A mesocorticolimbic signature of pleasure in the human brain

Abstract

Pleasure is a fundamental driver of human behavior, yet its neural basis remains largely unknown. Rodent studies highlight opioidergic neural circuits connecting the nucleus accumbens, ventral pallidum, insula, and orbitofrontal cortex as critical for the initiation and regulation of pleasure, and human neuroimaging studies exhibit some translational parity. However, whether activation observed across these regions reflects a common, generalizable code for pleasure driven by opioidergic mechanisms remains unclear. Here we use pattern recognition techniques to develop a human functional magnetic resonance imaging signature of mesocorticolimbic activity unique to states of pleasure. In independent validation tests, we find this signature has high sensitivity to pleasant tastes and positive affect evoked by humor. The signature is spatially coextensive with mu-opioid receptor gene expression, and its response is attenuated by the opioid antagonist naloxone. These findings provide evidence of a basis for pleasure derived from primary and secondary rewards in humans that is distributed across brain systems, and suggest that similar mechanisms underlie hedonic impact across mammalian species.

Understanding human amygdala function with artificial neural networks

“…Together, these findings are broadly consistent with studies reporting the amygdala is involved in reward learning and evaluating social images (Baxter and Murray, 2002;Adolphs and Spezio, 2006). They are also congruent with work in nonhuman primates showing that both pleasant and unpleasant stimuli engage distributed neural populations in the amygdala (Paton et al, 2006;Belova et al, 2008), and with fMRI evidence showing that the amygdala participates in a distributed network of brain regions sensitive to fluctuations in hedonic valence (Kragel et al, 2023).…”

“…Future studies using different movies, videos, or other dynamic visual stimuli to train encoding models are needed to identify the set of variables encoded by the amygdala, and to assess the extent to which they are context dependent or generalize across stimulus types (Čeko et al, 2022) and situations (Kragel et al, 2023).…”

A neural signature for the subjective experience of threat anticipation under uncertainty

“…Specifically, multivariate predictive modeling, a machine learning technique based on MVPA, is suggested to be more suitable for identifying neuromarkers for specific subjective emotional states with high effect sizes and clinical significance by providing predictions (instead of local mapping) about emotional experience from distributed neural activity patterns 51,57 . Chang et al showed for instance that a whole-brain multivariate model explained much more variance in predicting experiencing negative affect than local regions 51,52 , and this predictive modeling approach has been successfully utilized to develop models that can sensitively and specifically predict subjective experiences of fear 21 ,pain 53 and pleasure 58 .…”

Decoding mindfulness with multivariate predictive models

“…The potential utility of neuromarkers for examining mindfulness effects are not limited to the example of somatic pain. Other neuromarkers have been developed for observed pain, negative affect, reward and pleasure (37)(38)(39)(40). In particular, a neuromarker called the Neural Craving Signature (NCS), comprising a multivariate pattern of activity involving regions previously established to be involved in reward and addiction (ventromedial PFC, ventral striatum, dorsal anterior cingulate), provides a predictive model of food and drug craving states (41).…”