Bipolar disorder is a dynamic, progressive illness in which treatment decisions are made empirically. Neuroimaging has helped to define neurophysiological models of bipolar disorder that offer the promise of improving treatment assignment by identifying markers and predictors of treatment response and disease progression. We propose the UC Bipolar Disorder Imaging &Treatment Research Center (BITREC) to meet this promise. The overall hypothesis and organizing theme guiding this center is that bipolar disorder results from dysfunctional brain metabolism within specific brain systems (i.e., the anterior limbic network) that can be monitored in response to treatments using magnetic resonance imaging (MRI) and spectroscopy (MRS). In this center, we will integrate MRI and MRS with outcome measures and treatment trials in order to develop specific neurophysiological models of treatment response early in the course of bipolar disorder. These models will provide the means to identify markers and predictors of treatment response to individualize medication prescription, thereby minimizing affective recurrences, preventing disease progression, and improving long-term course of illness. In order to achieve our goals, we will integrate our extensive prior experience and current resources to create specific research and administrative cores;these cores will be used to establish the BITREC infrastructure. With this infrastructure in place, we will recruit a large sample of bipolar patients early in the course of illness (prior to significant illness progression), as well as a cohort of at-risk subjects (adolescents without bipolar disorder, but with bipolar parents). These subjects will be asked to participate in specific projects integrating MRI, MRS, treatment and clinical outcome in order to refine existing models of the neurophysiology of bipolar disorder. We will use these models in order to identify specific neurophysiological markers of treatment response in order to clarify the effects that different medications have on bipolar neurophysiology. We will then use these markers to develop specific neurophysiological predictors of treatment response and illness progression in bipolar disorder. Finally, we will apply these models to at-risk patients (prior to developing a first manic episode) in order to prevent or delay the onset of bipolar disorder.

Public Health Relevance

Bipolar disorder is a common mental illness that is the 6th leading cause of disability worldwide. Treating people with bipolar disorder is complicated by the lack of specific indicators to help guide treatment decisions. With the proposed center, we hope to use advances in neuroimaging technologies to identify treatment indicators, in order to improve treatment assignment and diminish the disability and mortality from this severe psychiatric condition.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Specialized Center (P50)
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Special Emphasis Panel (ZMH1-ERB-S (01))
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Hillefors, MI
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University of Cincinnati
Schools of Medicine
United States
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Nery, Fabiano G; Norris, Matthew; Eliassen, James C et al. (2017) White matter volumes in youth offspring of bipolar parents. J Affect Disord 209:246-253
Fleck, David E; Ernest, Nicholas; Adler, Caleb M et al. (2017) Prediction of lithium response in first-episode mania using the LITHium Intelligent Agent (LITHIA): Pilot data and proof-of-concept. Bipolar Disord 19:259-272
Welge, Jeffrey A; Saliba, Lawrence J; Strawn, Jeffrey R et al. (2016) Neurofunctional Differences Among Youth With and at Varying Risk for Developing Mania. J Am Acad Child Adolesc Psychiatry 55:980-989
Strakowski, Stephen M; Fleck, David E; Welge, Jeffrey et al. (2016) fMRI brain activation changes following treatment of a first bipolar manic episode. Bipolar Disord 18:490-501
McNamara, Robert K; Jandacek, Ronald; Tso, Patrick et al. (2016) Adolescents with or at ultra-high risk for bipolar disorder exhibit erythrocyte docosahexaenoic acid and eicosapentaenoic acid deficits: a candidate prodromal risk biomarker. Early Interv Psychiatry 10:203-11
McNamara, Robert K; Moser, Ann B; Jones, Richard I et al. (2016) Familial risk for bipolar disorder is not associated with impaired peroxisomal function: Dissociation from docosahexaenoic acid deficits. Psychiatry Res 246:803-807
McNamara, Robert K; Jandacek, Ronald; Tso, Patrick et al. (2015) First-episode bipolar disorder is associated with erythrocyte membrane docosahexaenoic acid deficits: Dissociation from clinical response to lithium or quetiapine. Psychiatry Res 230:447-53
Jacob, Shawna N; Shear, Paula K; Norris, Matthew et al. (2015) Impact of functional magnetic resonance imaging (fMRI) scanner noise on affective state and attentional performance. J Clin Exp Neuropsychol 37:563-70
Strawn, Jeffrey R; Adler, Caleb M; McNamara, Robert K et al. (2014) Antidepressant tolerability in anxious and depressed youth at high risk for bipolar disorder: a prospective naturalistic treatment study. Bipolar Disord 16:523-30
Cerullo, Michael A; Eliassen, James C; Smith, Christopher T et al. (2014) Bipolar I disorder and major depressive disorder show similar brain activation during depression. Bipolar Disord 16:703-12

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