Active psychosis in schizophrenia is among the most severe and burdensome medical conditions worldwide. However, the mechanisms of psychotic symptoms in this disorder, such as hallucinations (i.e., abnormal percepts in the absence of external sensory stimuli), remain elusive. This K23 application presents a research and training program that will support the applicant on a path towards becoming an NIH-funded independent investigator focused on the application of functional neuroimaging to the study of psychotic symptoms in schizophrenia. The activities in this application build on the candidate's prior training and are set in a resource-rich environment that will foster his development of expertise in (1) advanced analytic methods and study conduct for functional magnetic resonance imaging (fMRI) research;(2) computational neuroscience;(3) perception and cognition research;(4) pathophysiology and clinical assessment of schizophrenia;and (5) responsible and ethical conduct in scientific research with vulnerable populations. Combining functional magnetic resonance imaging (fMRI) and computational modeling, the current research proposal seeks to (1) define the neural mechanisms that generate hallucinations in schizophrenia;and (2) inform the development of a computational model of hallucinations based on predictive coding, an empirically-validated theoretical framework that supports a role of sensory systems in learning and predicting regularities in the external environment. The overarching hypothesis is that abnormal prediction-based attenuation of sensory cortical function produces excessive activity in the sensory cortex that generates hallucinations. To test this hypothesis, the present study will employ a novel speech discrimination fMRI paradigm, two groups of patients with schizophrenia, those with active, frequent auditory verbal hallucinations and those without a significant history of hallucinations, and a third group of healthy controls. This design will allo for testing a direct link between dysfunction in sensory predictive-coding mechanisms and the online experience of hallucinations in patients with schizophrenia, and will thus inform the neurobiological basis of psychotic symptoms in this disorder. Together, this training and research program will facilitate the candidate's transition to an independent research career and will help identify new therapeutic targets for refractory psychosis.

Public Health Relevance

The novel application of the predictive-coding framework and model-based fMRI to the study of psychotic symptoms will shed new light on the mechanisms of generation of psychotic symptoms, thus filling an important gap in schizophrenia research. This project will serve to develop an explanatory model of hallucinations that can be used to generate specific, testable hypotheses for future neuroscience research in both humans and non-human animal models, and to uncover novel targets (sensory prediction deficits) likely modifiable by treatment via learning or pharmacotherapy.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Mentored Patient-Oriented Research Career Development Award (K23)
Project #
1K23MH101637-01A1
Application #
8700122
Study Section
Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section (NPAS)
Program Officer
Wynne, Debra K
Project Start
2014-06-01
Project End
2019-03-31
Budget Start
2014-06-01
Budget End
2015-03-31
Support Year
1
Fiscal Year
2014
Total Cost
$186,192
Indirect Cost
$13,792
Name
New York State Psychiatric Institute
Department
Type
DUNS #
167204994
City
New York
State
NY
Country
United States
Zip Code
10032
Lehembre-Shiah, Eugénie; Leong, Wei; Brucato, Gary et al. (2017) Distinct Relationships Between Visual and Auditory Perceptual Abnormalities and Conversion to Psychosis in a Clinical High-Risk Population. JAMA Psychiatry 74:104-106
Colibazzi, Tiziano; Horga, Guillermo; Wang, Zhishun et al. (2016) Neural Dysfunction in Cognitive Control Circuits in Persons at Clinical High-Risk for Psychosis. Neuropsychopharmacology 41:1241-50
Alderson-Day, Ben; Diederen, Kelly; Fernyhough, Charles et al. (2016) Auditory Hallucinations and the Brain's Resting-State Networks: Findings and Methodological Observations. Schizophr Bull 42:1110-23
Cassidy, Clifford M; Van Snellenberg, Jared X; Benavides, Caridad et al. (2016) Dynamic Connectivity between Brain Networks Supports Working Memory: Relationships to Dopamine Release and Schizophrenia. J Neurosci 36:4377-88
Reinen, Jenna M; Van Snellenberg, Jared X; Horga, Guillermo et al. (2016) Motivational Context Modulates Prediction Error Response in Schizophrenia. Schizophr Bull 42:1467-1475
Horga, Guillermo; Cassidy, Clifford M; Xu, Xiaoyan et al. (2016) Dopamine-Related Disruption of Functional Topography of Striatal Connections in Unmedicated Patients With Schizophrenia. JAMA Psychiatry 73:862-70
Van Snellenberg, Jared X; Girgis, Ragy R; Horga, Guillermo et al. (2016) Mechanisms of Working Memory Impairment in Schizophrenia. Biol Psychiatry 80:617-26
Abi-Dargham, Anissa; Horga, Guillermo (2016) The search for imaging biomarkers in psychiatric disorders. Nat Med 22:1248-1255
Horga, Guillermo; Maia, Tiago V; Marsh, Rachel et al. (2015) Changes in corticostriatal connectivity during reinforcement learning in humans. Hum Brain Mapp 36:793-803