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Skouras S, Kleinert ML, Lee EHM, Hui CLM, Suen YN, Camchong J, Chong CSY, Chang WC, Chan SKW, Lo WTL, Lim KO, Chen EYH. Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping. Brain Commun 2024; 6:fcae094. [PMID: 38707706 PMCID: PMC11069118 DOI: 10.1093/braincomms/fcae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 12/04/2023] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.
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Affiliation(s)
- Stavros Skouras
- Department of Fundamental Neurosciences, Faculty of Medicine, University of Geneva, CH-1211 Geneva, Switzerland
- Department of Neurology, Inselspital University Hospital Bern, CH3010 Bern, Switzerland
| | | | - Edwin H M Lee
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Christy L M Hui
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Jazmin Camchong
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | | | - Wing Chung Chang
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - Sherry K W Chan
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
| | - William T L Lo
- Department of Psychiatry, Kwai Chung Hospital, Hong Kong, China
| | - Kelvin O Lim
- Department of Psychiatry, University of Minnesota, Minneapolis, MN 55454, USA
| | - Eric Y H Chen
- Department of Psychiatry, University of Hong Kong, Hong Kong, China
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Fu L, Aximu R, Zhao G, Chen Y, Sun Z, Xue H, Wang S, Zhang N, Zhang Z, Lei M, Zhai Y, Xu J, Sun J, Ma J, Liu F. Mapping the landscape: a bibliometric analysis of resting-state fMRI research on schizophrenia over the past 25 years. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:35. [PMID: 38490990 PMCID: PMC10942978 DOI: 10.1038/s41537-024-00456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 03/05/2024] [Indexed: 03/18/2024]
Abstract
Schizophrenia, a multifaceted mental disorder characterized by disturbances in thought, perception, and emotion, has been extensively investigated through resting-state fMRI, uncovering changes in spontaneous brain activity among those affected. However, a bibliometric examination regarding publication trends in resting-state fMRI studies related to schizophrenia is lacking. This study obtained relevant publications from the Web of Science Core Collection spanning the period from 1998 to 2022. Data extracted from these publications included information on countries/regions, institutions, authors, journals, and keywords. The collected data underwent analysis and visualization using VOSviewer software. The primary analyses included examination of international and institutional collaborations, authorship patterns, co-citation analyses of authors and journals, as well as exploration of keyword co-occurrence and temporal trend networks. A total of 859 publications were retrieved, indicating an overall growth trend from 1998 to 2022. China and the United States emerged as the leading contributors in both publication outputs and citations, with Central South University and the University of New Mexico being identified as the most productive institutions. Vince D. Calhoun had the highest number of publications and citation counts, while Karl J. Friston was recognized as the most influential author based on co-citations. Key journals such as Neuroimage, Schizophrenia Research, Schizophrenia Bulletin, and Biological Psychiatry played pivotal roles in advancing this field. Recent popular keywords included support vector machine, antipsychotic medication, transcranial magnetic stimulation, and related terms. This study systematically synthesizes the historical development, current status, and future trends in resting-state fMRI research in schizophrenia, offering valuable insights for future research directions.
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Affiliation(s)
- Linhan Fu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Remilai Aximu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medical Imaging, Tianjin Medical University, Tianjin, 300070, China
| | - Guoshu Zhao
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
- School of Medicine, Nankai University, Tianjin, 300071, China
| | - Yayuan Chen
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zuhao Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Shaoying Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Nannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zhihui Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Minghuan Lei
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Ying Zhai
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jinglei Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Juanwei Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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Larsen KM, Madsen KS, Ver Loren van Themaat AH, Thorup AAE, Plessen KJ, Mors O, Nordentoft M, Siebner HR. Children at Familial High risk of Schizophrenia and Bipolar Disorder Exhibit Altered Connectivity Patterns During Pre-attentive Processing of an Auditory Prediction Error. Schizophr Bull 2024; 50:166-176. [PMID: 37379847 PMCID: PMC10754183 DOI: 10.1093/schbul/sbad092] [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] [Indexed: 06/30/2023]
Abstract
BACKGROUND AND HYPOTHESIS Individuals with schizophrenia or bipolar disorder have attenuated auditory mismatch negativity (MMN) responses, indicating impaired sensory information processing. Computational models of effective connectivity between brain areas underlying MMN responses show reduced connectivity between fronto-temporal areas in individuals with schizophrenia. Here we ask whether children at familial high risk (FHR) of developing a serious mental disorder show similar alterations. STUDY DESIGN We recruited 67 children at FHR for schizophrenia, 47 children at FHR for bipolar disorder as well as 59 matched population-based controls from the Danish High Risk and Resilience study. The 11-12-year-old participants engaged in a classical auditory MMN paradigm with deviations in frequency, duration, or frequency and duration, while we recorded their EEG. We used dynamic causal modeling (DCM) to infer on the effective connectivity between brain areas underlying MMN. STUDY RESULTS DCM yielded strong evidence for differences in effective connectivity among groups in connections from right inferior frontal gyrus (IFG) to right superior temporal gyrus (STG), along with differences in intrinsic connectivity within primary auditory cortex (A1). Critically, the 2 high-risk groups differed in intrinsic connectivity in left STG and IFG as well as effective connectivity from right A1 to right STG. Results persisted even when controlling for past or present psychiatric diagnoses. CONCLUSIONS We provide novel evidence that connectivity underlying MMN responses in children at FHR for schizophrenia and bipolar disorder is altered at the age of 11-12, echoing findings that have been found in individuals with manifest schizophrenia.
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Affiliation(s)
- Kit Melissa Larsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Anna Hester Ver Loren van Themaat
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Anne Amalie Elgaard Thorup
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Hellerup, Denmark
- Copenhagen Research Centre for Mental Health - CORE, Mental Health Centre Copenhagen, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
| | - Kerstin Jessica Plessen
- Child and Adolescent Mental Health Centre, Mental Health Services, Capital Region of Denmark, Hellerup, Denmark
- Department of Psychiatry, Service of Child and Adolescent Psychiatry, University Medical Center, University of Lausanne, Switzerland
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health - CORE, Mental Health Centre Copenhagen, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hartwig Roman Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
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Lim K, Yee JY, See YM, Ng BT, Zheng S, Tang C, Lencz T, Lee J, Lam M. Deconstructing the genetic architecture of treatment-resistant schizophrenia in East Asian ancestry. Asian J Psychiatr 2023; 90:103826. [PMID: 37944474 DOI: 10.1016/j.ajp.2023.103826] [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: 08/04/2023] [Revised: 10/29/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
BACKGROUND Treatment-resistant schizophrenia (TRS) affects a substantial proportion of patients who do not respond adequately to antipsychotic medications, yet the underlying biological mechanism remains poorly understood. This study investigates the link between the genetic predisposition to schizophrenia and TRS. METHODS 857 individuals diagnosed with schizophrenia were divided into TRS (n = 142) and non-TRS (n = 715) based on well-defined TRS criteria. Polygenic risk scores (PRS) were calculated using schizophrenia genome-wide association summary statistics from East-Asian and European ancestry populations. PRS was estimated using both P-value thresholding and Bayesian framework methods. Logistic regression analyses were performed to differentiate between TRS and non-TRS individuals. RESULTS The schizophrenia PRS derived from the East-Asian training dataset effectively distinguished between TRS and non-TRS individuals (R2 = 0.029, p = 4.86 ×10-5, pT = 0.1, OR = 1.52, 95% CI = 1.242-1.861), with higher PRS values observed in the TRS group. Similar PRS analysis was conducted based on the European ancestry GWAS summary statistics, but we found superior prediction based on the East-Asian ancestry discovery data. CONCLUSION This study reveals an association between common risk variants for schizophrenia and TRS status, suggesting that the genetic burden of schizophrenia may partly contribute to treatment resistance in individuals with schizophrenia. These findings propose the potential use of genetic risk factors for early TRS identification and timely access to clozapine. However, the ancestral background of the discovery sample is crucial for successfully implementing PRS in clinical settings.
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Affiliation(s)
- Keane Lim
- Research Division, Institute of Mental Health, Singapore
| | - Jie Yin Yee
- Research Division, Institute of Mental Health, Singapore
| | - Yuen Mei See
- Research Division, Institute of Mental Health, Singapore
| | - Boon Tat Ng
- Department of Pharmacy, Institute of Mental Health, Singapore
| | - Shushan Zheng
- Department of Psychosis, Institute of Mental Health, Singapore
| | - Charmaine Tang
- Department of Psychosis, Institute of Mental Health, Singapore
| | - Todd Lencz
- Feinstein Institutes for Medical Research, NY, USA
| | - Jimmy Lee
- Department of Psychosis, Institute of Mental Health, Singapore; Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
| | - Max Lam
- Research Division, Institute of Mental Health, Singapore; Feinstein Institutes for Medical Research, NY, USA; Population and Global Health, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
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Dominicus LS, van Rijn L, van der A J, van der Spek R, Podzimek D, Begemann M, de Haan L, van der Pluijm M, Otte WM, Cahn W, Röder CH, Schnack HG, van Dellen E. fMRI connectivity as a biomarker of antipsychotic treatment response: A systematic review. Neuroimage Clin 2023; 40:103515. [PMID: 37797435 PMCID: PMC10568423 DOI: 10.1016/j.nicl.2023.103515] [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: 07/17/2023] [Revised: 08/31/2023] [Accepted: 09/22/2023] [Indexed: 10/07/2023]
Abstract
BACKGROUND Antipsychotic drugs are the first-choice therapy for psychotic episodes, but antipsychotic treatment response (AP-R) is unpredictable and only becomes clear after weeks of therapy. A biomarker for AP-R is currently unavailable. We reviewed the evidence for the hypothesis that functional magnetic resonance imaging functional connectivity (fMRI-FC) is a predictor of AP-R or could serve as a biomarker for AP-R in psychosis. METHOD A systematic review of longitudinal fMRI studies examining the predictive performance and relationship between FC and AP-R was performed following PRISMA guidelines. Technical and clinical aspects were critically assessed for the retrieved studies. We addressed three questions: Q1) is baseline fMRI-FC related to subsequent AP-R; Q2) is AP-R related to a change in fMRI-FC; and Q3) can baseline fMRI-FC predict subsequent AP-R? RESULTS In total, 28 articles were included. Most studies were of good quality. fMRI-FC analysis pipelines included seed-based-, independent component- / canonical correlation analysis, network-based statistics, and graph-theoretical approaches. We found high heterogeneity in methodological approaches and results. For Q1 (N = 17) and Q2 (N = 18), the most consistent evidence was found for FC between the striatum and ventral attention network as a potential biomarker of AP-R. For Q3 (N = 9) accuracy's varied form 50 till 93%, and prediction models were based on FC between various brain regions. CONCLUSION The current fMRI-FC literature on AP-R is hampered by heterogeneity of methodological approaches. Methodological uniformity and further improvement of the reliability and validity of fMRI connectivity analysis is needed before fMRI-FC analysis can have a place in clinical applications of antipsychotic treatment.
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Affiliation(s)
- L S Dominicus
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - L van Rijn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - J van der A
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - R van der Spek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - D Podzimek
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - M Begemann
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - L de Haan
- Department Early Psychosis, Academical Medical Centre of the University of Amsterdam, Amsterdam, Amsterdam, The Netherlands
| | - M van der Pluijm
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, The Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, The Netherlands
| | - W M Otte
- Department of Child Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, and Utrecht University, Utrecht, The Netherlands
| | - W Cahn
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - C H Röder
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - H G Schnack
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - E van Dellen
- Department of Psychiatry, Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; Department of Intensive Care Medicine and UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Christoff RR, Nani JV, Lessa G, Rabello T, Rossi AD, Krenn V, Higa LM, Tanuri A, Garcez PP, Hayashi MAF. Assessing the role of Ndel1 oligopeptidase activity in congenital Zika syndrome: Potential predictor of congenital syndrome endophenotype and treatment response. J Neurochem 2023; 166:763-776. [PMID: 37497817 DOI: 10.1111/jnc.15918] [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: 01/17/2023] [Revised: 06/22/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023]
Abstract
Maternal infections are among the main risk factors for cognitive impairments in the offspring. Zika virus (ZIKV) can be transmitted vertically, causing a set of heterogeneous birth defects, such as microcephaly, ventriculomegaly and corpus callosum dysgenesis. Nuclear distribution element like-1 (Ndel1) oligopeptidase controls crucial aspects of cerebral cortex development underlying cortical malformations. Here, we examine Ndel1 activity in an animal model for ZIKV infection, which was associated with deregulated corticogenesis. We observed here a reduction in Ndel1 activity in the forebrain associated with the congenital syndrome induced by ZIKV isolates, in an in utero and postnatal injections of different inoculum doses in mice models. In addition, we observed a strong correlation between Ndel1 activity and brain size of animals infected by ZIKV, suggesting the potential of this measure as a biomarker for microcephaly. More importantly, the increase of interferon (IFN)-beta signaling, which was used to rescue the ZIKV infection outcomes, also recovered Ndel1 activity to levels similar to those of uninfected healthy control mice, but with no influence on Ndel1 activity in uninfected healthy control animals. Taken together, we demonstrate for the first time here an association of corticogenesis impairments determined by ZIKV infection and the modulation of Ndel1 activity. Although further studies are still necessary to clarify the possible role(s) of Ndel1 activity in the molecular mechanism(s) underlying the congenital syndrome induced by ZIKV, we suggest here the potential of monitoring the Ndel1 activity to predict this pathological condition at early stages of embryos or offspring development, during while the currently employed methods are unable to detect impaired corticogenesis leading to microcephaly. Ndel1 activity may also be possibly used to follow up the positive response to the treatment, such as that employing the IFN-beta that is able to rescue the ZIKV-induced brain injury.
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Affiliation(s)
- Raissa R Christoff
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - João V Nani
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
- National Institute for Translational Medicine (INCT-TM, CNPq/FAPESP/CAPES), Ribeirão Preto, Brazil
| | - Gabriel Lessa
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
| | - Tailene Rabello
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Atila D Rossi
- Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Veronica Krenn
- Department of Biotechnology and Bioscience, University of Milan-Bicocca, Milano, Italy
| | - Luiza M Higa
- Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Amilcar Tanuri
- Instituto de Biologia, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Patricia P Garcez
- Instituto de Ciências Biomédicas, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
| | - Mirian A F Hayashi
- Department of Pharmacology, Escola Paulista de Medicina (EPM), Universidade Federal de São Paulo (UNIFESP), São Paulo, Brazil
- National Institute for Translational Medicine (INCT-TM, CNPq/FAPESP/CAPES), Ribeirão Preto, Brazil
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7
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Tuovinen N, Hofer A. Resting-state functional MRI in treatment-resistant schizophrenia. FRONTIERS IN NEUROIMAGING 2023; 2:1127508. [PMID: 37554635 PMCID: PMC10406237 DOI: 10.3389/fnimg.2023.1127508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 03/17/2023] [Indexed: 08/10/2023]
Abstract
BACKGROUND Abnormalities in brain regions involved in the pathophysiology of schizophrenia (SCZ) may present insight into individual clinical symptoms. Specifically, functional connectivity irregularities may provide potential biomarkers for treatment response or treatment resistance, as such changes can occur before any structural changes are visible. We reviewed resting-state functional magnetic resonance imaging (rs-fMRI) findings from the last decade to provide an overview of the current knowledge on brain functional connectivity abnormalities and their associations to symptoms in treatment-resistant schizophrenia (TRS) and ultra-treatment-resistant schizophrenia (UTRS) and to look for support for the dysconnection hypothesis. METHODS PubMed database was searched for articles published in the last 10 years applying rs-fMRI in TRS patients, i.e., who had not responded to at least two adequate treatment trials with different antipsychotic drugs. RESULTS Eighteen articles were selected for this review involving 648 participants (TRS and control cohorts). The studies showed frontal hypoconnectivity before the initiation of treatment with CLZ or riluzole, an increase in frontal connectivity after riluzole treatment, fronto-temporal hypoconnectivity that may be specific for non-responders, widespread abnormal connectivity during mixed treatments, and ECT-induced effects on the limbic system. CONCLUSION Probably due to the heterogeneity in the patient cohorts concerning antipsychotic treatment and other clinical variables (e.g., treatment response, lifetime antipsychotic drug exposure, duration of illness, treatment adherence), widespread abnormalities in connectivity were noted. However, irregularities in frontal brain regions, especially in the prefrontal cortex, were noted which are consistent with previous SCZ literature and the dysconnectivity hypothesis. There were major limitations, as most studies did not differentiate between TRS and UTRS (i.e., CLZ-resistant schizophrenia) and investigated heterogeneous cohorts treated with mixed treatments (with or without CLZ). This is critical as in different subtypes of the disorder an interplay between dopaminergic and glutamatergic pathways involving frontal, striatal, and hippocampal brain regions in separate ways is likely. Better definitions of TRS and UTRS are necessary in future longitudinal studies to correctly differentiate brain regions underlying the pathophysiology of SCZ, which could serve as potential functional biomarkers for treatment resistance.
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Affiliation(s)
- Noora Tuovinen
- Division of Psychiatry I, Department of Psychiatry, Psychotherapy, Psychosomatics and Medical Psychology, Medical University of Innsbruck, Innsbruck, Austria
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8
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Jiao S, Cao T, Cai H. Peripheral biomarkers of treatment-resistant schizophrenia: Genetic, inflammation and stress perspectives. Front Pharmacol 2022; 13:1005702. [PMID: 36313375 PMCID: PMC9597880 DOI: 10.3389/fphar.2022.1005702] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 09/26/2022] [Indexed: 11/16/2022] Open
Abstract
Treatment-resistant schizophrenia (TRS) often results in severe disability and functional impairment. Currently, the diagnosis of TRS is largely exclusionary and emphasizes the improvement of symptoms that may not be detected early and treated according to TRS guideline. As the gold standard, clozapine is the most prescribed selection for TRS. Therefore, how to predict TRS in advance is critical for forming subsequent treatment strategy especially clozapine is used during the early stage of TRS. Although mounting studies have identified certain clinical factors and neuroimaging characteristics associated with treatment response in schizophrenia, the predictors for TRS remain to be explored. Biomarkers, particularly for peripheral biomarkers, show great potential in predicting TRS in view of their predictive validity, noninvasiveness, ease of testing and low cost that would enable their widespread use. Recent evidence supports that the pathogenesis of TRS may be involved in abnormal neurotransmitter systems, inflammation and stress. Due to the heterogeneity of TRS and the lack of consensus in diagnostic criteria, it is difficult to compare extensive results among different studies. Based on the reported neurobiological mechanisms that may be associated with TRS, this paper narratively reviews the updates of peripheral biomarkers of TRS, from genetic and other related perspectives. Although current evidence regarding biomarkers in TRS remains fragmentary, when taken together, it can help to better understand the neurobiological interface of clinical phenotypes and psychiatric symptoms, which will enable individualized prediction and therapy for TRS in the long run.
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Affiliation(s)
- Shimeng Jiao
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, Hunan, China
| | - Ting Cao
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, Hunan, China
| | - Hualin Cai
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, Changsha, China
- Institute of Clinical Pharmacy, Central South University, Changsha, China
- International Research Center for Precision Medicine, Transformative Technology and Software Services, Changsha, Hunan, China
- *Correspondence: Hualin Cai,
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9
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Fan F, Tan S, Huang J, Chen S, Fan H, Wang Z, Li CSR, Tan Y. Functional disconnection between subsystems of the default mode network in schizophrenia. Psychol Med 2022; 52:2270-2280. [PMID: 33183375 DOI: 10.1017/s003329172000416x] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND A dysfunctional default mode network (DMN) has been reported in patients with schizophrenia. However, the stability of the deficits has not been determined across different stages of the disorder. METHODS We examined the functional connectivity of the DMN subsystems of 125 patients with first-episode schizophrenia (FES) or recurrent schizophrenia (RES), compared to that of 82 healthy controls. We tested the robustness of the findings in an independent cohort of 158 patients and 39 healthy controls. We performed resting-state functional connectivity analysis, and examined the strength of the connections within and between the three subsystems of the DMN (core, dorsal medial prefrontal cortex [dMPFC], and medial temporal lobe [MTL]). We also analyzed the connectivity correlations to symptoms and illness duration. RESULTS We found reduced connectivity strength between the core and MTL subsystems in schizophrenia patients compared to controls, with no differences between the FES and RES patient groups; these findings were validated in the second sample. Schizophrenia patients also showed a significant reduction in connectivity within the MTL and between the dMPFC-MTL subsystems, similarly between FES and RES groups. The connectivity strength within the core subsystem was negatively correlated with clinical symptoms in schizophrenia. There was no significant correlation between the DMN subsystem connectivity and illness duration. CONCLUSIONS DMN subsystem connectivity deficits are present in schizophrenia, and the homochronicity of their appearance indicates the trait-like nature of these alterations. The DMN deficit may be useful for early diagnosis, and MTL dysfunction may be a crucial mechanism underlying schizophrenia.
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Affiliation(s)
- Fengmei Fan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
- State Key Laboratory of Cognitive Neuroscience and Learning & International Data Group/McGovern Institute for Brain Research, Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China
| | - Shuping Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Junchao Huang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Song Chen
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Hongzhen Fan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Zhiren Wang
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Yunlong Tan
- Beijing Huilongguan Hospital, Peking University Huilongguan Clinical Medical School, Beijing 100096, China
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10
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Jiang Y, Duan M, He H, Yao D, Luo C. Structural and Functional MRI Brain Changes in Patients with Schizophrenia Following Electroconvulsive Therapy: A Systematic Review. Curr Neuropharmacol 2022; 20:1241-1252. [PMID: 34370638 PMCID: PMC9886826 DOI: 10.2174/1570159x19666210809101248] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 07/17/2021] [Accepted: 07/31/2021] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Schizophrenia (SZ) is a severe psychiatric disorder typically characterized by multidimensional psychotic syndromes. Electroconvulsive therapy (ECT) is a treatment option for medication-resistant patients with SZ or treating acute symptoms. Although the efficacy of ECT has been demonstrated in clinical use, its therapeutic mechanisms in the brain remain elusive. OBJECTIVE This study aimed to summarize brain changes on structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) after ECT. METHODS According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was carried out. The PubMed and Medline databases were systematically searched using the following medical subject headings (MeSH): (electroconvulsive therapy OR ECT) AND (schizophrenia) AND (MRI OR fMRI OR DTI OR DWI). RESULTS This review yielded 12 MRI studies, including 4 with sMRI, 5 with fMRI and 3 with multimodal MRI. Increases in volumes of the hippocampus and its adjacent regions (parahippocampal gyrus and amygdala), as well as the insula and frontotemporal regions, were noted after ECT. fMRI studies found ECT-induced changes in different brain regions/networks, including the hippocampus, amygdala, default model network, salience network and other regions/networks that are thought to highly correlate with the pathophysiologic characteristics of SZ. The results of the correlation between brain changes and symptom remissions are inconsistent. CONCLUSION Our review provides evidence supporting ECT-induced brain changes on sMRI and fMRI in SZ and explores the relationship between these changes and symptom remission.
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Affiliation(s)
- Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China;
| | - Mingjun Duan
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,Address correspondence to these authors at the The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu 610054, China; Tel: 86-28-83201018; Fax: 86-28-83208238; E-mails: (C. Luo) and (M. Duan)
| | - Hui He
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China;
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China; ,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, P.R. China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, P.R. China; ,High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, P.R. China; ,Research Unit of NeuroInformation (2019RU035), Chinese Academy of Medical Sciences, Chengdu, P.R. China,Address correspondence to these authors at the The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Second North Jianshe Road, Chengdu 610054, China; Tel: 86-28-83201018; Fax: 86-28-83208238; E-mails: (C. Luo) and (M. Duan)
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11
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Fountoulakis KN, Stahl SM. The effect of first- and second-generation antipsychotics on brain morphology in schizophrenia: A systematic review of longitudinal magnetic resonance studies with a randomized allocation to treatment arms. J Psychopharmacol 2022; 36:428-438. [PMID: 35395911 DOI: 10.1177/02698811221087645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Schizophrenia manifests as loss of brain volume in specific areas in a progressive nature and an important question concerns whether long-term treatment with medications contributes to this. The aim of the current PRISMA systematic review was to search for prospective studies involving randomization to treatment. PROSPERO ID: CRD42020197874. The MEDLINE/PUBMED was searched and it returned 2638 articles; 3 were fulfilling the inclusion criteria. A fourth was published later; they included 359 subjects, of whom 86 were healthy controls, while the rest were first-episode patients, with 91 under olanzapine, 93 under haloperidol, 48 under risperidone, 5 under paliperidone, 6 under ziprasidone, and 30 under placebo. Probably one-third of patients were suffering from a psychotic disorder other than schizophrenia. The consideration of their results suggested that there is no significant difference between these medications concerning their effects on brain structure and also in comparison to healthy subjects. There does not seem to be any strong support to the opinion that medications that treat psychosis cause loss of brain volume in patients with schizophrenia. On the contrary, the data might imply the possible presence of a protective effect for D2, 5-HT2, and NE alpha-2 antagonists (previously called SGAs). However, the literature is limited and focused research in large study samples is essential to clarify the issue, since important numerical differences do exist. The possibility of the results and their heterogeneity to be artifacts secondary to a modification of magnetic resonance imaging (MRI) signal by antipsychotics should not be easily rejected until relevant data are available.
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Affiliation(s)
- Konstantinos N Fountoulakis
- 3rd Department of Psychiatry, Faculty of Medicine, School of Health Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Stephen M Stahl
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA.,Department of Psychiatry, Cambridge University, Cambridge, UK
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12
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Resting-state functional connectivity predictors of treatment response in schizophrenia - A systematic review and meta-analysis. Schizophr Res 2021; 237:153-165. [PMID: 34534947 DOI: 10.1016/j.schres.2021.09.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/18/2021] [Accepted: 09/06/2021] [Indexed: 11/21/2022]
Abstract
We aimed to systematically synthesize and quantify the utility of pre-treatment resting-state functional magnetic resonance imaging (rs-fMRI) in predicting antipsychotic response in schizophrenia. We searched the PubMed/MEDLINE database for studies that examined the magnitude of association between baseline rs-fMRI assessment and subsequent response to antipsychotic treatment in persons with schizophrenia. We also performed meta-analyses for quantifying the magnitude and accuracy of predicting response defined continuously and categorically. Data from 22 datasets examining 1280 individuals identified striatal and default mode network functional segregation and integration metrics as consistent determinants of treatment response. The pooled correlation coefficient for predicting improvement in total symptoms measured continuously was ~0.47 (12 datasets; 95% CI: 0.35 to 0.59). The pooled odds ratio of predicting categorically defined treatment response was 12.66 (nine datasets; 95% CI: 7.91-20.29), with 81% sensitivity and 76% specificity. rs-fMRI holds promise as a predictive biomarker of antipsychotic treatment response in schizophrenia. Future efforts need to focus on refining feature characterization to improve prediction accuracy, validate prediction models, and evaluate their implementation in clinical practice.
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13
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Ajnakina O, Das T, Lally J, Di Forti M, Pariante CM, Marques TR, Mondelli V, David AS, Murray RM, Palaniyappan L, Dazzan P. Structural Covariance of Cortical Gyrification at Illness Onset in Treatment Resistance: A Longitudinal Study of First-Episode Psychoses. Schizophr Bull 2021; 47:1729-1739. [PMID: 33851203 PMCID: PMC8530394 DOI: 10.1093/schbul/sbab035] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Treatment resistance (TR) in patients with first-episode psychosis (FEP) is a major cause of disability and functional impairment, yet mechanisms underlying this severe disorder are poorly understood. As one view is that TR has neurodevelopmental roots, we investigated whether its emergence relates to disruptions in synchronized cortical maturation quantified using gyrification-based connectomes. Seventy patients with FEP evaluated at their first presentation to psychiatric services were followed up using clinical records for 4 years; of these, 17 (24.3%) met the definition of TR and 53 (75.7%) remained non-TR at 4 years. Structural MRI images were obtained within 5 weeks from first exposure to antipsychotics. Local gyrification indices were computed for 148 contiguous cortical regions using FreeSurfer; each subject's contribution to group-based structural covariance was quantified using a jack-knife procedure, providing a single deviation matrix for each subject. The latter was used to derive topological properties that were compared between TR and non-TR patients using a Functional Data Analysis approach. Compared to the non-TR patients, TR patients showed a significant reduction in small-worldness (Hedges's g = 2.09, P < .001) and a reduced clustering coefficient (Hedges's g = 1.07, P < .001) with increased length (Hedges's g = -2.17, P < .001), indicating a disruption in the organizing principles of cortical folding. The positive symptom burden was higher in patients with more pronounced small-worldness (r = .41, P = .001) across the entire sample. The trajectory of synchronized cortical development inferred from baseline MRI-based structural covariance highlights the possibility of identifying patients at high-risk of TR prospectively, based on individualized gyrification-based connectomes.
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Affiliation(s)
- Olesya Ajnakina
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, UK
| | - Tushar Das
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - John Lally
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland
- Department of Psychiatry, St Vincent’s Hospital Fairview, Dublin, Ireland
- Department of Psychiatry, Mater Misericordiae University Hospital, Dublin, Ireland
| | - Marta Di Forti
- MRC Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
| | - Carmine M Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Tiago Reis Marques
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Psychiatric Imaging Group, MRC London Institute of Medical Sciences (LMS), Hammersmith Hospital, Imperial College London, London, UK
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK
| | - Anthony S David
- Institute of Mental Health, University College London, London, UK
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy
| | - Lena Palaniyappan
- Departments of Psychiatry & Medical Biophysics, Robarts Research Institute & Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, UK
- National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, London, UK
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14
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Cao J, Chai-Zhang TC, Huang Y, Eshel MN, Kong J. Potential scalp stimulation targets for mental disorders: evidence from neuroimaging studies. J Transl Med 2021; 19:343. [PMID: 34376209 PMCID: PMC8353731 DOI: 10.1186/s12967-021-02993-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 07/17/2021] [Indexed: 11/16/2022] Open
Abstract
Mental disorders widely contribute to the modern global disease burden, creating a significant need for improvement of treatments. Scalp stimulation methods (such as scalp acupuncture and transcranial electrical stimulation) have shown promising results in relieving psychiatric symptoms. However, neuroimaging findings haven’t been well-integrated into scalp stimulation treatments. Identifying surface brain regions associated with mental disorders would expand target selection and the potential for these interventions as treatments for mental disorders. In this study, we performed large-scale meta-analyses separately on eight common mental disorders: attention deficit hyperactivity disorder, anxiety disorder, autism spectrum disorder, bipolar disorder, compulsive disorder, major depression, post-traumatic stress disorder and schizophrenia; utilizing modern neuroimaging literature to summarize disorder-associated surface brain regions, and proposed neuroimaging-based target protocols. We found that the medial frontal gyrus, the supplementary motor area, and the dorsal lateral prefrontal cortex are commonly involved in the pathophysiology of mental disorders. The target protocols we proposed may provide new brain targets for scalp stimulation in the treatment of mental disorders, and facilitate its clinical application.
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Affiliation(s)
- Jin Cao
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Thalia Celeste Chai-Zhang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Yiting Huang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Maya Nicole Eshel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, 02129, USA.
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15
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Chekroud AM, Bondar J, Delgadillo J, Doherty G, Wasil A, Fokkema M, Cohen Z, Belgrave D, DeRubeis R, Iniesta R, Dwyer D, Choi K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry 2021; 20:154-170. [PMID: 34002503 PMCID: PMC8129866 DOI: 10.1002/wps.20882] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real-world clinical practice. Relatively few retrospective studies to-date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
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Affiliation(s)
- Adam M Chekroud
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Spring Health, New York City, NY, USA
| | | | - Jaime Delgadillo
- Clinical Psychology Unit, Department of Psychology, University of Sheffield, Sheffield, UK
| | - Gavin Doherty
- School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland
| | - Akash Wasil
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Marjolein Fokkema
- Department of Methods and Statistics, Institute of Psychology, Leiden University, Leiden, The Netherlands
| | - Zachary Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Robert DeRubeis
- Department of Psychology, University of Pennsylvania, Philadelphia, PA, USA
| | - Raquel Iniesta
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neurosciences, King's College London, London, UK
| | - Dominic Dwyer
- Department of Psychiatry and Psychotherapy, Section for Neurodiagnostic Applications, Ludwig-Maximilian University, Munich, Germany
| | - Karmel Choi
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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16
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Wang Y, Jiang Y, Su W, Xu L, Wei Y, Tang Y, Zhang T, Tang X, Hu Y, Cui H, Wang J, Yao D, Luo C, Wang J. Temporal Dynamics in Degree Centrality of Brain Functional Connectome in First-Episode Schizophrenia with Different Short-Term Treatment Responses: A Longitudinal Study. Neuropsychiatr Dis Treat 2021; 17:1505-1516. [PMID: 34079256 PMCID: PMC8166279 DOI: 10.2147/ndt.s305117] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 04/14/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE This study investigated temporal dynamics in degree centrality (DC) of the brain functional connectome in first-episode schizophrenia with different short-term treatment responses. METHODS A total of 127 first-episode patients (FEPs) with schizophrenia and 133 healthy controls (HCs) were recruited in this study. All subjects underwent resting-state functional magnetic resonance imaging. FEPs were scanned at baseline (pretreatment) and at follow-up (posttreatment), while HCs were scanned only at baseline. The patients were exposed to naturalistic antipsychotic treatment for 12 weeks, and classified as schizophrenia responders (SRs) or nonresponders (NRs). Voxel-wise dynamic DC analyses were conducted among the SRs (n=75), NRs (n=52), and HCs (n=133) to assess temporal variability in functional connectivity across the entire neuronal network. RESULTS The SRs and NRs showed dissimilar dynamic DC at baseline, with differences mainly involving the temporal lobe. Different DC alteration was observed in the left fusiform gyrus, right fusiform gyrus, left middle cingulate cortex, and left superior parietal gyrus in the SRs and NRs pre- and posttreatment. SRs group and NRs presented opposite changing patterns of dynamic DC in particular regions of the brain. CONCLUSION These findings indicate that dynamic DC abnormalities exist in unmedicated patients with schizophrenia. The NRs differed from the SRs in dynamic DC not only at baseline but in the characteristics of changes before and after treatment as well. Our study may contribute to understanding pathophysiology in schizophrenia with different treatment responses.
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Affiliation(s)
- Yingchan Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yuchao Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Wenjun Su
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yanyan Wei
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Xiaochen Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Yegang Hu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Huiru Cui
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Jinhong Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China
| | - Dezhong Yao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Cheng Luo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, Center for Information in Medicine, School of life Science and technology, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030, People's Republic of China.,CAS Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, 200031, People's Republic of China.,Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, 200030, People's Republic of China
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17
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Ajnakina O, Agbedjro D, Lally J, Forti MD, Trotta A, Mondelli V, Pariante C, Dazzan P, Gaughran F, Fisher HL, David A, Murray RM, Stahl D. Predicting onset of early- and late-treatment resistance in first-episode schizophrenia patients using advanced shrinkage statistical methods in a small sample. Psychiatry Res 2020; 294:113527. [PMID: 33126015 DOI: 10.1016/j.psychres.2020.113527] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 10/18/2020] [Indexed: 01/09/2023]
Abstract
Evidence suggests there are two treatment-resistant schizophrenia subtypes (i.e. early treatment resistant (E-TR) and late-treatment resistant (L-TR)). We aimed to develop prediction models for estimating individual risk for these outcomes by employing advanced statistical shrinkage methods. 239 first-episode schizophrenia (FES) patients were followed-up for approximately 5 years after first presentation to psychiatric services; of these, n=56 (25.2%) were defined as E-TR and n=24 (12.6%) were defined as L-TR. Using known risk factors for poor schizophrenia outcomes, we developed prediction models for E-TR and L-TR using LASSO and RIDGE logistic regression models. Models' internal validation was performed employing Harrell's optimism-correction with repeated cross-validation; their predictive accuracy was assessed through discrimination and calibration. Both LASSO and RIDGE models had high discrimination, good calibration. While LASSO had moderate sensitivity for estimating an individual risk for E-TR and L-TR, sensitivity estimated for RIDGE model for these outcomes was extremely low, which was due to having a very large estimated optimism. Although it was possible to discriminate with sufficient accuracy who would meet criteria for E-TR and L-TR during the 5-year follow-up after first contact with mental health services for schizophrenia, further work is necessary to improve sensitivity for these models.
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Affiliation(s)
- Olesya Ajnakina
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London, United Kingdom.
| | - Deborah Agbedjro
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - John Lally
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin, Ireland; Department of Psychiatry, St Vincent's Hospital Fairview, Dublin, Ireland
| | - Marta Di Forti
- Social, Genetic, & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Antonella Trotta
- Social, Genetic, & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Tony Hillis Unit, South London and Maudsley NHS Foundation Trust, London United Kingdom
| | - Valeria Mondelli
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Carmine Pariante
- Department of Psychological Medicine, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Paola Dazzan
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Fiona Gaughran
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; National Psychosis Service, South London and Maudsley NHS Foundation Trust, London United Kingdom
| | - Helen L Fisher
- Social, Genetic, & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Anthony David
- Institute of Mental Health, University College London, London, United Kingdom
| | - Robin M Murray
- Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom; Department of Psychiatry, Experimental Biomedicine and Clinical Neuroscience, University of Palermo, Italy
| | - Daniel Stahl
- Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
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18
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Zhuo C, Xu X, Lin X, Chen M, Ji F, Jiang D, Xu Y, Wang L, Li Y, Tian H, Wang W, Zhou C. Depressive symptoms combined with auditory hallucinations are accompanied with severe gray matter brain impairments in patients with first-episode untreated schizophrenia - A pilot study in China. Neurosci Lett 2020; 730:135033. [PMID: 32417389 DOI: 10.1016/j.neulet.2020.135033] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 04/07/2020] [Accepted: 05/03/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND Depressive symptoms and auditory hallucinations (AHs) are often accompanied by gray matter volume (GMV) alterations in schizophrenia. However, little is known about the effects of concurrent depressive symptoms and AHs on the GMV of patients with schizophrenia. AIM To investigate the pathological features of gray brain matter in patients with first-episode untreated schizophrenia (FUSCH) who have concurrent moderate-to-severe depressive symptoms and AHs (FUSCH-DAH). METHODS The Calgary Depression Scale for Schizophrenia (CDSS) and Auditory Hallucinations Rating Scale (AHRS) were adopted. Voxel-based morphometry (VBM)-based GMV analyses were used to measure cortical alterations. FUSCH-DAH patients were compared to FUSCH patients with depressive symptoms but without AHs, denoted as FUSCH-D, along with healthy controls. RESULTS GMV reductions were more substantial in the FUSCH-DAH patients than FUSCH-D patients or healthy controls. Both FUSCH-DAH and FUSCH-D groups showed GMV reductions of the parietal, frontal, and temporal lobes, which were not apparent in the healthy controls. Compared to FUSCH-D patients, FUSCH-DAH patients demonstrated more substantial GMV reductions in the Broca area, Wernicke region, insular lobe, and prefrontal lobe. The GMV reductions were 1.06% and 0.58% in FUSCH-DAH and FUSCH-D patients, respectively, as compared with the healthy controls. CONCLUSIONS This is the first report showing that concurrent depressive symptoms and AHs leads to severe GMV deterioration in FUSCH-DAH patients. Hence, there is a reciprocal relationship between AHs and depressive symptoms in FUSCH-DAH patients. However, the potential additive effects of concurrent AHs and depressive symptoms require further investigation in order to identify future targeted therapies for schizophrenia.
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Affiliation(s)
- Chuanjun Zhuo
- Department of Biological Psychiatry, School of Mental Health, Jining Medical University, Jining 272191, Shandong Province, China; Department of Psychiatry and Neuroimaging Center, Wenzhou Seventh People's Hospital, Wenzhou, 325000, Zhejiang Province, China; Psychiatric-Neuroimaging-Genetics and Comorbidity Laboratory (PNGC_Lab), Tianjin Anding Hospital, 300300 Tianjin, China.
| | - Xuexin Xu
- Department of Radiology, MRI Center, Tianjin Children Hospital, Tianjin Medical University Affiliated Tianjin Children Hospital, Tianjin 300444, China
| | - Xiaodong Lin
- Department of Psychiatry and Neuroimaging Center, Wenzhou Seventh People's Hospital, Wenzhou, 325000, Zhejiang Province, China
| | - Min Chen
- Department of Biological Psychiatry, School of Mental Health, Jining Medical University, Jining 272191, Shandong Province, China
| | - Feng Ji
- Department of Biological Psychiatry, School of Mental Health, Jining Medical University, Jining 272191, Shandong Province, China
| | - Deguo Jiang
- Department of Psychiatry and Neuroimaging Center, Wenzhou Seventh People's Hospital, Wenzhou, 325000, Zhejiang Province, China
| | - Yong Xu
- Department of Psychiatry, First Hospital/First Clinical Medical College of Shanxi Medical University, Taiyuan, China; MDT Center for Cognitive Impairment and Sleep Disorders, First Hospital of Shanxi Medical University, Taiyuan 030001, China
| | - Lina Wang
- Psychiatric-Neuroimaging-Genetics and Comorbidity Laboratory (PNGC_Lab), Tianjin Anding Hospital, 300300 Tianjin, China
| | - Yancheng Li
- Psychiatric-Neuroimaging-Genetics and Comorbidity Laboratory (PNGC_Lab), Tianjin Anding Hospital, 300300 Tianjin, China
| | - Hongjun Tian
- Psychiatric-Neuroimaging-Genetics and Comorbidity Laboratory (PNGC_Lab), Tianjin Anding Hospital, 300300 Tianjin, China
| | - Wenqiang Wang
- Canada and China Joint Laboratory of Biological Psychiatry, Xiamen Xianye Hospital, Xiamen 361000, Fujian Province, China
| | - Chunhua Zhou
- Department of Pharmacology, The First Hospital of Hebei Medical University, Shijiazhuang 050000, Hebei Province, China
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de Leon J, Ruan CJ, Schoretsanitis G, De las Cuevas C. A Rational Use of Clozapine Based on Adverse Drug Reactions, Pharmacokinetics, and Clinical Pharmacopsychology. PSYCHOTHERAPY AND PSYCHOSOMATICS 2020; 89:200-214. [PMID: 32289791 PMCID: PMC7206357 DOI: 10.1159/000507638] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Accepted: 03/30/2020] [Indexed: 12/11/2022]
Abstract
Using Richardson and Davidson's model and the sciences of pharmacokinetics and clinical pharmacopsychology, this article reviewed the: (1) poor life expectancy associated with treatment-resistant schizophrenia (TRS), which may be improved in patients who adhere to clozapine; (2) findings that clozapine is the best treatment for TRS (according to efficacy, effectiveness and well-being); and (3) potential for clozapine to cause vulnerabilities, including potentially lethal adverse drug reactions such as agranulocytosis, pneumonia, and myocarditis. Rational use requires: (1) modification of the clozapine package insert worldwide to include lower doses for Asians and to avoid the lethality associated with pneumonia, (2) the use of clozapine levels for personalizing dosing, and (3) the use of slow and personalized titration. This may make clozapine as safe as possible and contribute to increased life expectancy and well-being. In the absence of data on COVID-19 in clozapine patients, clozapine possibly impairs immunological mechanisms and may increase pneumonia risk in infected patients. Psychiatrists should call their clozapine patients and families and explain to them that if the patient develops fever or flu-like symptoms, the psychiatrist should be called and should consider halving the clozapine dose. If the patient is hospitalized with pneumonia, the treating physician needs to assess for symptoms of clozapine intoxication since halving the dose may not be enough for all patients; consider decreasing it to one-third or even stopping it. Once the signs of inflammation and fever have disappeared, the clozapine dose can be slowly increased to the prior dosage level.
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Affiliation(s)
- Jose de Leon
- Mental Health Research Center at Eastern State Hospital, Lexington, Kentucky, USA, .,Psychiatry and Neurosciences Research Group (CTS-549), Institute of Neurosciences, University of Granada, Granada, Spain, .,Biomedical Research Centre in Mental Health Net (CIBERSAM), Santiago Apóstol Hospital, University of the Basque Country, Vitoria, Spain,
| | - Can-Jun Ruan
- The National Clinical Research Centre for Mental Disorders, Beijing Key Laboratory of Mental Disorders, and Laboratory of Clinical Psychopharmacology, Beijing Anding Hospital, Capital Medical University, Beijing, China,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Georgios Schoretsanitis
- Department of Psychiatry, Zucker Hillside Hospital, Northwell Health, Glen Oaks, New York, USA
| | - Carlos De las Cuevas
- Department of Internal Medicine, Dermatology and Psychiatry, University of La Laguna, San Cristóbal de La Laguna, Spain
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20
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Indicated association between polygenic risk score and treatment-resistance in a naturalistic sample of patients with schizophrenia spectrum disorders. Schizophr Res 2020; 218:55-62. [PMID: 32171635 DOI: 10.1016/j.schres.2020.03.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/02/2020] [Accepted: 03/05/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND One third of people diagnosed with schizophrenia fail to respond adequately to antipsychotic medication, resulting in persisting disabling symptoms, higher rates of hospitalization and higher costs for society. In an effort to better understand the mechanisms behind resistance to antipsychotic treatment in schizophrenia, we investigated its potential relationship to the genetic architecture of the disorder. METHODS Patients diagnosed with a schizophrenia spectrum disorder (N = 321) were classified as either being treatment-resistant (N = 108) or non-treatment-resistant (N = 213) to antipsychotic medication using defined consensus criteria. A schizophrenia polygenic risk score based on genome-wide association studies (GWAS) was calculated for each patient and binary logistic regression was performed to investigate the association between polygenetic risk and treatment resistance. We adjusted for principal components, batch number, age and sex. Additional analyses were performed to investigate associations with demographic and clinical variables. RESULTS High levels of polygenic risk score for schizophrenia significantly predicted treatment resistance (p = 0.003). The positive predictive value of the model was 61.5% and the negative predictive value was 71.7%. The association was significant for one (p = 0.01) out of five tested SNP significance thresholds. Season of birth was able to predict treatment-resistance in the regression model (p = 0.05). CONCLUSIONS The study indicates that treatment-resistance to antipsychotic medication is associated with higher polygenetic risk of schizophrenia, suggesting a link between antipsychotics mechanism of action and the genetic underpinnings of the disorder.
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21
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Takahashi T, Kido M, Sasabayashi D, Nakamura M, Furuichi A, Takayanagi Y, Noguchi K, Suzuki M. Gray Matter Changes in the Insular Cortex During the Course of the Schizophrenia Spectrum. Front Psychiatry 2020; 11:659. [PMID: 32754066 PMCID: PMC7366364 DOI: 10.3389/fpsyt.2020.00659] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 06/25/2020] [Indexed: 11/17/2022] Open
Abstract
Progressive gray matter reductions in the insular cortex have been reported in the early phases of schizophrenia (Sz); however, the trajectory of these reductions during the course of the illness currently remains unclear. Furthermore, it has not yet been established whether patients with schizotypal (SzTypal) features exhibit progressive changes in the insular cortex. This follow-up magnetic resonance imaging study examined volume changes in the short and long insular cortices (mean inter-scan interval = 2.6 years) of 23 first-episode (FE) and 17 chronic patients with Sz, 14 with SzTypal disorder, and 21 healthy controls. Baseline comparisons revealed smaller insular cortex volumes bilaterally in Sz patients (particularly in the chronic group) than in SzTypal patients and healthy controls. FESz patients showed significantly larger gray matter reductions in the insular cortex over time (left: -3.4%/year; right: -2.9%/year) than those in healthy controls (-0.1%/year for both hemispheres) without the effect of subregion or antipsychotic medication, whereas chronic Sz (left: -1.5%/year; right: -1.6%/year) and SzTypal (left: 0.5%/year; right: -0.6%/year) patients did not. Active atrophy of the right insular cortex during FE correlated with fewer improvements in positive symptoms in the Sz groups, while mild atrophy of the left insular cortex during the chronic phase was associated with the severity of negative symptoms in the follow-up period. The present results support dynamic volumetric changes in the insular cortex being specific to overt Sz among the spectrum disorders examined and their degree and role in symptomatology appear to differ across the illness stages.
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Affiliation(s)
- Tsutomu Takahashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Mikio Kido
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Daiki Sasabayashi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Mihoko Nakamura
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Atsushi Furuichi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
| | - Yoichiro Takayanagi
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Arisawabashi Hospital, Toyama, Japan
| | - Kyo Noguchi
- Department of Radiology, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan
| | - Michio Suzuki
- Department of Neuropsychiatry, University of Toyama Graduate School of Medicine and Pharmaceutical Sciences, Toyama, Japan.,Research Center for Idling Brain Science, University of Toyama, Toyama, Japan
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