Maintaining an adaptive balance of emotions is central to well-being, and dysregulated emotions contribute
broadly to clinical disorders that impart high personal and societal burdens. Recognizing the transdiagnostic
importance of emotion to mental health, the National Institute of Health's Research Domain Criteria (RDoC)
matrix contains overarching domains of Negative Valence, Positive Valence, and Arousal. However, the matrix
underspecifies how specific affective states like sadness, anxiety, or craving are organized within and across
these domains, in part because it is unknown whether representations of discrete emotions are reliably
differentiated. Other RDoC constructs, such as rumination and worry, modify the temporal parameters of
emotions that confer psychopathology risk and exacerbate symptom maintenance. Nonetheless, it is unknown
how these processes interface with emotional brain circuits to impact affect dynamics, particularly as they often
occur spontaneously during mind wandering. The proposed research promises to improve the RDoC depiction
of these emotion-related constructs by taking an affective computing approach. During combined recording of
psychophysiology and functional magnetic resonance imaging (fMRI), adult participants will experience
emotions to vignettes and movie clips spanning the arousal and valence dimensions, and will report on their
spontaneous emotions during resting-state fMRI scans. Machine learning algorithms will decode emotion-
specific signals across the levels of analysis, which will be integrated using Bayesian state-space modeling. An
analysis of classifier errors will test competing predictions from emotion theories regarding the optimal
structure of affective space. Using graph theoretic tools, we will characterize the neural network architecture of
the discrete emotion representations to identify provincial and connector hubs that can be used as novel targets
for future symptom-specific or co-morbid neuromodulation interventions, respectively. We will apply the
emotion-specific maps to resting-state data from the same participants to create neurophysiological indices of
spontaneous emotions and to relate their frequencies to measures of trait and state affect as a validation step.
Using stochastic modeling of the resting-state data, we will derive temporal dynamics metrics to test the
hypothesis that rumination and worry promote emotional inertia during mind wandering. Finally, we will use
existing data repositories to demonstrate that our novel indices of affect dynamics transdiagnostically
differentiate resting-state fMRI activity patterns in mental health disorders from healthy controls. The
proposed research will improve upon current RDoC formulations of Negative Affect, Positive Affect, and
Arousal domains by informing how discrete emotions are organized within and across these domains, by
integrating emotion representations across multiple RDoC units of analysis, by informing how rumination and
worry impact neurophysiological signatures of spontaneous emotions, and by establishing the clinical utility of
computationally-derived metrics of emotion dynamics.