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Toro VD, Antonucci LA, Quarto T, Passiatore R, Fazio L, Ursini G, Chen Q, Masellis R, Torretta S, Sportelli L, Kikidis GC, Massari F, D'Ambrosio E, Rampino A, Pergola G, Weinberger DR, Bertolino A, Blasi G. The interaction between early life complications and a polygenic risk score for schizophrenia is associated with brain activity during emotion processing in healthy participants. Psychol Med 2024; 54:1876-1885. [PMID: 38305128 DOI: 10.1017/s0033291724000011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
BACKGROUND Previous evidence suggests that early life complications (ELCs) interact with polygenic risk for schizophrenia (SCZ) in increasing risk for the disease. However, no studies have investigated this interaction on neurobiological phenotypes. Among those, anomalous emotion-related brain activity has been reported in SCZ, even if evidence of its link with SCZ-related genetic risk is not solid. Indeed, it is possible this relationship is influenced by non-genetic risk factors. Thus, this study investigated the interaction between SCZ-related polygenic risk and ELCs on emotion-related brain activity. METHODS 169 healthy participants (HP) in a discovery and 113 HP in a replication sample underwent functional magnetic resonance imaging (fMRI) during emotion processing, were categorized for history of ELCs and genome-wide genotyped. Polygenic risk scores (PRSs) were computed using SCZ-associated variants considering the most recent genome-wide association study. Furthermore, 75 patients with SCZ also underwent fMRI during emotion processing to verify consistency of their brain activity patterns with those associated with risk factors for SCZ in HP. RESULTS Results in the discovery and replication samples indicated no effect of PRSs, but an interaction between PRS and ELCs in left ventrolateral prefrontal cortex (VLPFC), where the greater the activity, the greater PRS only in presence of ELCs. Moreover, SCZ had greater VLPFC response than HP. CONCLUSIONS These results suggest that emotion-related VLPFC response lies in the path from genetic and non-genetic risk factors to the clinical presentation of SCZ, and may implicate an updated concept of intermediate phenotype considering early non-genetic factors of risk for SCZ.
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Affiliation(s)
- Veronica Debora Toro
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Linda A Antonucci
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Tiziana Quarto
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Roberta Passiatore
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Leonardo Fazio
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Medicine and Surgery, Libera Università Mediterranea "Giuseppe Degennaro", Bari, Italy
| | - Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Rita Masellis
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Silvia Torretta
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Leonardo Sportelli
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Gianluca Christos Kikidis
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Francesco Massari
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Enrico D'Ambrosio
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, SE5 8AF, UK
| | - Antonio Rampino
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giulio Pergola
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Alessandro Bertolino
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giuseppe Blasi
- Psychiatric Neuroscience Group, Department of Translational Biomedicine and Neuroscience, University of Bari "Aldo Moro", Bari, Italy
- U.O.C. Psichiatria Universitaria, Azìenda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
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2
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Tüscher O, Muthuraman M, Horstmann JP, Horta G, Radyushkin K, Baumgart J, Sigurdsson T, Endle H, Ji H, Kuhnhäuser P, Götz J, Kepser LJ, Lotze M, Grabe HJ, Völzke H, Leehr EJ, Meinert S, Opel N, Richers S, Stroh A, Daun S, Tittgemeyer M, Uphaus T, Steffen F, Zipp F, Groß J, Groppa S, Dannlowski U, Nitsch R, Vogt J. Altered cortical synaptic lipid signaling leads to intermediate phenotypes of mental disorders. Mol Psychiatry 2024:10.1038/s41380-024-02598-2. [PMID: 38806692 DOI: 10.1038/s41380-024-02598-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 04/23/2024] [Accepted: 05/03/2024] [Indexed: 05/30/2024]
Abstract
Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1R345T/WT). Prg-1R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.
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Affiliation(s)
- Oliver Tüscher
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
- Leibniz Institute for Resilience Research Mainz, Mainz, Germany
- Institute for Molecular Biology Mainz, Mainz, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany
- Department of Neurology, Neural engineering with Signal Analytics and Artificial Intelligence (NESA-AI), University Hospital of Würzburg, Würzburg, Germany
- Informatics for Medical Technology, University Augsburg, Augsburg, Germany
| | - Johann-Philipp Horstmann
- Department of Psychiatry and Psychotherapy, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Guilherme Horta
- Focus Program Translational Neuroscience, Johannes Gutenberg-University Mainz, Mainz, Germany
- Institute of Anatomy, University Medical Center Mainz, Mainz, Germany
| | - Konstantin Radyushkin
- TARC, Translational Animal Research Center, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Jan Baumgart
- TARC, Translational Animal Research Center, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Torfi Sigurdsson
- Institute of Neurophysiology, University Medical Center, Goethe-University Frankfurt, Frankfurt, Germany
| | - Heiko Endle
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Haichao Ji
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Prisca Kuhnhäuser
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Jan Götz
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Lara-Jane Kepser
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany
| | - Martin Lotze
- Functional Imaging Unit, Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Greifswald, Germany
| | - Hans J Grabe
- Department of Psychiatry and Psychotherapy, University Medicine Greifswald, Greifswald, Germany
| | - Henry Völzke
- Department SHIP/Clinical Epidemiological Research, Institute of Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Elisabeth J Leehr
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Susanne Meinert
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Nils Opel
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Sebastian Richers
- Institute for Translational Neuroscience, University of Münster, Münster, Germany
| | - Albrecht Stroh
- Institute of Pathophysiology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Silvia Daun
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (IMN-3), Research Centre Jülich, Jülich, Germany
| | - Marc Tittgemeyer
- Max Planck Institute of Metabolism Research, Cologne, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Cologne, Germany
| | - Timo Uphaus
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Falk Steffen
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Frauke Zipp
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Joachim Groß
- Institute for Biomagnetism and Biosignalanalysis, University of Münster, Münster, Germany
| | - Sergiu Groppa
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Robert Nitsch
- Institute for Translational Neuroscience, University of Münster, Münster, Germany.
| | - Johannes Vogt
- Department of Neurology, Johannes Gutenberg-University Mainz, Mainz, Germany.
- Department of Molecular and Translational Neuroscience, Institute of Anatomy II, Cluster of Excellence-Cellular Stress Response in Aging-Associated Diseases (CECAD), Center of Molecular Medicine Cologne (CMMC), University of Cologne, Cologne, Germany.
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3
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Pergola G, Rampino A, Sportelli L, Borcuk CJ, Passiatore R, Di Carlo P, Marakhovskaia A, Fazio L, Amoroso N, Castro MN, Domenici E, Gennarelli M, Khlghatyan J, Kikidis GC, Lella A, Magri C, Monaco A, Papalino M, Parihar M, Popolizio T, Quarto T, Romano R, Torretta S, Valsecchi P, Zunuer H, Blasi G, Dukart J, Beaulieu JM, Bertolino A. A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024; 9:356-366. [PMID: 38000716 DOI: 10.1016/j.bpsc.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/04/2023] [Accepted: 11/09/2023] [Indexed: 11/26/2023]
Abstract
BACKGROUND miR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-wide association studies have implicated miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing. METHODS Using RNA sequencing data from postmortem prefrontal cortex (N = 522), we identified a coexpression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of coexpression prediction and associated them with functional magnetic resonance imaging activation in healthy volunteers (n1 = 214; n2 = 136; n3 = 2075; n4 = 1800) and with short-term treatment response in patients with schizophrenia (N = 427). RESULTS In 4652 human participants, we found that 1) schizophrenia risk genes were coexpressed in a biologically validated set enriched for miR-137 targets; 2) increased expression of miR-137 target risk genes was mediated by low prefrontal miR-137 expression; 3) alleles that predict greater gene set coexpression were associated with greater prefrontal activation during emotion processing in 3 independent healthy cohorts (n1, n2, n3) in interaction with age (n4); and 4) these alleles predicted less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia. CONCLUSIONS The functional translation of miR-137 target gene expression linked with schizophrenia involves the neural substrates of emotion processing.
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Affiliation(s)
- Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy.
| | - Leonardo Sportelli
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Christopher James Borcuk
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Roberta Passiatore
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Institute of Neuroscience and Medicine, Brain & Behaviour, Research Centre Jülich, Jülich, Germany
| | - Pasquale Di Carlo
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | | | - Leonardo Fazio
- Department of Medicine and Surgery, Libera Università Mediterranea Giuseppe Degennaro, Casamassima, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, Bari, Italy; Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy
| | - Mariana Nair Castro
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina (MNC); Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Fleni-Consejo Nacional de Investigaciones Científicas y Técnicas Neurosciences Institute, Ciudad Autónoma de Buenos Aires, Argentina
| | - Enrico Domenici
- Department of Cellular, Computational and Integrative Biology, University of Trento, Trento, Italy; Fondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology, Rovereto, Italy
| | - Massimo Gennarelli
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy; Genetics Unit, Istituto di Ricovero e Cura a Carattere Sanitario Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Jivan Khlghatyan
- Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy; Department of Neuroscience, Novartis Institutes for Biomedical Research, Cambridge, Massachusetts
| | - Gianluca Christos Kikidis
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Annalisa Lella
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Chiara Magri
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, Bari, Italy; Consejo Nacional de Investigaciones Científicas y Técnicas, Ciudad Autónoma de Buenos Aires, Argentina (MNC); Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Fleni-Consejo Nacional de Investigaciones Científicas y Técnicas Neurosciences Institute, Ciudad Autónoma de Buenos Aires, Argentina; Università degli Studi di Bari Aldo Moro, Dipartimento Interateneo di Fisica M. Merlin, Bari, Italy
| | - Marco Papalino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Madhur Parihar
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Teresa Popolizio
- Istituto di Ricovero e Cura a Carattere Sanitario Istituto Centro San Giovanni di Dio Fatebenefratelli, Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
| | - Tiziana Quarto
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Department of Law, University of Foggia, Foggia, Italy
| | - Raffaella Romano
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Silvia Torretta
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Paolo Valsecchi
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy; Department of Mental Health and Addiction Services, Azienda Socio Sanitaria Territoriale Spedali Civili of Brescia, Brescia, Italy
| | - Hailiqiguli Zunuer
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour, Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
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Jameei H, Rakesh D, Zalesky A, Cairns MJ, Reay WR, Wray NR, Di Biase MA. Linking Polygenic Risk of Schizophrenia to Variation in Magnetic Resonance Imaging Brain Measures: A Comprehensive Systematic Review. Schizophr Bull 2024; 50:32-46. [PMID: 37354489 PMCID: PMC10754175 DOI: 10.1093/schbul/sbad087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/26/2023]
Abstract
BACKGROUND AND HYPOTHESIS Schizophrenia is highly heritable, with a polygenic effect of many genes conferring risk. Evidence on whether cumulative risk also predicts alterations in brain morphology and function is inconsistent. This systematic review examined evidence for schizophrenia polygenic risk score (sczPRS) associations with commonly used magnetic resonance imaging (MRI) measures. We expected consistent evidence to emerge for significant sczPRS associations with variation in structure and function, specifically in frontal, temporal, and insula cortices that are commonly implicated in schizophrenia pathophysiology. STUDY DESIGN In accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we searched MEDLINE, Embase, and PsycINFO for peer-reviewed studies published between January 2013 and March 2022. Studies were screened against predetermined criteria and National Institutes of Health (NIH) quality assessment tools. STUDY RESULTS In total, 57 studies of T1-weighted structural, diffusion, and functional MRI were included (age range = 9-80 years, Nrange = 64-76 644). We observed moderate, albeit preliminary, evidence for higher sczPRS predicting global reductions in cortical thickness and widespread variation in functional connectivity, and to a lesser extent, region-specific reductions in frontal and temporal volume and thickness. Conversely, sczPRS does not predict whole-brain surface area or gray/white matter volume. Limited evidence emerged for sczPRS associations with diffusion tensor measures of white matter microstructure in a large community sample and smaller cohorts of children and young adults. These findings were broadly consistent across community and clinical populations. CONCLUSIONS Our review supports the hypothesis that schizophrenia is a disorder of disrupted within and between-region brain connectivity, and points to specific whole-brain and regional MRI metrics that may provide useful intermediate phenotypes.
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Affiliation(s)
- Hadis Jameei
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Divyangana Rakesh
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Faculty of Engineering and Information Technology, The University of Melbourne, Parkville, VIC, Australia
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - William R Reay
- School of Biomedical Sciences and Pharmacy, The University of Newcastle, Newcastle, NSW, Australia
- Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, NSW, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
- Queensland Brain Institute, The University of Queensland, Brisbane, QLD, Australia
| | - Maria A Di Biase
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
- Department of Anatomy and Physiology, School of Biomedical Sciences, The University of Melbourne, VIC, Australia
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
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5
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Quarto T, Lella A, Di Carlo P, Rampino A, Paladini V, Papalino M, Romano R, Fazio L, Marvulli D, Popolizio T, Blasi G, Pergola G, Bertolino A. Heritability of amygdala reactivity to angry faces and its replicable association with the schizophrenia risk locus of miR-137. J Psychiatry Neurosci 2023; 48:E357-E366. [PMID: 37751917 PMCID: PMC10521919 DOI: 10.1503/jpn.230013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 07/11/2023] [Accepted: 07/30/2023] [Indexed: 09/28/2023] Open
Abstract
BACKGROUND Among healthy participants, the interindividual variability of brain response to facial emotions is associated with genetic variation, including common risk variants for schizophrenia, a heritable brain disorder characterized by anomalies in emotion processing. We aimed to identify genetic variants associated with heritable brain activity during processing of facial emotions among healthy participants and to explore the impact of these identified variants among patients with schizophrenia. METHODS We conducted a data-driven stepwise study including samples of healthy twins, unrelated healthy participants and patients with schizophrenia. Participants approached or avoided pictures of faces with negative emotional valence during functional magnetic resonance imaging (fMRI). RESULTS We investigated 3 samples of healthy participants - including 28 healthy twin pairs, 289 unrelated healthy participants (genome-wide association study [GWAS] discovery sample) and 90 unrelated healthy participants (replication sample) - and 1 sample of 48 patients with schizophrenia. Among healthy twins, we identified the amygdala as the brain region with the highest heritability during processing of angry faces (heritability estimate 0.54, p < 0.001). Subsequent GWAS in both discovery and replication samples of healthy non-twins indicated that amygdala activity was associated with a polymorphism in the miR-137 locus (rs1198575), a micro-RNA strongly involved in risk for schizophrenia. A significant effect in the same direction was found among patients with schizophrenia (p = 0.03). LIMITATIONS The limited sample size available for GWAS analyses may require further replication of results. CONCLUSION Our data-driven approach shows preliminary evidence that amygdala activity, as evaluated with our task, is heritable. Our genetic associations preliminarily suggest a role for miR-137 in brain activity during explicit processing of facial emotions among healthy participants and patients with schizophrenia, pointing to the amygdala as a brain region whose activity is related to miR-137.
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Affiliation(s)
- Tiziana Quarto
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Annalisa Lella
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Pasquale Di Carlo
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Antonio Rampino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Vittoria Paladini
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Marco Papalino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Raffaella Romano
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Leonardo Fazio
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Daniela Marvulli
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Teresa Popolizio
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Giuseppe Blasi
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Giulio Pergola
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
| | - Alessandro Bertolino
- From the Department of Translational Biomedicine and Neuroscience, University of Bari Aldo Moro, Bari, Italy (Quarto, Lella, Di Carlo, Rampino, Paladini, Papalino, Romano, Fazio, Marvulli, Blasi, Pergola, Bertolino); the Department of Humanities, University of Foggia, Foggia, Italy (Quarto); the Psychiatry Unit, Bari University Hospital, Bari, Italy (Rampino, Blasi, Bertolino); the LUM (Fazio); the IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy (Popolizio); the Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD (Pergola)
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6
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Cao H, Hong X, Tost H, Meyer-Lindenberg A, Schwarz E. Advancing translational research in neuroscience through multi-task learning. Front Psychiatry 2022; 13:993289. [PMID: 36465289 PMCID: PMC9714033 DOI: 10.3389/fpsyt.2022.993289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Accepted: 10/24/2022] [Indexed: 11/18/2022] Open
Abstract
Translational research in neuroscience is increasingly focusing on the analysis of multi-modal data, in order to account for the biological complexity of suspected disease mechanisms. Recent advances in machine learning have the potential to substantially advance such translational research through the simultaneous analysis of different data modalities. This review focuses on one of such approaches, the so-called "multi-task learning" (MTL), and describes its potential utility for multi-modal data analyses in neuroscience. We summarize the methodological development of MTL starting from conventional machine learning, and present several scenarios that appear particularly suitable for its application. For these scenarios, we highlight different types of MTL algorithms, discuss emerging technological adaptations, and provide a step-by-step guide for readers to apply the MTL approach in their own studies. With its ability to simultaneously analyze multiple data modalities, MTL may become an important element of the analytics repertoire used in future neuroscience research and beyond.
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Affiliation(s)
- Han Cao
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Xudong Hong
- Department of Computer Vision and Machine Learning, Max Planck Institute for Informatics, Saarbrücken, Germany
- Department of Language Science and Technology, Saarland University, Saarbrücken, Germany
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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7
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Raucher-Chéné D, Lavigne KM, Lepage M. Episodic Memory and Schizophrenia: From Characterization of Relational Memory Impairments to Neuroimaging Biomarkers. Curr Top Behav Neurosci 2022; 63:115-136. [PMID: 35902545 DOI: 10.1007/7854_2022_379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Episodic memory research in schizophrenia has a long history already which has clearly established significant impairments and strong associations with brain measures and functional outcome. The purpose of this chapter is not to make an exhaustive review of the recent literature but to highlight some relatively recent developments in the cognitive neuroscience field of episodic memory and schizophrenia. Hence, we present a contemporary view focusing specifically of relational memory which represents a form of episodic memory that refers to associations or binding among items or elements presented together. We describe the major tasks used and illustrate how their combination with brain imaging has: (1) favored the use of experimental memory tasks to isolate specific processes with specific neural correlates, (2) led to a distributed view of the neural correlates of memory impairments in schizophrenia where multiple regions are contributing, and (3) made possible the identification of fMRI biomarkers specific to episodic memory. We then briefly propose what we see as the next steps for memory research in schizophrenia so that the impact of this work can be maximized.
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Affiliation(s)
- Delphine Raucher-Chéné
- Cognition, Health, and Society Laboratory (EA 6291), University of Reims Champagne-Ardenne, Reims, France.,Academic Department of Psychiatry, University Hospital of Reims, EPSM Marne, Reims, France
| | - Katie M Lavigne
- Douglas Research Centre, Verdun, QC, Canada.,Department of Psychiatry, McGill University, Montréal, QC, Canada.,McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Martin Lepage
- Douglas Research Centre, Verdun, QC, Canada. .,Department of Psychiatry, McGill University, Montréal, QC, Canada.
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8
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Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J Neurodev Disord 2022; 14:28. [PMID: 35501679 PMCID: PMC9059371 DOI: 10.1186/s11689-022-09438-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/07/2022] [Indexed: 12/31/2022] Open
Abstract
Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Pramod Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Qiang Chang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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9
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Yi SY, Barnett BR, Poetzel MJ, Stowe NA, Yu JPJ. Clinical translational neuroimaging of the antioxidant effect of N-acetylcysteine on neural microstructure. Magn Reson Med 2022; 87:820-836. [PMID: 34590731 PMCID: PMC8627450 DOI: 10.1002/mrm.29035] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 08/20/2021] [Accepted: 09/15/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE Oxidative stress and downstream effectors have emerged as important pathological processes that drive psychiatric illness, suggesting that antioxidants may have a therapeutic role in psychiatric disease. However, no imaging biomarkers are currently available to track therapeutic response. The purpose of this study was to examine whether advanced DWI techniques are able to sensitively detect the potential therapeutic effects of the antioxidant N-acetylcysteine (NAC) in a Disc1 svΔ2 preclinical rat model of psychiatric illness. METHODS Male and female Disc1 svΔ2 rats and age-matched, sex-matched Sprague-Dawley wild-type controls were treated with a saline vehicle or NAC before ex vivo MRI acquisition at P50. Imaging data were fit to DTI and neurite orientation dispersion and density imaging models and analyzed for region-specific changes in quantitative diffusion metrics. Brains were further processed for cellular quantification of microglial density and morphology. All experiments were repeated for Disc1 svΔ2 rats exposed to chronic early-life stress to test how gene-environment interactions might alter effectiveness of NAC therapy. RESULTS The DTI and neurite orientation dispersion and density imaging analyses demonstrated amelioration of early-life, sex-specific neural microstructural deficits with concomitant differences in microglial morphology across multiple brain regions relevant to neuropsychiatric illness with NAC treatment, but only in male Disc1 svΔ2 rats. Addition of chronic early-life stress reduced the ability of NAC to restore microstructural deficits. CONCLUSION These findings provide evidence for a treatment pathway targeting endogenous antioxidant capacity, and the clinical translational utility of neurite orientation dispersion and density imaging microstructural imaging to sensitively detect microstructural alterations resulting from antioxidant treatment.
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Affiliation(s)
- Sue Y. Yi
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin–Madison, Madison, WI 53705, USA
| | - Brian R. Barnett
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin–Madison, Madison, WI 53705, USA
| | - McKenzie J. Poetzel
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - Nicholas A. Stowe
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - John-Paul J. Yu
- Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin–Madison, Madison, WI 53705, USA
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
- Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI 53706, USA
- Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
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10
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Hirjak D, Schwarz E, Meyer-Lindenberg A. [Twelve years of research domain criteria in psychiatric research and practice: claim and reality]. DER NERVENARZT 2021; 92:857-867. [PMID: 34342676 DOI: 10.1007/s00115-021-01174-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 12/12/2022]
Abstract
The research domain criteria (RDoC) initiative of the National Institute of Mental Health (NIMH) was presented 12 years ago. The RDoC provides a matrix for the systematic, dimensional and domain-based study of mental disorders that is not based on established disease entities as defined by the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). The primary aim of RDoC is to understand the nature of mental health and illness in terms of different extents of dysfunction in psychological/biological systems with interconnected diagnoses. This selective review article aims to provide a comprehensive overview of RDoC-based studies that have contributed to a better conceptual organization of mental disorders. Numerous promising and methodologically sophisticated studies on RDoC were identified. The number of scientific studies increased over time, indicating that dimensional research is increasingly being pursued in psychiatry. In summary, the RDoC initiative has a considerable potential to more precisely define the complexity of pathomechanisms underlying mental disorders; however, major challenges (e.g. small and heterogeneous study samples, unclear biomarker definitions and lack of replication studies) remain to be overcome in the future. Furthermore, it is plausible that a diagnostic system of the future will integrate categorical and dimensional approaches to arrive at a stratification that can underpin a precision medical approach in psychiatry.
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Affiliation(s)
- Dusan Hirjak
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland.
| | - Emanuel Schwarz
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland
| | - Andreas Meyer-Lindenberg
- Zentralinstitut für Seelische Gesundheit, Klinik für Psychiatrie und Psychotherapie, Medizinische Fakultät Mannheim, Universität Heidelberg, 68159, Mannheim, Deutschland
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11
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Chen J, Dong G, Song L, Zhao X, Cao J, Luo X, Feng J, Zhao XM. Integration of Multimodal Data for Deciphering Brain Disorders. Annu Rev Biomed Data Sci 2021; 4:43-56. [PMID: 34465176 DOI: 10.1146/annurev-biodatasci-092820-020354] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The accumulation of vast amounts of multimodal data for the human brain, in both normal and disease conditions, has provided unprecedented opportunities for understanding why and how brain disorders arise. Compared with traditional analyses of single datasets, the integration of multimodal datasets covering different types of data (i.e., genomics, transcriptomics, imaging, etc.) has shed light on the mechanisms underlying brain disorders in greater detail across both the microscopic and macroscopic levels. In this review, we first briefly introduce the popular large datasets for the brain. Then, we discuss in detail how integration of multimodal human brain datasets can reveal the genetic predispositions and the abnormal molecular pathways of brain disorders. Finally, we present an outlook on how future data integration efforts may advance the diagnosis and treatment of brain disorders.
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Affiliation(s)
- Jingqi Chen
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
| | - Guiying Dong
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jixin Cao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Xiaohui Luo
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; ,
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, United Kingdom
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China; , .,MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, Ministry of Education, Shanghai 200433, China.,Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai 200433, China
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12
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A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space. Neuroimage 2021; 238:118200. [PMID: 34118398 DOI: 10.1016/j.neuroimage.2021.118200] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/08/2021] [Accepted: 05/22/2021] [Indexed: 11/21/2022] Open
Abstract
We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.
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13
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Peng W, Tan Q, Yu M, Wang P, Wang T, Yuan J, Liu D, Chen D, Huang C, Tan Y, Liu K, Xiang B, Liang X. Transcriptome Sequencing Reveals the Potential Mechanisms of Modified Electroconvulsive Therapy in Schizophrenia. Psychiatry Investig 2021; 18:385-391. [PMID: 33910328 PMCID: PMC8169330 DOI: 10.30773/pi.2020.0410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 03/13/2021] [Indexed: 11/27/2022] Open
Abstract
OBJECTIVE Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs). METHODS Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways. RESULTS Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10-16 and 1.09×10-13, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data. CONCLUSION It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.
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Affiliation(s)
- Wanhong Peng
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Qingyu Tan
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Minglan Yu
- Medical Laboratory Center, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Ping Wang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Tingting Wang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China.,Zigong Mental Health Research Center, Zigong Mental Health Center, Zigong, China
| | - Jixiang Yuan
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Dongmei Liu
- Department of Psychiatry, Yibin Fourth People's Hospital, Yibin, China
| | - Dechao Chen
- Department of Psychiatry, Yibin Fourth People's Hospital, Yibin, China
| | - Chaohua Huang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Youguo Tan
- Zigong Mental Health Center, Zigong, China
| | - Kezhi Liu
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Bo Xiang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China.,Zigong Mental Health Research Center, Zigong Mental Health Center, Zigong, China
| | - Xuemei Liang
- Department of Psychiatry, Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Affiliated Hospital of Southwest Medical University, Luzhou, China
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14
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Barker ED, Ing A, Biondo F, Jia T, Pingault JB, Du Rietz E, Zhang Y, Ruggeri B, Banaschewski T, Hohmann S, Bokde ALW, Bromberg U, Büchel C, Quinlan EB, Sounga-Barke E, Bowling AB, Desrivières S, Flor H, Frouin V, Garavan H, Asherson P, Gowland P, Heinz A, Ittermann B, Martinot JL, Martinot MLP, Nees F, Papadopoulos-Orfanos D, Poustka L, Smolka MN, Vetter NC, Walter H, Whelan R, Schumann G. Do ADHD-impulsivity and BMI have shared polygenic and neural correlates? Mol Psychiatry 2021; 26:1019-1028. [PMID: 31227801 PMCID: PMC7910212 DOI: 10.1038/s41380-019-0444-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 04/29/2019] [Accepted: 05/03/2019] [Indexed: 01/29/2023]
Abstract
There is an extensive body of literature linking ADHD to overweight and obesity. Research indicates that impulsivity features of ADHD account for a degree of this overlap. The neural and polygenic correlates of this association have not been thoroughly examined. In participants of the IMAGEN study, we found that impulsivity symptoms and body mass index (BMI) were associated (r = 0.10, n = 874, p = 0.014 FWE corrected), as were their respective polygenic risk scores (PRS) (r = 0.17, n = 874, p = 6.5 × 10-6 FWE corrected). We then examined whether the phenotypes of impulsivity and BMI, and the PRS scores of ADHD and BMI, shared common associations with whole-brain grey matter and the Monetary Incentive Delay fMRI task, which associates with reward-related impulsivity. A sparse partial least squared analysis (sPLS) revealed a shared neural substrate that associated with both the phenotypes and PRS scores. In a last step, we conducted a bias corrected bootstrapped mediation analysis with the neural substrate score from the sPLS as the mediator. The ADHD PRS associated with impulsivity symptoms (b = 0.006, 90% CIs = 0.001, 0.019) and BMI (b = 0.009, 90% CIs = 0.001, 0.025) via the neuroimaging substrate. The BMI PRS associated with BMI (b = 0.014, 95% CIs = 0.003, 0.033) and impulsivity symptoms (b = 0.009, 90% CIs = 0.001, 0.025) via the neuroimaging substrate. A common neural substrate may (in part) underpin shared genetic liability for ADHD and BMI and the manifestation of their (observable) phenotypic association.
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Affiliation(s)
- Edward D Barker
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK.
| | - Alex Ing
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Francesca Biondo
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Tianye Jia
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Ministry of Education, Fudan University, Shanghai, China
| | | | - Ebba Du Rietz
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Yuning Zhang
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Barbara Ruggeri
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Tobias Banaschewski
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Sarah Hohmann
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
| | - Arun L W Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Uli Bromberg
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Christian Büchel
- University Medical Centre Hamburg-Eppendorf, House W34, 3.OG, Martinistr. 52, 20246, Hamburg, Germany
| | - Erin Burke Quinlan
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Edmund Sounga-Barke
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - April B Bowling
- School of Health Science, Merrimack College, 315 Turnpike Street North Andover, North Andover, MA, 01845, USA
| | - Sylvane Desrivières
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
| | - Herta Flor
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
| | - Vincent Frouin
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Hugh Garavan
- Departments of Psychiatry and Psychology, University of Vermont, 05405, Burlington, VT, USA
| | - Philip Asherson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Penny Gowland
- Sir Peter Mansfield Imaging Centre School of Physics and Astronomy, University of Nottingham, University Park, Nottingham, UK
| | - Andreas Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Bernd Ittermann
- Physikalisch-Technische Bundesanstalt (PTB), Abbestr. 2 - 12, Berlin, Germany
| | - Jean-Luc Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes - Sorbonne Paris Cité; and Maison de Solenn, Paris, France
| | - Marie-Laure Paillère Martinot
- Institut National de la Santé et de la Recherche Médicale, INSERM Unit 1000 "Neuroimaging & Psychiatry", University Paris Sud, University Paris Descartes; Sorbonne Université; and AP-HP, Department of Child and Adolescent Psychiatry, Pitié-Salpêtrière Hospital, Paris, France
| | - Frauke Nees
- Department of Child and Adolescent Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, 68159, Mannheim, Germany
- Department of Cognitive and Clinical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Square J5, Mannheim, Germany
| | - Dimitri Papadopoulos-Orfanos
- Department of Psychology, School of Social Sciences, University of Mannheim, 68131, Mannheim, Germany
- NeuroSpin, CEA, Université Paris-Saclay, F-91191, Gif-sur-Yvette, France
| | - Luise Poustka
- Department of Child and Adolescent Psychiatry and Psychotherapy, University Medical Centre Göttingen, von-Siebold-Str. 5, 37075, Göttingen, Germany
| | - Michael N Smolka
- Department of Psychiatry and Neuroimaging Center, Technische Universität Dresden, Dresden, Germany
| | - Nora C Vetter
- Department of Psychiatry and Psychotherapy, Technische Universität Dresden, Dresden, Germany
| | - Henrik Walter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charitéplatz 1, Berlin, Germany
| | - Robert Whelan
- School of Psychology and Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Gunter Schumann
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Centre for Population Neuroscience and Stratified Medicine (PONS), MRC Social, Genetic and Developmental Psychiatry (SGDP) Centre, London, UK
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15
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Barlati S, Minelli A, Ceraso A, Nibbio G, Carvalho Silva R, Deste G, Turrina C, Vita A. Social Cognition in a Research Domain Criteria Perspective: A Bridge Between Schizophrenia and Autism Spectra Disorders. Front Psychiatry 2020; 11:806. [PMID: 33005149 PMCID: PMC7485015 DOI: 10.3389/fpsyt.2020.00806] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 07/27/2020] [Indexed: 12/27/2022] Open
Abstract
Schizophrenia and autism spectra disorders are currently conceptualized as distinct clinical categories. However, the relationship between these two nosological entities has been revisited in recent years due to the evidence that they share some important clinical and neurobiological features, putting into question the nature and the extent of their commonalities and differences. In this respect, some core symptoms that are present in both disorders, such as social cognitive deficits, could be a primary target of investigation. This review briefly summarizes the commonalities and overlapping features between schizophrenia and autism spectra disorders in social cognitive functions, considering this construct in a Research Domain Criteria perspective. The clinical manifestation of deficits in social cognition are similar in schizophrenia spectrum disorders and autism spectrum disorders, and brain areas that appear to be altered in relation to these impairments are largely shared; however, the results of various studies suggest that, in some cases, the qualitative nature of these alterations may be different in the two spectra. Moreover, relevant differences could be present at the level of brain networks and connections. More research is required in this field, regarding molecular and genetic aspects of both spectra, to better define the neurobiological mechanisms involved in social cognition deficits, with the objective of developing specific and targeted treatments.
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Affiliation(s)
- Stefano Barlati
- Department of Clinical and Experimental Sciences, University of Brescia, and Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Alessandra Minelli
- Genetics Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Anna Ceraso
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Gabriele Nibbio
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Rosana Carvalho Silva
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Giacomo Deste
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Cesare Turrina
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
| | - Antonio Vita
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Department of Mental Health and Addiction Services, ASST Spedali Civili of Brescia, Brescia, Italy
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16
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Chen J, Zang Z, Braun U, Schwarz K, Harneit A, Kremer T, Ma R, Schweiger J, Moessnang C, Geiger L, Cao H, Degenhardt F, Nöthen MM, Tost H, Meyer-Lindenberg A, Schwarz E. Association of a Reproducible Epigenetic Risk Profile for Schizophrenia With Brain Methylation and Function. JAMA Psychiatry 2020; 77:628-636. [PMID: 32049268 PMCID: PMC7042900 DOI: 10.1001/jamapsychiatry.2019.4792] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
IMPORTANCE Schizophrenia is a severe mental disorder in which epigenetic mechanisms may contribute to illness risk. Epigenetic profiles can be derived from blood cells, but to our knowledge, it is unknown whether these predict established brain alterations associated with schizophrenia. OBJECTIVE To identify an epigenetic signature (quantified as polymethylation score [PMS]) of schizophrenia using machine learning applied to genome-wide blood DNA-methylation data; evaluate whether differences in blood-derived PMS are mirrored in data from postmortem brain samples; test whether the PMS is associated with alterations of dorsolateral prefrontal cortex hippocampal (DLPFC-HC) connectivity during working memory in healthy controls (HC); explore the association between interactions between polygenic and epigenetic risk with DLPFC-HC connectivity; and test the specificity of the signature compared with other serious psychiatric disorders. DESIGN, SETTING, AND PARTICIPANTS In this case-control study conducted from 2008 to 2018 in sites in Germany, the United Kingdom, the United States, and Australia, blood DNA-methylation data from 2230 whole-blood samples from 6 independent cohorts comprising HC (1238 [55.5%]) and participants with schizophrenia (803 [36.0%]), bipolar disorder (39 [1.7%]), major depressive disorder 35 [1.6%]), and autism (27 [1.2%]), and first-degree relatives of all patient groups (88 [3.9%]) were analyzed. DNA-methylation data were further explored from 244 postmortem DLPFC samples from 136 HC and 108 patients with schizophrenia. Neuroimaging and genome-wide association data were available for 393 HC. The latter data was used to calculate a polygenic risk score (PRS) for schizophrenia. The data were analyzed in 2019. MAIN OUTCOMES AND MEASURES The accuracy of schizophrenia control classification based on machine learning using epigenetic data; association of schizophrenia PMS scores with DLPFC-HC connectivity; and association of the interaction between PRS and PMS with DLPFC-HC connectivity. RESULTS This study included 7488 participants (4395 men [58.7%]), of whom 3158 (2230 men [70.6%]) received a diagnosis of schizophrenia. The PMS signature was associated with schizophrenia across 3 independent data sets (area under the curve [AUC] from 0.69 to 0.78; P value from 0.049 to 1.24 × 10-7) and data from postmortem DLPFC samples (AUC = 0.63; P = 1.42 × 10-4), but not with major depressive disorder (AUC = 0.51; P = .16), autism (AUC = 0.53; P = .66), or bipolar disorder (AUC = 0.58; P = .21). Pathways contributing most to the classification included synaptic processes. Healthy controls with schizophrenia-like PMS showed significantly altered DLPFC-HC connectivity (validation methylation/magnetic resonance imaging, t < -3.81; P for familywise error, <.04; validation magnetic resonance imaging, t < -3.54; P for familywise error, <.02), mirroring the lack of functional decoupling in schizophrenia. There was no significant association of the interaction between PMS and PRS with DLPFC-HC connectivity (P > .19). CONCLUSIONS AND RELEVANCE We identified a reproducible blood DNA-methylation signature specific for schizophrenia that was correlated with altered functional DLPFC-HC coupling during working memory and mapped to methylation differences found in DLPFC postmortem samples. This indicates a possible epigenetic contribution to a schizophrenia intermediate phenotype and suggests that PMS could be of interest to be studied in the context of multimodal biomarkers for disease stratification and treatment personalization.
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Affiliation(s)
- Junfang Chen
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Zhenxiang Zang
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Urs Braun
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Kristina Schwarz
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Anais Harneit
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Thomas Kremer
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Ren Ma
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Janina Schweiger
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Carolin Moessnang
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Lena Geiger
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Han Cao
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Franziska Degenhardt
- School of Medicine & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - Markus M. Nöthen
- School of Medicine & University Hospital Bonn, Institute of Human Genetics, University of Bonn, Bonn, Germany,Life & Brain Center, Department of Genomics, University of Bonn, Bonn, Germany
| | - Heike Tost
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Emanuel Schwarz
- Central Institute of Mental Health, Department of Psychiatry and Psychotherapy, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
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17
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Appaji A, Nagendra B, Chako DM, Padmanabha A, Jacob A, Hiremath CV, Varambally S, Kesavan M, Venkatasubramanian G, Rao SV, Webers CAB, Berendschot TTJM, Rao NP. Examination of retinal vascular trajectory in schizophrenia and bipolar disorder. Psychiatry Clin Neurosci 2019; 73:738-744. [PMID: 31400288 DOI: 10.1111/pcn.12921] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 07/24/2019] [Accepted: 08/06/2019] [Indexed: 12/11/2022]
Abstract
AIM Evidence suggests microvascular dysfunction (wider retinal venules and narrower arterioles) in schizophrenia (SCZ) and bipolar disorder (BD). The vascular development is synchronous with neuronal development in the retina and brain. The retinal vessel trajectory is related to retinal nerve fiber layer thinning and cerebrovascular abnormalities in SCZ and BD and has not yet been examined. Hence, in this study we examined the retinal vascular trajectory in SCZ and BD in comparison with healthy volunteers (HV). METHODS Retinal images were acquired from 100 HV, SCZ patients, and BD patients, respectively, with a non-mydriatic fundus camera. Images were quantified to obtain the retinal arterial and venous trajectories using a validated, semiautomated algorithm. Analysis of covariance and regression analyses were conducted to examine group differences. A supervised machine-learning ensemble of bagged-trees method was used for automated classification of trajectory values. RESULTS There was a significant difference among groups in both the retinal venous trajectory (HV: 0.17 ± 0.08; SCZ: 0.25 ± 0.17; BD: 0.27 ± 0.20; P < 0.001) and the arterial trajectory (HV: 0.34 ± 0.15; SCZ: 0.29 ± 0.10; BD: 0.29 ± 0.11; P = 0.003) even after adjusting for age and sex (P < 0.001). On post-hoc analysis, the SCZ and BD groups differed from the HV on retinal venous and arterial trajectories, but there was no difference between SCZ and BD patients. The machine learning showed an accuracy of 86% and 73% for classifying HV versus SCZ and BD, respectively. CONCLUSION Smaller trajectories of retinal arteries indicate wider and flatter curves in SCZ and BD. Considering the relation between retinal/cerebral vasculatures and retinal nerve fiber layer thinness, the retinal vascular trajectory is a potential marker for SCZ and BD. As a relatively affordable investigation, retinal fundus photography should be further explored in SCZ and BD as a potential screening measure.
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Affiliation(s)
- Abhishek Appaji
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India.,University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Bhargavi Nagendra
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Dona M Chako
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Ananth Padmanabha
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India
| | - Arpitha Jacob
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Chaitra V Hiremath
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Shivarama Varambally
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Muralidharan Kesavan
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
| | | | - Shyam V Rao
- Department of Medical Electronics, B. M. S. College of Engineering, Bangalore, India.,University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Carroll A B Webers
- University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Tos T J M Berendschot
- University Eye Clinic Maastricht, Maastricht University, Maastricht, The Netherlands
| | - Naren P Rao
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore, India
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18
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Spalthoff R, Degenhardt F, Awasthi S, Heilmann-Heimbach S, Besteher B, Gaser C, Ripke S, Nöthen MM, Nenadić I. Effects of a neurodevelopmental genes based polygenic risk score for schizophrenia and single gene variants on brain structure in non-clinical subjects: A preliminary report. Schizophr Res 2019; 212:225-228. [PMID: 31395486 DOI: 10.1016/j.schres.2019.07.061] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 05/27/2019] [Accepted: 07/30/2019] [Indexed: 11/18/2022]
Abstract
We tested whether a polygenic risk score integrating the effects of genes affecting neurodevelopment is associated to brain structural variation in healthy subjects. We acquired magnetic resonance imaging and genetic data of 167 healthy adults and computed a neurodevelopmental polygenic risk score (nPRS). We correlated the nPRS with local gyrification, cortical thickness and grey matter density and explored effects of single nucleotide polymorphisms included in the score. We did not find significant correlations of this nPRS with either measure. Individuals with the risk allele at rs11139497 show increases in cortical thickness (p < 0.05, FWE corrected) of the left superior temporal gyrus.
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Affiliation(s)
- Robert Spalthoff
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Swapnil Awasthi
- Dept. of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin 10117, Germany
| | - Stefanie Heilmann-Heimbach
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Bianca Besteher
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Christian Gaser
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Neurology, Jena University Hospital, Jena, Germany
| | - Stephan Ripke
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Dept. of Psychiatry and Psychotherapy, Charité - Universitätsmedizin, Berlin 10117, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life and Brain Center, University of Bonn, Bonn, Germany
| | - Igor Nenadić
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany; Department of Psychiatry and Psychotherapy, Phillips University Marburg/Marburg University Hospital UKGM, Marburg, Germany.
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19
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Chen Q, Ursini G, Romer AL, Knodt AR, Mezeivtch K, Xiao E, Pergola G, Blasi G, Straub RE, Callicott JH, Berman KF, Hariri AR, Bertolino A, Mattay VS, Weinberger DR. Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain 2019; 141:1218-1228. [PMID: 29415119 DOI: 10.1093/brain/awy004] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 11/21/2017] [Indexed: 01/01/2023] Open
Abstract
The use of polygenic risk scores has become a practical translational approach to investigating the complex genetic architecture of schizophrenia, but the link between polygenic risk scores and pathophysiological components of this disorder has been the subject of limited research. We investigated in healthy volunteers whether schizophrenia polygenic risk score predicts hippocampal activity during simple memory encoding, which has been proposed as a risk-associated intermediate phenotype of schizophrenia. We analysed the relationship between polygenic risk scores and hippocampal activity in a discovery sample of 191 unrelated healthy volunteers from the USA and in two independent replication samples of 76 and 137 healthy unrelated participants from Europe and the USA, respectively. Polygenic risk scores for each individual were calculated as the sum of the imputation probability of reference alleles weighted by the natural log of odds ratio from the recent schizophrenia genome-wide association study. We examined hippocampal activity during simple memory encoding of novel visual stimuli assessed using blood oxygen level-dependent functional MRI. Polygenic risk scores were significantly associated with hippocampal activity in the discovery sample [P = 0.016, family-wise error (FWE) corrected within Anatomical Automatic Labeling (AAL) bilateral hippocampal-parahippocampal mask] and in both replication samples (P = 0.033, FWE corrected within AAL right posterior hippocampal-parahippocampal mask in Bari sample, and P = 0.002 uncorrected in the Duke Neurogenetics Study sample). The relationship between polygenic risk scores and hippocampal activity was consistently negative, i.e. lower hippocampal activity in individuals with higher polygenic risk scores, consistent with previous studies reporting decreased hippocampal-parahippocampal activity during declarative memory tasks in patients with schizophrenia and in their healthy siblings. Polygenic risk scores accounted for more than 8% of variance in hippocampal activity during memory encoding in discovery sample. We conclude that polygenic risk scores derived from the most recent schizophrenia genome-wide association study predict significant variability in hippocampal activity during memory encoding in healthy participants. Our findings validate mnemonic hippocampal activity as a genetic risk associated intermediate phenotype of schizophrenia, indicating that the aggregate neurobiological effect of schizophrenia risk alleles converges on this pattern of neural activity.awy004media15749593779001.
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Affiliation(s)
- Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA
| | - Gianluca Ursini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA.,Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adrienne L Romer
- Laboratory of NeuroGenetics, Department of Psychology and Neurosicence, Duke University, Durham, NC, USA
| | - Annchen R Knodt
- Laboratory of NeuroGenetics, Department of Psychology and Neurosicence, Duke University, Durham, NC, USA
| | - Karleigh Mezeivtch
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA
| | - Ena Xiao
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA
| | - Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Bari, Italy
| | - Richard E Straub
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA
| | - Joseph H Callicott
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health Intramural Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Ahmad R Hariri
- Laboratory of NeuroGenetics, Department of Psychology and Neurosicence, Duke University, Durham, NC, USA
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari, Bari, Italy
| | - Venkata S Mattay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 855 North Wolfe Street, MD, USA.,Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD USA.,Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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20
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Dezhina Z, Ranlund S, Kyriakopoulos M, Williams SCR, Dima D. A systematic review of associations between functional MRI activity and polygenic risk for schizophrenia and bipolar disorder. Brain Imaging Behav 2019; 13:862-877. [PMID: 29748770 PMCID: PMC6538577 DOI: 10.1007/s11682-018-9879-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Genetic factors account for up to 80% of the liability for schizophrenia (SCZ) and bipolar disorder (BD). Genome-wide association studies have successfully identified several genes associated with increased risk for both disorders. This has allowed researchers to model the aggregate effect of genes associated with disease status and create a polygenic risk score (PGRS) for each individual. The interest in imaging genetics using PGRS has grown in recent years, with several studies now published. We have conducted a systematic review to examine the effects of PGRS of SCZ, BD and cross psychiatric disorders on brain function and connectivity using fMRI data. Results indicate that the effect of genetic load for SCZ and BD on brain function affects task-related recruitment, with frontal areas having a more prominent role, independent of task. Additionally, the results suggest that the polygenic architecture of psychotic disorders is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions. Future imaging genetics studies with large samples, especially population studies, would be uniquely informative in mapping the spatial distribution of the genetic risk to psychiatric disorders on brain processes during various cognitive tasks and may lead to the discovery of biological pathways that could be crucial in mediating the link between genetic factors and alterations in brain networks.
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Affiliation(s)
- Zalina Dezhina
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Siri Ranlund
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Marinos Kyriakopoulos
- National and Specialist Acorn Lodge Inpatient Children Unit, South London and Maudsley NHS Foundation Trust, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Steve C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Danai Dima
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- Department of Psychology, School of Arts and Social Sciences, City, University of London, 10 Northampton Square, London, EC1V 0HB, UK.
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21
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Xia H, Jahr FM, Kim NK, Xie L, Shabalin AA, Bryois J, Sweet DH, Kronfol MM, Palasuberniam P, McRae M, Riley BP, Sullivan PF, van den Oord EJ, McClay JL. Building a schizophrenia genetic network: transcription factor 4 regulates genes involved in neuronal development and schizophrenia risk. Hum Mol Genet 2019; 27:3246-3256. [PMID: 29905862 DOI: 10.1093/hmg/ddy222] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2018] [Accepted: 06/04/2018] [Indexed: 01/05/2023] Open
Abstract
The transcription factor 4 (TCF4) locus is a robust association finding with schizophrenia (SCZ), but little is known about the genes regulated by the encoded transcription factor. Therefore, we conducted chromatin immunoprecipitation sequencing (ChIP-seq) of TCF4 in neural-derived (SH-SY5Y) cells to identify genome-wide TCF4 binding sites, followed by data integration with SCZ association findings. We identified 11 322 TCF4 binding sites overlapping in two ChIP-seq experiments. These sites are significantly enriched for the TCF4 Ebox binding motif (>85% having ≥1 Ebox) and implicate a gene set enriched for genes downregulated in TCF4 small-interfering RNA (siRNA) knockdown experiments, indicating the validity of our findings. The TCF4 gene set was also enriched among (1) gene ontology categories such as axon/neuronal development, (2) genes preferentially expressed in brain, in particular pyramidal neurons of the somatosensory cortex and (3) genes downregulated in postmortem brain tissue from SCZ patients (odds ratio, OR = 2.8, permutation P < 4x10-5). Considering genomic alignments, TCF4 binding sites significantly overlapped those for neural DNA-binding proteins such as FOXP2 and the SCZ-associated EP300. TCF4 binding sites were modestly enriched among SCZ risk loci from the Psychiatric Genomic Consortium (OR = 1.56, P = 0.03). In total, 130 TCF4 binding sites occurred in 39 of the 108 regions published in 2014. Thirteen genes within the 108 loci had both a TCF4 binding site ±10kb and were differentially expressed in siRNA knockdown experiments of TCF4, suggesting direct TCF4 regulation. These findings confirm TCF4 as an important regulator of neural genes and point toward functional interactions with potential relevance for SCZ.
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Affiliation(s)
- Hanzhang Xia
- Center for Biomarker Research and Precision Medicine
| | - Fay M Jahr
- Department of Pharmacotherapy and Outcomes Science
| | - Nak-Kyeong Kim
- Department of Biostatistics, Virginia Commonwealth University, Richmond, VA, USA
| | - Linying Xie
- Center for Biomarker Research and Precision Medicine
| | - Andrey A Shabalin
- Department of Psychiatry, University of Utah, Salt Lake City, UT, USA
| | - Julien Bryois
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
| | - Douglas H Sweet
- Department of Pharmaceutics, Virginia Commonwealth University, Richmond, VA, USA
| | | | | | | | - Brien P Riley
- Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA, USA
| | - Patrick F Sullivan
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden.,Departments of Genetics and Psychiatry, University of North Carolina School of Medicine, Chapel Hill, NC, USA
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22
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Abstract
The prenatal period is increasingly considered as a crucial target for the primary prevention of neurodevelopmental and psychiatric disorders. Understanding their pathophysiological mechanisms remains a great challenge. Our review reveals new insights from prenatal brain development research, involving (epi)genetic research, neuroscience, recent imaging techniques, physical modeling, and computational simulation studies. Studies examining the effect of prenatal exposure to maternal distress on offspring brain development, using brain imaging techniques, reveal effects at birth and up into adulthood. Structural and functional changes are observed in several brain regions including the prefrontal, parietal, and temporal lobes, as well as the cerebellum, hippocampus, and amygdala. Furthermore, alterations are seen in functional connectivity of amygdalar-thalamus networks and in intrinsic brain networks, including default mode and attentional networks. The observed changes underlie offspring behavioral, cognitive, emotional development, and susceptibility to neurodevelopmental and psychiatric disorders. It is concluded that used brain measures have not yet been validated with regard to sensitivity, specificity, accuracy, or robustness in predicting neurodevelopmental and psychiatric disorders. Therefore, more prospective long-term longitudinal follow-up studies starting early in pregnancy should be carried out, in order to examine brain developmental measures as mediators in mediating the link between prenatal stress and offspring behavioral, cognitive, and emotional problems and susceptibility for disorders.
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23
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Xiao X, Zhang C, Grigoroiu-Serbanescu M, Wang L, Li L, Zhou D, Yuan TF, Wang C, Chang H, Wu Y, Li Y, Wu DD, Yao YG, Li M. The cAMP responsive element-binding (CREB)-1 gene increases risk of major psychiatric disorders. Mol Psychiatry 2018; 23:1957-1967. [PMID: 29158582 DOI: 10.1038/mp.2017.243] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2017] [Revised: 08/25/2017] [Accepted: 09/14/2017] [Indexed: 12/11/2022]
Abstract
Bipolar disorder (BPD), schizophrenia (SCZ) and unipolar major depressive disorder (MDD) are primary psychiatric disorders sharing substantial genetic risk factors. We previously reported that two single-nucleotide polymorphisms (SNPs) rs2709370 and rs6785 in the cAMP responsive element-binding (CREB)-1 gene (CREB1) were associated with the risk of BPD and abnormal hippocampal function in populations of European ancestry. In the present study, we further expanded our analyses of rs2709370 and rs6785 in multiple BPD, SCZ and MDD data sets, including the published Psychiatric Genomics Consortium (PGC) genome-wide association study, the samples used in our previous CREB1 study, and six additional cohorts (three new BPD samples, two new SCZ samples and one new MDD sample). Although the associations of both CREB1 SNPs with each illness were not replicated in the new cohorts (BPD analysis in 871 cases and 1089 controls (rs2709370, P=0.0611; rs6785, P=0.0544); SCZ analysis in 1273 cases and 1072 controls (rs2709370, P=0.230; rs6785, P=0.661); and MDD analysis in 129 cases and 100 controls (rs2709370, P=0.114; rs6785, P=0.188)), an overall meta-analysis of all included samples suggested that both SNPs were significantly associated with increased risk of BPD (11 105 cases and 51 331 controls; rs2709370, P=2.33 × 10-4; rs6785, P=6.33 × 10-5), SCZ (34 913 cases and 44 528 controls; rs2709370, P=3.96 × 10-5; rs6785, P=2.44 × 10-5) and MDD (9369 cases and 9619 controls; rs2709370, P=0.0144; rs6785, P=0.0314), with the same direction of allelic effects across diagnostic categories. We then examined the impact of diagnostic status on CREB1 mRNA expression using data obtained from independent brain tissue samples, and observed that the mRNA expression of CREB1 was significantly downregulated in psychiatric patients compared with healthy controls. The protein-protein interaction analyses showed that the protein encoded by CREB1 directly interacted with several risk genes of psychiatric disorders identified by GWAS. In conclusion, the current study suggests that CREB1 might be a common risk gene for major psychiatric disorders, and further investigations are necessary.
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Affiliation(s)
- X Xiao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China
| | - C Zhang
- Schizophrenia Program, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - M Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania.
| | - L Wang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China
| | - L Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China
| | - D Zhou
- Ningbo Kangning Hospital, Ningbo, China
| | - T-F Yuan
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - C Wang
- Department of Pharmacology, and Provincial Key Laboratory of Pathophysiology in Ningbo University School of Medicine, Ningbo, China
| | - H Chang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China
| | - Y Wu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China
| | - Y Li
- Laboratory for Conservation and Utilization of Bio-Resource, Yunnan University, Kunming, China
| | - D-D Wu
- State Key Laboratory of Genetic Resources and Evolution, Chinese Academy of Sciences, Kunming, China
| | - Y-G Yao
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - M Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province, Kunming Institute of Zoology, Kunming, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.
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24
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Bogdan R, Baranger DAA, Agrawal A. Polygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences. Annu Rev Clin Psychol 2018; 14:119-157. [PMID: 29579395 PMCID: PMC7772939 DOI: 10.1146/annurev-clinpsy-050817-084847] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Genomewide association studies (GWASs) across psychiatric phenotypes have shown that common genetic variants generally confer risk with small effect sizes (odds ratio < 1.1) that additively contribute to polygenic risk. Summary statistics derived from large discovery GWASs can be used to generate polygenic risk scores (PRS) in independent, target data sets to examine correlates of polygenic disorder liability (e.g., does genetic liability to schizophrenia predict cognition?). The intuitive appeal and generalizability of PRS have led to their widespread use and new insights into mechanisms of polygenic liability. However, when currently applied across traits they account for small amounts of variance (<3%), are relatively uninformative for clinical treatment, and, in isolation, provide no insight into molecular mechanisms. Larger GWASs are needed to increase the precision of PRS, and novel approaches integrating various data sources (e.g., multitrait analysis of GWASs) may improve the utility of current PRS.
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Affiliation(s)
- Ryan Bogdan
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - David A A Baranger
- BRAINLab, Department of Psychological and Brain Sciences, and Division of Biology and Biomedical Sciences, Washington University in St. Louis, St. Louis, Missouri 63110, USA;
| | - Arpana Agrawal
- Department of Psychiatry, Washington University in St. Louis School of Medicine, St. Louis, Missouri 63110, USA
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25
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Piel JH, Lett TA, Wackerhagen C, Plichta MM, Mohnke S, Grimm O, Romanczuk-Seiferth N, Degenhardt F, Tost H, Witt S, Nöthen M, Rietschel M, Heinz A, Meyer-Lindenberg A, Walter H, Erk S. The effect of 5-HTTLPR and a serotonergic multi-marker score on amygdala, prefrontal and anterior cingulate cortex reactivity and habituation in a large, healthy fMRI cohort. Eur Neuropsychopharmacol 2018; 28:415-427. [PMID: 29358097 DOI: 10.1016/j.euroneuro.2017.12.014] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Revised: 12/06/2017] [Accepted: 12/08/2017] [Indexed: 12/16/2022]
Abstract
Major depressive disorder (MDD) is characterized by low mood for at least two weeks. Impaired emotion regulation has been suggested to be the consequence of dysfunctional serotonergic regulation of limbic and prefrontal regions, especially the amygdala, the anterior cingulate cortex (ACC) and the prefrontal cortex (PFC). The impact of genetic variation on brain function can be investigated with intermediate phenotypes. A suggested intermediate phenotype of MDD is emotion recognition: The 5-HTTLPR polymorphism of SLC6A4 as well as other serotonergic genes have been associated with amygdala and prefrontal function during emotion recognition. Previously, it has been suggested that habituation is a more reliable index of emotion recognition than functional activation. We examined the relationship of genes involved in serotonergic signaling with amygdala as well as prefrontal functional activation and habituation during an emotion recognition task in 171 healthy subjects. While effects of 5-HTTLPR and of a serotonergic multi-marker score (5-HTTLPR, TPH1(rs1800532), TPH2(rs4570625), HTR1A(rs6295) and HTR2A(rs6311)) on amygdala activation did not withstand correction for multiple regions of interest, we observed a strong correlation of the multi-marker score and habituation in the amygdala, DLPFC, and ACC. We replicated a well-studied intermediate phenotype for association with 5-HTTLPR and provided additional evidence for polygenic involvement. Furthermore, we showed that task habituation may be influenced by genetic variation in serotonergic signaling, particularly by a serotonergic multi-marker score. We provided preliminary evidence that PFC activation is an important intermediate phenotype of MDD. Future studies are needed to corroborate the results in larger samples.
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Affiliation(s)
- J H Piel
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - T A Lett
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - C Wackerhagen
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - M M Plichta
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe-University, Frankfurt, Germany
| | - S Mohnke
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - O Grimm
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe-University, Frankfurt, Germany
| | - N Romanczuk-Seiferth
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - F Degenhardt
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany; Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - H Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - S Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - M Nöthen
- Department of Genomics, Life & Brain Center, University of Bonn, Bonn, Germany; Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - M Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - A Heinz
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - A Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany
| | - H Walter
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany
| | - S Erk
- Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany; Division of Mind and Brain Research, Charité Universitätsmedizin Berlin, Campus Mitte, Berlin, Germany.
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26
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Unschuld PG. Novel Translational Research Methodology and the Prospect to a Better Understanding of Neurodegenerative Disease. NEURODEGENER DIS 2018; 18:1-4. [PMID: 29339665 DOI: 10.1159/000486565] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Affiliation(s)
- Paul G Unschuld
- Hospital for Psychogeriatric Medicine, Psychiatric University Hospital Zurich, Zurich, Switzerland.,Institute for Regenerative Medicine (IREM), University of Zurich, Zurich, Switzerland
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27
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Arslan A. Mapping the Schizophrenia Genes by Neuroimaging: The Opportunities and the Challenges. Int J Mol Sci 2018; 19:ijms19010219. [PMID: 29324666 PMCID: PMC5796168 DOI: 10.3390/ijms19010219] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2017] [Revised: 01/05/2018] [Accepted: 01/07/2018] [Indexed: 12/18/2022] Open
Abstract
Schizophrenia (SZ) is a heritable brain disease originating from a complex interaction of genetic and environmental factors. The genes underpinning the neurobiology of SZ are largely unknown but recent data suggest strong evidence for genetic variations, such as single nucleotide polymorphisms, making the brain vulnerable to the risk of SZ. Structural and functional brain mapping of these genetic variations are essential for the development of agents and tools for better diagnosis, treatment and prevention of SZ. Addressing this, neuroimaging methods in combination with genetic analysis have been increasingly used for almost 20 years. So-called imaging genetics, the opportunities of this approach along with its limitations for SZ research will be outlined in this invited paper. While the problems such as reproducibility, genetic effect size, specificity and sensitivity exist, opportunities such as multivariate analysis, development of multisite consortia for large-scale data collection, emergence of non-candidate gene (hypothesis-free) approach of neuroimaging genetics are likely to contribute to a rapid progress for gene discovery besides to gene validation studies that are related to SZ.
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Affiliation(s)
- Ayla Arslan
- Genetics and Bioengineering Program, Faculty of Engineering and Natural Sciences, International University of Sarajevo, Hrasnica cesta, 15 Ilidza, Sarajevo 71210, Bosnia and Herzegovina.
- Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul 34662, Turkey.
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28
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Papiol S, Popovic D, Keeser D, Hasan A, Schneider-Axmann T, Degenhardt F, Rossner MJ, Bickeböller H, Schmitt A, Falkai P, Malchow B. Polygenic risk has an impact on the structural plasticity of hippocampal subfields during aerobic exercise combined with cognitive remediation in multi-episode schizophrenia. Transl Psychiatry 2017; 7:e1159. [PMID: 28654095 PMCID: PMC5537649 DOI: 10.1038/tp.2017.131] [Citation(s) in RCA: 49] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2017] [Revised: 05/12/2017] [Accepted: 05/17/2017] [Indexed: 02/06/2023] Open
Abstract
Preliminary studies suggest that, besides improving cognition, aerobic exercise might increase hippocampal volume in schizophrenia patients; however, results are not consistent. Individual mechanisms of volume changes are unknown but might be connected to the load of risk genes. Genome-wide association studies have uncovered the polygenic architecture of schizophrenia. The secondary analysis presented here aimed to determine the modulatory role of schizophrenia polygenic risk scores (PRSs) on volume changes in the total hippocampus and cornu ammonis (CA) 1, CA2/3, CA4/dentate gyrus (DG) and subiculum over time. We studied 20 multi-episode schizophrenia patients and 23 healthy controls who performed aerobic exercise (endurance training) combined with cognitive remediation for 3 months and 21 multi-episode schizophrenia patients allocated to a control intervention (table soccer) combined with cognitive remediation. Magnetic resonance imaging-based assessments were performed at baseline and after 3 months with FreeSurfer. No effects of PRSs were found on total hippocampal volume change. Subfield analyses showed that the volume changes between baseline and 3 months in the left CA4/DG were significantly influenced by PRSs in schizophrenia patients performing aerobic exercise. A larger genetic risk burden was associated with a less pronounced volume increase or a decrease in volume over the course of the exercise intervention. Results of exploratory enrichment analyses reinforced the notion of genetic risk factors modulating biological processes tightly related to synaptic ion channel activity, calcium signaling, glutamate signaling and regulation of cell morphogenesis. We hypothesize that a high polygenic risk may negatively influence neuroplasticity in CA4/DG during aerobic exercise in schizophrenia.
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Affiliation(s)
- S Papiol
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Institute of Psychiatric Phenomics and Genomics (IPPG), Medical Center of the University of Munich, Munich, Germany
| | - D Popovic
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - D Keeser
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Institute of Clinical Radiology, Ludwig Maximilian University Munich, Munich, Germany
| | - A Hasan
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - T Schneider-Axmann
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - F Degenhardt
- Institute of Human Genetics, University of Bonn, Bonn, Germany
| | - M J Rossner
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - H Bickeböller
- Department of Genetic Epidemiology, University Medical Center, Georg-August-Universität Göttingen, Göttingen, Germany
| | - A Schmitt
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
- Laboratory of Neuroscience, Institute of Psychiatry, University of Sao Paulo, Sao Paulo, Brazil
| | - P Falkai
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
| | - B Malchow
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University Munich, Munich, Germany
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