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Imaging-based parcellations of the human brain

Nature Reviews Neurosciencevolume 19pages672686 (2018) | Download Citation


A defining aspect of brain organization is its spatial heterogeneity, which gives rise to multiple topographies at different scales. Brain parcellation — defining distinct partitions in the brain, be they areas or networks that comprise multiple discontinuous but closely interacting regions — is thus fundamental for understanding brain organization and function. The past decade has seen an explosion of in vivo MRI-based approaches to identify and parcellate the brain on the basis of a wealth of different features, ranging from local properties of brain tissue to long-range connectivity patterns, in addition to structural and functional markers. Given the high diversity of these various approaches, assessing the convergence and divergence among these ensuing maps is a challenge. Inter-individual variability adds to this challenge but also provides new opportunities when coupled with cross-species and developmental parcellation studies.

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  1. 1.

    Tononi, G., Sporns, O. & Edelman, G. M. A measure for brain complexity: relating functional segregation and integration in the nervous system. Proc. Natl Acad. Sci. USA 91, 5033–5037 (1994).

  2. 2.

    Fox, P. T. & Friston, K. J. Distributed processing; distributed functions? NeuroImage 61, 407–426 (2012).

  3. 3.

    Bijsterbosch, J. D. et al. The relationship between spatial configuration and functional connectivity of brain regions. eLife 7, e32992 (2018).

  4. 4.

    Cachia, A. et al. How interindividual differences in brain anatomy shape reading accuracy. Brain Struct. Function 223, 701–712 (2018).

  5. 5.

    Kong, R. et al. Spatial topography of individual-specific cortical networks predicts human cognition, personality and emotion. Cereb. Cortex (2018). References 3–5 demonstrate that fine-scale inter-individual differences in brain anatomy and parcellations are predictive of individuals’ behaviour, such as cognitive performance and personality traits.

  6. 6.

    Amunts, K. et al. Cytoarchitectonic mapping of the human amygdala, hippocampal region and entorhinal cortex: intersubject variability and probability maps. Anat. Embryol. 210, 343–352 (2005).

  7. 7.

    Strange, B. A., Witter, M. P., Lein, E. S. & Moser, E. I. Functional organization of the hippocampal longitudinal axis. Nat. Rev. Neurosci. 15, 655–669 (2014).

  8. 8.

    Geyer, S. & Zilles, K. in Higher-Order Motor Disorders: From Neuroanatomy and Neurobiology to Clinical Neurology (eds Freund, H.-J., Jeannerod, M., Hallett, M. & Leiguarda, R.) 3–22 (Oxford Univ. Press, 2005).

  9. 9.

    Schubotz, R. I., Anwander, A., Knösche, T. R., von Cramon, D. Y. & Tittgemeyer, M. Anatomical and functional parcellation of the human lateral premotor cortex. NeuroImage 50, 396–408 (2010).

  10. 10.

    Churchland, P. S. & Sejnowski, T. J. Perspectives on cognitive neuroscience. Science 242, 741–745 (1988).

  11. 11.

    Eickhoff, S. B., Constable, R. T. & Yeo, B. T. Topographic organization of the cerebral cortex and brain cartography. NeuroImage 170, 332–347 (2017).

  12. 12.

    Felleman, D. J. & Van Essen, D. C. Distributed hierarchical processing in the primate cerebral cortex. Cereb. Cortex 1, 1–47 (1991).

  13. 13.

    Amunts, K. & Zilles, K. Architectonic mapping of the human brain beyond Brodmann. Neuron 88, 1086–1107 (2015).

  14. 14.

    Brodmann, K. Vergleichende Lokalisationslehre der Grosshirnrinde in ihren Prinzipien dargestellt auf Grund des Zellenbaues (Johann Ambrosius Barth, 1909).

  15. 15.

    Preuss, T. M. & Goldman-Rakic, P. S. Architectonics of the parietal and temporal association cortex in the strepsirhine primate Galago compared to the anthropoid primate Macaca. J. Comp. Neurol. 310, 475–506 (1991).

  16. 16.

    Glasser, M. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016). This study represents an impressive endeavour to build a comprehensive multimodal brain map using in vivo MRI.

  17. 17.

    Amunts, K. et al. Broca’s region revisited: cytoarchitecture and intersubject variability. J. Comp. Neurol. 412, 319–341 (1999).

  18. 18.

    Wang, D. et al. Parcellating cortical functional networks in individuals. Nat. Neurosci. 18, 1853–1860 (2015).

  19. 19.

    Gordon, E. M., Laumann, T. O., Adeyemo, B. & Petersen, S. E. Individual variability of the system-level organization of the human brain. Cereb. Cortex 27, 386–399 (2017).

  20. 20.

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

  21. 21.

    Davatzikos, C. Computational neuroanatomy using brain deformations: from brain parcellation to multivariate pattern analysis and machine learning. Med. Image Anal. 33, 149–154 (2016).

  22. 22.

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

  23. 23.

    Varikuti, D. P. et al. Evaluation of non-negative matrix factorization of grey matter in age prediction. NeuroImage 173, 394–410 (2018).

  24. 24.

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

  25. 25.

    Arslan, S. et al. Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage 170, 5–30 (2017). This study provides an extensive evaluation of parcellation approaches based on resting-state functional connectivity.

  26. 26.

    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).

  27. 27.

    Lutti, A., Dick, F., Sereno, M. I. & Weiskopf, N. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. NeuroImage 93, 176–188 (2014).

  28. 28.

    Glasser, M. F. & Van Essen, D. C. Mapping human cortical areas in vivo based on myelin content as revealed by T1-and T2-weighted MRI. J. Neurosci. 31, 11597–11616 (2011).

  29. 29.

    De Martino, F. et al. High-resolution mapping of myeloarchitecture in vivo: localization of auditory areas in the human brain. Cereb. Cortex 25, 3394–3405 (2015).

  30. 30.

    Sereno, M. I., Lutti, A., Weiskopf, N. & Dick, F. Mapping the human cortical surface by combining quantitative T1 with retinotopy. Cereb. Cortex 23, 2261–2268 (2012).

  31. 31.

    Wilms, M. et al. Comparison of functional and cytoarchitectonic maps of human visual areas V1, V2, V3d, V3v, and V4 (v). NeuroImage 49, 1171–1179 (2010).

  32. 32.

    Orban, P. et al. The richness of task-evoked hemodynamic responses defines a pseudohierarchy of functionally meaningful brain networks. Cereb. Cortex 25, 2658–2669 (2015).

  33. 33.

    Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).

  34. 34.

    Kurth, F., Zilles, K., Fox, P. T., Laird, A. R. & Eickhoff, S. B. A link between the systems: functional differentiation and integration within the human insula revealed by meta-analysis. Brain Struct. Funct. 214, 519–534 (2010).

  35. 35.

    Yang, Y. et al. Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation. NeuroImage 124, 300–309 (2016).

  36. 36.

    Laird, A. R., Lancaster, J. L. & Fox, P. T. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3, 65–78 (2005).

  37. 37.

    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).

  38. 38.

    Gorgolewski, K. J. et al. a repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain. NeuroImage 124, 1242–1244 (2016).

  39. 39.

    Langner, R., Rottschy, C., Laird, A. R., Fox, P. T. & Eickhoff, S. B. Meta-analytic connectivity modeling revisited: controlling for activation base rates. NeuroImage 99, 559–570 (2014).

  40. 40.

    Pinho, A. L. et al. Individual brain charting, a high-resolution fMRI dataset for cognitive mapping. Sci. Data 5, 180105 (2018).

  41. 41.

    Glasser, M. F., Goyal, M. S., Preuss, T. M., Raichle, M. E. & Van Essen, D. C. Trends and properties of human cerebral cortex: correlations with cortical myelin content. NeuroImage 93 Pt. 2, 165–175 (2014).

  42. 42.

    Fischl, B. & Sereno, M. I. Microstructural parcellation of the human brain. NeuroImage (2018).

  43. 43.

    Augustinack, J. C. et al. MRI parcellation of ex vivo medial temporal lobe. NeuroImage 93 Pt. 2, 252–259 (2014).

  44. 44.

    Gao, Y. et al. Tests of cortical parcellation based on white matter connectivity using diffusion tensor imaging. NeuroImage 170, 321–331 (2017).

  45. 45.

    Eickhoff, S. et al. High-resolution MRI reflects myeloarchitecture and cytoarchitecture of human cerebral cortex. Hum. Brain Mapp. 24, 206–215 (2005).

  46. 46.

    Walters, N. B. et al. Observer-independent analysis of high-resolution MR images of the human cerebral cortex: in vivo delineation of cortical areas. Hum. Brain Mapp. 28, 1–8 (2007).

  47. 47.

    Toga, A. W., Thompson, P. M., Mori, S., Amunts, K. & Zilles, K. Towards multimodal atlases of the human brain. Nat. Rev. Neurosci. 7, 952–966 (2006).

  48. 48.

    Passingham, R. E., Stephan, K. E. & Kotter, R. The anatomical basis of functional localization in the cortex. Nat. Rev. Neurosci. 3, 606–616 (2002). This influential paper reviews the relationships between structure, connectivity and function and introduces the concept of a connectivity fingerprint.

  49. 49.

    Behrens, T. E. J. et al. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat. Neurosci. 6, 750–757 (2003).

  50. 50.

    Raichle, M. E. The restless brain: how intrinsic activity organizes brain function. Phil. Trans. R. Soc. B Biol. Sci. 370, 20140172 (2015).

  51. 51.

    Gilbert, S. J., Gonen-Yaacovi, G., Benoit, R. G., Volle, E. & Burgess, P. W. Distinct functional connectivity associated with lateral versus medial rostral prefrontal cortex: a meta-analysis. NeuroImage 53, 1359–1367 (2010).

  52. 52.

    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).

  53. 53.

    Cha, J., Jo, H. J., Gibson, W. S. & Lee, J. M. Functional organization of the human posterior cingulate cortex, revealed by multiple connectivity-based parcellation methods. Hum. Brain Mapp. 38, 2808–2818 (2017).

  54. 54.

    Schaefer, A. et al. Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI. Cereb. Cortex 28, 3095–3114 (2017). This is one of the earliest studies to develop a local–global parcellation technique integrating both clustering and local boundary detection approaches.

  55. 55.

    Cohen, A. L. et al. Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage 41, 45–57 (2008).

  56. 56.

    Barnes, K. A. et al. Identifying basal ganglia divisions in individuals using resting-state functional connectivity MRI. Front. Syst. Neurosci. (2010).

  57. 57.

    Nelson, S. M. et al. Role of the anterior insula in task-level control and focal attention. Brain Struct. Funct. 214, 669–680 (2010).

  58. 58.

    Nelson, S. M. et al. A parcellation scheme for human left lateral parietal cortex. Neuron 67, 156–170 (2010).

  59. 59.

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

  60. 60.

    Johansen-Berg, H. et al. Changes in connectivity profiles define functionally distinct regions in human medial frontal cortex. Proc. Natl Acad. Sci. USA 101, 13335–13340 (2004). This is a pioneering study using structural connectivity for brain parcellation, in which many conceptual ideas (for example, expected convergence between markers and cluster organization) are first developed.

  61. 61.

    Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H. & Evans, A. C. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51, 1126–1139 (2010).

  62. 62.

    Ryali, S., Chen, T., Padmanabhan, A., Cai, W. & Menon, V. Development and validation of consensus clustering-based framework for brain segmentation using resting fMRI. J. Neurosci. Methods 240, 128–140 (2015).

  63. 63.

    Cauda, F. et al. Meta-analytic clustering of the insular cortex: characterizing the meta-analytic connectivity of the insula when involved in active tasks. NeuroImage 62, 343–355 (2012).

  64. 64.

    Kelly, C. et al. A convergent functional architecture of the insula emerges across imaging modalities. NeuroImage 61, 1129–1142 (2012). This is one of the first studies using different connectivity modalities (meta-analytic co-activation modelling, resting-state functional connectivity and structural covariance) to parcellate the insula.

  65. 65.

    Eickhoff, S. B. et al. Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation. NeuroImage 57, 938–949 (2011). This is one of the first studies demonstrating the use of meta-analytic co-activation for brain parcellation.

  66. 66.

    Cohen, M. X., Lombardo, M. V. & Blumenfeld, R. S. Covariance-based subdivision of the human striatum using T1-weighted MRI. Eur. J. Neurosci. 27, 1534–1546 (2008).

  67. 67.

    Genon, S. et al. The right dorsal premotor mosaic: organization, functions, and connectivity. Cereb. Cortex 27, 2095–2110 (2017).

  68. 68.

    Genon, S. et al. The heterogeneity of the left dorsal premotor cortex evidenced by multimodal connectivity-based parcellation and functional characterization. NeuroImage 170, 400–411 (2018).

  69. 69.

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

  70. 70.

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

  71. 71.

    Jain, A. K. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 651–666 (2010).

  72. 72.

    Clos, M., Amunts, K., Laird, A. R., Fox, P. T. & Eickhoff, S. B. Tackling the multifunctional nature of Broca’s region meta-analytically: co-activation-based parcellation of area 44. NeuroImage 83, 174–188 (2013).

  73. 73.

    Kahnt, T., Chang, L. J., Park, S. Q., Heinzle, J. & Haynes, J.-D. Connectivity-based parcellation of the human orbitofrontal cortex. J. Neurosci. 32, 6240–6250 (2012).

  74. 74.

    Kelly, C. et al. Broca’s region: linking human brain functional connectivity data and non-human primate tracing anatomy studies. Eur. J. Neurosci. 32, 383–398 (2010).

  75. 75.

    van Oort, E. S. B. et al. Functional parcellation using time courses of instantaneous connectivity. NeuroImage 170, 31–40 (2017).

  76. 76.

    Laumann, T. O. et al. Functional system and areal organization of a highly sampled individual human brain. Neuron 87, 657–670 (2015).

  77. 77.

    Zilles, K. et al. Architectonics of the human cerebral cortex and transmitter receptor fingerprints: reconciling functional neuroanatomy and neurochemistry. Eur. Neuropsychopharmacol. 12, 587–599 (2002).

  78. 78.

    van den Heuvel, M. P., Scholtens, L. H., Feldman Barrett, L., Hilgetag, C. C. & de Reus, M. A. Bridging cytoarchitectonics and connectomics in human cerebral cortex. J. Neurosci. 35, 13943–13948 (2015).

  79. 79.

    Sporns, O. Cerebral cartography and connectomics. Phil. Trans. R. Soc. B Biol. Sci. (2015).

  80. 80.

    Cloutman, L. L. & Ralph, M. A. L. Connectivity-based structural and functional parcellation of the human cortex using diffusion imaging and tractography. Front. Neuroanat. (2012). This is one of the earliest reviews that critically discusses evidence of divergence between parcellation schemes based on connectivity and cytoarchitecture mapping.

  81. 81.

    Chase, H. W. et al. Evidence for an anterior–posterior differentiation in the human hippocampal formation revealed by meta-analytic parcellation of fMRI coordinate maps: focus on the subiculum. NeuroImage 113, 44–60 (2015).

  82. 82.

    Adnan, A. et al. Distinct hippocampal functional networks revealed by tractography-based parcellation. Brain Struct. Funct. 221, 2999–3012 (2016).

  83. 83.

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

  84. 84.

    Cerliani, L., D’Arceuil, H. & Thiebaut de Schotten, M. Connectivity-based parcellation of the macaque frontal cortex, and its relation with the cytoarchitectonic distribution described in current atlases. Brain Struct. Funct. 222, 1331–1349 (2017).

  85. 85.

    Ding, S. L. et al. Comprehensive cellular-resolution atlas of the adult human brain. J. Comp. Neurol. 524, 3127–3481 (2016).

  86. 86.

    Wang, J. et al. Convergent functional architecture of the superior parietal lobule unraveled with multimodal neuroimaging approaches. Hum. Brain Mapp. 36, 238–257 (2015).

  87. 87.

    Xia, X. et al. Multimodal connectivity-based parcellation reveals a shell-core dichotomy of the human nucleus accumbens. Hum. Brain Mapp. 38, 3878–3898 (2017).

  88. 88.

    Nachev, P., Kennard, C. & Husain, M. The functional anatomy of the frontal lobes. Nat. Rev. Neurosci. 10, 829 (2009).

  89. 89.

    Wang, C., Yoldemir, B. & Abugharbieh, R. in International Conference on Medical Image Computing and Computer-Assisted Intervention 21–28 (Springer, 2015).

  90. 90.

    Kaas, J. H. Evolution of columns, modules, and domains in the neocortex of primates. Proc. Natl Acad. Sci. USA 109, 10655–10660 (2012).

  91. 91.

    Kaas, J. H. & Stepniewska, I. Evolution of posterior parietal cortex and parietal-frontal networks for specific actions in primates. J. Comp. Neurol. 524, 595–608 (2016).

  92. 92.

    Vogt, C. & Vogt, O. Die vergleichend-architektonische und die vergleichend-reizphysiologische Felderung der Großhirnrinde unter besonderer Berücksichtigung der menschlichen [German]. Naturwissenschaften 14, 1190–1194 (1926).

  93. 93.

    Genon, S., Reid, A., Langner, R., Amunts, K. & Eickhoff, S. B. How to characterize the function of a brain region. Trends Cogn. Sci. 22, 350–364 (2018).

  94. 94.

    Fischl, B. et al. Cortical folding patterns and predicting cytoarchitecture. Cereb. Cortex 18, 1973–1980 (2007).

  95. 95.

    Mueller, S. et al. Individual variability in functional connectivity architecture of the human brain. Neuron 77, 586–595 (2013).

  96. 96.

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

  97. 97.

    Gordon, E. M. et al. Precision functional mapping of individual human brains. Neuron 95, 791–807.e7 (2017). References 96 and 97 demonstrate that extensive resting-state data collected from the same individuals allow the delineation of high-quality cortical parcellations in individuals.

  98. 98.

    Zilles, K. & Amunts, K. Individual variability is not noise. Trends Cogn. Sci. 17, 153–155 (2013).

  99. 99.

    Salehi, M., Karbasi, A., Shen, X., Scheinost, D. & Constable, R. T. An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks. NeuroImage 170, 54–67 (2017).

  100. 100.

    Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N. & Petersen, S. E. Evidence for hubs in human functional brain networks. Neuron 79, 798–813 (2013).

  101. 101.

    Sepulcre, J. et al. The organization of local and distant functional connectivity in the human brain. PLoS Comput. Biol. 6, e1000808 (2010).

  102. 102.

    Tzourios-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).

  103. 103.

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

  104. 104.

    Robinson, J. L. et al. Neurofunctional topography of the human hippocampus. Hum. Brain Mapp. 36, 5018–5037 (2015).

  105. 105.

    Chi, J. G., Dooling, E. C. & Gilles, F. H. Gyral development of the human brain. Ann. Neurol. 1, 86–93 (1977).

  106. 106.

    Semendeferi, K., Lu, A., Schenker, N. & Damásio, H. Humans and great apes share a large frontal cortex. Nat. Neurosci. 5, 272–276 (2002).

  107. 107.

    Wood, J. N. & Grafman, J. Human prefrontal cortex: processing and representational perspectives. Nat. Rev. Neurosci. 4, 139–147 (2003).

  108. 108.

    Geyer, S., Matelli, M., Luppino, G. & Zilles, K. Functional neuroanatomy of the primate isocortical motor system. Anat. Embryol. 202, 443–474 (2000).

  109. 109.

    Rizzolatti, G., Luppino, G. & Matelli, M. The organization of the cortical motor system: new concepts. Electroencephalogr. Clin. Neurophysiol. 106, 283–296 (1998).

  110. 110.

    Rizzolatti, G. & Luppino, G. The cortical motor system. Neuron 31, 889–901 (2001).

  111. 111.

    Petrides, M. & Pandya, D. Dorsolateral prefrontal cortex: comparative cytoarchitectonic analysis in the human and the macaque brain and corticocortical connection patterns. Eur. J. Neurosci. 11, 1011–1036 (1999).

  112. 112.

    Petrides, M. & Pandya, D. Comparative cytoarchitectonic analysis of the human and the macaque ventrolateral prefrontal cortex and corticocortical connection patterns in the monkey. Eur. J. Neurosci. 16, 291–310 (2002).

  113. 113.

    Vincent, J. L. et al. Intrinsic functional architecture in the anaesthetized monkey brain. Nature 447, 83–86 (2007).

  114. 114.

    Orban, G. A., Van Essen, D. & Vanduffel, W. Comparative mapping of higher visual areas in monkeys and humans. Trends Cogn. Sci. 8, 315–324 (2004).

  115. 115.

    Neubert, F.-X., Mars, R. B., Thomas, A. G., Sallet, J. & Rushworth, M. F. Comparison of human ventral frontal cortex areas for cognitive control and language with areas in monkey frontal cortex. Neuron 81, 700–713 (2014).

  116. 116.

    Xu, T. et al. Delineating the macroscale areal organization of the macaque cortex in vivo. Cell Rep. 23, 429–441 (2018).

  117. 117.

    Croxson, P. L., Forkel, S. J., Cerliani, L. & Thiebaut de Schotten, M. Structural variability across the primate brain: a cross-species comparison. Cereb. Cortex (2017).

  118. 118.

    Zilles, K. & Amunts, K. Centenary of Brodmann’s map — conception and fate. Nat. Rev. Neurosci. 11, 139–145 (2010).

  119. 119.

    Klatzo, I. Cécile and Oskar Vogt: the visionaries of modern neuroscience Vol. 80 (ed. Reulen, H.-J.) (Springer Science & Business Media, 2002).

  120. 120.

    Talairach, J. & Tournoux, P. Co-Planar Stereotaxic Atlas of the Human Brain: 3-Dimensional Proportional System: An Approach to Cerebral Imaging. (Thieme, New York, 1987).

  121. 121.

    Frackowiak, R. & Markram, H. The future of human cerebral cartography: a novel approach. Phil. Trans. R. Soc. B Biol. Sci. 370, 20140171 (2015).

  122. 122.

    Schleicher, A., Amunts, K., Geyer, S., Morosan, P. & Zilles, K. Observer-independent method for microstructural parcellation of cerebral cortex: a quantitative approach to cytoarchitectonics. NeuroImage 9, 165–177 (1999).

  123. 123.

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

  124. 124.

    Ding, C., He, X. & Simon, H. D. On the equivalence of nonnegative matrix factorization and spectral clustering. Proc. 2005 SIAM Int. Conf. Data Mining (2005).

  125. 125.

    Sotiras, A., Resnick, S. M. & Davatzikos, C. Finding imaging patterns of structural covariance via non-negative matrix factorization. NeuroImage 108, 1–16 (2015).

  126. 126.

    Yeo, B. T., Krienen, F. M., Chee, M. W. & Buckner, R. L. Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. NeuroImage 88, 212–227 (2014).

  127. 127.

    Catani, M. in Diffusion MRI: Theory, Methods, and Applications 5–18 (Oxford Univ. Press, 2010).

  128. 128.

    Maier-Hein, K. H. et al. The challenge of mapping the human connectome based on diffusion tractography. Nat. Commun. 8, 1349 (2017).

  129. 129.

    Biswal, B., Zerrin Yetkin, F., 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).

  130. 130.

    Van Essen, D. C. et al. in Diffusion MRI 2nd edn (eds Johansen-Berg, H. & Behrens, T. E. J.), 337–358 (Academic Press, 2014).

  131. 131.

    Jbabdi, S. & Johansen-Berg, H. Tractography: where do we go from here? Brain Connectiv. 1, 169–183 (2011).

  132. 132.

    Catani, M. et al. Short frontal lobe connections of the human brain. Cortex 48, 273–291 (2012).

  133. 133.

    Birn, R. M. The role of physiological noise in resting-state functional connectivity. NeuroImage 62, 864–870 (2012).

  134. 134.

    Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59, 2142–2154 (2012).

  135. 135.

    He, Y., Chen, Z. J. & Evans, A. C. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407–2419 (2007).

  136. 136.

    Tootell, R. B. H. et al. Functional analysis of primary visual cortex (V1) in humans. Proc. Natl Acad. Sci. USA 95, 811 (1998).

  137. 137.

    Amunts, K., Malikovic, A., Mohlberg, H., Schormann, T. & Zilles, K. Brodmann’s areas 17 and 18 brought into stereotaxic space-where and how variable? NeuroImage 11, 66–84 (2000).

  138. 138.

    Frazier, J. A. et al. Structural brain magnetic resonance imaging of limbic and thalamic volumes in pediatric bipolar disorder. Am. J. Psychiatry 162, 1256–1265 (2005).

  139. 139.

    Makris, N. et al. Decreased volume of left and total anterior insular lobule in schizophrenia. Schizophr. Res. 83, 155–171 (2006).

  140. 140.

    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).

  141. 141.

    Goldstein, J. M. et al. Hypothalamic abnormalities in schizophrenia: sex effects and genetic vulnerability. Biol. Psychiatry 61, 935–945 (2007).

  142. 142.

    Destrieux, C., Fischl, B., Dale, A. & Halgren, E. Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature. NeuroImage 53, 1–15 (2010).

  143. 143.

    Auzias, G., Coulon, O. & Brovelli, A. MarsAtlas: a cortical parcellation atlas for functional mapping. Hum. Brain Mapp. 37, 1573–1592 (2016).

  144. 144.

    Power, J. D. et al. Functional network organization of the human brain. Neuron 72, 665–678 (2011).

  145. 145.

    Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C. & Yeo, B. T. The organization of the human cerebellum estimated by intrinsic functional connectivity. J. Neurophysiol. 106, 2322–2345 (2011).

  146. 146.

    Choi, E. Y., Yeo, B. T. & Buckner, R. L. The organization of the human striatum estimated by intrinsic functional connectivity. J. Neurophysiol. 108, 2242–2263 (2012).

  147. 147.

    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).

  148. 148.

    Joliot, M. et al. AICHA: an atlas of intrinsic connectivity of homotopic areas. J. Neurosci. Methods 254, 46–59 (2015).

  149. 149.

    Huth, A. G., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. PrAGMATiC: a probabilistic and generative model of areas tiling the cortex. Preprint at (2015).

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The work of S.B.E. and S.G. is supported by the Deutsche Forschungsgemeinschaft (DFG, GE 2835/1-1, EI 816/4-1), the Helmholtz Portfolio Theme ‘Supercomputing and Modelling for the Human Brain’ and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1) and Grant Agreement No. 785907 (HBP SGA2). B.T.T.Y. is supported by the Singapore Ministry Of Education Tier 2 (MOE2014-T2-2-016), the National University of Singapore (NUS) Strategic Research (DPRT/944/09/14), the National University of Singapore (NUS) School of Medicine Aspiration Fund (R185000271720), Singapore National Medical Research Council (CBRG/0088/2015), NUS Young Investigator Award and the Singapore National Research Foundation Fellowship (Class of 2017). The authors also thank N. Palomero-Gallagher for helpful discussion and Q. Yang and R. Kong for their help with the figures.

Reviewer information

Nature Reviews Neuroscience thanks M. Joliot, H. Liu and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Author information


  1. Institute of Neuroscience and Medicine, Brain and Behavior (INM-7), Research Centre Jülich, Jülich, Germany

    • Simon B. Eickhoff
    •  & Sarah Genon
  2. Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany

    • Simon B. Eickhoff
    •  & Sarah Genon
  3. Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore, Singapore

    • B. T. Thomas Yeo
  4. NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, Singapore

    • B. T. Thomas Yeo
  5. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

    • B. T. Thomas Yeo
  6. Centre for Cognitive Neuroscience, Duke-NUS Graduate Medical School, Singapore, Singapore

    • B. T. Thomas Yeo


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S.B.E., B.T.T.Y. and S.G. researched data for the article, made substantial contributions to discussion of content, wrote the manuscript and reviewed or edited the manuscript before submission.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Simon B. Eickhoff.

Supplementary information


Large-scale networks

Constellations of brain areas that are strongly connected to each other, presumably subserving specific functions.

Connectivity fingerprints

Patterns of the interactions of brain regions with other brain regions.

Brain cartography

The study of brain organization with the particular objective of representing the organization of the brain as a map of distinct areas.

Brain areas

Brain regions showing specific structure, function and connectivity.

Universal map

A unique division of the brain into individual areas, each having specific structure, connectivity and function, which can be found in all humans.

Graph theory

The use of graphs to study and model relationships between objects with elements such as nodes and edges.


Tissue composition with regard to cell characteristics.


The pattern of myelinated fibres.

Visuotopic mapping

The identification of visual areas based on differential cortical responses to different visual stimuli. An example of a mapping stimulus would be a rotating sector of a flashing checkerboard.

Non-negative matrix factorization

A multivariate statistical approach to factorize data into components promoting a part-based representation of the data.

Spectral clustering

A clustering approach based on the eigenvectors of the matrix of similarity (such as connectivity) between brain locations (voxels or vertices). The term ‘spectral’ refers to the spectrum (eigenvalues) of the similarity matrix.

Hierarchical clustering

A clustering approach that disentangles clusters in a hierarchical fashion, in such a way that relationships between clusters can be visualized as a tree structure.

Principal component analysis

A multivariate statistical approach to factorize data into orthogonal components that best represent variance in the data.

Fuzzy or soft clustering

A clustering approach in which points are not assigned to one single group but have a fractional value that represents their relative membership in each group.

Echo planar imaging

An MRI sequence used for functional and diffusion imaging.

Meta-analytic connectivity modelling

A method that aims to model functional connectivity in the brain based on a co-activation pattern across various activation studies.

Probabilistic tractography

An approach to estimate white-matter tract pathways in the brain from diffusion MRI images.

Structural covariance

The pattern of covariations in measures of morphometry (such as grey-matter volume) across brain regions.

Crossing fibres

Individual white-matter fibres whose spatial direction result in points where they meet or cross each other, complicating the estimation of their respective paths.


A clustering algorithm that divides a set of data points into k clusters by iteratively optimizing the definition of each cluster centroid and data points assigned to the clusters.


Spatial units in the brain that are smaller than usual brain regions and show specific functions.

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