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Multi-method Approaches to Identifying Reproducible, Robust, and Replicable Neural Network Risk Markers of Bipolar Disorder

Year: 2024
Type: article
Abstract: Bipolar Disorder (BD) is a debilitating neuropsychiatric disorder characterized by recurrent alternating episodes of mania/hypomania, depression, mixed states, and psychosis, although it is mania and hypomania that define BD and set it apart from other psychiatric disorders and brain- based diseases. While having a familial history of BD can increase one’s risk by tenfold, there are many individuals that become diagnosed with BD who do not have a genetic predisposition, yet we do not have established methods or biomarkers for identifying these individuals. Neuroimaging can identify objective risk markers for BD by capitalizing on its neural basis to yield neurobiologically-relevant markers that can subsequently inform targets for future treatments. However, for neuroimaging-defined biomarkers of BD to be clinically informative, they must also be reproducible, robust, and replicable, and this dissertation proposes three different methods to derive such markers. We started with a large-scale meta-analysis, synthesizing three decades of functional neuroimaging studies in BD, that sought to determine whether the extant literature provided justification for reproducible and robust markers of BD given substantial clinical and methodological heterogeneity, and this meta-analysis identified robust group-level markers distinguishing adults with BD from controls that were also condition-dependent (Chapter 3). Next, in a cross-sectional analysis of three independent samples of young adults with spectrum-level mania/hypomania and depression, we aimed to determine whether there were objective, reproducible neural markers specific to risk for mania/hypomania (i.e., BD-specific risk) versus risk for depression, and we identified several patterns of neural network connectivity distinguishing, and common to, risk for mania/hypomania and depression that also replicated across samples (Chapter 4). Finally, we aimed to identify neural markers of low versus high risk for BD, defined by trait emotion-related impulsivity i.e., negative and positive urgency (Chapter 5). The neuroimaging findings identified by these three approaches highlight the involvement of four neural networks—the salience, default mode, ventral attention, and central executive networks—and both reinforce long-held consensus views about the neurobiology implicated in BD and BD risk while also expanding this consensus, thereby validating and challenging our broader understanding of the neural mechanisms underlying and predisposing to the starting point of BD.
Language: English
Field: Medicine
Open Access status: green
OpenAlex ID: W6983591693
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