Abstract
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.
Highlights
Within the subset of experiments focused on decoding affective information from complex visual imagery, Multivariate pattern analysis (MVPA) has been deployed to significantly classify brain states across both the discrete[17] and dimensional[7,18,19,20] models of emotion processing
The primary goal of this work is to test a conceptual model of the brain state as the central unit of affect processing within the dimensional model of emotion by (1) predicting multiple, continuously-valued dimensions of affect from brain states defined by a single measurement modality, the functional magnetic resonance imaging (fMRI)-derived blood oxygen-level dependent (BOLD) signal, while (2) simultaneously predicting an independent measure of stimulus-driven affect perception based on well-studied psychophysiological response
(3) Verifying the anatomical consistency of the derived brain states with respect to functional regions long-established in the fMRI-based emotion processing literature across multiple paradigms as well as (4) verifying the anatomical orthogonality of valence and arousal processing performed by brain states that is imposed by the circumplex model of dimensional emotion
Summary
Within the subset of experiments focused on decoding affective information from complex visual imagery, MVPA has been deployed to significantly classify brain states across both the discrete[17] and dimensional[7,18,19,20] models of emotion processing. The primary goal of this work is to test a conceptual model of the brain state as the central unit of affect processing within the dimensional model of emotion by (1) predicting multiple, continuously-valued dimensions of affect from brain states defined by a single measurement modality, the fMRI-derived blood oxygen-level dependent (BOLD) signal, while (2) simultaneously predicting an independent measure of stimulus-driven affect perception based on well-studied psychophysiological response. (3) Verifying the anatomical consistency of the derived brain states with respect to functional regions long-established in the fMRI-based emotion processing literature across multiple paradigms as well as (4) verifying the anatomical orthogonality of valence and arousal processing performed by brain states that is imposed by the circumplex model of dimensional emotion To achieve this goal we conducted analysis of fMRI and SCR measurements that were concurrently recorded during a visual stimulus-based affective perception experiment.
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