Identifying the Neural Correlates of Resting State Affect Processing Dynamics

There exists growing interest in understanding the dynamics of resting state functional magnetic resonance imaging (rs-fMRI) to establish mechanistic links between individual patterns of spontaneous neural activation and corresponding behavioral measures in both normative and clinical populations. Here we propose and validate a novel approach in which whole-brain rs-fMRI data are mapped to a specific low-dimensional representation-affective valence and arousal processing-prior to dynamic analysis. This mapping process constrains the state space such that both independent validation and visualization of the system's dynamics become tractable. To test this approach, we constructed neural decoding models of affective valence and arousal processing from brain states induced by International Affective Picture Set image stimuli during task-related fMRI in (n = 97) healthy control subjects. We applied these models to decode moment-to-moment affect processing in out-of-sample subjects' rs-fMRI data and computed first and second temporal derivatives of the resultant valence and arousal time-series. Finally, we fit a second set of neural decoding models to these derivatives, which function as neurally constrained ordinary differential equations (ODE) underlying affect processing dynamics. To validate these decodings, we simulated affect processing by numerical integration of the true temporal sequence of neurally decoded derivatives for each subject and demonstrated that these decodings generate significantly less (p < 0.05) group-level simulation error than integration based upon decoded derivatives sampled uniformly randomly from the true temporal sequence. Indeed, simulations of valence and arousal processing were significant for up to four steps of closed-loop simulation (Δt = 2.0 s) for both valence and arousal, respectively. Moreover, neural encoding representations of the ODE decodings include significant clusters of activation within brain regions associated with affective reactivity and regulation. Our work has methodological implications for efforts to identify unique and actionable biomarkers of possible future or current psychopathology, particularly those related to mood and emotional instability.