou
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qi Wang
- Fengtai Mental Health Center, Beijing, China
| | - Hang Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xiangqin Qin
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Yushen Ding
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Fang Dong
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Qijing Bo
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Anning Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Liang Zhang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zhenzhu Chen
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Zhimin Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xianbin Li
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Jimmy Lee
- Institute of Mental Health, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Chuanyue Wang
- Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders & National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China.
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7
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Zhang T, Cui H, Tang X, Xu L, Wei Y, Hu Y, Tang Y, Wang Z, Liu H, Chen T, Li C, Wang J. Models of mild cognitive deficits in risk assessment in early psychosis. Psychol Med 2024; 54:2230-2241. [PMID: 38433595 DOI: 10.1017/s0033291724000382] [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] [Indexed: 03/05/2024]
Abstract
BACKGROUND Mild cognitive deficits (MCD) emerge before the first episode of psychosis (FEP) and persist in the clinical high-risk (CHR) stage. This study aims to refine risk prediction by developing MCD models optimized for specific early psychosis stages and target populations. METHODS A comprehensive neuropsychological battery assessed 1059 individuals with FEP, 794 CHR, and 774 matched healthy controls (HCs). CHR subjects, followed up for 2 years, were categorized into converters (CHR-C) and non-converters (CHR-NC). The MATRICS Consensus Cognitive Battery standardized neurocognitive tests were employed. RESULTS Both the CHR and FEP groups exhibited significantly poorer performance compared to the HC group across all neurocognitive tests (all p < 0.001). The CHR-C group demonstrated poorer performance compared to the CHR-NC group on three sub-tests: visuospatial memory (p < 0.001), mazes (p = 0.005), and symbol coding (p = 0.023) tests. Upon adjusting for sex and age, the performance of the MCD model was excellent in differentiating FEP from HC, as evidenced by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.895 (p < 0.001). However, when applied in the CHR group for predicting CHR-C (AUC = 0.581, p = 0.008), the performance was not satisfactory. To optimize the efficiency of psychotic risk assessment, three distinct MCD models were developed to distinguish FEP from HC, predict CHR-C from CHR-NC, and identify CHR from HC, achieving accuracies of 89.3%, 65.6%, and 80.2%, respectively. CONCLUSIONS The MCD exhibits variations in domains, patterns, and weights across different stages of early psychosis and diverse target populations. Emphasizing precise risk assessment, our findings highlight the importance of tailored MCD models for different stages and risk levels.
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Affiliation(s)
- TianHong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - HuiRu Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - XiaoChen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - LiHua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YanYan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YeGang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - YingYing Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - ZiXuan Wang
- Shanghai Xinlianxin Psychological Counseling Co., Ltd, Shanghai, China
| | - HaiChun Liu
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Tao Chen
- Big Data Research Lab, University of Waterloo, Waterloo, ON, Canada
- Labor and Worklife Program, Harvard University, Cambridge, MA, USA
| | - ChunBo Li
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, Shanghai 200030, People's Republic of China
| | - JiJun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai Engineering Research Center of Intelligent Psychological Evaluation and Intervention, 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
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Fromm AE, Grittner U, Brodt S, Flöel A, Antonenko D. No Object-Location Memory Improvement through Focal Transcranial Direct Current Stimulation over the Right Temporoparietal Cortex. Life (Basel) 2024; 14:539. [PMID: 38792561 PMCID: PMC11122124 DOI: 10.3390/life14050539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Revised: 04/14/2024] [Accepted: 04/17/2024] [Indexed: 05/26/2024] Open
Abstract
Remembering objects and their associated location (object-location memory; OLM), is a fundamental cognitive function, mediated by cortical and subcortical brain regions. Previously, the combination of OLM training and transcranial direct current stimulation (tDCS) suggested beneficial effects, but the evidence remains heterogeneous. Here, we applied focal tDCS over the right temporoparietal cortex in 52 participants during a two-day OLM training, with anodal tDCS (2 mA, 20 min) or sham (40 s) on the first day. The focal stimulation did not enhance OLM performance on either training day (stimulation effect: -0.09, 95%CI: [-0.19; 0.02], p = 0.08). Higher electric field magnitudes in the target region were not associated with individual performance benefits. Participants with content-related learning strategies showed slightly superior performance compared to participants with position-related strategies. Additionally, training gains were associated with individual verbal learning skills. Consequently, the lack of behavioral benefits through focal tDCS might be due to the involvement of different cognitive processes and brain regions, reflected by participant's learning strategies. Future studies should evaluate whether other brain regions or memory-relevant networks may be involved in the modulation of object-location associations, investigating other target regions, and further exploring individualized stimulation parameters.
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Affiliation(s)
- Anna Elisabeth Fromm
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany
| | - Ulrike Grittner
- Berlin Institute of Health (BIH), 10178 Berlin, Germany
- Institute of Biometry and Clinical Epidemiology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany
| | - Svenja Brodt
- Max Planck Institute for Biological Cybernetics, 72076 Tübingen, Germany
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Agnes Flöel
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany
- German Centre for Neurodegenerative Diseases (DZNE) Standort Greifswald, 17489 Greifswald, Germany
| | - Daria Antonenko
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany
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