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A mesocorticolimbic signature of pleasure in the human brain

Nature Human Behaviour volume 7, pages 1332–1343 (2023)Cite this article

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Abstract

Pleasure is a fundamental driver of human behaviour, 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 in these regions conveys a generalizable representation of pleasure regulated 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, this signature is sensitive to pleasant tastes and affect evoked by humour. The signature is spatially co-extensive with mu-opioid receptor gene expression, and its response is attenuated by the opioid antagonist naloxone. These findings provide evidence for a basis of pleasure in humans that is distributed across brain systems.

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Fig. 1: An fMRI-based signature for pleasure.
Fig. 2: Validation of the pleasure signature.
Fig. 3: Cortical and subcortical representations of pleasure.
Fig. 4: Opioid contributions to the pleasure signature.

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Data availability

Data used to train and validate the signature are available at https://osf.io/vs84r/. Data from the Allen Brain Atlas are available at https://neurosynth.org/genes/ and http://portal.brain-map.org/.

Code availability

Code for reproducing the findings presented in this manuscript is available at https://github.com/ecco-laboratory/PMA. SPM can be downloaded from https://www.fil.ion.ucl.ac.uk/spm/software/download/, and the CanlabCore Tools are available at https://github.com/canlab/CanlabCore.

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Acknowledgements

This project was supported by grants R01MH126083 and R00MH102355 to M.T.T. D.A.P. was partially supported by grants P50MH119467 and R37MH068376. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Department of Psychology, Emory University, Atlanta, GA, USA

    Philip A. Kragel, Michael T. Treadway & Evan C. Hahn

  2. Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, USA

    Philip A. Kragel & Michael T. Treadway

  3. Department of Psychiatry, Harvard Medical School and McLean Hospital, Belmont, MA, USA

    Roee Admon & Diego A. Pizzagalli

  4. School of Psychological Sciences, University of Haifa, Haifa, Israel

    Roee Admon

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Conceptualization, methodology, formal analysis, writing—original draft, writing—review and editing, validation and visualization, P.A.K.; conceptualization, review and editing, and resources, M.T.T.; writing—review and editing, and resources, R.A.; writing—review and editing, and resources, D.A.P.; conceptualization, writing—original draft, review and editing, and formal analysis, E.C.H.

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Correspondence to Philip A. Kragel.

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Competing interests

The authors declare the following competing interests: in the past 3 years M.T.T. has served as a paid consultant to Neumora Therapeutics (formerly BlackThorn Therapeutics) and Boehringer Ingelheim. Over the past 3 years, D.A.P. has received consulting fees from Albright Stonebridge Group, Boehringer Ingelheim, Compass Pathways, Engrail Therapeutics, Neumora Therapeutics (formerly BlackThorn Therapeutics), Neurocrine Biosciences, Neuroscience Software, Otsuka, Sunovion and Takeda; he has received honoraria from the Psychonomic Society and the American Psychological Association (for editorial work) and Alkermes; he has received research funding from the Brain and Behavior Research Foundation, the Dana Foundation, Millennium Pharmaceuticals, National Institute of Mental Health (NIMH) and Wellcome Leap; he has received stock options from Compass Pathways, Engrail Therapeutics, Neumora Therapeutics and Neuroscience Software. No funding from these entities was used to support the current work, and all views expressed are solely those of the authors. The remaining authors declare no competing interests.

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Nature Human Behaviour thanks Junichi Chikazoe, Dylan Nielson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Multivariate models discriminate brain states during manipulations of pleasure, pain, cognitive control, and negative affect.

(a) Rendering of z-scores for beta estimates from Partial Least Squares regression fit on training data (n = 499) overlaid on the ICBM152 template. Warm colors are positively associated with predictions of each domain, whereas cool colors are negatively associated with each domain. (b) Confusion matrix estimated using stratified 5-fold cross-validation in the training dataset (4-way accuracy = 47.17%, all four classes are statistically distinguishable at p < .05). Rows have been normalized to sum to 1. (c) Clustering of domains based on classification errors. Dendrogram shows clustering of errors using Ward’s linkage. Dashed vertical line depicts the optimal cut point, in which all four domains are assigned to separate clusters.

Extended Data Fig. 2 Signature coefficients within ventral pallidum and nucleus accumbens.

(a) Volumetric rendering of anatomically defined regions of interest overlaid on the ICBM152 template. (b) Signature coefficients (beta estimates from Partial Least Squares regression) within the ventral pallidum that predict states of pleasure. Warm colors are positively associated with predictions of pleasure, whereas cool colors are negatively associated with pleasure. MNI coordinates (mm in the y dimension) are shown next to each section. (c) Signature coefficients in the nucleus accumbens.

Extended Data Fig. 3 Estimated spatial smoothness of the pleasure signature.

The empirical spatial autocorrelation of the pleasure signature (black) and estimates using both Gaussian (green) and mono-exponential fit (red) are shown. Figure generated from the AFNI program 3dFWHMx.

Extended Data Fig. 4 A simplified, region-average model of the signature response is neither sensitive nor specific to pleasure.

(a) The simplified model defined by the average of coefficients from the optimized signature. Warm colors indicate regions in which increases in brain activity contribute to predictions of pleasure, whereas cool colors indicate regions in which increased brain activity leads to fewer classifications of pleasure. (b) Box and whisker plot shows differences in the region-average signature response for each study (ns = 26, 26, 13, 24 independent participants; *mean = .0412, t25 = 2.948, p = .00685, d = .578, 95% Confidence Interval = [.0138 .0686], uncorrected two-sided paired t-test). Black lines depict the mean response, light-shaded regions depict one standard deviation, and darker-shaded regions two standard errors. Each point corresponds to the response of a single subject. (c) Receiver operating characteristic curves for each of the four studies.

Extended Data Fig. 5 Brain reward signature is sensitive to reward feedback, but not pleasure.

(a) Reward signature trained to discriminate gains and losses in a monetary incentive delay task. Warm colors indicate regions in which increases in brain activity contribute to predictions of greater reward, whereas cool colors indicate regions in which increases in brain activity lead to lower levels of reward. (b) Box and whisker plot shows differences in the region-average signature response for each study (ns = 26, 26, 13, 24 independent participants; *mean = .0279, t25 = 3.221, p = .00353, d = .632, 95% Confidence Interval = [.0109 .0448], uncorrected two-sided paired t-test). Black lines depict the mean response, light-shaded regions depict one standard deviation, and darker-shaded regions two standard errors. Each point corresponds to the response of a single subject. (c) Receiver operating characteristic curves for each of the four studies.

Extended Data Fig. 6 Spatial correlation of neurotransmitter gene expression maps from the Allen Brain Atlas.

Heatmap depicts the Pearson correlation coefficient between pairs of gene expression maps, with cool colors indicating negative correlations and warm colors positive correlations.

Extended Data Fig. 7 Spatial correlation between Partial Least Squares regression coefficients and neurotransmitter gene expression maps from the Allen Brain Atlas.

*p < .05; **qFDR < .05, two-sided bootstrap test. Full statistics are reported in Supplementary Table 7.

Extended Data Fig. 8 Group-average contrast maps showing the effect of naloxone on brain activity during the presentation of erotic images and feedback about monetary rewards.

Warm colors indicate greater activity during saline placebo administration compared to naloxone, whereas cool colors indicate a greater response during naloxone administration compared to placebo.

Supplementary information

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Supplementary Figs. 1–3 and Tables 1–9.

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Kragel, P.A., Treadway, M.T., Admon, R. et al. A mesocorticolimbic signature of pleasure in the human brain. Nat Hum Behav 7, 1332–1343 (2023). https://doi.org/10.1038/s41562-023-01639-0

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