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Morita K, Miura K, Toyomaki A, Makinodan M, Ohi K, Hashimoto N, Yasuda Y, Mitsudo T, Higuchi F, Numata S, Yamada A, Aoki Y, Honda H, Mizui R, Honda M, Fujikane D, Matsumoto J, Hasegawa N, Ito S, Akiyama H, Onitsuka T, Satomura Y, Kasai K, Hashimoto R. Tablet-Based Cognitive and Eye Movement Measures as Accessible Tools for Schizophrenia Assessment: Multisite Usability Study. JMIR Ment Health 2024; 11:e56668. [PMID: 38815257 PMCID: PMC11176872 DOI: 10.2196/56668] [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: 01/25/2024] [Revised: 04/10/2024] [Accepted: 05/01/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Schizophrenia is a complex mental disorder characterized by significant cognitive and neurobiological alterations. Impairments in cognitive function and eye movement have been known to be promising biomarkers for schizophrenia. However, cognitive assessment methods require specialized expertise. To date, data on simplified measurement tools for assessing both cognitive function and eye movement in patients with schizophrenia are lacking. OBJECTIVE This study aims to assess the efficacy of a novel tablet-based platform combining cognitive and eye movement measures for classifying schizophrenia. METHODS Forty-four patients with schizophrenia, 67 healthy controls, and 41 patients with other psychiatric diagnoses participated in this study from 10 sites across Japan. A free-viewing eye movement task and 2 cognitive assessment tools (Codebreaker task from the THINC-integrated tool and the CognitiveFunctionTest app) were used for conducting assessments in a 12.9-inch iPad Pro. We performed comparative group and logistic regression analyses for evaluating the diagnostic efficacy of the 3 measures of interest. RESULTS Cognitive and eye movement measures differed significantly between patients with schizophrenia and healthy controls (all 3 measures; P<.001). The Codebreaker task showed the highest classification effectiveness in distinguishing schizophrenia with an area under the receiver operating characteristic curve of 0.90. Combining cognitive and eye movement measures further improved accuracy with a maximum area under the receiver operating characteristic curve of 0.94. Cognitive measures were more effective in differentiating patients with schizophrenia from healthy controls, whereas eye movement measures better differentiated schizophrenia from other psychiatric conditions. CONCLUSIONS This multisite study demonstrates the feasibility and effectiveness of a tablet-based app for assessing cognitive functioning and eye movements in patients with schizophrenia. Our results suggest the potential of tablet-based assessments of cognitive function and eye movement as simple and accessible evaluation tools, which may be useful for future clinical implementation.
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Affiliation(s)
- Kentaro Morita
- Department of Rehabilitation, The University of Tokyo Hospital, Bunkyo-ku Tokyo, Japan
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku Tokyo, Japan
| | - Kenichiro Miura
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Atsuhito Toyomaki
- Department of Psychiatry, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Manabu Makinodan
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | - Kazutaka Ohi
- Department of Psychiatry, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Naoki Hashimoto
- Department of Psychiatry, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | - Yuka Yasuda
- Life Grow Brilliant Mental Clinic, Medical Corporation Foster, Kita-ku Osaka, Japan
| | - Takako Mitsudo
- Division of Clinical Research, National Hospital Organization Hizen Psychiatric Center, Kanzaki-gun, Japan
| | - Fumihiro Higuchi
- Department of Neuroscience, Division of Neuropsychiatry, Yamaguchi University School of Medicine, Ube City, Japan
| | - Shusuke Numata
- Department of Psychiatry, Graduate School of Biomedical Science, Tokushima University, Tokushima, Japan
| | - Akiko Yamada
- Department of Neuropsychiatry, Graduate School of Medicine, University of Kyoto, Sakyo-ku Kyoto, Japan
| | - Yohei Aoki
- Healthcare Innovation Group, Future Corporation, Shinagawa-ku Tokyo, Japan
| | - Hiromitsu Honda
- Healthcare Innovation Group, Future Corporation, Shinagawa-ku Tokyo, Japan
| | - Ryo Mizui
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | - Masato Honda
- Department of Psychiatry, Nara Medical University, Kashihara, Japan
| | - Daisuke Fujikane
- Department of Psychiatry, Graduate School of Medicine, Gifu University, Gifu, Japan
| | - Junya Matsumoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Naomi Hasegawa
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Satsuki Ito
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Hisashi Akiyama
- Department of Psychiatry, Graduate School of Medicine, Hokkaido University, Sapporo, Japan
| | | | - Yoshihiro Satomura
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku Tokyo, Japan
- Center for Diversity in Medical Education and Research, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Bunkyo-ku Tokyo, Japan
| | - Kiyoto Kasai
- Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku Tokyo, Japan
| | - Ryota Hashimoto
- Department of Pathology of Mental Diseases, National Institute of Mental Health, National Center of Neurology and Psychiatry, Kodaira, Japan
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Ma K, Zhou T, Pu C, Cheng Z, Han X, Yang L, Yu X. The Bidirectional Relationship between Weight Gain and Cognitive Function in First-Episode Schizophrenia: A Longitudinal Study in China. Brain Sci 2024; 14:310. [PMID: 38671962 PMCID: PMC11048552 DOI: 10.3390/brainsci14040310] [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: 02/25/2024] [Revised: 03/13/2024] [Accepted: 03/17/2024] [Indexed: 04/28/2024] Open
Abstract
Patients with schizophrenia often encounter notable weight gain during their illness, heightening the risk of metabolic diseases. While previous studies have noted a correlation between obesity and cognitive impairment in schizophrenia, many were cross-sectional, posing challenges in establishing a causal relationship between weight gain and cognitive function. The aim of this longitudinal study is to examine the relationship between weight gain and cognitive function in patients with first-episode schizophrenia (FES) during the initial 6-month antipsychotic treatments. Employing linear and logistic regression analyses, the study involved 337 participants. Significantly, baseline scores in processing speed (OR = 0.834, p = 0.007), working memory and attention (OR = 0.889, p = 0.043), and executive function (OR = 0.862, p = 0.006) were associated with clinically relevant weight gain (CRW, defined as an increase in body weight > 7%) at the 6-month endpoint. On the other hand, CRW correlated with improvements in the Brief Visuospatial Memory Test (p = 0.037). These findings suggest that patients with lower baseline cognitive performance undergo more substantial weight gain. Conversely, weight gain was correlated with cognitive improvements, particularly in the domain of visual learning and memory. This suggested a potential bidirectional relationship between weight gain and cognitive function in first-episode schizophrenia patients.
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Affiliation(s)
- Ke Ma
- Department of Clinical Psychology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China
| | - Tianhang Zhou
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Chengcheng Pu
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Zhang Cheng
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Xue Han
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Lei Yang
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
| | - Xin Yu
- Peking University Sixth Hospital, Beijing 100191, China
- Institute of Mental Health, Peking University Sixth Hospital, Beijing 100191, China
- NHC Key Laboratory of Mental Health, Peking University, Beijing 100191, China
- National Clinical Research Center for Mental Disorders, Peking University Sixth Hospital, Beijing 100191, China
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Rao PS, Rangaswamy M, Evans J, Dutt A. Prospective memory in early and established psychosis: An Indian perspective. J Neuropsychol 2023; 17:461-476. [PMID: 37070648 DOI: 10.1111/jnp.12314] [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: 05/19/2022] [Revised: 03/12/2023] [Accepted: 03/23/2023] [Indexed: 04/19/2023]
Abstract
Individuals affected by psychosis often have deficits in several neurocognitive functions. Prospective memory (PM), the ability to remember to do things, is crucial for activities of daily living, social and occupational functioning, but very few studies have attempted to examine this domain of functioning in people with psychosis, particularly in India. A total of 71 patients with psychosis, (both early and established psychosis), and 140 age, gender and education-matched healthy controls were assessed using the Positive and Negative Symptom Scale, Hospital Anxiety and Depression scale, and Addenbrooke's Cognitive Examination. PM was assessed using the Cambridge Prospective Memory Test and the Prospective and Retrospective Memory Questionnaire (PRMQ). Group differences were evaluated using Mann-Whitney U-tests. Significantly greater cognitive deficits, higher anxiety and depression were evident in the psychosis group compared with controls. The psychosis group performed significantly poorer on both time- and event-based tests in CAMPROMPT than controls. These differences remained when controlling for age, education, general cognitive functioning and mood. The subjective measure of PM (PRMQ) did not differentiate the two groups. The PM performance of early and established psychosis patients was similar. Comparisons with cross-cultural data (PRMQ UK norms and CAMPROMPT and PRMQ Chinese data) revealed important differences in PM performance. Individuals with psychosis have significant deficits in both time- and event-based PM. CAMPROMPT emerged as a more sensitive PM measure compared with PRMQ. Results from cross-cultural comparisons underscore the need for cultural contextualization of assessments.
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Affiliation(s)
- Pulijala Sulakshana Rao
- Department of Psychology, Christ University, Bangalore, Karnataka, 560029, India
- Duttanagar Mental Health Centre, Kolkata, 700077, India
| | - Madhavi Rangaswamy
- Department of Psychology, Christ University, Bangalore, Karnataka, 560029, India
| | - Jonathan Evans
- Department of Psychology, Christ University, Bangalore, Karnataka, 560029, India
- Institute of Health and Wellbeing, University of Glasgow, Glasgow, UK
| | - Anirban Dutt
- Department of Psychology, Christ University, Bangalore, Karnataka, 560029, India
- Duttanagar Mental Health Centre, Kolkata, 700077, India
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
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Habiba U, Malik A, Raja GK, Memon MR, Nizami ATD, Ishaq R, Ilyas M, Valadi H, Nawaz M, Shaiq PA. Differential Treatment Responses in Pakistani Schizophrenia Samples: Correlation with Sociodemographic Parameters, Drug Addiction, Attitude to the Treatment and Antipsychotic Agents. Brain Sci 2023; 13:brainsci13030407. [PMID: 36979217 PMCID: PMC10046393 DOI: 10.3390/brainsci13030407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
Schizophrenia patients demonstrate variations in response to different therapies that are currently being used for the treatment of disorders, such as augmentation therapy (ECT or mood stabilizer) and combination therapy (with antipsychotics). These therapies are also used to treat schizophrenia patients in Pakistan; however, patients show poor overall response. Therefore, this study was conducted to investigate the association between the patients’ response to treatment and the use of antipsychotic agents, with variability in overall response, within different groups of patients. Methods: We conducted a retrospective study that included schizophrenia subjects (N = 200) belonging to different age groups, ethnicities, and regions from different outpatient and inpatient departments in psychiatric institutes located in different cities of Pakistan. These patients were assessed for their response to treatment therapies and categorized into four groups (non-responders (N-R), slow response (S-R), patients with relapse, and completely recovered patients (C-R)) according to their responses. Results: The final analysis included 200 subjects, of which 73.5% were males. Mean age was 34 ± 10 years. Percentage of N-R was 5%, S-R was 42%, patients with relapse were 24%, and C-R was 1.5%. The generalized linear regression model shows a significant association between medication response and age (p = 0.0231), age of onset (p = 0.0086), gender (p = 0.005), and marital status (p = 0.00169). Variability within the medication responses was a result of the treatment regime followed. Antipsychotic agents were significantly associated with the treatment response (p = 0.00258, F = 4.981) of the patients. Significant variation was also observed in the treatment response (p = 0.00128) of the patients that were given augmentation therapy as well as combination therapy. Conclusion: The data suggests proper monitoring of patients’ behavior in response to treatment therapies to implement tailored interventions. Despite several genetic studies supporting the heritability of schizophrenia, an insignificant association between characteristic features and family history might have been due to the limited sample size, suggesting collaborative work with massive sample sizes.
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Affiliation(s)
- Umme Habiba
- University Institute of Biochemistry and Biotechnology, Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Shamsabad, Rawalpindi 46300, Pakistan
| | - Aafia Malik
- Department of Psychiatry, Jinnah Hospital Usmani Road, Quaid-i-Azam Campus, Lahore 54550, Pakistan
| | - Ghazala Kaukab Raja
- University Institute of Biochemistry and Biotechnology, Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Shamsabad, Rawalpindi 46300, Pakistan
| | - Muhammad Raza Memon
- Department of Psychiatry, Liaquat University of Medical and Health Sciences, Jamshoro 76090, Pakistan
| | - Asad Tameezud din Nizami
- Institute of Psychiatry, WHO Collaborating Center for Mental Health, Benazir Bhutto Hospital, Murree Road, Rawalpindi 23000, Pakistan
| | - Rafaqat Ishaq
- University Institute of Biochemistry and Biotechnology, Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Shamsabad, Rawalpindi 46300, Pakistan
| | - Muhammad Ilyas
- University Institute of Biochemistry and Biotechnology, Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Shamsabad, Rawalpindi 46300, Pakistan
| | - Hadi Valadi
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 41346 Gothenburg, Sweden
| | - Muhammad Nawaz
- Department of Rheumatology and Inflammation Research, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, 41346 Gothenburg, Sweden
- Correspondence: (M.N.); (P.A.S.)
| | - Pakeeza Arzoo Shaiq
- University Institute of Biochemistry and Biotechnology, Pir Mehr Ali Shah, Arid Agriculture University Rawalpindi, Shamsabad, Rawalpindi 46300, Pakistan
- Correspondence: (M.N.); (P.A.S.)
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Using Nonhuman Primate Models to Reverse-Engineer Prefrontal Circuit Failure Underlying Cognitive Deficits in Schizophrenia. Curr Top Behav Neurosci 2023; 63:315-362. [PMID: 36607528 DOI: 10.1007/7854_2022_407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
In this chapter, I review studies in nonhuman primates that emulate the circuit failure in prefrontal cortex responsible for working memory and cognitive control deficits in schizophrenia. These studies have characterized how synaptic malfunction, typically induced by blockade of NMDAR, disrupts neural function and computation in prefrontal networks to explain errors in cognitive tasks that are seen in schizophrenia. This work is finding causal relationships between pathogenic events of relevance to schizophrenia at vastly different levels of scale, from synapses, to neurons, local, circuits, distributed networks, computation, and behavior. Pharmacological manipulation, the dominant approach in primate models, has limited construct validity for schizophrenia pathogenesis, as the disease results from a complex interplay between environmental, developmental, and genetic factors. Genetic manipulation replicating schizophrenia risk is more advanced in rodent models. Nonetheless, gene manipulation in nonhuman primates is rapidly advancing, and primate developmental models have been established. Integration of large scale neural recording, genetic manipulation, and computational modeling in nonhuman primates holds considerable potential to provide a crucial schizophrenia model moving forward. Data generated by this approach is likely to fill several crucial gaps in our understanding of the causal sequence leading to schizophrenia in humans. This causal chain presents a vexing problem largely because it requires understanding how events at very different levels of scale relate to one another, from genes to circuits to cognition to social interactions. Nonhuman primate models excel here. They optimally enable discovery of causal relationships across levels of scale in the brain that are relevant to cognitive deficits in schizophrenia. The mechanistic understanding of prefrontal circuit failure they promise to provide may point the way to more effective therapeutic interventions to restore function to prefrontal networks in the disease.
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Levchenko A, Gusev F, Rogaev E. The evolutionary origin of psychosis. Front Psychiatry 2023; 14:1115929. [PMID: 36741116 PMCID: PMC9894884 DOI: 10.3389/fpsyt.2023.1115929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 01/05/2023] [Indexed: 01/21/2023] Open
Abstract
Imagination, the driving force of creativity, and primary psychosis are human-specific, since we do not observe behaviors in other species that would convincingly suggest they possess the same traits. Both these traits have been linked to the function of the prefrontal cortex, which is the most evolutionarily novel region of the human brain. A number of evolutionarily novel genetic and epigenetic changes that determine the human brain-specific structure and function have been discovered in recent years. Among them are genomic loci subjected to increased rates of single nucleotide substitutions in humans, called human accelerated regions. These mostly regulatory regions are involved in brain development and sometimes contain genetic variants that confer a risk for schizophrenia. On the other hand, neuroimaging data suggest that mind wandering and related phenomena (as a proxy of imagination) are in many ways similar to rapid eye movement dreaming, a function also present in non-human species. Furthermore, both functions are similar to psychosis in several ways: for example, the same brain areas are activated both in dreams and visual hallucinations. In the present Perspective we hypothesize that imagination is an evolutionary adaptation of dreaming, while primary psychosis results from deficient control by higher-order brain areas over imagination. In the light of this, human accelerated regions might be one of the key drivers in evolution of human imagination and the pathogenesis of psychotic disorders.
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Affiliation(s)
- Anastasia Levchenko
- Institute of Translational Biomedicine, Saint Petersburg State University, Saint Petersburg, Russia
| | - Fedor Gusev
- Center for Genetics and Life Sciences, Department of Genetics, Sirius University of Science and Technology, Sochi, Russia.,Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia
| | - Evgeny Rogaev
- Vavilov Institute of General Genetics, Russian Academy of Sciences, Moscow, Russia.,Department of Psychiatry, UMass Chan Medical School, Shrewsbury, MA, United States
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