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Abstract #3166

Decoding the neural representation of value in schizophrenia with negative symptoms

Reihaneh Forouhandehpour 1,2, Guillermo Horga3, and Clifford Cassidy1,2
1Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada, 2The Royal’s institute of mental health research, Ottawa, ON, Canada, 3Department of Psychiatry, Columbia University, New York, NY, United States

Synopsis

Keywords: Task/Intervention Based fMRI, fMRI, Schizophrenia, machine learning, MVPA, decision maiking, MRI, decoding, tasked-based fmri

Motivation: The neural mechanisms underlying motivation and pleasure deficits in schizophrenia have not yet been elucidated.

Goal(s): We aimed to develop a method to disentangle different aspects of value-based decision making to understand these deficits in schizophrenia.

Approach: We developed a novel fMRI task and combined it with machine learning approaches to effectively measure multiple components of value-based decision making.

Results: Our preliminary results revealed that patterns of brain activity in the value regions of the brain (ventromedial PFC and ventral striatum) are predictive of healthy individuals’ decisions; however, such patterns do not provide value representation to support decision making in schizophrenia.

Impact: Neural patterns in the value regions of the brain were shown to not represent value to support decision making in schizophrenia. These findings will provide targets for treatment developments for motivation and pleasure deficits in schizophrenia and other psychiatric conditions.

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Keywords