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Multivariate pattern analysis (MVPA) of functional magnetic resonance imaging (fMRI) data has critically advanced the neuroanatomical understanding of affect processing in the human brain. Central to these advancements is the brain state, a temporally-succinct fMRI-derived pattern of neural activation, which serves as a processing unit. Establishing the brain state’s central role in affect processing, however, requires that it predicts multiple independent measures of affect. We employed MVPA-based regression to predict the valence and arousal properties of visual stimuli sampled from the International Affective Picture System (IAPS) along with the corollary skin conductance response (SCR) for demographically diverse healthy human participants (n = 19). We found that brain states significantly predicted the normative valence and arousal scores of the stimuli as well as the attendant individual SCRs. In contrast, SCRs significantly predicted arousal only. The prediction effect size of the brain state was more than three times greater than that of SCR. Moreover, neuroanatomical analysis of the regression parameters found remarkable agreement with regions long-established by fMRI univariate analyses in the emotion processing literature. Finally, geometric analysis of these parameters also found that the neuroanatomical encodings of valence and arousal are orthogonal as originally posited by the circumplex model of dimensional emotion. 

Patterns of estimated neural activity derived from resting state functional magnetic resonance imaging (rs-fMRI) have been shown to predict a wide range of cognitive and behavioral outcomes in both normative and clinical populations. Yet, without links to established cognitive processes, the functional brain states associated with the resting brain will remain unexplained, and potentially confounded, markers of individual differences. In this work we demonstrate the application of multivoxel pattern classifiers (MVPCs) to predict the valence and arousal properties of spontaneous affect processing in the task-non-engaged resting state. rs-fMRI data were acquired from subjects that were held out from a subject set that underwent image-based affect induction concurrent with fMRI to train the MVPCs. We also validated these affective predictions against a well-established, independent measure of autonomic arousal, skin conductance response. These findings suggest a new neuroimaging methodology for resting state analysis in which models, trained on cognition-specific task-based fMRI acquired from well-matched cohorts, capably predict hidden cognitive processes operating within the resting brain.