DEcoding Emotional Dynamics Driving Mood Instability in Bipolar Disorder

Bipolar disorder (BD) is a serious mental health condition characterized by recurring episodes of mania, hypomania, and depression. A defining feature of BD is mood instability, which contributes significantly to emotional dysregulation, impaired functioning, and poor clinical outcomes. However, little is known about the neural mechanisms underlying these emotional dynamics, in part due to the limitations of conventional neuroimaging analyses, which average brain activity over time and fail to capture dynamic affective processes. To address this gap, the proposed study leverages advances in functional neuroimaging and machine learning to investigate how emotional states unfold and fluctuate over time in individuals with BD. Using a validated task-based fMRI paradigm, the Think and Regulate Affective states Task (TReAT), we will decode emotional states at each time point based on whole-brain activation patterns. We will also quantify brain state variability using computational metrics from complexity science, such as metastability and fractal scaling, and identify brain regions that drive emotional transitions using network control theory. Combining these approaches will allow us to characterize emotional state variability over time and examine its underlying dynamics. This approach is innovative in its use of moment-to-moment emotional state decoding, providing a high-resolution view of emotional instability not possible with traditional neuroimaging analyses. Pilot data support the feasibility of this method, showing that emotional states can be accurately decoded. Building on this foundation, we will test the hypothesis that individuals with BD exhibit greater emotional lability, reduced temporal coherence, and altered network controllability during emotional reflection. In addition to identifying neural signatures of mood instability, the study will evaluate how emotion regulation strategies, particularly the upregulation of positive affect, influence emotional trajectories in BD. This work has direct translational potential: identifying the neural mechanisms of emotional instability will lay the groundwork for developing personalized, brain-based interventions aimed at stabilizing mood. The long-term goal is to inform the design of real-time, state-adaptive treatments that target the dynamic brain processes underlying emotional dysregulation in BD.