A major goal of cognitive neuroscience is to delineate how brain systems give rise to mental function. Here we review the increasingly large role informatics-driven approaches are playing in such efforts. We begin by reviewing a number of challenges conventional neuroimaging approaches face in trying to delineate brain-cognition mappings—for example, the difficulty in establishing the specificity of postulated associations. Next, we demonstrate how these limitations can potentially be overcome using complementary approaches that emphasize large-scale analysis—including meta-analytic methods that synthesize hundreds or thousands of studies at a time; latent-variable approaches that seek to extract structure from data in a bottom-up manner; and predictive modeling approaches capable of quantitatively inferring mental states from patterns of brain activity. We highlight the underappreciated but critical role for formal cognitive ontologies in helping to clarify, refine, and test theories of brain and cognitive function. Finally, we conclude with a speculative discussion of what future informatics developments may hold for cognitive neuroscience.


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Literature Cited

  1. Aron AR, Robbins TW, Poldrack RA. 2004. Inhibition and the right inferior frontal cortex. Trends Cogn. Sci. 8:170–77 [Google Scholar]
  2. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H. et al. 2000. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25:25–29 [Google Scholar]
  3. Ashton MC, Lee K, Paunonen SV. 2002. What is the central feature of extraversion? Social attention versus reward sensitivity. J. Personal. Soc. Psychol. 83:245–52 [Google Scholar]
  4. Baddeley A. 1992. Working memory. Science 255:556–59 [Google Scholar]
  5. Bard JBL, Rhee SY. 2004. Ontologies in biology: design, applications and future challenges. Nat. Rev. Genet. 5:213–22 [Google Scholar]
  6. Beckmann CF, Smith SM. 2004. Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans. Med. Imaging 23:137–52 [Google Scholar]
  7. Biswal B, Yetkin FZ, Haughton VM, Hyde JS. 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34:537–41 [Google Scholar]
  8. Blei D, Ng A, Jordan M. 2003. Latent Dirichlet allocation. J. Mach. Learn. Res. 3:993–1022 [Google Scholar]
  9. Bodenreider O, Stevens R. 2006. Bio-ontologies: current trends and future directions. Brief Bioinform. 7:256–74 [Google Scholar]
  10. Chang LJ, Yarkoni T, Khaw MW, Sanfey AG. 2013. Decoding the role of the insula in human cognition: functional parcellation and large-scale reverse inference. Cereb. Cortex 23:739–49 [Google Scholar]
  11. Chronbach LJ, Meehl PE. 1955. Construct validity in psychological tests. Psychol. Bull. 52:281–302 [Google Scholar]
  12. Churchland PM. 1981. Eliminative materialism and the propositional attitudes. J. Philos. 78:67–90 [Google Scholar]
  13. Cox DD, Savoy RL. 2003. Functional magnetic resonance imaging (fMRI) “brain reading”: detecting and classifying distributed patterns of fMRI activity in human visual cortex. NeuroImage 19:261–70 [Google Scholar]
  14. Craddock RC, James GA, Holtzheimer PE 3rd, Hu XP, Mayberg HS. 2012. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33:1914–28 [Google Scholar]
  15. Critchley HD, Wiens S, Rotshtein P, Ohman A, Dolan RJ. 2004. Neural systems supporting interoceptive awareness. Nat. Neurosci. 7:189–95 [Google Scholar]
  16. Dale AM, Buckner RL. 1997. Selective averaging of rapidly presented individual trials using fMRI. Hum. Brain Mapp. 5:329–40 [Google Scholar]
  17. Derrfuss J, Mar RA. 2009. Lost in localization: the need for a universal coordinate database. NeuroImage 48:1–7 [Google Scholar]
  18. Dickson J, Drury H, Van Essen DC. 2001. “The Surface Management System” (SuMS) database: a surface-based database to aid cortical surface reconstruction, visualization and analysis. Philos. Trans. R. Soc. B 356:1277–92 [Google Scholar]
  19. Dosenbach NUF, Visscher KM, Palmer ED, Miezin FM, Wenger KK. et al. 2006. A core system for the implementation of task sets. Neuron 50:799–812 [Google Scholar]
  20. Egeth H, Marcus N, Bevan W. 1972. Target-set and response-set interaction: implications for models of human information processing. Science 176:1447–48 [Google Scholar]
  21. Fan Y, Duncan NW, de Greck M, Northoff G. 2011. Is there a core neural network in empathy? An fMRI based quantitative meta-analysis. Neurosci. Biobehav. Rev. 35:903–11 [Google Scholar]
  22. Fodor JA. 1974. Special sciences (or: the disunity of science as a working hypothesis). Synthese 28:97–115 [Google Scholar]
  23. Friston KJ, Price CJ, Fletcher P, Moore C, Frackowiak RS, Dolan RJ. 1996. The trouble with cognitive subtraction. NeuroImage 4:97–104 [Google Scholar]
  24. Garner WR, Hake HW, Eriksen CW. 1956. Operationism and the concept of perception. Psychol. Rev. 63:149–59 [Google Scholar]
  25. Gordon EM, Laumann TO, Adeyemo B, Huckins JF, Kelley WM, Petersen SE. 2014. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex. doi: 10.1093/cercor/bhu239
  26. Gorgolewski K, Burns CD, Madison C, Clark D, Halchenko YO. et al. 2011. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5:13 [Google Scholar]
  27. Gorgolewski KJ, Varoquaux G, Rivera G, Schwartz Y, Ghosh SS. et al. 2015. NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Front. Neuroinform. 9:8 [Google Scholar]
  28. Greenwald AG. 2012. There is nothing so theoretical as a good method. Perspect. Psychol. Sci. 7:99–108 [Google Scholar]
  29. Gruber T. 1993. A translation approach to portable ontology specifications. Knowl. Acquis. 5:199–220 [Google Scholar]
  30. Haynes JD, Rees G. 2006. Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7:523–34 [Google Scholar]
  31. Huth AG, Nishimoto S, Vu AT, Gallant JL. 2012. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron 76:1210–24 [Google Scholar]
  32. Hutzler F. 2014. Reverse inference is not a fallacy per se: Cognitive processes can be inferred from functional imaging data. NeuroImage 84:1061–69 [Google Scholar]
  33. Jeneson A, Squire LR. 2012. Working memory, long-term memory, and medial temporal lobe function. Learn. Mem. 19:15–25 [Google Scholar]
  34. Jennings JM, McIntosh AR, Kapur S, Tulving E, Houle S. 1997. Cognitive subtractions may not add up: the interaction between semantic processing and response mode. NeuroImage 5:229–39 [Google Scholar]
  35. Kane MJ, Conway ARA, Miura TK, Colflesh GJH. 2007. Working memory, attention control, and the N-back task: a question of construct validity. J. Exp. Psychol.: Learn. Mem. Cogn. 33:615–22 [Google Scholar]
  36. Kay KN, Naselaris T, Prenger RJ, Gallant JL. 2008. Identifying natural images from human brain activity. Nature 452:352–55 [Google Scholar]
  37. Khatib F, Cooper S, Tyka MD, Xu K, Makedon I. et al. 2011. Algorithm discovery by protein folding game players. PNAS 108:18949–53 [Google Scholar]
  38. Kim H. 2011. Neural activity that predicts subsequent memory and forgetting: a meta-analysis of 74 fMRI studies. NeuroImage 54:2446–61 [Google Scholar]
  39. Klein C. 2012. Cognitive ontology and region- versus network-oriented analyses. Philos. Sci. 79:952–60 [Google Scholar]
  40. Klein TA, Endrass T, Kathmann N, Neumann J, von Cramon DY, Ullsperger M. 2007. Neural correlates of error awareness. NeuroImage 34:1774–81 [Google Scholar]
  41. Koyejo O, Poldrack RA. 2013. Decoding cognitive processes from functional MRI Presented at NIPS Workshop Mach. Learn. Interpret. Neuroimaging, Dec. 5–10, Lake Tahoe, CA
  42. Laird AR, Lancaster JL, Fox PT. 2005. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3:65–78 [Google Scholar]
  43. Larocque JJ, Lewis-Peacock JA, Postle BR. 2014. Multiple neural states of representation in short-term memory? It's a matter of attention. Front. Hum. Neurosci. 8:5 [Google Scholar]
  44. Lucas RE, Diener E. 2001. Understanding extraverts' enjoyment of social situations: the importance of pleasantness. J. Personal. Soc. Psychol. 81:343–56 [Google Scholar]
  45. Lucas RE, Diener E, Grob A, Suh EM, Shao L. 2000. Cross-cultural evidence for the fundamental features of extraversion. J. Personal. Soc. Psychol. 79:452–68 [Google Scholar]
  46. Lucas RE, Le K, Dyrenforth PS. 2008. Explaining the extraversion/positive affect relation: Sociability cannot account for extraverts' greater happiness. J. Personal. 76:385–414 [Google Scholar]
  47. Machery E. 2014. In defense of reverse inference. Br. J. Philos. Sci. 65:251–267 [Google Scholar]
  48. Mischel W. 2008. The toothbrush problem. APS Obs. 21:11 [Google Scholar]
  49. Mitchell TM, Hutchinson R, Just MA, Niculescu RS, Pereira F, Wang X. 2003. Classifying instantaneous cognitive states from fMRI data. AMIA Annu. Symp. Proc. 2003:465–69 [Google Scholar]
  50. Mitchell TM, Shinkareva SV, Carlson A, Chang KM, Malave VL. et al. 2008. Predicting human brain activity associated with the meanings of nouns. Science 320:1191–95 [Google Scholar]
  51. Mourão-Miranda J, Bokde ALW, Born C, Hampel H, Stetter M. 2005. Classifying brain states and determining the discriminating activation patterns: Support Vector Machine on functional MRI data. NeuroImage 28:980–95 [Google Scholar]
  52. Munakata Y, Herd SA, Chatham CH, Depue BE, Banich MT, O'Reilly RC. 2011. A unified framework for inhibitory control. Trends Cogn. Sci. 15:453–59 [Google Scholar]
  53. Nielsen FA, Hansen LK, Balslev D. 2004. Mining for associations between text and brain activation in a functional neuroimaging database. Neuroinformatics 2:369–80 [Google Scholar]
  54. Nishimoto S, Vu AT, Naselaris T, Benjamini Y, Yu B, Gallant JL. 2011. Reconstructing visual experiences from brain activity evoked by natural movies. Curr. Biol. 21:1641–46 [Google Scholar]
  55. Norman KA, Polyn SM, Detre GJ, Haxby JV. 2006. Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends Cogn. Sci. 10:424–30 [Google Scholar]
  56. Owen AM, McMillan KM, Laird AR, Bullmore E. 2005. N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies. Hum. Brain. Mapp. 25:46–59 [Google Scholar]
  57. Poldrack RA. 2006. Can cognitive processes be inferred from neuroimaging data?. Trends Cogn. Sci. 10:59–63 [Google Scholar]
  58. Poldrack RA. 2010. An exchange about localism. Foundational Issues in Human Brain Mapping SJ Hanson, M Bunzl 147–60 Cambridge, MA: MIT Press [Google Scholar]
  59. Poldrack RA. 2011. Inferring mental states from neuroimaging data: from reverse inference to large-scale decoding. Neuron 72:692–97 [Google Scholar]
  60. Poldrack RA, Barch DM, Mitchell JP, Wager TD, Wagner AD. et al. 2013. Toward open sharing of task-based fMRI data: the OpenfMRI project. Front. Neuroinform. 7:12 [Google Scholar]
  61. Poldrack RA, Halchenko YO, Hanson SJ. 2009. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychol. Sci. 20:1364–72 [Google Scholar]
  62. Poldrack RA, Kittur A, Kalar D, Miller E, Seppa C. et al. 2011. The Cognitive Atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform. 5:17 [Google Scholar]
  63. Poldrack RA, Mumford JA, Schonberg T, Kalar D, Barman B, Yarkoni T. 2012. Discovering relations between mind, brain, and mental disorders using topic mapping. PLOS Comput. Biol. 8:e1002707 [Google Scholar]
  64. Price C, Friston K. 2005. Functional ontologies for cognition: the systematic definition of structure and function. Cogn. Neuropsychol. 22:262–75 [Google Scholar]
  65. Quine WV. 1948. On what there is. Rev. Metaphys. 2:21–38 [Google Scholar]
  66. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. 2001. A default mode of brain function. PNAS 98:676–82 [Google Scholar]
  67. Rhee SY, Wood V, Dolinski K, Draghici S. 2008. Use and misuse of the gene ontology annotations. Nat. Rev. Genet. 9:509–15 [Google Scholar]
  68. Rogers RD, Monsell S. 1995. Costs of a predictable switch between simple cognitive tasks. J. Exp. Psychol.: Gen. 124:207–31 [Google Scholar]
  69. Rottschy C, Langner R, Dogan I, Reetz K, Laird AR. et al. 2012. Modelling neural correlates of working memory: a coordinate-based meta-analysis. NeuroImage 60:830–46 [Google Scholar]
  70. Rubin DL, Shah NH, Noy NF. 2008. Biomedical ontologies: a functional perspective. Brief Bioinform. 9:75–90 [Google Scholar]
  71. Salimi-Khorshidi G, Smith SM, Keltner JR, Wager TD, Nichols TE. 2009. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. NeuroImage 45:810–23 [Google Scholar]
  72. Schwartz Y, Thirion B, Varoquaux G. 2013. Mapping paradigm ontologies to and from the brain. Presented at NIPS Workshop Mach. Learn. Interpret. Neuroimaging, Dec. 5–10, Lake Tahoe, CA
  73. Shinkareva SV, Mason RA, Malave VL, Wang W, Mitchell TM, Just MA. 2008. Using fMRI brain activation to identify cognitive states associated with perception of tools and dwellings. PLOS ONE 3:e1394 [Google Scholar]
  74. Smillie LD, Cooper AJ, Wilt J, Revelle W. 2012. Do extraverts get more bang for the buck? Refining the affective-reactivity hypothesis of extraversion. J. Personal. Soc. Psychol. 103:306–26 [Google Scholar]
  75. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM. et al. 2009. Correspondence of the brain's functional architecture during activation and rest. PNAS 106:13040–45 [Google Scholar]
  76. Sternberg S. 1969. Memory-scanning: mental processes revealed by reaction-time experiments. Am. Sci. 57:421–57 [Google Scholar]
  77. Tillisch K, Mayer EA, Labus JS. 2011. Quantitative meta-analysis identifies brain regions activated during rectal distension in irritable bowel syndrome. Gastroenterology 140:91–100 [Google Scholar]
  78. Turkeltaub PE, Eden GF, Jones KM, Zeffiro TA. 2002. Meta-analysis of the functional neuroanatomy of single-word reading: method and validation. NeuroImage 16:765–80 [Google Scholar]
  79. Turner JA, Laird AR. 2012. The cognitive paradigm ontology: design and application. Neuroinformatics 10:57–66 [Google Scholar]
  80. Uttal W. 2001. The New Phrenology: The Limits of Localizing Cognitive Processes in the Brain. Cambridge, MA: MIT Press
  81. Varoquaux G, Craddock RC. 2013. Learning and comparing functional connectomes across subjects. NeuroImage 80:405–15 [Google Scholar]
  82. Wager TD, Atlas LY, Lindquist MA, Roy M, Woo CW, Kross E. 2013. An fMRI-based neurologic signature of physical pain. N. Engl. J. Med. 368:1388–97 [Google Scholar]
  83. Wager TD, Lindquist M, Kaplan L. 2007. Meta-analysis of functional neuroimaging data: current and future directions. Soc. Cogn. Affect. Neurosci. 2:150–58 [Google Scholar]
  84. Wager TD, Rilling JK, Smith EE, Sokolik A, Casey KL. et al. 2004. Placebo-induced changes in fMRI in the anticipation and experience of pain. Science 303:1162–67 [Google Scholar]
  85. Wiech K, Lin CS, Brodersen KH, Bingel U, Ploner M, Tracey I. 2010. Anterior insula integrates information about salience into perceptual decisions about pain. J. Neurosci. 30:16324–31 [Google Scholar]
  86. Wilhelm O, Hildebrandt A, Oberauer K. 2013. What is working memory capacity, and how can we measure it?. Front. Psychol. 4:433 [Google Scholar]
  87. Wise RJ, Greene J, Büchel C, Scott SK. 1999. Brain regions involved in articulation. Lancet 353:1057–61 [Google Scholar]
  88. Yarkoni T, Barch DM, Gray JR, Conturo TE, Braver TS. 2009. Bold correlates of trial-by-trial reaction time variability in gray and white matter: a multi-study fMRI analysis. PLOS ONE 4:e4257 [Google Scholar]
  89. Yarkoni T, Poldrack RA, Nichols TE, Van Essen DC, Wager TD. 2011. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8:665–70 [Google Scholar]
  90. Yarkoni T, Poldrack RA, Van Essen DC, Wager TD. 2010. Cognitive neuroscience 2.0: building a cumulative science of human brain function. Trends Cogn. Sci. 14:489–96 [Google Scholar]
  91. Yeo BTT, Krienen FM, Eickhoff SB, Yaakub SN, Fox PT. et al. 2014. Functional specialization and flexibility in human association cortex. Cereb. Cortex. doi: 10.1093/cercor/bhu217

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