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Varathan A, Senthooran S, Jeyananthan P. Role of different omics data in the diagnosis of schizophrenia disorder: A machine learning study. Schizophr Res 2024; 271:38-46. [PMID: 39003990 DOI: 10.1016/j.schres.2024.07.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 06/19/2024] [Accepted: 07/07/2024] [Indexed: 07/16/2024]
Abstract
Schizophrenia is a serious mental disorder that affects millions of people worldwide. This disorder slowly disintegrates thinking ability and changes behaviours of patients. These patients will show some psychotic symptoms such as hallucinations, delusions, thought disorder and movement disorder. These symptoms are in common with some other psychiatric disorders such as bipolar disorder, major depressive disorder and mood spectrum disorder. As patients would require immediate treatment, an on-time diagnosis is critical. This study explores the use of omics data in diagnosis of schizophrenia. Transcriptome, miRNA and epigenome data are used in diagnosis of patients with schizophrenia with the aid of machine learning algorithms. As the data is in high dimension, mutual information and feature importance are independently used for selecting relevant features for the study. Selected sets of features (biomarkers) are individually used with different machine learning algorithms and their performances are compared to select the best-performing model. This study shows that the top 140 miRNA features selected using mutual information along with support vector machines give the highest accuracy (0.86 ± 0.07) in the diagnosis of schizophrenia. All reported accuracies are validated using 5-fold cross validation. They are further validated using leave one out cross validation and the accuracies are reported in the supplementary material.
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Affiliation(s)
- Aarthy Varathan
- Department of Computer Engineering, Faculty of Engineering, University of Jaffna, Sri Lanka.
| | | | - Pratheeba Jeyananthan
- Department of Computer Engineering, Faculty of Engineering, University of Jaffna, Sri Lanka.
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Zhang F, Li Y, Liu L, Liu Y, Wang P, Biswal BB. Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder. Psychiatry Clin Neurosci 2024; 78:291-299. [PMID: 38444215 PMCID: PMC11469573 DOI: 10.1111/pcn.13650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/07/2024]
Abstract
AIM The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD. METHODS We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways. RESULTS Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD. CONCLUSION The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.
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Affiliation(s)
- Fanyu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Lin Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yefen Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Bharat B. Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology. University of Electronic Science and Technology of China, Chengdu 611731, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA
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Fiorito AM, Blasi G, Brunelin J, Chowdury A, Diwadkar VA, Goghari VM, Gur RC, Kwon JS, Quarto T, Rolland B, Spilka MJ, Wolf DH, Yun JY, Fakra E, Sescousse G. Blunted brain responses to neutral faces in healthy first-degree relatives of patients with schizophrenia: an image-based fMRI meta-analysis. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:38. [PMID: 38503766 PMCID: PMC10951276 DOI: 10.1038/s41537-024-00452-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 02/16/2024] [Indexed: 03/21/2024]
Abstract
Schizophrenia is characterized by the misattribution of emotional significance to neutral faces, accompanied by overactivations of the limbic system. To understand the disorder's genetic and environmental contributors, investigating healthy first-degree relatives is crucial. However, inconsistent findings exist regarding their ability to recognize neutral faces, with limited research exploring the cerebral correlates of neutral face processing in this population. Thus, we here investigated brain responses to neutral face processing in healthy first-degree relatives through an image-based meta-analysis of functional magnetic resonance imaging studies. We included unthresholded group-level T-maps from 5 studies comprising a total of 120 first-degree relatives and 150 healthy controls. In sensitivity analyses, we ran a combined image- and coordinate-based meta-analysis including 7 studies (157 first-degree relatives, 207 healthy controls) aiming at testing the robustness of the results in a larger sample of studies. Our findings revealed a pattern of decreased brain responses to neutral faces in relatives compared with healthy controls, particularly in limbic areas such as the bilateral amygdala, hippocampus, and insula. The same pattern was observed in sensitivity analyses. These results contrast with the overactivations observed in patients, potentially suggesting that this trait could serve as a protective factor in healthy relatives. However, further research is necessary to test this hypothesis.
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Affiliation(s)
- Anna M Fiorito
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France.
- Centre Hospitalier Le Vinatier, Bron, France.
| | - 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
| | - Jérôme Brunelin
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
| | - Asadur Chowdury
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Vaibhav A Diwadkar
- Department of Psychiatry & Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Vina M Goghari
- Department of Psychological Clinical Science, University of Toronto, Toronto, ON, Canada
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Tiziana Quarto
- Department of Humanities, University of Foggia, Foggia, Italy
| | - Benjamin Rolland
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
| | | | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Je-Yeon Yun
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
| | - Eric Fakra
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Department of Psychiatry, University Hospital of Saint-Etienne, Saint-Etienne, France
| | - Guillaume Sescousse
- Université Claude Bernard Lyon 1, CNRS, INSERM, Centre de Recherche en Neurosciences de Lyon CRNL U1028 UMR5292, PSYR2, 69500, Bron, France
- Centre Hospitalier Le Vinatier, Bron, France
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Zhu Y, Gong W, Lu X, Wang H. Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223819] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.
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Almodóvar-Payá C, Guardiola-Ripoll M, Giralt-López M, Gallego C, Salgado-Pineda P, Miret S, Salvador R, Muñoz MJ, Lázaro L, Guerrero-Pedraza A, Parellada M, Carrión MI, Cuesta MJ, Maristany T, Sarró S, Fañanás L, Callado LF, Arias B, Pomarol-Clotet E, Fatjó-Vilas M. NRN1 Gene as a Potential Marker of Early-Onset Schizophrenia: Evidence from Genetic and Neuroimaging Approaches. Int J Mol Sci 2022; 23:ijms23137456. [PMID: 35806464 PMCID: PMC9267632 DOI: 10.3390/ijms23137456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 06/27/2022] [Accepted: 06/28/2022] [Indexed: 12/10/2022] Open
Abstract
Included in the neurotrophins family, the Neuritin 1 gene (NRN1) has emerged as an attractive candidate gene for schizophrenia (SZ) since it has been associated with the risk for the disorder and general cognitive performance. In this work, we aimed to further investigate the association of NRN1 with SZ by exploring its role on age at onset and its brain activity correlates. First, we developed two genetic association analyses using a family-based sample (80 early-onset (EO) trios (offspring onset ≤ 18 years) and 71 adult-onset (AO) trios) and an independent case–control sample (120 healthy subjects (HS), 87 EO and 138 AO patients). Second, we explored the effect of NRN1 on brain activity during a working memory task (N-back task; 39 HS, 39 EO and 39 AO; matched by age, sex and estimated IQ). Different haplotypes encompassing the same three Single Nucleotide Polymorphisms(SNPs, rs3763180–rs10484320–rs4960155) were associated with EO in the two samples (GCT, TCC and GTT). Besides, the GTT haplotype was associated with worse N-back task performance in EO and was linked to an inefficient dorsolateral prefrontal cortex activity in subjects with EO compared to HS. Our results show convergent evidence on the NRN1 association with EO both from genetic and neuroimaging approaches, highlighting the role of neurotrophins in the pathophysiology of SZ.
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Affiliation(s)
- Carmen Almodóvar-Payá
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
| | - Maria Guardiola-Ripoll
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
| | - Maria Giralt-López
- Departament de Psiquiatria, Hospital Universitari Germans Trias i Pujol (HUGTP), 08916 Badalona, Barcelona, Spain;
- Departament de Psiquiatria i Medicina Legal, Universitat Autònoma de Barcelona (UAB), 08193 Bellaterra, Barcelona, Spain
| | - Carme Gallego
- Department of Cell Biology, Molecular Biology Institute of Barcelona (IBMB-CSIC), 08028 Barcelona, Barcelona, Spain;
| | - Pilar Salgado-Pineda
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
| | - Salvador Miret
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Centre de Salut Mental d’Adults de Lleida, Servei de Psiquiatria, Salut Mental i Addiccions, Hospital Universitari Santa Maria de Lleida, 25198 Lleida, Lleida, Spain
- Institut de Recerca Biomèdica (IRB), 25198 Lleida, Lleida, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
| | - María J. Muñoz
- Complex Assistencial en Salut Mental Benito Menni, 08830 Sant Boi de Llobregat, Barcelona, Spain;
| | - Luisa Lázaro
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Department of Child and Adolescent Psychiatry and Psychology, Institute of Neurosciences, Hospital Clinic de Barcelona, 08036 Barcelona, Barcelona, Spain
- Departament de Medicina, Universitat de Barcelona (UB), 08036 Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), 08036 Barcelona, Barcelona, Spain
| | - Amalia Guerrero-Pedraza
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Complex Assistencial en Salut Mental Benito Menni, 08830 Sant Boi de Llobregat, Barcelona, Spain;
| | - Mara Parellada
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Servicio de Psiquiatría del Niño y del Adolescente, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Madrid, Spain
- Instituto de Investigación Sanitaria del Hospital Gregorio Marañón (IiSGM), 28007 Madrid, Madrid, Spain
- Departamento de Psiquiatría, Facultad de Medicina, Universidad Complutense, 28040 Madrid, Madrid, Spain
| | | | - Manuel J. Cuesta
- Servicio de Psiquiatría, Hospital Universitario de Navarra, 31008 Pamplona, Navarra, Spain;
- Instituto de Investigación Sanitaria de Navarra (IdiSNA), 31008 Pamplona, Navarra, Spain
| | - Teresa Maristany
- Departament de Diagnòstic per la Imatge, Hospital Sant Joan de Déu Fundació de Recerca, 08950 Esplugues de Llobregat, Barcelona, Spain;
| | - Salvador Sarró
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
| | - Lourdes Fañanás
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Departament de Biologia Evolutiva, Ecología i Ciències Ambientals, Universitat de Barcelona (UB), 08028 Barcelona, Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), 08028 Barcelona, Barcelona, Spain
| | - Luis F. Callado
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Department of Pharmacology, University of the Basque Country, UPV/EHU, 48940 Leioa, Bizkaia, Spain
- Biocruces Bizkaia Health Research Institute, 48903 Barakaldo, Bizkaia, Spain
| | - Bárbara Arias
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Departament de Biologia Evolutiva, Ecología i Ciències Ambientals, Universitat de Barcelona (UB), 08028 Barcelona, Barcelona, Spain
- Institut de Biomedicina de la Universitat de Barcelona (IBUB), 08028 Barcelona, Barcelona, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Correspondence: (E.P.-C.); (M.F.-V.)
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, 08830 Sant Boi de Llobregat, Barcelona, Spain; (C.A.-P.); (M.G.-R.); (P.S.-P.); (R.S.); (A.G.-P.); (S.S.)
- Instituto de Salud Carlos III, Biomedical Research Network in Mental Health (CIBERSAM), 28029 Madrid, Madrid, Spain; (S.M.); (L.L.); (M.P.); (L.F.); (L.F.C.); (B.A.)
- Departament de Biologia Evolutiva, Ecología i Ciències Ambientals, Universitat de Barcelona (UB), 08028 Barcelona, Barcelona, Spain
- Correspondence: (E.P.-C.); (M.F.-V.)
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Sharaev M, Malashenkova I, Maslennikova A, Zakharova N, Bernstein A, Burnaev E, Mamedova G, Krynskiy S, Ogurtsov D, Kondrateva E, Druzhinina P, Zubrikhina M, Arkhipov A, Strelets V, Ushakov V. Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review). Sovrem Tekhnologii Med 2022; 14:53-75. [PMID: 37181835 PMCID: PMC10171060 DOI: 10.17691/stm2022.14.5.06] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Indexed: 05/16/2023] Open
Abstract
Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.
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Affiliation(s)
- M.G. Sharaev
- Senior Researcher; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia; Department Senior Researcher; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
- Corresponding author: Maksim G. Sharaev, e-mail:
| | - I.K. Malashenkova
- Head of the Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia; Senior Researcher, Laboratory of Clinical Immunology; Federal Research and Clinical Center of Physical-Chemical Medicine, Federal Medical Biological Agency of Russia, 1A Malaya Pirogovskaya St., Moscow, 119435, Russia
| | - A.V. Maslennikova
- Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - N.V. Zakharova
- Head of the Laboratory for Fundamental Research Methods, Research Clinical Center of Neuropsychiatry; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
| | - A.V. Bernstein
- Professor, Professor of the Center of Applied Artificial Intelligence; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - E.V. Burnaev
- Associate Professor, Professor of the Center of Applied Artificial Intelligence; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - G.S. Mamedova
- Junior Researcher, Laboratory for Fundamental Research Methods, Research Clinical Center of Neuropsychiatry; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia
| | - S.A. Krynskiy
- Researcher, Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia
| | - D.P. Ogurtsov
- Researcher, Laboratory of Molecular Immunology and Virology; National Research Center “Kurchatov Institute”, 1 Akademika Kurchatova Square, Moscow, 123182, Russia
| | - E.A. Kondrateva
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - P.V. Druzhinina
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - M.O. Zubrikhina
- PhD Student; Skolkovo Institute of Science and Technology (Skoltech), Territory of Skolkovo Innovation Center, Bldg 1, 30 Bolshoy Boulevard, Moscow, 121205, Russia
| | - A.Yu. Arkhipov
- Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - V.B. Strelets
- Chief Researcher, Laboratory of Human Higher Nervous Activity; Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 5A Butlerova St., Moscow, 117485, Russia
| | - V.L. Ushakov
- Associate Professor, Chief Researcher, Institute for Advanced Brain Research; Lomonosov Moscow State University, 27/1 Lomonosov Avenue, Moscow, 119192, Russia; Head of the Department; N.A. Alekseyev Psychiatric Clinical Hospital No.1, 2 Zagorodnoye Shosse, Moscow, 117152, Russia; Senior Researcher; National Research Nuclear University MEPhI, 31 Kashirskoye Shosse, Moscow, 115409, Russia
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Hwang H, Cho G, Jin MJ, Ryoo JH, Choi Y, Lee SH. A knowledge-based multivariate statistical method for examining gene-brain-behavioral/cognitive relationships: Imaging genetics generalized structured component analysis. PLoS One 2021; 16:e0247592. [PMID: 33690643 PMCID: PMC7946325 DOI: 10.1371/journal.pone.0247592] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/10/2021] [Indexed: 12/30/2022] Open
Abstract
With advances in neuroimaging and genetics, imaging genetics is a naturally emerging field that combines genetic and neuroimaging data with behavioral or cognitive outcomes to examine genetic influence on altered brain functions associated with behavioral or cognitive variation. We propose a statistical approach, termed imaging genetics generalized structured component analysis (IG-GSCA), which allows researchers to investigate such gene-brain-behavior/cognitive associations, taking into account well-documented biological characteristics (e.g., genetic pathways, gene-environment interactions, etc.) and methodological complexities (e.g., multicollinearity) in imaging genetic studies. We begin by describing the conceptual and technical underpinnings of IG-GSCA. We then apply the approach for investigating how nine depression-related genes and their interactions with an environmental variable (experience of potentially traumatic events) influence the thickness variations of 53 brain regions, which in turn affect depression severity in a sample of Korean participants. Our analysis shows that a dopamine receptor gene and an interaction between a serotonin transporter gene and the environment variable have statistically significant effects on a few brain regions' variations that have statistically significant negative impacts on depression severity. These relationships are largely supported by previous studies. We also conduct a simulation study to safeguard whether IG-GSCA can recover parameters as expected in a similar situation.
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Affiliation(s)
- Heungsun Hwang
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Gyeongcheol Cho
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Min Jin Jin
- Institute of Liberal Education, Kongju National University, Gongju, Korea
| | - Ji Hoon Ryoo
- Department of Education, Yonsei University, Seoul, Korea
| | - Younyoung Choi
- Department of Counseling Psychology, Hanyang Cyber University, Seoul, Korea
| | - Seung Hwan Lee
- Department of Psychiatry, Inje University Ilsan-Paik Hospital and Inje University, Goyang, Korea
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8
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Antonucci LA, Pergola G, Passiatore R, Taurisano P, Quarto T, Dispoto E, Rampino A, Bertolino A, Cassibba R, Blasi G. The interaction between OXTR rs2268493 and perceived maternal care is associated with amygdala-dorsolateral prefrontal effective connectivity during explicit emotion processing. Eur Arch Psychiatry Clin Neurosci 2020; 270:553-565. [PMID: 31471679 DOI: 10.1007/s00406-019-01062-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 08/23/2019] [Indexed: 12/15/2022]
Abstract
Previous studies have indicated a link between socio-emotional processing and the oxytocin receptor. In this regard, a single nucleotide polymorphism in the oxytocin receptor coding gene (OXTR rs2268493) has been linked with lower social functioning, increased risk for autism spectrum disorders (ASDs) and with post-mortem OXTR mRNA expression levels. Indeed, the levels of expression of OXTR in brain regions involved in emotion processing are also associated with maternal care. Furthermore, maternal care has been associated with emotional correlates. Taken together, these previous findings suggest a possible combined effect of rs2268493 and maternal care on emotion-related brain phenotypes. A crucial biological mechanism subtending emotional processing is the amygdala-dorsolateral prefrontal cortex (DLPFC) functional connection. On this basis, our aim was to investigate the interaction between rs2268493 and maternal care on amygdala-DLPFC effective connectivity during emotional evaluation. We characterized through dynamic causal modeling (DCM) patterns of amygdala-DLPFC effective connectivity during explicit emotion processing in healthy controls (HC), profiled based on maternal care and rs2268493 genotype. In the whole sample, right top-down DLPFC-to-amygdala pattern was the most likely directional model of effective connectivity. This pattern of connectivity was the most likely for all rs2268493/maternal care subgroups, except for thymine homozygous (TT)/low maternal care individuals. Here, a right bottom-up amygdala-to-DLPFC was the most likely directional model. These results suggest a gene by environment interaction mediated by the oxytocin receptor on biological phenotypes relevant to emotion processing.
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Affiliation(s)
- Linda A Antonucci
- Section for Neurodiagnostic Applications, Department of Psychiatry and Psychotherapy, Ludwig-Maximilians Universität, 80336, Munich, Germany.,Department of Educational Science, Psychology and Communication Science, University of Bari "Aldo Moro", 70121, Bari, Italy.,Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy
| | - Giulio Pergola
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy
| | - Roberta Passiatore
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy
| | - Paolo Taurisano
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy.,IRCCS "Casa Sollievo della Sofferenza", San Giovanni Rotondo, 71013, Foggia, Italy
| | - Tiziana Quarto
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy
| | - Eleonora Dispoto
- Department of Educational Science, Psychology and Communication Science, University of Bari "Aldo Moro", 70121, Bari, Italy
| | - Antonio Rampino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy.,Psychiatry Unit, Bari University Hospital, 70124, Bari, Italy
| | - Alessandro Bertolino
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy.,Psychiatry Unit, Bari University Hospital, 70124, Bari, Italy
| | - Rosalinda Cassibba
- Department of Educational Science, Psychology and Communication Science, University of Bari "Aldo Moro", 70121, Bari, Italy
| | - Giuseppe Blasi
- Department of Basic Medical Science, Neuroscience and Sense Organs, University of Bari "Aldo Moro", Piazza Giulio Cesare, 11, 70124, Bari, Italy. .,Psychiatry Unit, Bari University Hospital, 70124, Bari, Italy.
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9
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Pergola G, Di Carlo P, Jaffe AE, Papalino M, Chen Q, Hyde TM, Kleinman JE, Shin JH, Rampino A, Blasi G, Weinberger DR, Bertolino A. Prefrontal Coexpression of Schizophrenia Risk Genes Is Associated With Treatment Response in Patients. Biol Psychiatry 2019; 86:45-55. [PMID: 31126695 DOI: 10.1016/j.biopsych.2019.03.981] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Revised: 03/13/2019] [Accepted: 03/14/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND Gene coexpression networks are relevant to functional and clinical translation of schizophrenia risk genes. We hypothesized that schizophrenia risk genes converge into coexpression pathways that may be associated with gene regulation mechanisms and with response to treatment in patients with schizophrenia. METHODS We identified gene coexpression networks in two prefrontal cortex postmortem RNA sequencing datasets (n = 688) and replicated them in four more datasets (n = 1295). We identified and replicated (p values < .001) a single module enriched for schizophrenia risk loci (13 risk genes in 10 loci). In silico screening of potential regulators of the schizophrenia risk module via bioinformatic analyses identified two transcription factors and three microRNAs associated with the risk module. To translate postmortem information into clinical phenotypes, we identified polymorphisms predicting coexpression and combined them to obtain an index approximating module coexpression (Polygenic Coexpression Index [PCI]). RESULTS The PCI-coexpression association was successfully replicated in two independent brain transcriptome datasets (n = 131; p values < .05). Finally, we tested the association between the PCI and short-term treatment response in two independent samples of patients with schizophrenia treated with olanzapine (n = 167). The PCI was associated with treatment response in the positive symptom domain in both clinical cohorts (p values < .05). CONCLUSIONS In summary, our findings in 1983 samples of human postmortem prefrontal cortex show that coexpression of a set of genes enriched for schizophrenia risk genes is relevant to treatment response. This coexpression pathway may be coregulated by transcription factors and microRNA associated with it.
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Affiliation(s)
- Giulio Pergola
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland.
| | - Pasquale Di Carlo
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland
| | - Marco Papalino
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Qiang Chen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joel E Kleinman
- 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
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland
| | - Antonio Rampino
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Giuseppe Blasi
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, Maryland; Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Alessandro Bertolino
- Group of Psychiatric Neuroscience, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari Aldo Moro, Bari, Italy; Azienda Ospedaliero-Universitaria Consorziale Policlinico, Bari, Italy.
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10
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Li X, Wang A, Xu J, Sun Z, Xia J, Wang P, Wang B, Zhang M, Tian J. Reduced Dynamic Interactions Within Intrinsic Functional Brain Networks in Early Blind Patients. Front Neurosci 2019; 13:268. [PMID: 30983956 PMCID: PMC6448007 DOI: 10.3389/fnins.2019.00268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 03/07/2019] [Indexed: 11/16/2022] Open
Abstract
Neuroimaging studies in early blind (EB) patients have shown altered connections or brain networks. However, it remains unclear how the causal relationships are disrupted within intrinsic brain networks. In our study, we used spectral dynamic causal modeling (DCM) to estimate the causal interactions using resting-state data in a group of 20 EB patients and 20 healthy controls (HC). Coupling parameters in specific regions were estimated, including the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), and inferior parietal lobule (IPC) in the default mode network (DMN); dorsal anterior cingulate cortex (dACC) and bilateral anterior insulae (AI) in the salience network (SN), and bilateral frontal eye fields (FEF) and superior parietal lobes (SPL) within the dorsal attention network (DAN). Statistical analyses found that all endogenous connections and the connections from the mPFC to bilateral IPCs in EB patients were significantly reduced within the DMN, and the effective connectivity from the PCC and lIPC to the mPFC, and from the mPFC to the PCC were enhanced. For the SN, all significant connections in EB patients were significantly decreased, except the intrinsic right AI connections. Within the DAN, more significant effective connections were observed to be reduced between the EB and HC groups, while only the connections from the right SPL to the left SPL and the intrinsic connection in the left SPL were significantly enhanced. Furthermore, discovery of more decreased effective connections in the EB subjects suggested that the disrupted causal interactions between specific regions are responsive to the compensatory brain plasticity in early deprivation.
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Affiliation(s)
- Xianglin Li
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Ailing Wang
- Department of Clinical Laboratory, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Junhai Xu
- Tianjin Key Laboratory of Cognitive Computing and Application, School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, Tianjin, China
| | - Zhenbo Sun
- Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Jikai Xia
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Peiyuan Wang
- Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Bin Wang
- Medical Imaging Research Institute, Binzhou Medical University, Yantai, China
| | - Ming Zhang
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Jie Tian
- Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.,School of Life Sciences and Technology, Xidian University, Xi'an, China
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11
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Alloza C, Cox SR, Blesa Cábez M, Redmond P, Whalley HC, Ritchie SJ, Muñoz Maniega S, Valdés Hernández MDC, Tucker-Drob EM, Lawrie SM, Wardlaw JM, Deary IJ, Bastin ME. Polygenic risk score for schizophrenia and structural brain connectivity in older age: A longitudinal connectome and tractography study. Neuroimage 2018; 183:884-896. [PMID: 30179718 PMCID: PMC6215331 DOI: 10.1016/j.neuroimage.2018.08.075] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 08/28/2018] [Accepted: 08/31/2018] [Indexed: 12/14/2022] Open
Abstract
Higher polygenic risk score for schizophrenia (szPGRS) has been associated with lower cognitive function and might be a predictor of decline in brain structure in apparently healthy populations. Age-related declines in structural brain connectivity-measured using white matter diffusion MRI -are evident from cross-sectional data. Yet, it remains unclear how graph theoretical metrics of the structural connectome change over time, and whether szPGRS is associated with differences in ageing-related changes in human brain connectivity. Here, we studied a large, relatively healthy, same-year-of-birth, older age cohort over a period of 3 years (age ∼ 73 years, N = 731; age ∼76 years, N = 488). From their brain scans we derived tract-averaged fractional anisotropy (FA) and mean diffusivity (MD), and network topology properties. We investigated the cross-sectional and longitudinal associations between these structural brain variables and szPGRS. Higher szPGRS showed significant associations with longitudinal increases in MD in the splenium (β = 0.132, pFDR = 0.040), arcuate (β = 0.291, pFDR = 0.040), anterior thalamic radiations (β = 0.215, pFDR = 0.040) and cingulum (β = 0.165, pFDR = 0.040). Significant declines over time were observed in graph theory metrics for FA-weighted networks, such as mean edge weight (β = -0.039, pFDR = 0.048) and strength (β = -0.027, pFDR = 0.048). No significant associations were found between szPGRS and graph theory metrics. These results are consistent with the hypothesis that szPGRS confers risk for ageing-related degradation of some aspects of structural connectivity.
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Affiliation(s)
- C Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK.
| | - S R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, UK
| | - P Redmond
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - H C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - S J Ritchie
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - S Muñoz Maniega
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - M Del C Valdés Hernández
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - E M Tucker-Drob
- Department of Psychology, University of Texas, Austin, TX, USA
| | - S M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, UK
| | - J M Wardlaw
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - I J Deary
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
| | - M E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, University of Edinburgh, Edinburgh, UK; Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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12
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Dzafic I, Burianová H, Periyasamy S, Mowry B. Association between schizophrenia polygenic risk and neural correlates of emotion perception. Psychiatry Res Neuroimaging 2018; 276:33-40. [PMID: 29723776 DOI: 10.1016/j.pscychresns.2018.04.005] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2017] [Revised: 02/26/2018] [Accepted: 04/23/2018] [Indexed: 11/30/2022]
Abstract
The neural correlates of emotion perception have been shown to be significantly altered in schizophrenia (SCZ) patients as well as their healthy relatives, possibly reflecting genetic susceptibility to the disease. The aim of the study was to investigate the association between SCZ polygenic risk and brain activity whilst testing perception of multisensory, dynamic emotional stimuli. We created SCZ polygenic risk scores (PRS) for a sample of twenty-eight healthy individuals. The PRS was based on data from the Psychiatric Genomics Consortium and was used as a regressor score in the neuroimaging analysis. The results of a multivariate brain-behaviour analysis show that higher SCZ PRS are related to increased activity in brain regions critical for emotion during the perception of threatening (angry) emotions. These results suggest that individuals with higher SCZ PRS over-activate the neural correlates underlying emotion during perception of threat, perhaps due to an increased experience of fear or neural inefficiency in emotion-regulation areas. Moreover, over-recruitment of emotion regulation regions might function as a compensation to maintain normal emotion regulation during threat perception. If replicated in larger studies, these findings may have important implications for understanding the neurophysiological biomarkers relevant in SCZ.
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Affiliation(s)
- Ilvana Dzafic
- Queensland Brain Institute, University of Queensland, Brisbane, Australia; Centre for Advanced Imaging, University of Queensland, Brisbane, Australia.
| | - Hana Burianová
- Centre for Advanced Imaging, University of Queensland, Brisbane, Australia; Department of Psychology, Swansea University, Swansea, United Kingdom
| | - Sathish Periyasamy
- Queensland Brain Institute, University of Queensland, Brisbane, Australia; Queensland Centre for Mental Health Research, Brisbane, Australia
| | - Bryan Mowry
- Queensland Brain Institute, University of Queensland, Brisbane, Australia; Queensland Centre for Mental Health Research, Brisbane, Australia
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13
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Quarto T, Paparella I, De Tullio D, Viscanti G, Fazio L, Taurisano P, Romano R, Rampino A, Masellis R, Popolizio T, Selvaggi P, Pergola G, Bertolino A, Blasi G. Familial Risk and a Genome-Wide Supported DRD2 Variant for Schizophrenia Predict Lateral Prefrontal-Amygdala Effective Connectivity During Emotion Processing. Schizophr Bull 2018; 44:834-843. [PMID: 28981847 PMCID: PMC6007415 DOI: 10.1093/schbul/sbx128] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
The brain functional mechanisms translating genetic risk into emotional symptoms in schizophrenia (SCZ) may include abnormal functional integration between areas key for emotion processing, such as the amygdala and the lateral prefrontal cortex (LPFC). Indeed, investigation of these mechanisms is also complicated by emotion processing comprising different subcomponents and by disease-associated state variables. Here, our aim was to investigate the relationship between risk for SCZ and effective connectivity between the amygdala and the LPFC during different subcomponents of emotion processing. Thus, we first characterized with dynamic causal modeling (DCM) physiological patterns of LPFC-amygdala effective connectivity in healthy controls (HC) during implicit and explicit emotion processing. Then, we compared DCM patterns in a subsample of HC, in patients with SCZ and in healthy siblings of patients (SIB), matched for demographics. Finally, we investigated in HC association of LPFC-amygdala effective connectivity with a genome-wide supported variant increasing genetic risk for SCZ and possibly relevant to emotion processing (DRD2 rs2514218). In HC, we found that a "bottom-up" amygdala-to-LPFC pattern during implicit processing and a "top-down" LPFC-to-amygdala pattern during explicit processing were the most likely directional models of effective connectivity. Differently, implicit emotion processing in SIB, SCZ, and HC homozygous for the SCZ risk rs2514218 C allele was associated with decreased probability for the "bottom-up" as well as with increased probability for the "top-down" model. These findings suggest that task-specific anomaly in the directional flow of information or disconnection between the amygdala and the LPFC is a good candidate endophenotype of SCZ.
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Affiliation(s)
- Tiziana Quarto
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy,Cognitive Brain Research Unit, Department of Psychology and Logopedics, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Isabella Paparella
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Davide De Tullio
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Giovanna Viscanti
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Leonardo Fazio
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Paolo Taurisano
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Raffaella Romano
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Antonio Rampino
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Rita Masellis
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Teresa Popolizio
- IRCCS “Casa Sollievo della Sofferenza”, San Giovanni Rotondo, Italy
| | - Pierluigi Selvaggi
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy,Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
| | - Giulio Pergola
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Alessandro Bertolino
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe Blasi
- Psychiatric Neuroscience Group, Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy,To whom correspondence should be addressed; tel: +390 8055 93629; fax: +390 8055 93204; e-mail:
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14
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Schiavone S, Trabace L. The use of antioxidant compounds in the treatment of first psychotic episode: Highlights from preclinical studies. CNS Neurosci Ther 2018. [PMID: 29542255 DOI: 10.1111/cns.12847] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Recent evidence highlighted a pathogenetic link between redox dysregulation and the early stages of psychosis. Indeed, an increasing number of studies have pointed toward an association between oxidative stress, both at central and peripheral levels, and first psychotic episode. Moreover, basal low antioxidant capacity has been shown to directly correlate with cognitive impairment in the early onset of psychosis. In this context, the possibility to use antioxidant compounds in first psychotic episode, especially as supplementation to antipsychotic therapy, has become the focus of numerous investigations on rodents with the aim to translate data on the possible effects of antioxidant therapies to large populations of patients, with a diagnosis of the first psychotic episode. In this review, we will discuss studies, published from January 1st, 2007 to July 31st, 2017, investigating the effects of antioxidant compounds on neuropathological alterations observed in different rodent models characterized by a cluster of psychotic-like symptoms reminiscent of what observed in human first psychotic episode. A final focus on the effective possibility to directly translate data obtained on rodents to humans will be also provided.
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Affiliation(s)
- Stefania Schiavone
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
| | - Luigia Trabace
- Department of Clinical and Experimental Medicine, University of Foggia, Foggia, Italy
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15
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Dauvermann MR, Moorhead TW, Watson AR, Duff B, Romaniuk L, Hall J, Roberts N, Lee GL, Hughes ZA, Brandon NJ, Whitcher B, Blackwood DH, McIntosh AM, Lawrie SM. Verbal working memory and functional large-scale networks in schizophrenia. Psychiatry Res Neuroimaging 2017; 270:86-96. [PMID: 29111478 DOI: 10.1016/j.pscychresns.2017.10.004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2017] [Revised: 09/16/2017] [Accepted: 10/20/2017] [Indexed: 12/17/2022]
Abstract
The aim of this study was to test whether bilinear and nonlinear effective connectivity (EC) measures of working memory fMRI data can differentiate between patients with schizophrenia (SZ) and healthy controls (HC). We applied bilinear and nonlinear Dynamic Causal Modeling (DCM) for the analysis of verbal working memory in 16 SZ and 21 HC. The connection strengths with nonlinear modulation between the dorsolateral prefrontal cortex (DLPFC) and the ventral tegmental area/substantia nigra (VTA/SN) were evaluated. We used Bayesian Model Selection at the group and family levels to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging was used to assess the connection strengths with nonlinear modulation. The DCM analyses revealed that SZ and HC used different bilinear networks despite comparable behavioral performance. In addition, the connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area showed differences between SZ and HC. The adoption of different functional networks in SZ and HC indicated neurobiological alterations underlying working memory performance, including different connection strengths with nonlinear modulation between the DLPFC and the VTA/SN area. These novel findings may increase our understanding of connectivity in working memory in schizophrenia.
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Affiliation(s)
- Maria R Dauvermann
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK; School of Psychology, National University of Ireland Galway, University Road, Galway, Ireland; McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139, USA.
| | - Thomas Wj Moorhead
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Andrew R Watson
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Barbara Duff
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Liana Romaniuk
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Jeremy Hall
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - Neil Roberts
- Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, UK; British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
| | - Graham L Lee
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, MA 02139, USA
| | - Zoë A Hughes
- Neuroscience Research Unit, Pfizer Inc., Cambridge, MA, USA
| | - Nicholas J Brandon
- Neuroscience Research Unit, Pfizer Inc., Cambridge, MA, USA; IMED Neuroscience Unit, AstraZeneca, Waltham, MA, USA
| | - Brandon Whitcher
- Clinical and Translational Imaging, Pfizer Inc., Cambridge, MA, USA
| | - Douglas Hr Blackwood
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Andrew M McIntosh
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
| | - Stephen M Lawrie
- Division of Psychiatry, Royal Edinburgh Hospital, Morningside Park, University of Edinburgh, Edinburgh EH10 5HF, UK
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Alloza C, Bastin ME, Cox SR, Gibson J, Duff B, Semple SI, Whalley HC, Lawrie SM. Central and non-central networks, cognition, clinical symptoms, and polygenic risk scores in schizophrenia. Hum Brain Mapp 2017; 38:5919-5930. [PMID: 28881417 DOI: 10.1002/hbm.23798] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 08/02/2017] [Accepted: 08/24/2017] [Indexed: 12/25/2022] Open
Abstract
Schizophrenia is a complex disorder that may be the result of aberrant connections between specific brain regions rather than focal brain abnormalities. Here, we investigate the relationships between brain structural connectivity as described by network analysis, intelligence, symptoms, and polygenic risk scores (PGRS) for schizophrenia in a group of patients with schizophrenia and a group of healthy controls. Recently, researchers have shown an interest in the role of high centrality networks in the disorder. However, the importance of non-central networks still remains unclear. Thus, we specifically examined network-averaged fractional anisotropy (mean edge weight) in central and non-central subnetworks. Connections with the highest betweenness centrality within the average network (>75% of centrality values) were selected to represent the central subnetwork. The remaining connections were assigned to the non-central subnetwork. Additionally, we calculated graph theory measures from the average network (connections that occur in at least 2/3 of participants). Density, strength, global efficiency, and clustering coefficient were significantly lower in patients compared with healthy controls for the average network (pFDR < 0.05). All metrics across networks were significantly associated with intelligence (pFDR < 0.05). There was a tendency towards significance for a correlation between intelligence and PGRS for schizophrenia (r = -0.508, p = 0.052) that was significantly mediated by central and non-central mean edge weight and every graph metric from the average network. These results are consistent with the hypothesis that intelligence deficits are associated with a genetic risk for schizophrenia, which is mediated via the disruption of distributed brain networks. Hum Brain Mapp 38:5919-5930, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Clara Alloza
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Mark E Bastin
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Centre for Clinical Brain Sciences, Western General Hospital, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Simon R Cox
- Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, United Kingdom.,Department of Psychology, University of Edinburgh, Edinburgh, United Kingdom.,Scottish Imaging Network: A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, United Kingdom
| | - Jude Gibson
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Barbara Duff
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Scott I Semple
- Clinical Research Imaging Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Heather C Whalley
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
| | - Stephen M Lawrie
- Division of Psychiatry, University of Edinburgh, Edinburgh, United Kingdom
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Ibrahim EC, Guillemot V, Comte M, Tenenhaus A, Zendjidjian XY, Cancel A, Belzeaux R, Sauvanaud F, Blin O, Frouin V, Fakra E. Modeling a linkage between blood transcriptional expression and activity in brain regions to infer the phenotype of schizophrenia patients. NPJ SCHIZOPHRENIA 2017; 3:25. [PMID: 28883405 PMCID: PMC5589880 DOI: 10.1038/s41537-017-0027-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 07/05/2017] [Accepted: 07/21/2017] [Indexed: 11/20/2022]
Abstract
Hundreds of genetic loci participate to schizophrenia liability. It is also known that impaired cerebral connectivity is directly related to the cognitive and affective disturbances in schizophrenia. How genetic susceptibility and brain neural networks interact to specify a pathological phenotype in schizophrenia remains elusive. Imaging genetics, highlighting brain variations, has proven effective to establish links between vulnerability loci and associated clinical traits. As previous imaging genetics works in schizophrenia have essentially focused on structural DNA variants, these findings could be blurred by epigenetic mechanisms taking place during gene expression. We explored the meaningful links between genetic data from peripheral blood tissues on one hand, and regional brain reactivity to emotion task assayed by blood oxygen level-dependent functional magnetic resonance imaging on the other hand, in schizophrenia patients and matched healthy volunteers. We applied Sparse Generalized Canonical Correlation Analysis to identify joint signals between two blocks of variables: (i) the transcriptional expression of 33 candidate genes, and (ii) the blood oxygen level-dependent activity in 16 region of interest. Results suggested that peripheral transcriptional expression is related to brain imaging variations through a sequential pathway, ending with the schizophrenia phenotype. Generalization of such an approach to larger data sets should thus help in outlining the pathways involved in psychiatric illnesses such as schizophrenia. IMAGING SEARCHING FOR LINKS TO AID DIAGNOSIS: Researchers explore links between the expression of genes associated with schizophrenia in blood cells and variations in brain activity during emotion processing. El Chérif Ibrahim and Eric Fakra at Aix-Marseille Université, France, and colleagues have developed a method to relate the expression levels of 33 schizophrenia susceptibility genes in blood cells and functional magnetic resonance imaging (fMRI) data obtained as individuals carry out a task that triggers emotional responses. Although they found no significant differences in the expression of genes between the 26 patients with schizophrenia and 26 healthy controls they examined, variations in activity in the superior temporal gyrus were strongly linked to schizophrenia-associated gene expression and presence of disease. Similar analyses of larger data sets will shed further light on the relationship between peripheral molecular changes and disease-related behaviors and ultimately, aid the diagnosis of neuropsychiatric disease.
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Affiliation(s)
- El Chérif Ibrahim
- Aix-Marseille Univ, CNRS, CRN2M, Marseille, France.
- Fondation FondaMental, Fondation de Recherche et de Soins en Santé Mentale, Créteil, France.
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France.
| | - Vincent Guillemot
- INSERM, U 1127, Paris, France
- CNRS, 7225, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, UMRS_1127, Paris, France
- ICM, Département des maladies du système nerveux and Département de Génétique, Hôpital Pitié-Salpêtrière, Paris, France
| | - Magali Comte
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France
| | - Arthur Tenenhaus
- Laboratoire des Signaux et Systèmes (L2S, UMR CNRS 8506), CentraleSupélec-CNRS Université Paris-Sud, Gif-sur-Yvette, France
- Bioinformatics/Biostatistics Platform IHU-A-ICM, Brain and Spine Institute, Paris, France
| | - Xavier Yves Zendjidjian
- Pôle Psychiatrie centre, Hôpital de la Conception, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | - Aida Cancel
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France
- Service Hospitalo-Universitaire de Psychiatrie Secteur Saint-Etienne, Hôpital Nord, Saint-Etienne, France
| | - Raoul Belzeaux
- Aix-Marseille Univ, CNRS, CRN2M, Marseille, France
- Fondation FondaMental, Fondation de Recherche et de Soins en Santé Mentale, Créteil, France
- McGill Group for Suicide Studies, Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Florence Sauvanaud
- Service Hospitalo-Universitaire de Psychiatrie Secteur Saint-Etienne, Hôpital Nord, Saint-Etienne, France
| | - Olivier Blin
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France
- CIC-UPCET et Pharmacologie Clinique, Hôpital de la Timone, Assistance Publique des Hôpitaux de Marseille, Marseille, France
| | | | - Eric Fakra
- Aix-Marseille Univ, CNRS, INT, Inst Neurosci Timone, Marseille, France.
- Service Hospitalo-Universitaire de Psychiatrie Secteur Saint-Etienne, Hôpital Nord, Saint-Etienne, France.
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Kumar V, Shivakumar V, Chhabra H, Bose A, Venkatasubramanian G, Gangadhar BN. Functional near infra-red spectroscopy (fNIRS) in schizophrenia: A review. Asian J Psychiatr 2017; 27:18-31. [PMID: 28558892 DOI: 10.1016/j.ajp.2017.02.009] [Citation(s) in RCA: 37] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 02/02/2017] [Accepted: 02/12/2017] [Indexed: 01/14/2023]
Abstract
The research on the alterations in functional connectivity in schizophrenia has been facilitated by development of an array of functional neuroimaging techniques. Functional Near Infra Red Spectroscopy (fNIRS) is a novel diffuse optical neuromonitring method with its own advantages and limitations. The advantages of fNIRS have made it to be frequently used as a research tool by medical community in different settings. In fNIRS the property of haemoglobin to absorb near infrared light is used to measure brain activity. It provides the indirect measurement of the neuronal activity in the areas of interest. The advantage of fNIRS being less restrictive has made it to be used more commonly in the research of psychiatric disorders in general, schizophrenia in particular. The fNIRS studies on patients with schizophrenia have shown haemodynamic hypo activation primarily in the prefrontal cortex during various cognitive tasks. In this review, initially we have briefly explained the basic principles of fNIRS followed by detailed review of fNIRS findings in patients with schizophrenia.
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Affiliation(s)
- Vijay Kumar
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India.
| | - Venkataram Shivakumar
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Harleen Chhabra
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Anushree Bose
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Ganesan Venkatasubramanian
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India
| | - Bangalore N Gangadhar
- The Schizophrenia Clinic, Department of Psychiatry & Translational Psychiatry Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India
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Chahine G, Richter A, Wolter S, Goya-Maldonado R, Gruber O. Disruptions in the left frontoparietal network underlie resting state endophenotypic markers in schizophrenia. Hum Brain Mapp 2016; 38:1741-1750. [PMID: 28009080 DOI: 10.1002/hbm.23477] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 11/12/2016] [Accepted: 11/15/2016] [Indexed: 11/10/2022] Open
Abstract
Advances in functional brain imaging have improved the search for potential endophenotypic markers in schizophrenia. Here, we employed independent component analysis (ICA) and dynamic causal modeling (DCM) in resting state fMRI on a sample of 35 schizophrenia patients, 20 first-degree relatives and 35 control subjects. Analysis on ICA-derived networks revealed increased functional connectivity between the left frontoparietal network (FPN) and left temporal and parietal regions in schizophrenia patients (P < 0.001). First-degree relatives shared this hyperconnectivity, in particular in the supramarginal gyrus (SMG; P = 0.008). DCM analysis was employed to further explore underlying effective connectivity. Results showed increased inhibitory connections to the left angular gyrus (AG) in schizophrenia patients from all other nodes of the left FPN (P < 0.001), and in particular from the left SMG (P = 0.001). Relatives also showed a pattern of increased inhibitory connections to the left AG (P = 0.008). Furthermore, the patient group showed increased excitatory connectivity between the left fusiform gyrus and the left SMG (P = 0.002). This connection was negatively correlated to inhibitory afferents to the left AG (P = 0.005) and to the negative symptom score on the PANSS scale (P = 0.001, r = -0.51). Left frontoparietotemporal dysfunction in schizophrenia has been previously associated with a range of abnormalities, including formal thought disorder, working memory dysfunction and sensory hallucinations. Our analysis uncovered new potential endophenotypic markers of schizophrenia and shed light on the organization of the left FPN in patients and their first-degree relatives. Hum Brain Mapp 38:1741-1750, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- George Chahine
- Department of Psychiatry and Psychotherapy, Centre for Translational Research in Systems, Neuroscience and Clinical Psychiatry, George August University, Göttingen, Germany.,Department of Psychiatry, Maimonides Medical Center, Brooklyn, New York
| | - Anja Richter
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
| | - Sarah Wolter
- Department of Psychiatry and Psychotherapy, Centre for Translational Research in Systems, Neuroscience and Clinical Psychiatry, George August University, Göttingen, Germany
| | - Roberto Goya-Maldonado
- Department of Psychiatry and Psychotherapy, Centre for Translational Research in Systems, Neuroscience and Clinical Psychiatry, George August University, Göttingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany
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Raab K, Kirsch P, Mier D. Understanding the impact of 5-HTTLPR, antidepressants, and acute tryptophan depletion on brain activation during facial emotion processing: A review of the imaging literature. Neurosci Biobehav Rev 2016; 71:176-197. [DOI: 10.1016/j.neubiorev.2016.08.031] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2016] [Revised: 07/28/2016] [Accepted: 08/26/2016] [Indexed: 12/22/2022]
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Affiliation(s)
- Larry W. Swanson
- Department of Biological Sciences, University of Southern California, Los Angeles, California 90089;
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, Massachusetts 02138;
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Pergola G, Di Carlo P, Andriola I, Gelao B, Torretta S, Attrotto MT, Fazio L, Raio A, Albergo D, Masellis R, Rampino A, Blasi G, Bertolino A. Combined effect of genetic variants in the GluN2B coding gene (GRIN2B) on prefrontal function during working memory performance. Psychol Med 2016; 46:1135-1150. [PMID: 26690829 DOI: 10.1017/s0033291715002639] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
BACKGROUND The GluN2B subunit of N-methyl-d-aspartate receptors is crucially involved in the physiology of the prefrontal cortex during working memory (WM). Consistently, genetic variants in the GluN2B coding gene (GRIN2B) have been associated with cognitive phenotypes. However, it is unclear how GRIN2B genetic variation affects gene expression and prefrontal cognitive processing. Using a composite score, we tested the combined effect of GRIN2B variants on prefrontal activity during WM performance in healthy subjects. METHOD We computed a composite score to combine the effects of single nucleotide polymorphisms on post-mortem prefrontal GRIN2B mRNA expression. We then computed the composite score in independent samples of healthy participants in a peripheral blood expression study (n = 46), in a WM behavioural study (n = 116) and in a WM functional magnetic resonance imaging study (n = 122). RESULTS Five polymorphisms were associated with GRIN2B expression: rs2160517, rs219931, rs11055792, rs17833967 and rs12814951 (all corrected p < 0.05). The score computed to account for their combined effect reliably indexed gene expression. GRIN2B composite score correlated negatively with intelligence quotient, WM behavioural efficiency and dorsolateral prefrontal cortex activity. Moreover, there was a non-linear association between GRIN2B genetic score and prefrontal activity, i.e. both high and low putative genetic score levels were associated with high blood oxygen level-dependent signals in the prefrontal cortex. CONCLUSIONS Multiple genetic variants in GRIN2B are jointly associated with gene expression, prefrontal function and behaviour during WM. These results support the role of GRIN2B genetic variants in WM prefrontal activity in human adults.
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Affiliation(s)
- G Pergola
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - P Di Carlo
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - I Andriola
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - B Gelao
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - S Torretta
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - M T Attrotto
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - L Fazio
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Raio
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - D Albergo
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - R Masellis
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Rampino
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - G Blasi
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
| | - A Bertolino
- Department of Basic Medical Sciences,Neuroscience and Sensory Organs,University of Bari 'Aldo Moro',Piazza Giulio Cesare 11,70124 Bari,Italy
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Abstract
Despite a lack of recent progress in the treatment of schizophrenia, our understanding of its genetic and environmental causes has considerably improved, and their relationship to aberrant patterns of neurodevelopment has become clearer. This raises the possibility that 'disease-modifying' strategies could alter the course to - and of - this debilitating disorder, rather than simply alleviating symptoms. A promising window for course-altering intervention is around the time of the first episode of psychosis, especially in young people at risk of transition to schizophrenia. Indeed, studies performed in both individuals at risk of developing schizophrenia and rodent models for schizophrenia suggest that pre-diagnostic pharmacotherapy and psychosocial or cognitive-behavioural interventions can delay or moderate the emergence of psychosis. Of particular interest are 'hybrid' strategies that both relieve presenting symptoms and reduce the risk of transition to schizophrenia or another psychiatric disorder. This Review aims to provide a broad-based consideration of the challenges and opportunities inherent in efforts to alter the course of schizophrenia.
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26
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Chen JE, Glover GH. Functional Magnetic Resonance Imaging Methods. Neuropsychol Rev 2015; 25:289-313. [PMID: 26248581 PMCID: PMC4565730 DOI: 10.1007/s11065-015-9294-9] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Accepted: 07/28/2015] [Indexed: 12/11/2022]
Abstract
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,
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Tesli M, Espeseth T, Bettella F, Mattingsdal M, Aas M, Melle I, Djurovic S, Andreassen OA. Polygenic risk score and the psychosis continuum model. Acta Psychiatr Scand 2014; 130:311-7. [PMID: 24961959 DOI: 10.1111/acps.12307] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/03/2014] [Indexed: 11/29/2022]
Abstract
OBJECTIVE Schizophrenia (SZ) and bipolar disorder (BD) are heritable, polygenic disorders with shared clinical characteristics and genetic risk indicating a psychosis continuum. This is the first study using polygenic risk score (PGRS) to investigate the localization of diagnostic subcategories along the entire psychosis spectrum. METHOD Based on results from the Psychiatric Genomics Consortium (PGC), we assigned a SZ and BD PGRS to each individual in our independent sample [N=570 BD spectrum cases, 452 SZ spectrum cases and 415 healthy controls (CTR)]. Potential differences in mean SZ and BD PGRS across diagnostic spectrums and subcategories were explored. RESULTS SZ and BD PGRSs were significantly associated with both SZ and BD spectrums compared with CTR. For the subcategories, SZ PGRS was significantly associated with SZ, schizoaffective disorder, psychosis not otherwise specified, and BD1, while BD PGRS was significantly associated with BD1 and BD2. There were no significant differences between any of the diagnostic spectrums or subgroups for neither the SZ nor BD PGRS. Lifetime psychosis was significantly associated with SZ PGRS but not with BD PGRS. CONCLUSION These findings further support the psychosis continuum model and provide molecular polygenetic validation of the localization of diagnostic subcategories within this continuum.
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Affiliation(s)
- M Tesli
- NORMENT, KG Jebsen Centre for Psychosis Research, Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Lin D, Cao H, Calhoun VD, Wang YP. Sparse models for correlative and integrative analysis of imaging and genetic data. J Neurosci Methods 2014; 237:69-78. [PMID: 25218561 DOI: 10.1016/j.jneumeth.2014.09.001] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2014] [Revised: 08/27/2014] [Accepted: 09/01/2014] [Indexed: 11/29/2022]
Abstract
The development of advanced medical imaging technologies and high-throughput genomic measurements has enhanced our ability to understand their interplay as well as their relationship with human behavior by integrating these two types of datasets. However, the high dimensionality and heterogeneity of these datasets presents a challenge to conventional statistical methods; there is a high demand for the development of both correlative and integrative analysis approaches. Here, we review our recent work on developing sparse representation based approaches to address this challenge. We show how sparse models are applied to the correlation and integration of imaging and genetic data for biomarker identification. We present examples on how these approaches are used for the detection of risk genes and classification of complex diseases such as schizophrenia. Finally, we discuss future directions on the integration of multiple imaging and genomic datasets including their interactions such as epistasis.
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Affiliation(s)
- Dongdong Lin
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA.
| | - Hongbao Cao
- Unit on Statistical Genomics, Intramural Program of Research, National Institute of Mental Health, NIH, Bethesda 20852, USA.
| | - Vince D Calhoun
- The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM 87131, USA.
| | - Yu-Ping Wang
- Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA; Center of Genomics and Bioinformatics, Tulane University, New Orleans, LA, 70112, USA.
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29
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Genetic risk variants of schizophrenia associated with left superior temporal gyrus volume. Cortex 2014; 58:23-6. [DOI: 10.1016/j.cortex.2014.05.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2014] [Revised: 04/04/2014] [Accepted: 05/21/2014] [Indexed: 11/17/2022]
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Abstract
Psychosis is an abnormal mental state characterized by disorganization, delusions and hallucinations. Animal models have become an increasingly important research tool in the effort to understand both the underlying pathophysiology and treatment of psychosis. There are multiple animal models for psychosis, with each formed by the coupling of a manipulation and a measurement. In this manuscript we do not address the diseases of which psychosis is a prominent comorbidity. Instead, we summarize the current state of affairs and future directions for animal models of psychosis. To accomplish this, our manuscript will first discuss relevant behavioral and electrophysiological measurements. We then provide an overview of the different manipulations that are combined with these measurements to produce animal models. The strengths and limitations of each model will be addressed in order to evaluate its cross-species comparability.
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