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Common and stimulus-type-specific brain representations of negative affect

Abstract

The brain contains both generalized and stimulus-type-specific representations of aversive events, but models of how these are integrated and related to subjective experience are lacking. We combined functional magnetic resonance imaging with predictive modeling to identify representations of generalized (common) and stimulus-type-specific negative affect across mechanical pain, thermal pain, aversive sounds and aversive images of four intensity levels each. This allowed us to examine how generalized and stimulus-specific representations jointly contribute to aversive experience. Stimulus-type-specific negative affect was largely encoded in early sensory pathways, whereas generalized negative affect was encoded in a distributed set of midline, forebrain, insular and somatosensory regions. All models specifically predicted negative affect rather than general salience or arousal and accurately predicted negative affect in independent samples, demonstrating robustness and generalizability. Common and stimulus-type-specific models were jointly important for predicting subjective experience. Together, these findings offer an integrated account of how negative affect is constructed in the brain and provide predictive neuromarkers for future studies.

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Fig. 1: Task design and main analyses.
Fig. 2: Model evaluation and joint contributions to predicting negative affect.
Fig. 3: Core brain systems for multimodal and stimulus-type-specific negative affect.
Fig. 4: The proposed neural architecture of multimodal negative affect.
Fig. 5: Prediction of negative affect in new datasets.
Fig. 6: Valence specificity and additional validation tests.

Data availability

Brain patterns generated and analyzed during the current study, as well as source data for figures are freely available via . The dataset used in study 6 is available at https://github.com/cocoanlab/interpret_ml_neuroimaging/.

Code availability

Code for analysis and for generating figures is openly shared at https://github.com/canlab/2021_Ceko_MPA2_Aversive/. Analyses reported in this paper were performed using code release v1.0.1 (https://doi.org/10.5281/zenodo.6452244).

References

  1. Berridge, K. C. & Kringelbach, M. L. Neuroscience of affect: brain mechanisms of pleasure and displeasure. Curr. Opin. Neurobiol. 23, 294–303 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Tye, K. M. Neural circuit motifs in valence processing. Neuron 100, 436–452 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Russell, J. A. A circumplex model of affect. J. Pers. Soc. Psychol. 39, 1161–1178 (1980).

    Google Scholar 

  4. Russell, J. A. & Barrett, L. F. Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J. Pers. Soc. Psychol. 76, 805–819 (1999).

    CAS  PubMed  Google Scholar 

  5. Gray, J. A. The Psychology of Fear and Stress. (CUP Archive, 1987).

  6. Montague, P. R. & Berns, G. S. Neural economics and the biological substrates of valuation. Neuron 36, 265–284 (2002).

    CAS  PubMed  Google Scholar 

  7. Padoa-Schioppa, C. & Assad, J. A. Neurons in the orbitofrontal cortex encode economic value. Nature 441, 223–226 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hariri, A. R. et al. A susceptibility gene for affective disorders and the response of the human amygdala. Arch. Gen. Psychiatry 62, 146–152 (2005).

    CAS  PubMed  Google Scholar 

  9. Bishop, S., Duncan, J., Brett, M. & Lawrence, A. D. Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli. Nat. Neurosci. 7, 184–188 (2004).

    CAS  PubMed  Google Scholar 

  10. Duerden, E. G. & Albanese, M.-C. Localization of pain-related brain activation: a meta-analysis of neuroimaging data. Hum. Brain Mapp. 34, 109–149 (2013).

    PubMed  Google Scholar 

  11. Insel, T. et al. Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. Am. J. Psychiatry 167, 748–751 (2010).

    PubMed  Google Scholar 

  12. Hayden, B. Y. & Niv, Y. The case against economic values in the orbitofrontal cortex (or anywhere else in the brain). Behav. Neurosci. 135, 192–201 (2021).

    PubMed  Google Scholar 

  13. Gehrlach, D. A. et al. Aversive state processing in the posterior insular cortex. Nat. Neurosci. 22, 1424–1437 (2019).

    CAS  PubMed  Google Scholar 

  14. Corradi-Dell’Acqua, C., Tusche, A., Vuilleumier, P. & Singer, T. Cross-modal representations of first-hand and vicarious pain, disgust and fairness in insular and cingulate cortex. Nat. Commun. 7, 10904 (2016).

    PubMed  PubMed Central  Google Scholar 

  15. Kragel, P. A. et al. Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. Nat. Neurosci. 21, 283–289 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Chikazoe, J., Lee, D. H., Kriegeskorte, N. & Anderson, A. K. Population coding of affect across stimuli, modalities and individuals. Nat. Neurosci. 17, 1114–1122 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Kober, H. et al. Functional grouping and cortical-subcortical interactions in emotion: a meta-analysis of neuroimaging studies. Neuroimage 42, 998–1031 (2008).

    PubMed  Google Scholar 

  18. Satpute, A. B. et al. Involvement of sensory regions in affective experience: a meta-analysis. Front. Psychol. 6, 1860 (2015).

    PubMed  PubMed Central  Google Scholar 

  19. Ekman, P. An argument for basic emotions. Cognition Emot. 6, 169–200 (1992).

    Google Scholar 

  20. Friedman, B. H. Feelings and the body: the Jamesian perspective on autonomic specificity of emotion. Biol. Psychol. 84, 383–393 (2010).

    PubMed  Google Scholar 

  21. Barrett, L. F. Solving the emotion paradox: categorization and the experience of emotion. Pers. Soc. Psychol. Rev. 10, 20–46 (2006).

    PubMed  Google Scholar 

  22. Saarimäki, H. et al. Discrete neural signatures of basic emotions. Cereb. Cortex 26, 2563–2573 (2016).

    PubMed  Google Scholar 

  23. Kragel, P. A. & LaBar, K. S. Multivariate neural biomarkers of emotional states are categorically distinct. Soc. Cogn. Affect. Neurosci. 10, 1437–1448 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. Stephens, C. L., Christie, I. C. & Friedman, B. H. Autonomic specificity of basic emotions: evidence from pattern classification and cluster analysis. Biol. Psychol. 84, 463–473 (2010).

    PubMed  Google Scholar 

  25. Horing, B., Sprenger, C. & Büchel, C. The parietal operculum preferentially encodes heat pain and not salience. PLoS Biol. 17, e3000205 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wager, T. D. et al. A Bayesian model of category-specific emotional brain responses. PLoS Comput. Biol. 11, e1004066 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. Kragel, P. A., Reddan, M. C., LaBar, K. S. & Wager, T. D. Emotion schemas are embedded in the human visual system. Sci. Adv. 5, eaaw4358 (2019).

    PubMed  PubMed Central  Google Scholar 

  28. Corder, G. et al. An amygdalar neural ensemble that encodes the unpleasantness of pain. Science 363, 276–281 (2019).

    CAS  PubMed  Google Scholar 

  29. Hua, T. et al. General anesthetics activate a potent central pain-suppression circuit in the amygdala. Nat. Neurosci. 23, 854–868 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Chiang, M. C. et al. Divergent neural pathways emanating from the lateral parabrachial nucleus mediate distinct components of the pain response. Neuron 106, 927–939 (2020).

    CAS  PubMed  Google Scholar 

  31. Allen, W. E. et al. Thirst-associated preoptic neurons encode an aversive motivational drive. Science 357, 1149–1155 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Allen, W. E. et al. Thirst regulates motivated behavior through modulation of brainwide neural population dynamics. Science 364, 253 (2019).

    PubMed  PubMed Central  Google Scholar 

  33. Pool, A.-H. et al. The cellular basis of distinct thirst modalities. Nature 588, 112–117 (2020).

    PubMed  PubMed Central  Google Scholar 

  34. McIntosh, A. R. & Lobaugh, N. J. Partial least squares analysis of neuroimaging data: applications and advances. Neuroimage 23, 250–263 (2004).

    Google Scholar 

  35. Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).

    CAS  Google Scholar 

  36. Woo, C.-W., Chang, L. J., Lindquist, M. A. & Wager, T. D. Building better biomarkers: brain models in translational neuroimaging. Nat. Neurosci. 20, 365–377 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Poldrack, R. A., Huckins, G. & Varoquaux, G. Establishment of best practices for evidence for prediction: a review. JAMA Psychiatry 77, 534–540 (2020).

    PubMed  PubMed Central  Google Scholar 

  38. Nimon, K., Lewis, M., Kane, R. & Haynes, R. M. An R package to compute commonality coefficients in the multiple regression case: an introduction to the package and a practical example. Behav. Res. Methods 40, 457–466 (2008).

    PubMed  Google Scholar 

  39. Haufe, S. et al. On the interpretation of weight vectors of linear models in multivariate neuroimaging. Neuroimage 87, 96–110 (2014).

    PubMed  Google Scholar 

  40. Hayes, D. J. & Northoff, G. Identifying a network of brain regions involved in aversion-related processing: a cross-species translational investigation. Front. Integr. Neurosci. 5, 49 (2011).

    PubMed  PubMed Central  Google Scholar 

  41. Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J. & Barrett, L. F. The brain basis of positive and negative affect: evidence from a meta-analysis of the human neuroimaging literature. Cereb. Cortex 26, 1910–1922 (2016).

    PubMed  Google Scholar 

  42. Panzeri, S., Macke, J. H., Gross, J. & Kayser, C. Neural population coding: combining insights from microscopic and mass signals. Trends Cogn. Sci. 19, 162–172 (2015).

    PubMed  PubMed Central  Google Scholar 

  43. Mouraux, A., Diukova, A., Lee, M. C., Wise, R. G. & Iannetti, G. D. A multisensory investigation of the functional significance of the ‘pain matrix’. Neuroimage 54, 2237–2249 (2011).

    PubMed  Google Scholar 

  44. Liang, M., Mouraux, A., Hu, L. & Iannetti, G. D. Primary sensory cortices contain distinguishable spatial patterns of activity for each sense. Nat. Commun. 4, 1979 (2013).

    CAS  PubMed  Google Scholar 

  45. Shuler, M. G. & Bear, M. F. Reward timing in the primary visual cortex. Science 311, 1606–1609 (2006).

    CAS  PubMed  Google Scholar 

  46. Kragel, P. A. et al. A human colliculus-pulvinar-amygdala pathway encodes negative emotion. Neuron https://doi.org/10.1016/j.neuron.2021.06.001 (2021).

  47. Pessoa, L. & Adolphs, R. Emotion processing and the amygdala: from a ‘low road’ to ‘many roads’ of evaluating biological significance. Nat. Rev. Neurosci. 11, 773–783 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, C., Cheng, M., Ito, T. & Song, S. Neuronal organization in the inferior colliculus revisited with cell-type-dependent monosynaptic tracing. J. Neurosci. 38, 3318–3332 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Tan, L. L. & Kuner, R. Neocortical circuits in pain and pain relief. Nat. Rev. Neurosci. 22, 458–471 (2021).

    CAS  PubMed  Google Scholar 

  50. Mogil, J. S. The genetic mediation of individual differences in sensitivity to pain and its inhibition. Proc. Natl Acad. Sci. USA 96, 7744–7751 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Baron, R. et al. Peripheral neuropathic pain: a mechanism-related organizing principle based on sensory profiles. Pain 158, 261–272 (2017).

    PubMed  Google Scholar 

  52. Price, D. D. Psychological and neural mechanisms of the affective dimension of pain. Science 288, 1769–1772 (2000).

    CAS  PubMed  Google Scholar 

  53. Satpute, A. B. et al. Identification of discrete functional subregions of the human periaqueductal gray. Proc. Natl Acad. Sci. USA 110, 17101–17106 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Craig, A. D., Bushnell, M. C., Zhang, E. T. & Blomqvist, A. A thalamic nucleus specific for pain and temperature sensation. Nature 372, 770–773 (1994).

    CAS  PubMed  Google Scholar 

  55. Woo, C.-W. et al. Quantifying cerebral contributions to pain beyond nociception. Nat. Commun. 8, 14211 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Anderson, A. K. & Sobel, N. Dissociating intensity from valence as sensory inputs to emotion. Neuron 39, 581–583 (2003).

    CAS  PubMed  Google Scholar 

  57. Wehrum, S. et al. Gender commonalities and differences in the neural processing of visual sexual stimuli. J. Sex. Med. 10, 1328–1342 (2013).

    PubMed  Google Scholar 

  58. Neugebauer, V. Amygdala pain mechanisms. Handb. Exp. Pharmacol. 227, 261–284 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Kim, J., Shinkareva, S. V. & Wedell, D. H. Representations of modality-general valence for videos and music derived from fMRI data. Neuroimage 148, 42–54 (2017).

    PubMed  Google Scholar 

  60. Li, J., Schiller, D., Schoenbaum, G., Phelps, E. A. & Daw, N. D. Differential roles of human striatum and amygdala in associative learning. Nat. Neurosci. 14, 1250–1252 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Belova, M. A., Paton, J. J. & Salzman, C. D. Moment-to-moment tracking of state value in the amygdala. J. Neurosci. 28, 10023–10030 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Hayes, D. J. & Northoff, G. Common brain activations for painful and non-painful aversive stimuli. BMC Neurosci. 13, 60 (2012).

    PubMed  PubMed Central  Google Scholar 

  63. Villemure, C. & Bushnell, M. C. Mood influences supraspinal pain processing separately from attention. J. Neurosci. 29, 705–715 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Roy, M., Piché, M., Chen, J.-I., Peretz, I. & Rainville, P. Cerebral and spinal modulation of pain by emotions. Proc. Natl Acad. Sci. USA 106, 20900–20905 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Anders, S., Eippert, F., Weiskopf, N. & Veit, R. The human amygdala is sensitive to the valence of pictures and sounds irrespective of arousal: an fMRI study. Soc. Cogn. Affect. Neurosci. 3, 233–243 (2008).

    PubMed  PubMed Central  Google Scholar 

  66. Woo, C.-W. et al. Separate neural representations for physical pain and social rejection. Nat. Commun. 5, 5380 (2014).

    PubMed  Google Scholar 

  67. Peelen, M. V. & Downing, P. E. Using multi-voxel pattern analysis of fMRI data to interpret overlapping functional activations. Trends Cogn. Sci. 11, 4–5 (2007).

    PubMed  Google Scholar 

  68. Tomova, L. et al. Acute social isolation evokes midbrain craving responses similar to hunger. Nat. Neurosci. 23, 1597–1605 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Woolf, C. J. Central sensitization: implications for the diagnosis and treatment of pain. Pain 152, S2–S15 (2011).

    PubMed  Google Scholar 

  70. Ceko, M., Bushnell, M. C., Fitzcharles, M.-A. & Schweinhardt, P. Fibromyalgia interacts with age to change the brain. Neuroimage Clin. 3, 249–260 (2013).

    PubMed  PubMed Central  Google Scholar 

  71. López-Solà, M. et al. Towards a neurophysiological signature for fibromyalgia. Pain 158, 34–47 (2017).

    PubMed  PubMed Central  Google Scholar 

  72. Grothusen, J. R., Alexander, G., Erwin, K. & Schwartzman, R. Thermal pain in complex regional pain syndrome type I. Pain Physician 17, 71–79 (2014).

    PubMed  Google Scholar 

  73. Eippert, F. et al. Activation of the opioidergic descending pain control system underlies placebo analgesia. Neuron 63, 533–543 (2009).

    CAS  PubMed  Google Scholar 

  74. Wager, T. D. et al. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368, 1388–1397 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Bartoshuk, L. M. et al. Valid across-group comparisons with labeled scales: the gLMS versus magnitude matching. Physiol. Behav. 82, 109–114 (2004).

    CAS  PubMed  Google Scholar 

  76. Hayes, J. E., Allen, A. L. & Bennett, S. M. Direct comparison of the generalized visual analog scale and general labeled magnitude scale. Food Qual. Prefer. 28, 36–44 (2013).

    PubMed  Google Scholar 

  77. Kumar, S., Forster, H. M., Bailey, P. & Griffiths, T. D. Mapping unpleasantness of sounds to their auditory representation. J. Acoust. Soc. Am. 124, 3810–3817 (2008).

    PubMed  Google Scholar 

  78. Kumar, S., von Kriegstein, K., Friston, K. & Griffiths, T. D. Features versus feelings: dissociable representations of the acoustic features and valence of aversive sounds. J. Neurosci. 32, 14184–14192 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Lang, P. J., Bradley, M. M. & Cuthbert, B. N. International affective picture system (IAPS): affective ratings of pictures and instruction manual. Technical Report A-8. University of Florida, Gainesville (2008).

  80. Op de Beeck, H. P. Against hyperacuity in brain reading: spatial smoothing does not hurt multivariate fMRI analyses? Neuroimage 49, 1943–1948 (2010).

    PubMed  Google Scholar 

  81. Genovese, C. R., Lazar, N. A. & Nichols, T. Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage 15, 870–878 (2002).

    PubMed  Google Scholar 

  82. Thompson, B. & Borrello, G. M. The importance of structure coefficients in regression research. Educ. Psychol. Meas. 45, 203–209 (1985).

    Google Scholar 

  83. Parra, L. C., Spence, C. D., Gerson, A. D. & Sajda, P. Recipes for the linear analysis of EEG. Neuroimage 28, 326–341 (2005).

    PubMed  Google Scholar 

  84. Kohoutová, L. et al. Toward a unified framework for interpreting machine-learning models in neuroimaging. Nat. Protoc. https://doi.org/10.1038/s41596-019-0289-5 (2020).

  85. Mumford, J. A. & Nichols, T. Simple group fMRI modeling and inference. Neuroimage 47, 1469–1475 (2009).

    PubMed  Google Scholar 

  86. Bota, M. & Swanson, L. W. BAMS Neuroanatomical Ontology: design and implementation. Front. Neuroinformatics 2, 2 (2008).

    PubMed Central  Google Scholar 

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Acknowledgements

We thank D. Ott, J. Griffin, E. Biringen and T. Wilkes for assistance with data collection; P. Gianaros for sharing data included in study 4 and R. Stark for sharing data included in study 6; and R. Botvinik-Nezer, K. Zorina-Lichtenwalter and B. Petre for helpful comments on earlier versions of the manuscript. This work was funded by NIH R01DA035484 (to T.D.W.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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M.C., C.-W.W., M.L.-S. and T.D.W. conceived and designed the experiment for studies 1 and 2, and P.A.K. and T.D.W. conceived and designed the experiment for study 3. M.C. and C.-W.W. collected and preprocessed the data for studies 1 and 2, and P.A.K. collected and preprocessed the data for study 3. M.C., P.A.K. and T.D.W. analyzed the data and interpreted the results. M.C. created the figures, with intellectual input from all other authors. M.C. and T.D.W. wrote the manuscript. All authors edited the manuscript.

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Correspondence to Marta Čeko or Tor D. Wager.

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Nature Neuroscience thanks Junichi Chikazoe 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 PLS-R models trained to predict normative ratings to negative (aversive) and positive (pleasant) IAPS images.

(a) PLS-R procedure to estimate brain patterns for ‘arousal’ (common across stimuli) and for stimulus type-specific outcomes (IAPS norm ratings) simultaneously (b) Behavior plots. Left: normative ratings shown for each individual stimulus (that is, IAPS image); original IAPS scales (1–9 scales for Valence (higher score = less negative / more positive; 0 is neutral) and Arousal (higher score = more arousing). Right: norm ratings averaged per bin (‘stimulus intensity level’, used for PLS-R training) and shown on a 0–4 split scale (higher score = more negative / more positive; 0 is neutral); Pattern response plots. Relationship between observed and predicted ratings. Circles reflect mean values across participants for each stimulus type, error bars reflect within-participant SEM. ‘Arousal’ model (panel 1), trained on all stimuli, significantly predicted ratings across stimulus types. Stimulus type-specific models (panels 2–3) significantly predicted ratings to target (color-matched), but not off-target stimulus type. r, mean within-participant Pearson correlation between predicted and observed ratings; two-sided P-values based on a 10,000 samples bootstrap test of within-participant r values. (c) Left: PLS-R model weight maps showing which brain areas make a reliable contribution to each model’s prediction (based on bootstrapping with 10,000 samples and displayed here at t > 3, retaining positive values). Right: Model encoding maps showing where in the brain voxel-wise activity correlates with PLS model outcomes, corrected for multiple comparisons using q < 0.05 FDR and thresholded at t > 3, retaining positive values. (d) Violin plots showing average BOLD response per stimulus intensity (x-axis) in bilateral ventral striatum (vStr) and amygdala ROIs (Supplementary Table 7), * p = 0.047, ** p = 0.002, * p < 0.001 (left panels); Mean structure coefficient values for each model, averaged across in-ROI voxels across both hemispheres, * p < 0.001, only p-values associated with positive t-values are marked and interpreted, each dot is a participant (right panels); one-sample t-test on n = 55 participants, treating participant as random effect, bars reflect mean values across participants for each stimulus type, error bars reflect within-participant SEM.(e) 3D surface maps of vStr and amy are displaying FDR-corrected model encoding maps for PLS ‘norm’ models of positive and negative images, and for the PLS model trained on participants’ ratings of negative images (Analysis 1).

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Čeko, M., Kragel, P.A., Woo, CW. et al. Common and stimulus-type-specific brain representations of negative affect. Nat Neurosci 25, 760–770 (2022). https://doi.org/10.1038/s41593-022-01082-w

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