Abstract

The medial frontal cortex, including anterior midcingulate cortex, has been linked to multiple psychological domains, including cognitive control, pain, and emotion. However, it is unclear whether this region encodes representations of these domains that are generalizable across studies and subdomains. Additionally, if there are generalizable representations, do they reflect a single underlying process shared across domains or multiple domain-specific processes? We decomposed multivariate patterns of functional MRI activity from 270 participants across 18 studies into study-specific, subdomain-specific, and domain-specific components and identified latent multivariate representations that generalized across subdomains but were specific to each domain. Pain representations were localized to anterior midcingulate cortex, negative emotion representations to ventromedial prefrontal cortex, and cognitive control representations to portions of the dorsal midcingulate. These findings provide evidence for medial frontal cortex representations that generalize across studies and subdomains but are specific to distinct psychological domains rather than reducible to a single underlying process.

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References

  1. 1.

    Amodio, D. M. & Frith, C. D. Meeting of minds: the medial frontal cortex and social cognition. Nat. Rev. Neurosci. 7, 268–277 (2006).

  2. 2.

    Cosmides, L. & Tooby, J. Origins of domain specificity: the evolution of functional organization. in Mapping the Mind: Domain Specificity in Cognition and Culture (eds. Hirschfeld, L.A. & Gelman, S.A.) 85–116 (1994).

  3. 3.

    Vogt, B. A. Midcingulate cortex: structure, connections, homologies, functions and diseases. J. Chem. Neuroanat. 74, 28–46 (2016).

  4. 4.

    Botvinick, M., Nystrom, L. E., Fissell, K., Carter, C. S. & Cohen, J. D. Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402, 179–181 (1999).

  5. 5.

    Ridderinkhof, K. R., Ullsperger, M., Crone, E. A. & Nieuwenhuis, S. The role of the medial frontal cortex in cognitive control. Science 306, 443–447 (2004).

  6. 6.

    Ito, S., Stuphorn, V., Brown, J. W. & Schall, J. D. Performance monitoring by the anterior cingulate cortex during saccade countermanding. Science 302, 120–122 (2003).

  7. 7.

    Dosenbach, N. U. et al. A core system for the implementation of task sets. Neuron 50, 799–812 (2006).

  8. 8.

    Procyk, E., Tanaka, Y. L. & Joseph, J. P. Anterior cingulate activity during routine and non-routine sequential behaviors in macaques. Nat. Neurosci. 3, 502–508 (2000).

  9. 9.

    Kolling, N., Behrens, T. E., Mars, R. B. & Rushworth, M. F. Neural mechanisms of foraging. Science 336, 95–98 (2012).

  10. 10.

    Büchel, C. et al. Dissociable neural responses related to pain intensity, stimulus intensity, and stimulus awareness within the anterior cingulate cortex: a parametric single-trial laser functional magnetic resonance imaging study. J. Neurosci. 22, 970–976 (2002).

  11. 11.

    Rainville, P., Duncan, G. H., Price, D. D., Carrier, B. & Bushnell, M. C. Pain affect encoded in human anterior cingulate but not somatosensory cortex. Science 277, 968–971 (1997).

  12. 12.

    Etkin, A., Egner, T., Peraza, D. M., Kandel, E. R. & Hirsch, J. Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron 51, 871–882 (2006).

  13. 13.

    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).

  14. 14.

    Tomlin, D. et al. Agent-specific responses in the cingulate cortex during economic exchanges. Science 312, 1047–1050 (2006).

  15. 15.

    Rudebeck, P. H., Buckley, M. J., Walton, M. E. & Rushworth, M. F. S. A role for the macaque anterior cingulate gyrus in social valuation. Science 313, 1310–1312 (2006).

  16. 16.

    Ebitz, R. B. & Hayden, B. Y. Dorsal anterior cingulate: a Rorschach test for cognitive neuroscience. Nat. Neurosci. 19, 1278–1279 (2016).

  17. 17.

    Shackman, A. J. et al. The integration of negative affect, pain and cognitive control in the cingulate cortex. Nat. Rev. Neurosci. 12, 154–167 (2011).

  18. 18.

    Critchley, H. D. et al. Human cingulate cortex and autonomic control: converging neuroimaging and clinical evidence. Brain 126, 2139–2152 (2003).

  19. 19.

    Behrens, T. E., Woolrich, M. W., Walton, M. E. & Rushworth, M. F. Learning the value of information in an uncertain world. Nat. Neurosci. 10, 1214–1221 (2007).

  20. 20.

    Shenhav, A., Botvinick, M. M. & Cohen, J. D. The expected value of control: an integrative theory of anterior cingulate cortex function. Neuron 79, 217–240 (2013).

  21. 21.

    Eisenberger, N. I. & Lieberman, M. D. Why rejection hurts: a common neural alarm system for physical and social pain. Trends Cogn. Sci. 8, 294–300 (2004).

  22. 22.

    Haynes, J. D. A primer on pattern-based approaches to fMRI: principles, pitfalls, and perspectives. Neuron 87, 257–270 (2015).

  23. 23.

    Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).

  24. 24.

    Kvitsiani, D. et al. Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498, 363–366 (2013).

  25. 25.

    Krishnan, A. et al. Somatic and vicarious pain are represented by dissociable multivariate brain patterns. eLife 5, e15166 (2016).

  26. 26.

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

  27. 27.

    Vogt, B. A., Berger, G. R. & Derbyshire, S. W. Structural and functional dichotomy of human midcingulate cortex. Eur. J. Neurosci. 18, 3134–3144 (2003).

  28. 28.

    Kriegeskorte, N., Mur, M. & Bandettini, P. Representational similarity analysis - connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).

  29. 29.

    Peyron, R., Laurent, B. & García-Larrea, L. Functional imaging of brain responses to pain. A review and meta-analysis (2000). Neurophysiol. Clin. 30, 263–288 (2000).

  30. 30.

    Lieberman, M. D. & Eisenberger, N. I. The dorsal anterior cingulate cortex is selective for pain: results from large-scale reverse inference. Proc. Natl. Acad. Sci. USA 112, 15250–15255 (2015).

  31. 31.

    Hutchison, W. D., Davis, K. D., Lozano, A. M., Tasker, R. R. & Dostrovsky, J. O. Pain-related neurons in the human cingulate cortex. Nat. Neurosci. 2, 403–405 (1999).

  32. 32.

    McNamee, D., Rangel, A. & O’Doherty, J. P. Category-dependent and category-independent goal-value codes in human ventromedial prefrontal cortex. Nat. Neurosci. 16, 479–485 (2013).

  33. 33.

    Peelen, M. V., Atkinson, A. P. & Vuilleumier, P. Supramodal representations of perceived emotions in the human brain. J. Neurosci. 30, 10127–10134 (2010).

  34. 34.

    Levy, D. J. & Glimcher, P. W. The root of all value: a neural common currency for choice. Curr. Opin. Neurobiol. 22, 1027–1038 (2012).

  35. 35.

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

  36. 36.

    Roy, M., Shohamy, D. & Wager, T. D. Ventromedial prefrontal-subcortical systems and the generation of affective meaning. Trends Cogn. Sci. 16, 147–156 (2012).

  37. 37.

    Laird, A. R. et al. Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. J. Neurosci. 29, 14496–14505 (2009).

  38. 38.

    Fan, L. et al. The Human Brainnetome Atlas: a new brain atlas based on connectional architecture. Cereb. Cortex 26, 3508–3526 (2016).

  39. 39.

    Paus, T. et al. Human cingulate and paracingulate sulci: pattern, variability, asymmetry, and probabilistic map. Cereb. Cortex 6, 207–214 (1996).

  40. 40.

    Kriegeskorte, N., Goebel, R. & Bandettini, P. Information-based functional brain mapping. Proc. Natl Acad. Sci. USA 103, 3863–3868 (2006).

  41. 41.

    Van Snellenberg, J. X. & Wager, T. D. Cognitive and motivational functions of the human prefrontal cortex. in Luria’s Legacy in the 21st Century (Christiansen, A.-L., Goldberg, E. & Bougakov, D. eds.)30–61 (2009).

  42. 42.

    Amiezz, C. & Petrides, M. Neuroimaging evidence of the anatomo-functional organization of the human cingulate motor areas. Cereb. Cortex 24, 563–578 (2014).

  43. 43.

    Gallistel, C. R. The importance of proving the null. Psychol. Rev. 116, 439–453 (2009).

  44. 44.

    Dienes, Z. Using Bayes to get the most out of non-significant results. Front. Psychol. 5, 781 (2014).

  45. 45.

    de la Vega, A., Chang, L. J., Banich, M. T., Wager, T. D. & Yarkoni, T. Large-scale meta-analysis of human medial frontal cortex reveals tripartite functional organization. J. Neurosci. 36, 6553–6562 (2016).

  46. 46.

    Torta, D. M. & Cauda, F. Different functions in the cingulate cortex, a meta-analytic connectivity modeling study. Neuroimage 56, 2157–2172 (2011).

  47. 47.

    Jahn, A., Nee, D. E., Alexander, W. H. & Brown, J. W. Distinct regions within medial prefrontal cortex process pain and cognition. J. Neurosci. 36, 12385–12392 (2016).

  48. 48.

    Yeo, B. T. et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 1125–1165 (2011).

  49. 49.

    Cronbach, L. J. & Meehl, P. E. Construct validity in psychological tests. Psychol. Bull. 52, 281–302 (1955).

  50. 50.

    Campbell, D. T. & Fiske, D. W. Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol. Bull. 56, 81–105 (1959).

  51. 51.

    Barch, D. M. et al. Function in the human connectome: task-fMRI and individual differences in behavior. Neuroimage 80, 169–189 (2013).

  52. 52.

    Miller, K. L. et al. Multimodal population brain imaging in the UK Biobank prospective epidemiological study. Nat. Neurosci. 19, 1523–1536 (2016).

  53. 53.

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

  54. 54.

    Atlas, L. Y., Bolger, N., Lindquist, M. A. & Wager, T. D. Brain mediators of predictive cue effects on perceived pain. J. Neurosci. 30, 12964–12977 (2010).

  55. 55.

    Rubio, A. et al. Uncertainty in anticipation of uncomfortable rectal distension is modulated by the autonomic nervous system-a fMRI study in healthy volunteers. Neuroimage 107, 10–22 (2015).

  56. 56.

    Kano, M. et al. Influence of uncertain anticipation on brain responses to aversive rectal distension in patients with irritable bowel syndrome. Psychosom. Med. https://doi.org/10.1097/PSY.0000000000000484 (2017).

  57. 57.

    DeYoung, C. G., Shamosh, N. A., Green, A. E., Braver, T. S. & Gray, J. R. Intellect as distinct from Openness: differences revealed by fMRI of working memory. J. Pers. Soc. Psychol. 97, 883–892 (2009).

  58. 58.

    van Ast, V. A. et al. Brain mechanisms of social threat effects on working memory. Cereb. Cortex 26, 544–556 (2016).

  59. 59.

    Xue, G., Aron, A. R. & Poldrack, R. A. Common neural substrates for inhibition of spoken and manual responses. Cereb. Cortex 18, 1923–1932 (2008).

  60. 60.

    Aron, A. R., Behrens, T. E., Smith, S., Frank, M. J. & Poldrack, R. A. Triangulating a cognitive control network using diffusion-weighted magnetic resonance imaging (MRI) and functional MRI. J. Neurosci. 27, 3743–3752 (2007).

  61. 61.

    Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X. & Milham, M. P. Competition between functional brain networks mediates behavioral variability. Neuroimage 39, 527–537 (2008).

  62. 62.

    Gianaros, P. J. et al. An inflammatory pathway links atherosclerotic cardiovascular disease risk to neural activity evoked by the cognitive regulation of emotion. Biol. Psychiatry 75, 738–745 (2014).

  63. 63.

    Yarkoni, T., Poldrack, R. A., Nichols, T. E., Van Essen, D. C. & Wager, T. D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

  64. 64.

    Kross, E., Berman, M. G., Mischel, W., Smith, E. E. & Wager, T. D. Social rejection shares somatosensory representations with physical pain. Proc. Natl Acad. Sci. USA 108, 6270–6275 (2011).

  65. 65.

    Bradley, M. M. & Lang, P. J. The International Affective Digitized Sounds (IADS-2): Affective Ratings of Sounds and Instruction Manual. (University of Florida, Gainesville, FL, 2007). Tech. Rep. B-3.

  66. 66.

    Poldrack, R. A. et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform. 5, 17 (2011).

  67. 67.

    Kriegeskorte, N. & Kievit, R. A. Representational geometry: integrating cognition, computation, and the brain. Trends Cogn. Sci. 17, 401–412 (2013).

  68. 68.

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

  69. 69.

    Shattuck, D. W. et al. Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39, 1064–1080 (2008).

  70. 70.

    Lancaster, J. L. et al. Bias between MNI and Talairach coordinates analyzed using the ICBM-152 brain template. Hum. Brain Mapp. 28, 1194–1205 (2007).

  71. 71.

    Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl Acad. Sci. USA 107, 4734–4739 (2010).

  72. 72.

    Eklund, A., Nichols, T. E. & Knutsson, H. Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proc. Natl Acad. Sci. USA 113, 7900–7905 (2016).

  73. 73.

    Schwarz, G. Estimating dimension of a model. Ann. Stat. 6, 461–464 (1978).

  74. 74.

    Wagenmakers, E. J. & Farrell, S. AIC model selection using Akaike weights. Psychon. Bull. Rev. 11, 192–196 (2004).

  75. 75.

    Nichols, T., Brett, M., Andersson, J., Wager, T. & Poline, J. B. Valid conjunction inference with the minimum statistic. Neuroimage 25, 653–660 (2005).

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Acknowledgements

We thank S. Fukudo, T. Muratsubaki and J. Morishita for assistance with data collection; K. Ochsner for sharing data from studies of negative emotion; T. Braver and J. Gray for sharing working memory data; and R. Poldrack for sharing response selection data (available at https://openfmri.org/). This research was supported by grants R01 HL089850 to P.J.G.; P01 HL040962 to S.B.M.; grants OCI-1131801, R01 DA035484, and R01 MH076136 to T.D.W.; JSPS-FWO grant VS.014.13 N to L.V.O. and S. Fukudo; JSPS-KAKENHI grant 26460898 to M.K.; R01 MH076137 and R01 AG043463 to K.O.; by the Direction de la Recherche Clinique of the University Hospital of Grenoble Alpes; and by the pharmaceutical labs Ferring and Cephalon. L.V.O. is funded by the KU Leuven Special Research Fund. T.E.N. is supported by the Wellcome Trust.

Author information

Affiliations

  1. Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA

    • Philip A. Kragel
    • , Marta Ceko
    •  & Tor D. Wager
  2. Frontier Research Institute for Interdisciplinary Sciences, Tohoku University, Sendai, Japan

    • Michiko Kano
  3. Department of Behavioral Medicine, Tohoku University Graduate School of Medicine, Sendai, Japan

    • Michiko Kano
  4. Department of Clinical and Experimental Medicine, University of Leuven, Leuven, Belgium

    • Lukas Van Oudenhove
    •  & Huynh Giao Ly
  5. Department of Neurosciences, University of Leuven, Leuven, Belgium

    • Patrick Dupont
  6. Grenoble Institut des Neurosciences, GIN, Univ. Grenoble Alpes, Grenoble, France

    • Amandine Rubio
    • , Chantal Delon-Martin
    •  & Bruno L. Bonaz
  7. INSERM, Grenoble, France

    • Amandine Rubio
    • , Chantal Delon-Martin
    •  & Bruno L. Bonaz
  8. CHU Grenoble Alpes, Grenoble, France

    • Amandine Rubio
    •  & Bruno L. Bonaz
  9. Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA

    • Stephen B. Manuck
    •  & Peter J. Gianaros
  10. Department of Psychology, University of Miami, Miami, FL, USA

    • Elizabeth A. Reynolds Losin
  11. Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea

    • Choong-Wan Woo
  12. Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea

    • Choong-Wan Woo
  13. Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK

    • Thomas E. Nichols

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Contributions

P.A.K. and T.D.W. designed the experiment and drafted the manuscript. P.A.K. conducted data analysis. P.A.K., T.E.N., and T.D.W. developed simulated experiments for evaluating statistical procedures. A.R., B.L.B., M.C., C.D.-M., H.G.L., E.A.R.L., L.V.O., M.K., P.D., P.J.G., S.B.M., T.D.W., and C.-W.W. contributed neuroimaging data. All authors provided feedback and revised the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Philip A. Kragel or Tor D. Wager.

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