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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Main Authors: Steven M. Silverstein, Docia L. Demmin, James A. Bednar
Format: Article
Language:English
Published: The MIT Press 2017-12-01
Series:Computational Psychiatry
Subjects:
Online Access:https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00005
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spelling doaj-bdcc557cafd1474893c5a44e220530da2020-11-24T22:25:47ZengThe MIT PressComputational Psychiatry2379-62272017-12-01110213110.1162/CPSY_a_00005CPSY_a_00005Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic SchizophreniaSteven M. Silverstein0Docia L. Demmin1James A. Bednar2Rutgers University Behavioral Health Care, Piscataway, New Jersey, USARutgers University Behavioral Health Care, Piscataway, New Jersey, USASchool of Informatics, University of Edinburgh, Edinburgh, ScotlandComputational 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.https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00005schizophreniavisionperceptioncontrast sensitivityorientation tuningmodelingfirst episodeHebbian learninggain controlinhibitionexcitation
collection DOAJ
language English
format Article
sources DOAJ
author Steven M. Silverstein
Docia L. Demmin
James A. Bednar
spellingShingle Steven M. Silverstein
Docia L. Demmin
James A. Bednar
Computational Modeling of Contrast Sensitivity and Orientation Tuning in First-Episode and Chronic Schizophrenia
Computational Psychiatry
schizophrenia
vision
perception
contrast sensitivity
orientation tuning
modeling
first episode
Hebbian learning
gain control
inhibition
excitation
author_facet Steven M. Silverstein
Docia L. Demmin
James A. Bednar
author_sort Steven M. Silverstein
title 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_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_sort computational modeling of contrast sensitivity and orientation tuning in first-episode and chronic schizophrenia
publisher The MIT Press
series Computational Psychiatry
issn 2379-6227
publishDate 2017-12-01
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.
topic schizophrenia
vision
perception
contrast sensitivity
orientation tuning
modeling
first episode
Hebbian learning
gain control
inhibition
excitation
url https://www.mitpressjournals.org/doi/pdf/10.1162/CPSY_a_00005
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