Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Challenges and future directions for representations of functional brain organization


A key principle of brain organization is the functional integration of brain regions into interconnected networks. Functional MRI scans acquired at rest offer insights into functional integration via patterns of coherent fluctuations in spontaneous activity, known as functional connectivity. These patterns have been studied intensively and have been linked to cognition and disease. However, the field is fractionated. Diverging analysis approaches have segregated the community into research silos, limiting the replication and clinical translation of findings. A primary source of this fractionation is the diversity of approaches used to reduce complex brain data into a lower-dimensional set of features for analysis and interpretation, which we refer to as brain representations. In this Primer, we provide an overview of different brain representations, lay out the challenges that have led to the fractionation of the field and that continue to form obstacles for convergence, and propose concrete guidelines to unite the field.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Example brain representations.
Fig. 2: Different functional-connectivity-based versions of summary measures in different brain representations.
Fig. 3: Toy examples of representational ambiguity.
Fig. 4


  1. 1.

    Van Essen, D. C. & Glasser, M. F. Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans. Neuron 99, 640–663 (2018). This review of brain mapping (i.e., parcellating the brain into units) summarizes extensive research performed in non-human primates and discusses how these results inform cortical brain parcellation efforts in humans using noninvasive imaging techniques including rfMRI. This article provides both a historical and a future perspective on cortical brain parcellation, which is an important aspect of brain representations (related to our description defining the brain units in section “Defining a brain unit”).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Bijsterbosch, J., Smith, S.M. & Beckmann, C.F. Introduction to Resting State fMRI Functional Connectivity (Oxford Univ. Press, 2017).

  3. 3.

    Reid, A. T. et al. Advancing functional connectivity research from association to causation. Nat. Neurosci. 22, 1751–1760 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Botvinik-Nezer, R. et al. Variability in the analysis of a single neuroimaging dataset by many teams. Nature 582, 84–88 (2020).

    CAS  PubMed  Google Scholar 

  5. 5.

    Eickhoff, S. B., Yeo, B. T. T. & Genon, S. Imaging-based parcellations of the human brain. Nat. Rev. Neurosci. 19, 672–686 (2018).

    CAS  PubMed  Google Scholar 

  6. 6.

    Ferrante, M. et al. Computational psychiatry: a report from the 2017 NIMH workshop on opportunities and challenges. Mol. Psychiatry 24, 479–483 (2019).

    PubMed  Google Scholar 

  7. 7.

    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 

  8. 8.

    Lopes-dos-Santos, V., Ribeiro, S. & Tort, A. B. L. Detecting cell assemblies in large neuronal populations. J. Neurosci. Methods 220, 149–166 (2013).

    PubMed  Google Scholar 

  9. 9.

    Smith, S. M. et al. Resting-state fMRI in the Human Connectome Project. Neuroimage 80, 144–168 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. 10.

    Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Eickhoff, S. B., Thirion, B., Varoquaux, G. & Bzdok, D. Connectivity-based parcellation: critique and implications. Hum. Brain Mapp. 36, 4771–4792 (2015).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Uddin, L. Q., Yeo, B. T. T. & Spreng, R. N. Towards a universal taxonomy of macro-scale functional human brain networks. Brain Topogr. 32, 926–942 (2019). This article discusses the challenges of scale and dimensionality of brain representations (related to our discussion of dimensionality in the section “Heterogeneity and dimensionality of brain units”). Focusing on the level of large-scale brain networks, the authors propose a consistent nomenclature for the naming of networks grounded in anatomy to address widespread problems with inconsistent terminology in the literature.

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Eickhoff, S. B., Constable, R. T. & Yeo, B. T. T. Topographic organization of the cerebral cortex and brain cartography. Neuroimage 170, 332–347 (2018).

    PubMed  Google Scholar 

  14. 14.

    Tzourio-Mazoyer, N. et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002).

    CAS  PubMed  Google Scholar 

  15. 15.

    Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Google Scholar 

  16. 16.

    Eickhoff, S. B. et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage 25, 1325–1335 (2005).

    PubMed  Google Scholar 

  17. 17.

    Dadi, K. et al. Benchmarking functional connectome-based predictive models for resting-state fMRI. Neuroimage 192, 115–134 (2019). This paper is a great example of the benefit of comparing different brain representations to determine their relative value in addressing a specific research question. The results offer clear-cut insights that are important for brain representations. For example, functionally defined brain units clearly outperform anatomically defined brain units, which has informed our guideline #1 in Box 3.

    PubMed  Google Scholar 

  18. 18.

    Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2018).

    PubMed  Google Scholar 

  19. 19.

    Shen, X., Tokoglu, F., Papademetris, X. & Constable, R. T. Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82, 403–415 (2013).

    CAS  PubMed  Google Scholar 

  20. 20.

    Craddock, R. C., James, G. A., Holtzheimer, P. E. III, Hu, X. P. & Mayberg, H. S. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Hum. Brain Mapp. 33, 1914–1928 (2012).

    PubMed  Google Scholar 

  21. 21.

    Gordon, E. M. et al. Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26, 288–303 (2016).

    PubMed  Google Scholar 

  22. 22.

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

    PubMed  Google Scholar 

  23. 23.

    Beckmann, C. F., DeLuca, M., Devlin, J. T. & Smith, S. M. Investigations into resting-state connectivity using independent component analysis. Phil. Trans. R. Soc. Lond. B 360, 1001–1013 (2005).

    Google Scholar 

  24. 24.

    Harrison, S. J. et al. Large-scale probabilistic functional modes from resting state fMRI. Neuroimage 109, 217–231 (2015).

    PubMed  PubMed Central  Google Scholar 

  25. 25.

    Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V. & Thirion, B. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Inf. Process. Med. Imaging 22, 562–573 (2011).

    PubMed  Google Scholar 

  26. 26.

    Eavani, H. et al. Identifying sparse connectivity patterns in the brain using resting-state fMRI. Neuroimage 105, 286–299 (2015).

    PubMed  Google Scholar 

  27. 27.

    Friston, K. J. Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2, 56–78 (1994).

    Google Scholar 

  28. 28.

    Smith, S. M. et al. Functional connectomics from resting-state fMRI. Trends Cogn. Sci. 17, 666–682 (2013).

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Betzel, R. F. & Bassett, D. S. Multi-scale brain networks. Neuroimage 160, 73–83 (2017).

    PubMed  Google Scholar 

  30. 30.

    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 

  31. 31.

    Smith, S. M. et al. A positive-negative mode of population covariation links brain connectivity, demographics and behavior. Nat. Neurosci. 18, 1565–1567 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Nickerson, L. D., Smith, S. M., Öngür, D. & Beckmann, C. F. Using dual regression to investigate network shape and amplitude in functional connectivity analyses. Front. Neurosci. 11, 115 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Hutchison, R. M. et al. Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80, 360–378 (2013).

    PubMed  Google Scholar 

  34. 34.

    Preti, M. G., Bolton, T. A. & Van De Ville, D. The dynamic functional connectome: state-of-the-art and perspectives. Neuroimage 160, 41–54 (2017).

    PubMed  Google Scholar 

  35. 35.

    Allen, E. A. et al. Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24, 663–676 (2014).

    PubMed  Google Scholar 

  36. 36.

    Vidaurre, D. et al. Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks. Nat. Commun. 9, 2987 (2018).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Zou, Q.-H. et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J. Neurosci. Methods 172, 137–141 (2008).

    PubMed  PubMed Central  Google Scholar 

  38. 38.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Bijsterbosch, J. et al. Investigations into within- and between-subject resting-state amplitude variations. Neuroimage 159, 57–69 (2017).

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Bijsterbosch, J. D., Beckmann, C. F., Woolrich, M. W., Smith, S. M. & Harrison, S. J. The relationship between spatial configuration and functional connectivity of brain regions revisited. eLife 8, e44890 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality, and emotion. Cereb. Cortex 29, 2533–2551 (2019).

    PubMed  Google Scholar 

  42. 42.

    Duff, E. P., Makin, T., Cottaar, M., Smith, S. M. & Woolrich, M. W. Disambiguating brain functional connectivity. Neuroimage 173, 540–550 (2018).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Huntenburg, J. M., Bazin, P.-L. & Margulies, D. S. Large-scale gradients in human cortical organization. Trends Cogn. Sci. 22, 21–31 (2018).

    PubMed  Google Scholar 

  44. 44.

    Marquand, A. F., Haak, K. V. & Beckmann, C. F. Functional corticostriatal connection topographies predict goal directed behaviour in humans. Nat. Hum. Behav. 1, 0146 (2017).

    PubMed  PubMed Central  Google Scholar 

  45. 45.

    Margulies, D. S. et al. Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc. Natl. Acad. Sci. USA 113, 12574–12579 (2016).

    CAS  PubMed  Google Scholar 

  46. 46.

    Majeed, W. et al. Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. Neuroimage 54, 1140–1150 (2011).

    PubMed  Google Scholar 

  47. 47.

    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 

  48. 48.

    Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).

    CAS  PubMed  Google Scholar 

  49. 49.

    Jbabdi, S., Sotiropoulos, S. N. & Behrens, T. E. The topographic connectome. Curr. Opin. Neurobiol. 23, 207–215 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Paquola, C. et al. The cortical wiring scheme of hierarchical information processing. Preprint at bioRxiv (2020).

  51. 51.

    Betzel, R. F. et al. The community structure of functional brain networks exhibits scale-specific patterns of inter- and intra-subject variability. Neuroimage 202, 115990 (2019).

    PubMed  Google Scholar 

  52. 52.

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

    CAS  PubMed  Google Scholar 

  53. 53.

    Moeller, S. et al. Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn. Reson. Med. 63, 1144–1153 (2010).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Yu, Q. et al. Comparing brain graphs in which nodes are regions of interest or independent components: A simulation study. J. Neurosci. Methods 291, 61–68 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. 55.

    Abraham, A. et al. Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example. Neuroimage 147, 736–745 (2017).

    PubMed  Google Scholar 

  56. 56.

    Duff, E. P. et al. Task-driven ICA feature generation for accurate and interpretable prediction using fMRI. Neuroimage 60, 189–203 (2012).

    PubMed  Google Scholar 

  57. 57.

    Pervaiz, U., Vidaurre, D., Woolrich, M. W. & Smith, S. M. Optimising network modelling methods for fMRI. NeuroImage 211, 116604 (2020).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Smith, S. M. et al. Network modelling methods for FMRI. Neuroimage 54, 875–891 (2011).

    PubMed  Google Scholar 

  59. 59.

    Allen, E. A., Erhardt, E. B., Wei, Y., Eichele, T. & Calhoun, V. D. Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study. Neuroimage 59, 4141–4159 (2012).

    PubMed  Google Scholar 

  60. 60.

    Llera, A., Wolfers, T., Mulders, P. & Beckmann, C. F. Inter-individual differences in human brain structure and morphology link to variation in demographics and behavior. eLife 8, e44443 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Smith, S. et al. Structural variability in the human brain reflects fine-grained functional architecture at the population level. J. Neurosci. 39, 6136–6149 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Bijsterbosch, J. D. et al. The relationship between spatial configuration and functional connectivity of brain regions. eLife 7, e32992 (2018). This article uncovers an important source of representational ambiguity by showing that between-participant variance in node-to-node correlations is partly driven by variability in spatial organization. Data-driven simulations are used to interrogate interactions between different elements of brain representations, and shared versus unique variance is used to compare different brain representations.

    PubMed  PubMed Central  Google Scholar 

  63. 63.

    Tavor, I. et al. Task-free MRI predicts individual differences in brain activity during task performance. Science 352, 216–220 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Coalson, T. S., Van Essen, D. C. & Glasser, M. F. The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proc. Natl. Acad. Sci. USA 115, E6356–E6365 (2018).

    CAS  PubMed  Google Scholar 

  65. 65.

    Robinson, E. C. et al. MSM: a new flexible framework for multimodal surface matching. Neuroimage 100, 414–426 (2014).

    PubMed  PubMed Central  Google Scholar 

  66. 66.

    Braga, R. M. & Buckner, R. L. Parallel interdigitated distributed networks within the individual estimated by intrinsic functional connectivity. Neuron 95, 457–471.e5 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67.

    Poldrack, R. A. et al. Long-term neural and physiological phenotyping of a single human. Nat. Commun. 6, 8885 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Seitzman, B. A. et al. Trait-like variants in human functional brain networks. Proc. Natl. Acad. Sci. USA 116, 22851–22861 (2019).

    CAS  PubMed  Google Scholar 

  69. 69.

    Hacker, C. D. et al. Resting state network estimation in individual subjects. Neuroimage 82, 616–633 (2013).

    PubMed  PubMed Central  Google Scholar 

  70. 70.

    Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Harrison, S. J. et al. Modelling subject variability in the spatial and temporal characteristics of functional modes. NeuroImage 222, 117226 (2020).

    PubMed  Google Scholar 

  72. 72.

    Vanderwal, T. et al. Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 157, 521–530 (2017).

    PubMed  Google Scholar 

  73. 73.

    Guntupalli, J. S., Feilong, M. & Haxby, J. V. A computational model of shared fine-scale structure in the human connectome. PLOS Comput. Biol. 14, e1006120 (2018).

    PubMed  PubMed Central  Google Scholar 

  74. 74.

    Kieliba, P., Madugula, S., Filippini, N., Duff, E. P. & Makin, T. R. Large-scale intrinsic connectivity is consistent across varying task demands. PLoS One 14, e0213861 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Horien, C., Shen, X., Scheinost, D. & Constable, R. T. The individual functional connectome is unique and stable over months to years. Neuroimage 189, 676–687 (2019).

    PubMed  PubMed Central  Google Scholar 

  76. 76.

    Gratton, C. et al. Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98, 439–452.e5 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Salehi, M. et al. There is no single functional atlas even for a single individual: Functional parcel definitions change with task. Neuroimage 208, 116366 (2020).

    PubMed  Google Scholar 

  78. 78.

    Chen, J. E. et al. Resting-state “physiological networks”. NeuroImage 213, 116707 (2020).

    PubMed  Google Scholar 

  79. 79.

    Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154, 174–187 (2017).

    PubMed  PubMed Central  Google Scholar 

  80. 80.

    Murphy, K. & Fox, M. D. Towards a consensus regarding global signal regression for resting state functional connectivity MRI. Neuroimage 154, 169–173 (2017).

    PubMed  PubMed Central  Google Scholar 

  81. 81.

    Power, J. D., Laumann, T. O., Plitt, M., Martin, A. & Petersen, S. E. On global fMRI signals and simulations. Trends Cogn. Sci. 21, 911–913 (2017).

    PubMed  Google Scholar 

  82. 82.

    Hallquist, M. N., Hwang, K. & Luna, B. The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. Neuroimage 82, 208–225 (2013).

    PubMed  Google Scholar 

  83. 83.

    Smith, S. M. et al. Correspondence of the brain’s functional architecture during activation and rest. Proc. Natl. Acad. Sci. USA 106, 13040–13045 (2009).

    CAS  PubMed  Google Scholar 

  84. 84.

    Deco, G., Jirsa, V. K., Robinson, P. A., Breakspear, M. & Friston, K. The dynamic brain: from spiking neurons to neural masses and cortical fields. PLOS Comput. Biol. 4, e1000092 (2008).

    PubMed  PubMed Central  Google Scholar 

  85. 85.

    Buibas, M. & Silva, G. A. A framework for simulating and estimating the state and functional topology of complex dynamic geometric networks. Neural Comput. 23, 183–214 (2011).

    PubMed  Google Scholar 

  86. 86.

    Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).

    CAS  PubMed  Google Scholar 

  87. 87.

    Shmuel, A. & Leopold, D. A. Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest. Hum. Brain Mapp. 29, 751–761 (2008). This paper used simultaneous rfMRI and intracortical recordings to show that between-region correlations measured from resting state fMRI (i.e., functional connectivity) are linked to synchronization of neuronal signals. This study is one of the few examples that link rfMRI brain representations to the underlying neurophysiology. Similar research efforts are needed to explicitly differentiate between different brain representations, as we have proposed in section “Recommendations and future directions for brain representations” and Fig. 4.

    PubMed  PubMed Central  Google Scholar 

  88. 88.

    Kucyi, A. et al. Intracranial electrophysiology reveals reproducible intrinsic functional connectivity within human brain networks. J. Neurosci. 38, 4230–4242 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. 89.

    Baillet, S. Magnetoencephalography for brain electrophysiology and imaging. Nat. Neurosci. 20, 327–339 (2017).

    CAS  PubMed  Google Scholar 

  90. 90.

    Bentley, W. J., Li, J. M., Snyder, A. Z., Raichle, M. E. & Snyder, L. H. Oxygen level and LFP in task-positive and task-negative areas: bridging BOLD fMRI and electrophysiology. Cereb. Cortex 26, 346–357 (2016).

    PubMed  Google Scholar 

  91. 91.

    Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H. & Leopold, D. A. Neural basis of global resting-state fMRI activity. Proc. Natl. Acad. Sci. USA 107, 10238–10243 (2010).

    PubMed  Google Scholar 

  92. 92.

    Vidaurre, D. et al. Discovering dynamic brain networks from big data in rest and task. Neuroimage 180, 646–656 (2018). Pt B.

    PubMed  PubMed Central  Google Scholar 

  93. 93.

    Van Essen, D. C. & Maunsell, J. H. R. Hierarchical organization and functional streams in the visual cortex. Trends Neurosci. 6, 370–375 (1983).

    Google Scholar 

  94. 94.

    Biswal, B., Yetkin, F. Z., Haughton, V. M. & Hyde, J. S. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541 (1995).

    CAS  PubMed  Google Scholar 

  95. 95.

    Medaglia, J. D., Lynall, M.-E. & Bassett, D. S. Cognitive network neuroscience. J. Cogn. Neurosci. 27, 1471–1491 (2015).

    PubMed  PubMed Central  Google Scholar 

  96. 96.

    Park, H.-J., Friston, K. J., Pae, C., Park, B. & Razi, A. Dynamic effective connectivity in resting state fMRI. Neuroimage 180, 594–608 (2018). Pt B.

    PubMed  PubMed Central  Google Scholar 

  97. 97.

    Patel, R. S., Bowman, F. D. & Rilling, J. K. A Bayesian approach to determining connectivity of the human brain. Hum. Brain Mapp. 27, 267–276 (2006).

    PubMed  Google Scholar 

  98. 98.

    Hyvärinen, A. & Smith, S. M. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. J. Mach. Learn. Res. 14, 111–152 (2013).

    PubMed  PubMed Central  Google Scholar 

  99. 99.

    Mumford, J. A. & Ramsey, J. D. Bayesian networks for fMRI: a primer. Neuroimage 86, 573–582 (2014).

    PubMed  Google Scholar 

  100. 100.

    Schwab, S. et al. Directed functional connectivity using dynamic graphical models. Neuroimage 175, 340–353 (2018).

    PubMed  PubMed Central  Google Scholar 

  101. 101.

    Sala-Llonch, R., Smith, S. M., Woolrich, M. & Duff, E. P. Spatial parcellations, spectral filtering, and connectivity measures in fMRI: Optimizing for discrimination. Hum. Brain Mapp. 40, 407–419 (2019).

    PubMed  Google Scholar 

  102. 102.

    Noble, S., Scheinost, D. & Constable, R. T. A decade of test-retest reliability of functional connectivity: a systematic review and meta-analysis. Neuroimage 203, 116157 (2019).

    PubMed  Google Scholar 

  103. 103.

    Burgess, G. C. et al. Evaluation of denoising strategies to address motion-correlated artifacts in resting-state functional magnetic resonance imaging data from the Human Connectome Project. Brain Connect. 6, 669–680 (2016).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Glasser, M. F. et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013).

    PubMed  PubMed Central  Google Scholar 

  105. 105.

    Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).

    CAS  PubMed  Google Scholar 

  106. 106.

    Yu, M. et al. Statistical harmonization corrects site effects in functional connectivity measurements from multi-site fMRI data. Hum. Brain Mapp. 39, 4213–4227 (2018).

    PubMed  PubMed Central  Google Scholar 

  107. 107.

    Power, J. D., Plitt, M., Laumann, T. O. & Martin, A. Sources and implications of whole-brain fMRI signals in humans. Neuroimage 146, 609–625 (2017).

    PubMed  Google Scholar 

  108. 108.

    Saad, Z. S. et al. Trouble at rest: how correlation patterns and group differences become distorted after global signal regression. Brain Connect. 2, 25–32 (2012).

    PubMed  PubMed Central  Google Scholar 

  109. 109.

    Glover, G. H., Li, T. Q. & Ress, D. Image-based method for retrospective correction of physiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167 (2000).

    CAS  PubMed  Google Scholar 

  110. 110.

    Glasser, M. F. et al. Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data. Neuroimage 181, 692–717 (2018).

    PubMed  PubMed Central  Google Scholar 

  111. 111.

    Dinga, R. et al. Evaluating the evidence for biotypes of depression: methodological replication and extension of Drysdale et al. (2017). Neuroimage Clin. 22, 101796 (2019).

    PubMed  PubMed Central  Google Scholar 

  112. 112.

    Varoquaux, G. et al. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. Neuroimage 145, 166–179 (2017). Pt B.

    PubMed  Google Scholar 

  113. 113.

    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).

    CAS  PubMed  Google Scholar 

  114. 114.

    Arslan, S. et al. Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. Neuroimage 170, 5–30 (2018).

    PubMed  Google Scholar 

  115. 115.

    Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


E.P.D. was supported by the SSNAP “Support for Sick and Newborn Infants and their Parents” Medical Research Fund (University of Oxford Excellence Fellowship). S.J.H. was supported by grant #2017-403 of the Strategic Focal Area “Personalized Health and Related Technologies (PHRT)” of the ETH Domain. S.S. is supported by a Wellcome Trust Strategic Award 098369/Z/12/Z and a Wellcome Trust Collaborative Award 215573/Z/19/Z. M.W. is supported by the NIHR Oxford Health Biomedical Research Centre and by the Wellcome Trust (106183/Z/14/Z and 215573/Z/19/Z). We thank M. Glasser for his helpful comments and discussions in relation to this article.

Author information




J.B. and E.P.D. conceived of the topic and structure for this article. J.B. wrote the manuscript with input from E.P.D. All authors took part in extensive discussions to refine the arguments presented in this manuscript, and all authors commented on the final manuscript.

Corresponding authors

Correspondence to Janine Bijsterbosch or Eugene P. Duff.

Ethics declarations

Competing interests

The authors declare that they have no conflicts of interest.

Additional information

Peer review information Nature Neuroscience thanks Finnegan Calabro, Thomas Yeo and the other, anonymous, reviewer for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bijsterbosch, J., Harrison, S.J., Jbabdi, S. et al. Challenges and future directions for representations of functional brain organization. Nat Neurosci 23, 1484–1495 (2020).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing