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Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex

Nature Neuroscience volume 21, pages 283–289 (2018)Cite this article

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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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Fig. 1: Study selection and multivariate modeling.
Fig. 2: Regional assessment of generalizable representations in MFC.
Fig. 3: Identifying latent brain representations that predict the occurrence of distinct functional domains in each region of interest.
Fig. 4: Representational mapping of pain, cognitive control, and negative emotion in MFC.

References

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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. 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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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.

    Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

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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.

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Authors and 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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  1. Philip A. Kragel
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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.

Corresponding authors

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

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Integrated supplementary information

Supplementary Figure 1 Generalizability of MFC activation.

a Force-directed graph conveying the observed similarity of brain activation across MFC (see Fig. 1b) using the Fruchterman-Reingold algorithm. Each small circle corresponds to a brain activation pattern from a single subject. The number next to each circle indicates the study it came from, and the color of each circle indicates domain membership of each study. Large circles depict the mean location of contrasts from each domain. b Results from modeling the similarity of activation patterns spanning the full MFC. The model explains clustering of brain activation patterns as a combination of study (top 18 rows), subdomain (middle nine rows), and domain-specific effects (bottom three rows). Smoothed bootstrap distributions of the generalization index (b = 5,000 bootstrap samples, drawn by random resampling of n = 270 participants) are plotted for each term and indicate the extent to which contrasts that share this feature are more (or less) similar. In general, there is greater clustering of domains and individual studies. For example, the domain of negative emotion is distinct from the domains of pain and cognitive control, yet the subdomain of social emotion does not form a clear cluster, because studies 15 and 16 are dissimilar from one another. *FDR q < .05; + P < .05 uncorrected.

Supplementary Figure 2 Variance Inflation Factors (VIFs) for representational similarity-based models.

VIFs indicate how much multicollinearity in a design matrix has inflated the variance of parameter estimates. All values here are below the standard cutoff of 10 (see Ray, W.D. Applied Linear Statistical-Models, 3rd Edition - Neter,J, Wasserman,W, Kutner,Mh. J Oper Res Soc 42, 815–815 (1991).).

Supplementary Figure 3 Regional analyses using different model parameterizations yield similar results.

a Model parameterized to capture increases in dissimilarity (1–Pearson′s r, n = 270 participants) for patterns of brain activity observed in subjects engaged in different studies, subdomains, or domains. The average within study dissimilarity serves as a reference and is modeled with a constant term. b Model parameterized to capture increases in similarity (Pearson′s r, n = 270 participants) for patterns of brain activity from the same study, subdomain, or domain. The average between domain similarity serves as a reference and is modeled with a constant term. c Model parameterized to capture differences in similarity (Pearson′s r, n = 270 participants) for patterns of brain activity from the same study, subdomain, or domain versus those that come from different studies, subdomains, or domains. The average overall similarity serves as a reference and is modeled with a constant term. d-f Bootstrap distributions (b = 5,000 bootstrap samples, drawn by random resampling of n = 270 participants) of the generalization index for terms modeling ‘pain’, ‘cognitive control’, and ‘negative emotion’. *FDR q < .05 corrected. +P < .05 uncorrected.

Supplementary Figure 4 Results of model comparisons conducted using the Brainnetome Atlas.

Colors indicate Bayesian information criterion weights, which reflect the relative evidence in favor of generalizable representation of pain (red), cognitive control (green), and negative emotion (blue). The colormap ranges from values of 0 to 1 for each model and the colors are additive. Purple regions indicate brain regions that exhibit equivocal evidence for representation of pain and negative emotion, and transparent (gray) regions do not exhibit evidence for the representation of a single domain. For full details see Table S6.

Supplementary Figure 5 Evaluation of model bias and variance, as well as false positive rates using resting-state fMRI (rsfMRI) data.

a Null-hypothesis representational similarity analysis (RSA) modeling procedure for a single Monte Carlo iteration (this procedure was repeated 1,000 times). Mirroring our experimental procedure, rsfMRI data was sampled from 18 sites from the 1,000 functional connectomes project70 (15 subjects per site, total n = 270). In each iteration, a random GLM was fit for each subject, producing 270 independent activation maps. These maps were used to estimate a ‘null’ representational dissimilarity matrix that served as the outcome for RSA-based models. The mean and standard deviation of parameter estimates from these models (βnull) were computed to index the bias and variance of our modeling procedure. To provide an estimate of the false positive rate (FPR), P-values were calculated on each iteration using bootstrap resampling. The FPR was calculated as the proportion of significant effects across the 1,000 iterations. b Bias and variance of the modeling procedure. Violin plots indicate the distribution of parameter estimates from null models (across 1,000 MC iterations), error bars indicate the mean and bootstrap standard error. Results show little bias, with all bootstrap distributions centered near the expected value of zero. c Estimated FPR for each region of interest. Significant effects were identified using a threshold α = .05 on each MC iteration. Dashed lines indicate the nominal rate of 0.05, which is the expected frequency for a single test using a threshold of α = .05. Error bars reflect standard error of the mean based on a binomial distribution. The conservative false positive rates are likely due to the dependence structure in the dissimilarity measurements that we do not explicitly account for, but none the less is evidence of a valid statistical procedure.

Supplementary Figure 6 Evaluation of model sensitivity and false positive rates by generating synthetic data with Wishart noise.

a Mirroring our experimental procedure, synthetic data was generated with a covariance structure with unique effects for each of 270 “subjects” and either 3 domains or 18 studies (not shown). On each of 500 Monte Carlo (MC) iterations, a Wishart random matrix was generated from a pre-specified covariance structure and converted to a representational dissimilarity matrix that served as the outcome for representational similarity analysis-based models. P-values were calculated on each iteration using bootstrap resampling to provide estimates of the true and the false positive rates. This procedure was repeated three times with different levels of signal (the covariance across studies and domains was set to values of 0.1, 0.2, and 0.3). b Estimates of true and false positive rates for simulations where domain effects are present. Error bars reflect standard error of the mean based on a binomial distribution. c Estimates of true and false positive rates for simulations where study effects are present. Significant effects were identified using a threshold α = .05 on each MC iteration. Error bars reflect standard error of the mean based on a binomial distribution. Sensitivity increases monotonically with the true effect size, and false positives are at or below the nominal value.

Supplementary Figure 7 Methods for comparing the similarity of patterns of brain activity within and across studies, using bootstrap resampling to derive P-values and make inferences.

Unique pairwise correlations between patterns of brain activity from 270 subjects are displayed in each panel. This illustration shows actual data from the anterior midcingulate cortex. a Full inter-subject correlation matrix where the shaded dark region indicates across-domain correlations. The average similarity across domains is estimated as the average inter-subject correlation in this area. b Regions of the inter-subject correlation matrix averaged to compute domain-general effects of pain (red), cognitive control (green), negative emotion (blue). These effects are computed by averaging correlations within domains but across different subdomains. c Regions used to compute the average correlation within domains but across different studies. d Regions used to compute the average correlation within domains. e For bootstrap tests, we resampled individual correlation coefficients from all 10 regions of the correlation matrix (here shown for the region with pairs of contrasts that are within the pain domain, but from different subdomains). The solid line indicates the sample mean correlation coefficient; the dashed lines indicate 95% confidence intervals of the mean, computed by bootstrap resampling (full bootstrap distribution shown). f Comparisons of the means between different regions reveal generalizable domain effects b > a, average effects of subdomain c > b, and average effects of study d > c. Differences in the means of two regions of the correlation matrix are compared against zero using the same bootstrap procedure (here showing a comparison of the average similarity within the pain domain and from different subdomains versus the similarity between pain and other domains).

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Kragel, P.A., Kano, M., Van Oudenhove, L. et al. Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. Nat Neurosci 21, 283–289 (2018). https://doi.org/10.1038/s41593-017-0051-7

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