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      Searching for Cross-diagnostic Convergence: Neural Mechanisms Governing Excitation and Inhibition Balance in Schizophrenia and Autism Spectrum Disorders

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          Abstract

          Recent theoretical accounts have proposed excitation (E) and inhibition (I) imbalance as a possible mechanistic, network-level hypothesis underlying neural and behavioral dysfunction across neurodevelopmental disorders, particularly autism spectrum disorder (ASD) and schizophrenia (SCZ). These two disorders share some overlap in their clinical presentation as well as convergence in their underlying genes and neurobiology. However, there are also clear points of dissociation in terms of phenotypes and putatively affected neural circuitry. Here we highlight emerging work from the clinical neuroscience literature examining neural correlates of E/I imbalance across children and adults with ASD and adults with both chronic and early-course SCZ. We discuss findings from diverse neuroimaging studies across distinct modalities, conducted with EEG, MEG, 1H-MRS, and fMRI, including effects observed both during task and at rest. Throughout this review we discuss points of convergence and divergence in the ASD and SCZ literature, with a focus on disruptions in neural E/I balance. We also consider these findings in relation to predictions generated by theoretical neuroscience, particularly computational models predicting E/I imbalance across disorders. Finally, we discuss how human non-invasive neuroimaging can benefit from pharmacological challenge studies to reveal mechanisms in ASD and SCZ. Collectively, we attempt to shed light on shared and divergent neuroimaging effects across disorders with the goal of informing future research examining the mechanisms underlying the E/I imbalance hypothesis across neurodevelopmental disorders. We posit that such translational efforts are vital to facilitate development of neurobiologically informed treatment strategies across neuropsychiatric conditions.

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          Author and article information

          Journal
          0213264
          1117
          Biol Psychiatry
          Biol. Psychiatry
          Biological psychiatry
          0006-3223
          1873-2402
          18 March 2017
          14 March 2017
          15 May 2017
          15 May 2018
          : 81
          : 10
          : 848-861
          Affiliations
          [1 ]Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, One Gustave Levy Place, New York, NY 10028, USA
          [2 ]Seaver Autism Center, Icahn School of Medicine at Mount Sinai Hospital, One Gustave Levy Place, New York, NY 10028, USA
          [3 ]Child Study Center, Yale University School of Medicine, 230 South Frontage Road, New Haven, CT, 06520, USA
          [4 ]Department of Psychiatry, Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA
          [5 ]Interdepartmental Neuroscience Program, Yale University, New Haven, CT 06520, USA
          [6 ]Department of Psychology, Yale University, 2 Hillhouse Avenue, CT 06520, USA
          [7 ]Abraham Ribicoff Research Facilities, Connecticut Mental Health Center, New Haven, CT 06519, USA
          [8 ]Division of Neurocognition, Neurocomputation, & Neurogenetics (N3), Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA
          [9 ]Department of Neurobiology, Yale University School of Medicine, 300 George Street, New Haven, CT, 06511, USA
          Author notes
          Corresponding Authors: Jennifer Foss-Feig, Ph.D., Seaver Autism Center, Department of Psychiatry, Icahn School of Medicine at Mount Sinai Hospital, One Gustave Levy Place, Box 1230, New York, NY, 10020, USA, Phone: (212) 241-3276, jennifer.foss-feig@ 123456mssm.edu
          Article
          PMC5436134 PMC5436134 5436134 nihpa860008
          10.1016/j.biopsych.2017.03.005
          5436134
          28434615
          4b33f36e-a15f-4f39-b123-0ba0ca22bd98
          History
          Categories
          Article

          autism,schizophrenia,E/I balance,review,computational modeling,mechanism

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