Multivariate neural biomarkers of emotional states are categorically distinct

Abstract

Understanding how emotions are represented neurally is a central aim of affective neuroscience. Despite decades of neuroimaging efforts addressing this question, it remains unclear whether emotions are represented as distinct entities, as predicted by categorical theories, or are constructed from a smaller set of underlying factors, as predicted by dimensional accounts. Here, we capitalize on multivariate statistical approaches and computational modeling to directly evaluate these theoretical perspectives. We elicited discrete emotional states using music and films during functional magnetic resonance imaging scanning. Distinct patterns of neural activation predicted the emotion category of stimuli and tracked subjective experience. Bayesian model comparison revealed that combining dimensional and categorical models of emotion best characterized the information content of activation patterns. Surprisingly, categorical and dimensional aspects of emotion experience captured unique and opposing sources of neural information. These results indicate that diverse emotional states are poorly differentiated by simple models of valence and arousal, and that activity within separable neural systems can be mapped to unique emotion categories.

Multivariate Brain Prediction of Heart Rate and Skin Conductance Responses to Social Threat

“…Interestingly, recent neuroanatomical work in monkeys has shown multisynaptic projections to a peripheral sympathetic effector system (the adrenal medulla) from multiple discrete areas within the medial wall, including dorsal and ventral aspects of the mPFC and SMA (Dum et al, 2016). This diversity of regions parallels recent findings that emotional states are not only related to activity in single areas of the brain, but also to a complex network (Kassam et al, 2013;Nummenmaa et al, 2014;Kragel and LaBar, 2015). Our HR-predictive patterns, in particular, also identified multiple areas of the dorsal and ventral mPFC and SMA (Fig.…”

Emotion Perception from Face, Voice, and Touch: Comparisons and Convergence

“…Building on earlier work suggesting that perceptual representations feed into systems for judging emotions [89], neuroimaging findings point to specific nodes in regions such as the posterior STS that may mediate between early emotion perception and mental state inference [90]. Similarly, regions of right somatosensory cortex within which lesions [20] or brain stimulation [91] can alter multimodal emotion recognition [92,93], show multivoxel activations in fMRI that correlate with self-reported emotional experiences of the perceiver [30]. There are thus rich connections between early perceptual processing, later mental state inference, and emotion induction in the perceiver.…”

“…Additionally, an intriguing recent study found that multivoxel decoding of individual emotions could be achieved from somatosensory cortex. The accuracy of such decoding showed a somatotopic pattern: for example, the perception of fear, which is distinguished by wide eyes, could be decoded best from the region of somatosensory cortex that represents the eyes [30]. Case studies of rare lesion patients have provided evidence for emotion differentiation in subcortical structures as well.…”

The neuroscience of understanding the emotions of others

“…In support of this hypothesis, lesions of somatosensory-related cortices (particularly in the right hemisphere) disrupt people’s ability to recognize facial emotion (Adolphs, Damasio, Tranel, Cooper, & Damasio, 2000). Most interestingly, fMRI studies have found that MVPA of regions within right somatosensory cortex represented information about specific facial expressions, and that this representation showed some somatotopy such that specific emotions could be decoded from those regions of S1 that represent the facial features most diagnostic for that emotion (such as wide eyes for fear; Kragel & LaBar, 2015). …”