The long-term goal of the proposed study is to characterize the relationship between cerebello-prefrontal networks with respect to symptom severity and course of illness in individuals at ultra high-risk (UHR) for psychosis. UHR individuals are at much higher risk for the development of an Axis I psychotic disorder, and identifying neural differences associated with symptomatology and the course of illness is a key first step towards the development of predictive biomarkers for psychosis. Such biomarkers would open the door to more targeted preventative therapeutics. While movement abnormalities associated with striatal function are associated with the conversion to psychosis, we have also found evidence distinctly implicating the cerebellum in symptom severity in UHR individuals. While the cerebellum has been well studied in schizophrenia, and its networks, particularly networks associated with the prefrontal cortex, are implicated in the cognitive dysmetria framework for the dysfunction seen in schizophrenia, it has been relatively understudied in UHR populations. There is some evidence to indicate cerebellar volumetric decreases in UHR groups, and there is decreased resting state cerebello-cortical connectivity in first-degree relatives of schizophrenia patients, but the literature on this topic is generally sparse. Given ou recent finding of a relationship between cerebellar dysfunction and symptom severity, along with the contributions of the cerebellum to schizophrenia and cognitive dysmetria, cerebellar networks are an important target for research in UHR populations. Here, we aim to 1) investigate group differences in resting state functional cerebello-prefrontal cortical networks and 2) investigate group differences in brain structure and structural connectivity of cerebello-prefrontal cortical networks between UHR and healthy controls. Crucially, we will also investigate the relationship between the integrity of these networks (structural and functional), and the volume of cerebellar and prefrontal nodes in these networks, with respect to symptom severity, cognitive function, and the course of illness using a two year longitudinal design. Using multi-modal neuroimaging we will collect resting state connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) in conjunction with high-resolution anatomical scans annually. In addition, all participants will complete cognitive testing, along with clinical assessments to quantify symptom severity and disease progression in UHR individuals. I will receive key training in translational research and in both DTI and structural anatomical analysis methods. Knowledge of the relationships between cerebellar-prefrontal networks and the development of psychosis is crucial for gaining a complete picture of the etiology of schizophrenia. Doing so will help explain the role of the cerebellum in schizophrenia, and its etiology. Furthermore, this may facilitate the development of targeted interventions that may improve disease course and treatment outcomes in at-risk populations.

Public Health Relevance

The proposed research will investigate brain networks that may underlie the development of schizophrenia in at-risk individuals. Though schizophrenia affects only about 1% of the population, it has devastating personal and societal consequences. Identifying neural differences in these at-risk populations will aid in our understanding of the underlying factors causing schizophrenia, and importantly, may lead to preventative therapies that could ease some of the devastating effects of schizophrenia on the impacted individual, their family, and society as a whole.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Postdoctoral Individual National Research Service Award (F32)
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Special Emphasis Panel (ZRG1)
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Sarampote, Christopher S
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University of Colorado at Boulder
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Bernard, Jessica A; Orr, Joseph M; Mittal, Vijay A (2017) Cerebello-thalamo-cortical networks predict positive symptom progression in individuals at ultra-high risk for psychosis. Neuroimage Clin 14:622-628
Bernard, Jessica A; Orr, Joseph M; Mittal, Vijay A (2016) Differential motor and prefrontal cerebello-cortical network development: Evidence from multimodal neuroimaging. Neuroimage 124:591-601
Bernard, Jessica A; Orr, Joseph M; Mittal, Vijay A (2015) Abnormal hippocampal-thalamic white matter tract development and positive symptom course in individuals at ultra-high risk for psychosis. NPJ Schizophr 1:
Bernard, Jessica A; Leopold, Daniel R; Calhoun, Vince D et al. (2015) Regional cerebellar volume and cognitive function from adolescence to late middle age. Hum Brain Mapp 36:1102-20
Bernard, Jessica A; Mittal, Vijay A (2015) Dysfunctional Activation of the Cerebellum in Schizophrenia: A Functional Neuroimaging Meta-Analysis. Clin Psychol Sci 3:545-566
Bernard, Jessica A; B Millman, Zachary; Mittal, Vijay A (2015) Beat and metaphoric gestures are differentially associated with regional cerebellar and cortical volumes. Hum Brain Mapp 36:4016-30
Dean, Derek J; Kent, Jerillyn S; Bernard, Jessica A et al. (2015) Increased postural sway predicts negative symptom progression in youth at ultrahigh risk for psychosis. Schizophr Res 162:86-9
Pelletier-Baldelli, Andrea; Bernard, Jessica A; Mittal, Vijay A (2015) Intrinsic Functional Connectivity in Salience and Default Mode Networks and Aberrant Social Processes in Youth at Ultra-High Risk for Psychosis. PLoS One 10:e0134936
Bernard, J A; Mittal, V A (2015) Updating the research domain criteria: the utility of a motor dimension. Psychol Med 45:2685-9
Bernard, Jessica A; Dean, Derek J; Kent, Jerillyn S et al. (2014) Cerebellar networks in individuals at ultra high-risk of psychosis: impact on postural sway and symptom severity. Hum Brain Mapp 35:4064-78

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