Computational approaches to fMRI analysis


Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Types of MVPA.
Figure 2: Uses of real-time fMRI.
Figure 3: Schematic of model-based fMRI.
Figure 4: Process for SRM.
Figure 5: Comparison of SRM to other multisubject approaches.
Figure 6: Functional interactions within and between participants.
Figure 7: Real-time cloud system architecture.


  1. 1

    Bandettini, P.A., Wong, E.C., Hinks, R.S., Tikofsky, R.S. & Hyde, J.S. Time course EPI of human brain function during task activation. Magn. Reson. Med. 25, 390–397 (1992).

    CAS  PubMed  Google Scholar 

  2. 2

    Kwong, K.K. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. USA 89, 5675–5679 (1992).

    CAS  PubMed  Google Scholar 

  3. 3

    Ogawa, S. et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 89, 5951–5955 (1992).

    CAS  PubMed  Google Scholar 

  4. 4

    Posner, M.I., Petersen, S.E., Fox, P.T. & Raichle, M.E. Localization of cognitive operations in the human brain. Science 240, 1627–1631 (1988).

    CAS  PubMed  Google Scholar 

  5. 5

    Friston, K.J. et al. Statistical parametric maps in functional imaging: a general linear approach. Hum. Brain Mapp. 2, 189–210 (1994).

    Google Scholar 

  6. 6

    Braver, T.S. et al. A parametric study of prefrontal cortex involvement in human working memory. Neuroimage 5, 49–62 (1997).

    CAS  PubMed  Google Scholar 

  7. 7

    Dale, A.M. & Buckner, R.L. Selective averaging of rapidly presented individual trials using fMRI. Hum. Brain Mapp. 5, 329–340 (1997).

    CAS  PubMed  Google Scholar 

  8. 8

    Forman, S.D. et al. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33, 636–647 (1995).

    CAS  Google Scholar 

  9. 9

    Smith, S.M. & Nichols, T.E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–98 (2009).

    PubMed  PubMed Central  Google Scholar 

  10. 10

    Ashburner, J. SPM: a history. Neuroimage 62, 791–800 (2012).

    PubMed  Google Scholar 

  11. 11

    Cox, R.W. AFNI: what a long strange trip it's been. Neuroimage 62, 743–747 (2012).

    PubMed  Google Scholar 

  12. 12

    Goebel, R. BrainVoyager--past, present, future. Neuroimage 62, 748–756 (2012).

    PubMed  Google Scholar 

  13. 13

    Jenkinson, M., Beckmann, C.F., Behrens, T.E.J., Woolrich, M.W. & Smith, S.M. FSL. Neuroimage 62, 782–790 (2012).

    Google Scholar 

  14. 14

    Lewis-Peacock, J.A. & Norman, K.A. Multi-voxel pattern analysis of fMRI data. in The Cognitive Neurosciences (eds. Gazzaniga, M. & Mangun, R.) 911–920 (MIT Press, 2014).

  15. 15

    Haxby, J.V. et al. Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293, 2425–2430 (2001).

    CAS  Google Scholar 

  16. 16

    Kamitani, Y. & Tong, F. Decoding the visual and subjective contents of the human brain. Nat. Neurosci. 8, 679–685 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Freeman, J., Brouwer, G.J., Heeger, D.J. & Merriam, E.P. Orientation decoding depends on maps, not columns. J. Neurosci. 31, 4792–4804 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Kriegeskorte, N. & Diedrichsen, J. Inferring brain-computational mechanisms with models of activity measurements. Phil. Trans. R. Soc. B 371, 20160278 (2016).

    PubMed  Google Scholar 

  19. 19

    Ryali, S., Supekar, K., Abrams, D.A. & Menon, V. Sparse logistic regression for whole-brain classification of fMRI data. Neuroimage 51, 752–764 (2010).

    PubMed  PubMed Central  Google Scholar 

  20. 20

    Etzel, J.A., Zacks, J.M. & Braver, T.S. Searchlight analysis: promise, pitfalls, and potential. Neuroimage 78, 261–269 (2013).

    PubMed  PubMed Central  Google Scholar 

  21. 21

    Wimmer, G.E. & Shohamy, D. Preference by association: how memory mechanisms in the hippocampus bias decisions. Science 338, 270–273 (2012).

    CAS  PubMed  Google Scholar 

  22. 22

    Zeithamova, D., Dominick, A.L. & Preston, A.R. Hippocampal and ventral medial prefrontal activation during retrieval-mediated learning supports novel inference. Neuron 75, 168–179 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Kim, G., Lewis-Peacock, J.A., Norman, K.A. & Turk-Browne, N.B. Pruning of memories by context-based prediction error. Proc. Natl. Acad. Sci. USA 111, 8997–9002 (2014).

    CAS  PubMed  Google Scholar 

  24. 24

    Gordon, A.M., Rissman, J., Kiani, R. & Wagner, A.D. Cortical reinstatement mediates the relationship between content-specific encoding activity and subsequent recollection decisions. Cereb. Cortex 24, 3350–3364 (2014).

    PubMed  Google Scholar 

  25. 25

    Hindy, N.C., Ng, F.Y. & Turk-Browne, N.B. Linking pattern completion in the hippocampus to predictive coding in visual cortex. Nat. Neurosci. 19, 665–667 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Deuker, L. et al. Memory consolidation by replay of stimulus-specific neural activity. J. Neurosci. 33, 19373–19383 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    St-Laurent, M., Abdi, H. & Buchsbaum, B.R. Distributed patterns of reactivation predict vividness of recollection. J. Cogn. Neurosci. 27, 2000–2018 (2015).

    PubMed  Google Scholar 

  28. 28

    Todd, M.T., Nystrom, L.E. & Cohen, J.D. Confounds in multivariate pattern analysis: theory and rule representation case study. Neuroimage 77, 157–165 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29

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

    PubMed  PubMed Central  Google Scholar 

  30. 30

    Khaligh-Razavi, S.-M. & Kriegeskorte, N. Deep supervised, but not unsupervised, models may explain IT cortical representation. PLoS Comput. Biol. 10, e1003915 (2014).

    PubMed  PubMed Central  Google Scholar 

  31. 31

    Hsieh, L.-T., Gruber, M.J., Jenkins, L.J. & Ranganath, C. Hippocampal activity patterns carry information about objects in temporal context. Neuron 81, 1165–1178 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    Chan, S.C.Y., Niv, Y. & Norman, K.A. A probability distribution over latent causes, in the orbitofrontal cortex. J. Neurosci. 36, 7817–7828 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Schapiro, A.C., Kustner, L.V. & Turk-Browne, N.B. Shaping of object representations in the human medial temporal lobe based on temporal regularities. Curr. Biol. 22, 1622–1627 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Hulbert, J.C. & Norman, K.A. Neural differentiation tracks improved recall of competing memories following interleaved study and retrieval practice. Cereb. Cortex 25, 3994–4008 (2015).

    CAS  PubMed  Google Scholar 

  35. 35

    Poppenk, J. & Norman, K.A. Briefly cuing memories leads to suppression of their neural representations. J. Neurosci. 34, 8010–8020 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Milivojevic, B., Vicente-Grabovetsky, A. & Doeller, C.F. Insight reconfigures hippocampal-prefrontal memories. Curr. Biol. 25, 821–830 (2015).

    CAS  Google Scholar 

  37. 37

    Schlichting, M.L., Mumford, J.A. & Preston, A.R. Learning-related representational changes reveal dissociable integration and separation signatures in the hippocampus and prefrontal cortex. Nat. Commun. 6, 8151 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Wimber, M., Alink, A., Charest, I., Kriegeskorte, N. & Anderson, M.C. Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression. Nat. Neurosci. 18, 582–589 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    Favila, S.E., Chanales, A.J.H. & Kuhl, B.A. Experience-dependent hippocampal pattern differentiation prevents interference during subsequent learning. Nat. Commun. 7, 11066 (2016).

    PubMed  PubMed Central  Google Scholar 

  40. 40

    Davis, T. et al. What do differences between multi-voxel and univariate analysis mean? How subject-, voxel-, and trial-level variance impact fMRI analysis. Neuroimage 97, 271–283 (2014).

    PubMed  PubMed Central  Google Scholar 

  41. 41

    Alink, A., Walther, A., Krugliak, A., van den Bosch, J.J.F. & Kriegeskorte, N. Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift. Preprint at (2015).

  42. 42

    LaConte, S.M. Decoding fMRI brain states in real-time. Neuroimage 56, 440–454 (2011).

    PubMed  PubMed Central  Google Scholar 

  43. 43

    Sulzer, J. et al. Real-time fMRI neurofeedback: progress and challenges. Neuroimage 76, 386–399 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    deCharms, R.C. et al. Control over brain activation and pain learned by using real-time functional MRI. Proc. Natl. Acad. Sci. USA 102, 18626–18631 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Schnyer, D.M. et al. Neurocognitive therapeutics: from concept to application in the treatment of negative attention bias. Biol. Mood Anxiety Disord. 5, 1 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. 46

    Li, X. et al. Volitional reduction of anterior cingulate cortex activity produces decreased cue craving in smoking cessation: a preliminary real-time fMRI study. Addict. Biol. 18, 739–748 (2013).

    PubMed  Google Scholar 

  47. 47

    Shibata, K., Watanabe, T., Sasaki, Y. & Kawato, M. Perceptual learning incepted by decoded fMRI neurofeedback without stimulus presentation. Science 334, 1413–1415 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    deBettencourt, M.T., Cohen, J.D., Lee, R.F., Norman, K.A. & Turk-Browne, N.B. Closed-loop training of attention with real-time brain imaging. Nat. Neurosci. 18, 470–475 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Cox, R.W., Jesmanowicz, A. & Hyde, J.S. Real-time functional magnetic resonance imaging. Magn. Reson. Med. 33, 230–236 (1995).

    CAS  PubMed  Google Scholar 

  50. 50

    Goddard, N. et al. Online analysis of functional MRI datasets on parallel platforms. J. Supercomput. 11, 295–318 (1997).

    Google Scholar 

  51. 51

    Weiskopf, N. et al. Physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI): methodology and exemplary data. Neuroimage 19, 577–586 (2003).

    PubMed  Google Scholar 

  52. 52

    Bray, S., Shimojo, S. & O'Doherty, J.P. Direct instrumental conditioning of neural activity using functional magnetic resonance imaging-derived reward feedback. J. Neurosci. 27, 7498–7507 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Ramot, M., Grossman, S., Friedman, D. & Malach, R. Covert neurofeedback without awareness shapes cortical network spontaneous connectivity. Proc. Natl. Acad. Sci. USA 113, E2413–E2420 (2016).

    CAS  PubMed  Google Scholar 

  54. 54

    Harmelech, T., Friedman, D. & Malach, R. Differential magnetic resonance neurofeedback modulations across extrinsic (visual) and intrinsic (default-mode) nodes of the human cortex. J. Neurosci. 35, 2588–2595 (2015).

    PubMed  PubMed Central  Google Scholar 

  55. 55

    Emmert, K. et al. Meta-analysis of real-time fMRI neurofeedback studies using individual participant data: how is brain regulation mediated? Neuroimage 124 Pt A: 806–812 (2016).

    PubMed  Google Scholar 

  56. 56

    Yoo, J.J. et al. When the brain is prepared to learn: enhancing human learning using real-time fMRI. Neuroimage 59, 846–852 (2012).

    PubMed  PubMed Central  Google Scholar 

  57. 57

    Leeds, D.D. & Tarr, M.J. A method for real-time visual stimulus selection in the study of cortical object perception. Neuroimage 133, 529–548 (2016).

    PubMed  PubMed Central  Google Scholar 

  58. 58

    Daw, N.D. & Doya, K. The computational neurobiology of learning and reward. Curr. Opin. Neurobiol. 16, 199–204 (2006).

    CAS  PubMed  Google Scholar 

  59. 59

    Tom, S.M., Fox, C.R., Trepel, C. & Poldrack, R.A. The neural basis of loss aversion in decision-making under risk. Science 315, 515–518 (2007).

    CAS  PubMed  Google Scholar 

  60. 60

    Hampton, A.N., Bossaerts, P. & O'Doherty, J.P. Neural correlates of mentalizing-related computations during strategic interactions in humans. Proc. Natl. Acad. Sci. USA 105, 6741–6746 (2008).

    CAS  PubMed  Google Scholar 

  61. 61

    Daw, N.D., Gershman, S.J., Seymour, B., Dayan, P. & Dolan, R.J. Model-based influences on humans' choices and striatal prediction errors. Neuron 69, 1204–1215 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

    Niv, Y. et al. Reinforcement learning in multidimensional environments relies on attention mechanisms. J. Neurosci. 35, 8145–8157 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Doll, B.B., Duncan, K.D., Simon, D.A., Shohamy, D. & Daw, N.D. Model-based choices involve prospective neural activity. Nat. Neurosci. 18, 767–772 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Boorman, E.D., Rajendran, V.G., O'Reilly, J.X. & Behrens, T.E. Two anatomically and computationally distinct learning signals predict changes to stimulus-outcome associations in hippocampus. Neuron 89, 1343–1354 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    Chen, P.-H. (Cameron) et al. A reduced-dimension fMRI shared response model. in Advances in Neural Information Processing Systems 28 (eds. Cortes, C., Lawrence, N.D., Lee, D.D., Sugiyama, M. & Garnett, R.) 460–468 (Curran Associates, Inc., 2015).

  66. 66

    Haxby, J.V. et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72, 404–416 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Wang, X., Hutchinson, R. & Mitchell, T.M. Training fMRI classifiers to discriminate cognitive states across multiple subjects. in Advances in Neural Information Processing Systems 16 (eds. Thrun, S., Saul, L.K. & Schölkopf, P.B.) (2003).

    Google Scholar 

  68. 68

    Richter, F.R., Chanales, A.J.H. & Kuhl, B.A. Predicting the integration of overlapping memories by decoding mnemonic processing states during learning. Neuroimage 124, 323–335 (2016).

    PubMed  Google Scholar 

  69. 69

    Poldrack, R.A., Halchenko, Y.O. & Hanson, S.J. Decoding the large-scale structure of brain function by classifying mental States across individuals. Psychol. Sci. 20, 1364–1372 (2009).

    PubMed  PubMed Central  Google Scholar 

  70. 70

    Finn, E.S. et al. Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat. Neurosci. 18, 1664–1671 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71

    Guntupalli, J.S. et al. A model of representational spaces in human cortex. Cereb. Cortex 26, 2919–2934 (2016).

    PubMed  PubMed Central  Google Scholar 

  72. 72

    Chen, J. et al. Shared memories reveal shared structure in neural activity across individuals. Nat. Neurosci. 20, 115–125 (2017).

    CAS  PubMed  Google Scholar 

  73. 73

    Dubois, J. & Adolphs, R. Building a science of individual differences from fMRI. Trends Cogn. Sci. 20, 425–443 (2016).

    PubMed  PubMed Central  Google Scholar 

  74. 74

    Rosenberg, M.D. et al. A neuromarker of sustained attention from whole-brain functional connectivity. Nat. Neurosci. 19, 165–171 (2016).

    CAS  PubMed  Google Scholar 

  75. 75

    Manning, J.R., Ranganath, R., Norman, K.A. & Blei, D.M. Topographic factor analysis: a Bayesian model for inferring brain networks from neural data. PLoS One 9, e94914 (2014).

    PubMed  PubMed Central  Google Scholar 

  76. 76

    Turk-Browne, N.B. Functional interactions as big data in the human brain. Science 342, 580–584 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Al-Aidroos, N., Said, C.P. & Turk-Browne, N.B. Top-down attention switches coupling between low-level and high-level areas of human visual cortex. Proc. Natl. Acad. Sci. USA 109, 14675–14680 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78

    Shirer, W.R., Ryali, S., Rykhlevskaia, E., Menon, V. & Greicius, M.D. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22, 158–165 (2012).

    CAS  PubMed  Google Scholar 

  79. 79

    Cole, M.W. et al. Multi-task connectivity reveals flexible hubs for adaptive task control. Nat. Neurosci. 16, 1348–1355 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  80. 80

    Fornito, A., Zalesky, A. & Bullmore, E.T. Network scaling effects in graph analytic studies of human resting-state fMRI data. Front. Syst. Neurosci. 4, 22 (2010).

    PubMed  PubMed Central  Google Scholar 

  81. 81

    Wang, Y., Cohen, J.D., Li, K. & Turk-Browne, N.B. Full correlation matrix analysis (FCMA): an unbiased method for task-related functional connectivity. J. Neurosci. Methods 251, 108–119 (2015).

    PubMed  PubMed Central  Google Scholar 

  82. 82

    Bullmore, E. & Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186–198 (2009).

    CAS  Google Scholar 

  83. 83

    Giusti, C., Ghrist, R. & Bassett, D.S. Two's company, three (or more) is a simplex: algebraic-topological tools for understanding higher-order structure in neural data. Preprint at (2016).

  84. 84

    Mitchell, T.M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).

    CAS  PubMed  Google Scholar 

  85. 85

    Anderson, J.R. A spreading activation theory of memory. J. Verbal Learn. Verbal Behav. 22, 261–295 (1983).

    Google Scholar 

  86. 86

    Cohen, J.D., Dunbar, K. & McClelland, J.L. On the control of automatic processes: a parallel distributed processing account of the Stroop effect. Psychol. Rev. 97, 332–361 (1990).

    CAS  PubMed  Google Scholar 

  87. 87

    Yamins, D.L.K. & DiCarlo, J.J. Using goal-driven deep learning models to understand sensory cortex. Nat. Neurosci. 19, 356–365 (2016).

    CAS  PubMed  Google Scholar 

  88. 88

    Kriegeskorte, N. Deep neural networks: a new framework for modeling biological vision and brain information processing. Annu. Rev. Vis. Sci. 1, 417–446 (2015).

    PubMed  Google Scholar 

  89. 89

    Cichy, R.M., Khosla, A., Pantazis, D., Torralba, A. & Oliva, A. Deep neural networks predict hierarchical spatio-temporal cortical dynamics of human visual object recognition. Preprint at (2016).

  90. 90

    Güçlü, U. & van Gerven, M.A.J. Deep neural networks reveal a gradient in the complexity of neural representations across the ventral stream. J. Neurosci. 35, 10005–10014 (2015).

    PubMed  PubMed Central  Google Scholar 

  91. 91

    Anderson, J.R. Tracking problem solving by multivariate pattern analysis and Hidden Markov Model algorithms. Neuropsychologia 50, 487–498 (2012).

    PubMed  Google Scholar 

  92. 92

    Wang, Y. et al. Full correlation matrix analysis of fMRI data on Intel Xeon Phi coprocessors. in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (Supercomputing) (ACM, 2015).

  93. 93

    Anderson, M.J. et al. Enabling factor analysis on thousand-subject neuroimaging datasets. Preprint at (2016).

  94. 94

    Dean, J. et al. Large scale distributed deep networks. in Advances in Neural Information Processing Systems 25 (eds. Pereira, F., Burges, C.J.C., Bottou, L. & Weinberger, K.Q.) 1223–1231 (Curran Associates, Inc., 2012).

  95. 95

    Gorgolewski, K.J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci. Data 3, 160044 (2016).

    PubMed  PubMed Central  Google Scholar 

  96. 96

    Simony, E. et al. Dynamic reconfiguration of the default mode network during narrative comprehension. Nat. Commun. 7, 12141 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97

    Wang, Y. et al. Real-time full correlation matrix analysis of fMRI data. in 2016 IEEE International Conference on Big Data (Big Data 2016) (Curran Associates, Inc., in the press).

  98. 98

    Vodrahalli, K. et al. Mapping between natural movie fMRI responses and word-sequence representations. Preprint at (2016).

  99. 99

    Zhang, H. et al. A searchlight factor model approach for locating shared information in multi-subject fMRI analysis. Preprint at (2016).

  100. 100

    Freeman, J. et al. Mapping brain activity at scale with cluster computing. Nat. Methods 11, 941–950 (2014).

    CAS  PubMed  Google Scholar 

Download references


We thank C. Chen, M. Regev, Y. Wang, and H. Zhang for assistance and the members of our labs at Princeton University and Intel Labs for their numerous invaluable contributions to the work described herein. This work was made possible by support from Intel Corporation, the John Templeton Foundation, NIH grants R01 EY021755 and R01 MH069456, and NSF grant MRI BCS1229597. The opinions expressed in this publication do not necessarily reflect the views of these agencies.

Author information




All authors helped conceive the manuscript and wrote one or more sections. N.B.T.-B. edited the manuscript. N.B.T.-B. revised the manuscript with input from J.D.C., K.A.N. and P.J.R. Author order was determined alphabetically.

Corresponding author

Correspondence to Nicholas B Turk-Browne.

Ethics declarations

Competing interests

All authors receive research support from Intel Corporation. T.L.W. is employed by Intel Corporation.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cohen, J., Daw, N., Engelhardt, B. et al. Computational approaches to fMRI analysis. Nat Neurosci 20, 304–313 (2017).

Download citation

Further reading