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Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia

Computational modeling is a useful method for generating hypotheses about the contributions of impaired neurobiological mechanisms, and their interactions, to psychopathology. Modeling is being increasingly used to further our understanding of schizophrenia, but to date, it has not been applied to q...

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Detalles Bibliográficos
Autores principales: Silverstein, Steven M., Demmin, Docia L., Bednar, James A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MIT Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6067832/
https://www.ncbi.nlm.nih.gov/pubmed/30090855
http://dx.doi.org/10.1162/CPSY_a_00005
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author Silverstein, Steven M.
Demmin, Docia L.
Bednar, James A.
author_facet Silverstein, Steven M.
Demmin, Docia L.
Bednar, James A.
author_sort Silverstein, Steven M.
collection PubMed
description Computational modeling is a useful method for generating hypotheses about the contributions of impaired neurobiological mechanisms, and their interactions, to psychopathology. Modeling is being increasingly used to further our understanding of schizophrenia, but to date, it has not been applied to questions regarding the common perceptual disturbances in the disorder. In this article, we model aspects of low-level visual processing and demonstrate how this can lead to testable hypotheses about both the nature of visual abnormalities in schizophrenia and the relationships between the mechanisms underlying these disturbances and psychotic symptoms. Using a model that incorporates retinal, lateral geniculate nucleus (LGN), and V1 activity, as well as gain control in the LGN, homeostatic adaptation in V1, lateral excitation and inhibition in V1, and self-organization of synaptic weights based on Hebbian learning and divisive normalization, we show that (a) prior data indicating increased contrast sensitivity for low-spatial-frequency stimuli in first-episode schizophrenia can be successfully modeled as a function of reduced retinal and LGN efferent activity, leading to overamplification at the cortical level, and (b) prior data on reduced contrast sensitivity and broadened orientation tuning in chronic schizophrenia can be successfully modeled by a combination of reduced V1 lateral inhibition and an increase in the Hebbian learning rate at V1 synapses for LGN input. These models are consistent with many current findings, and they predict several relationships that have not yet been demonstrated. They also have implications for understanding changes in brain and visual function from the first psychotic episode to the chronic stage of illness.
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spelling pubmed-60678322018-08-06 Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia Silverstein, Steven M. Demmin, Docia L. Bednar, James A. Comput Psychiatr Research Computational modeling is a useful method for generating hypotheses about the contributions of impaired neurobiological mechanisms, and their interactions, to psychopathology. Modeling is being increasingly used to further our understanding of schizophrenia, but to date, it has not been applied to questions regarding the common perceptual disturbances in the disorder. In this article, we model aspects of low-level visual processing and demonstrate how this can lead to testable hypotheses about both the nature of visual abnormalities in schizophrenia and the relationships between the mechanisms underlying these disturbances and psychotic symptoms. Using a model that incorporates retinal, lateral geniculate nucleus (LGN), and V1 activity, as well as gain control in the LGN, homeostatic adaptation in V1, lateral excitation and inhibition in V1, and self-organization of synaptic weights based on Hebbian learning and divisive normalization, we show that (a) prior data indicating increased contrast sensitivity for low-spatial-frequency stimuli in first-episode schizophrenia can be successfully modeled as a function of reduced retinal and LGN efferent activity, leading to overamplification at the cortical level, and (b) prior data on reduced contrast sensitivity and broadened orientation tuning in chronic schizophrenia can be successfully modeled by a combination of reduced V1 lateral inhibition and an increase in the Hebbian learning rate at V1 synapses for LGN input. These models are consistent with many current findings, and they predict several relationships that have not yet been demonstrated. They also have implications for understanding changes in brain and visual function from the first psychotic episode to the chronic stage of illness. MIT Press 2017-12-01 /pmc/articles/PMC6067832/ /pubmed/30090855 http://dx.doi.org/10.1162/CPSY_a_00005 Text en © 2017 Massachusetts Institute of Technology http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research
Silverstein, Steven M.
Demmin, Docia L.
Bednar, James A.
Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title_full Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title_fullStr Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title_full_unstemmed Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title_short Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
title_sort computational modeling of contrast sensitivity and orientation tuning in first-episode and chronic schizophrenia
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6067832/
https://www.ncbi.nlm.nih.gov/pubmed/30090855
http://dx.doi.org/10.1162/CPSY_a_00005
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