Dataset Information

A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis.

ABSTRACT: Impaired visual integration is well documented in schizophrenia and related to functional outcomes. However, it is unclear if this deficit is specific to schizophrenia, or characteristic of psychosis more broadly. To address this question, this study used a Bayesian model comparison approach to examine the evidence of three grouping models of visual integration performance in 116 individuals with schizophrenia (SZ), schizoaffective disorder (SA), bipolar disorder (BD) with or without a history of prominent psychosis (BDP+ and BDP-, respectively), or no psychiatric diagnosis (healthy controls; HC). We compared: (1) Psychosis Model (psychosis, non-psychosis), where the psychosis group included SZ, SA, and BDP+, and the non-psychosis group included BDP- and HC; (2) Schizophrenia Model (SZ, non-SZ); and (3) DSM Model (SZ, SA, BD, HC). The relationship between visual integration and general cognition was also explored. The Psychosis Model showed the strongest evidence, and visual integration was associated with general cognition in participants with psychosis. The results were consistent with the Research Domain Criteria (RDoC) framework, indicating that visual integration impairment is characteristic of psychosis and not specific to SZ or DSM categories, and may share similar disease pathways with observed neurocognitive deficits in psychotic disorders.

SUBMITTER: Grove TB

PROVIDER: S-EPMC6448399 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis.

Grove Tyler B TB Yao Beier B Mueller Savanna A SA McLaughlin Merranda M Ellingrod Vicki L VL McInnis Melvin G MG Taylor Stephan F SF Deldin Patricia J PJ Tso Ivy F IF

Psychiatry research 20180507

Impaired visual integration is well documented in schizophrenia and related to functional outcomes. However, it is unclear if this deficit is specific to schizophrenia, or characteristic of psychosis more broadly. To address this question, this study used a Bayesian model comparison approach to examine the evidence of three grouping models of visual integration performance in 116 individuals with schizophrenia (SZ), schizoaffective disorder (SA), bipolar disorder (BD) with or without a history o ...[more]

PMID: 29768190

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Project description:The brain integrates information from different sensory modalities to generate a coherent and accurate percept of external events. Several experimental studies suggest that this integration follows the principle of Bayesian estimate. However, the neural mechanisms responsible for this behavior, and its development in a multisensory environment, are still insufficiently understood. We recently presented a neural network model of audio-visual integration (Neural Computation, 2017) to investigate how a Bayesian estimator can spontaneously develop from the statistics of external stimuli. Model assumes the presence of two unimodal areas (auditory and visual) topologically organized. Neurons in each area receive an input from the external environment, computed as the inner product of the sensory-specific stimulus and the receptive field synapses, and a cross-modal input from neurons of the other modality. Based on sensory experience, synapses were trained via Hebbian potentiation and a decay term. Aim of this work is to improve the previous model, including a more realistic distribution of visual stimuli: visual stimuli have a higher spatial accuracy at the central azimuthal coordinate and a lower accuracy at the periphery. Moreover, their prior probability is higher at the center, and decreases toward the periphery. Simulations show that, after training, the receptive fields of visual and auditory neurons shrink to reproduce the accuracy of the input (both at the center and at the periphery in the visual case), thus realizing the likelihood estimate of unimodal spatial position. Moreover, the preferred positions of visual neurons contract toward the center, thus encoding the prior probability of the visual input. Finally, a prior probability of the co-occurrence of audio-visual stimuli is encoded in the cross-modal synapses. The model is able to simulate the main properties of a Bayesian estimator and to reproduce behavioral data in all conditions examined. In particular, in unisensory conditions the visual estimates exhibit a bias toward the fovea, which increases with the level of noise. In cross modal conditions, the SD of the estimates decreases when using congruent audio-visual stimuli, and a ventriloquism effect becomes evident in case of spatially disparate stimuli. Moreover, the ventriloquism decreases with the eccentricity.

Dataset Information

A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis.

Publications

A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis.

Similar Datasets

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