Lithium Effects on the Brain's Functional and Structural Connectome in the Treatment of Bipolar Disorder

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ABSTRACT Lithium is one of the most specific and effective treatments for bipolar disorder (BD) yet its mechanism of action remains unclear. This has impeded methods to monitor its effects and the development of new medications with similar efficacy and specificity. Clinical efficacy of lithium in bipolar disorder, and its complex effects on multiple brain physiological functions, may be best deciphered using a network properties-metric approach. This approach is critical because it provides insight into the function of brain networks (e.g., resilience to disruption, central hubs), which is likely to be more closely linked to behavioral outcomes. Furthermore, the relationship of the neuroimaging effects of lithium and its molecular effects in humans has been studied even less. This proposal addresses these important gaps in knowledge by measuring brain functional and structural connectomics before and after acute and longer term treatment with lithium. We will study 90 medication free bipolar disorder depressed (BDD) type II subjects at baseline and after 2, 8 and 26 weeks of lithium monotherapy. We will also study 30 closely matched healthy controls who will be imaged at the same time points but will not receive any treatment. BDD II subjects will undergo resting state functional magnetic resonance imaging (fMRI) scans and diffusion weighted structural imaging scans. In addition to imaging, we will also collect RNA samples from peripheral blood lymphocytes to explore lithium induced peripheral gene expression changes. Functional and structural scans will be analyzed using graph theory metrics (GTM) and Independent Component Analysis (ICA). Graph theory metrics move beyond simple description of brain connectivity and provide insight into network function (e.g., resilience to disruption, regional hubs). ICA analysis has identified consistent and well defined independent components in resting state data e.g the default mode and salience networks. Preliminary data from our studies indicates significant effect of lithium on GTM and ICA metrics and correlation of the changes in these metrics to clinical improvement. In this proposal, we will acquire images on a state-of-the-art 7-T scanner, conduct rigorous motion correction and apply cutting-edge GTM and ICA analytics. We will correlate changes in GTA and ICA metrics to improvement in depression in the short term and mood stability in the longer term in BDD II subjects. Furthermore, we will conduct an exploratory analysis regarding association between changes in peripheral gene expression and improvement in symptomatology and mood stability using the GTA metrics change as mediators and using ICA multimodality fusion analysis. This will be an exploratory analysis to yield data for future more definitive human and basic science studies of lithium effect. This study will therefore provide unique data regarding imaging and molecular correlates of lithium monotherapy and its relationship to lithium efficacy in bipolar disorder. In addition, this study will provide a novel paradigm to investigate effects of lithium and other psychotropics using network-property metrics as biomarkers which can be used for treatment monitoring and drug development.