Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI

  • Jiajia Zhao
  • Chao Tang
  • Jingxin NieEmail author
Original Article


The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.


Functional map Resting-state fMRI Parcellation Cerebral cortex 


Funding Information

This study was found by National Natural Science Foundation of China (grant number 61403148).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

12021_2019_9445_MOESM1_ESM.docx (32 kb)
ESM 1 (DOCX 32 kb)


  1. Anderson, J. S., Ferguson, M. A., Lopez-Larson, M., & Yurgelun-Todd, D. (2011). Reproducibility of single-subject functional connectivity measurements. American Journal of Neuroradiology, 32(3), 548–555. Scholar
  2. Arslan, S., Parisot, S., & Rueckert, D. (2015). Joint spectral decomposition for the parcellation of the human cerebral cortex using resting-state fMRI. In Information Processing in Medical Imaging (pp. 85-97, Lecture Notes in Computer Science).Google Scholar
  3. Arslan, S., Ktena, S. I., Makropoulos, A., Robinson, E. C., Rueckert, D., & Parisot, S. (2018). Human brain mapping: A systematic comparison of parcellation methods for the human cerebral cortex. NeuroImage, 170, 5–30. Scholar
  4. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51(3), 1126–1139. Scholar
  5. Blumensath, T., Jbabdi, S., Glasser, M. F., Van Essen, D. C., Ugurbil, K., Behrens, T. E., et al. (2013). Spatially constrained hierarchical parcellation of the brain with resting-state fMRI. NeuroImage, 76, 313–324. Scholar
  6. Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. (2011). The organization of the human cerebellum estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(5), 2322–2345. Scholar
  7. Cohen, A. L., Fair, D. A., Dosenbach, N. U., Miezin, F. M., Dierker, D., Van Essen, D. C., et al. (2008). Defining functional areas in individual human brains using resting functional connectivity MRI. NeuroImage, 41(1), 45–57.CrossRefGoogle Scholar
  8. Craddock, R. C., James, G. A., Holtzheimer 3rd, P. E., Hu, X. P., & Mayberg, H. S. (2012). A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human Brain Mapping, 33(8), 1914–1928. Scholar
  9. Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., et al. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848–13853. Scholar
  10. Eickhoff, S. B., Thirion, B., Varoquaux, G., & Bzdok, D. (2015). Connectivity-based parcellation: Critique and implications. Human Brain Mapping, 36(12), 4771–4792. Scholar
  11. Fan, L., Hai, L., Zhuo, J., Yu, Z., Wang, J., Chen, L., et al. (2016). The Human Brainnetome Atlas: A new brain atlas based on connectional architecture. Cerebral Cortex, 26(8), 3508–3526.CrossRefGoogle Scholar
  12. Finn, E. S., Shen, X. L., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., et al. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664–1671. Scholar
  13. Fischl, B. (2012). FreeSurfer. NeuroImage, 62(2), 774–781. Scholar
  14. Gao, W., Elton, A., Zhu, H. T., Alcauter, S., Smith, J. K., Gilmore, J. H., et al. (2014). Intersubject variability of and genetic effects on the Brain’s functional connectivity during infancy. The Journal of Neuroscience, 34(34), 11288–11296. Scholar
  15. Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., et al. (2013). The minimal preprocessing pipelines for the human Connectome project. NeuroImage, 80(3), 105.CrossRefGoogle Scholar
  16. Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. Scholar
  17. Goulas, A., Uylings, H. B., & Stiers, P. (2012). Unravelling the intrinsic functional organization of the human lateral frontal cortex: A parcellation scheme based on resting state fMRI. The Journal of Neuroscience, 32(30), 10238–10252.CrossRefGoogle Scholar
  18. Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72. Scholar
  19. Guillot, A., Collet, C., Nguyen, V. A., Malouin, F., Richards, C., & Doyon, J. (2008). Functional neuroanatomical networks associated with expertise in motor imagery. NeuroImage, 41(4), 1471–1483. Scholar
  20. Hacker, C. D., Laumann, T. O., Szrama, N. P., Baldassarre, A., Snyder, A. Z., Leuthardt, E. C., et al. (2013). Resting state network estimation in individual subjects. NeuroImage, 82, 616–633. Scholar
  21. Hampson, M., Driesen, N. R., Skudlarski, P., Gore, J. C., & Constable, R. T. (2006). Brain connectivity related to working memory performance. The Journal of Neuroscience, 26(51), 13338–13343. Scholar
  22. Harrison, S. J., Woolrich, M. W., Robinson, E. C., Glasser, M. F., Beckmann, C. F., Jenkinson, M., et al. (2015). Large-scale probabilistic functional modes from resting state fMRI. NeuroImage, 109, 217–231.CrossRefGoogle Scholar
  23. Hill, J., Dierker, D., Neil, J., Inder, T., Knutsen, A., Harwell, J., et al. (2010). A surface-based analysis of hemispheric asymmetries and folding of cerebral cortex in term-born human infants. The Journal of Neuroscience, 30(6), 2268–2276. Scholar
  24. James, G. A., Hazaroglu, O., & Bush, K. A. (2016a). A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data. Magnetic Resonance Imaging, 34(2), 209–218. Scholar
  25. James, G. A., Kearney-Ramos, T. E., Young, J. A., Kilts, C. D., Gess, J. L., & Fausett, J. S. (2016b). Functional independence in resting-state connectivity facilitates higher-order cognition. Brain and Cognition, 105, 78–87. Scholar
  26. Jenkinson, M., Beckmann, C. F., Behrens, T. E., Woolrich, M. W., & Smith, S. M. (2012). Fsl Neuroimage, 62(2), 782–790.CrossRefGoogle Scholar
  27. Kelly, C., Toro, R., Di Martino, A., Cox, C. L., Bellec, P., Castellanos, F. X., et al. (2012). A convergent functional architecture of the insula emerges across imaging modalities. NeuroImage, 61(4), 1129–1142. Scholar
  28. Kim, J. H., Lee, J. M., Jo, H. J., Kim, S. H., Lee, J. H., Kim, S. T., et al. (2010). Defining functional SMA and pre-SMA subregions in human MFC using resting state fMRI: Functional connectivity-based parcellation method. NeuroImage, 49(3), 2375–2386. Scholar
  29. Kochunov, P., Glahn, D. C., Fox, P. T., Lancaster, J. L., Saleem, K., Shelledy, W., et al. (2010). Genetics of primary cerebral gyrification: Heritability of length, depth and area of primary sulci in an extended pedigree of Papio baboons. NeuroImage, 53(3), 1126–1134. Scholar
  30. Liao, X. H., Cao, M., Xia, M. R., & He, Y. (2017). Individual differences and time-varying features of modular brain architecture. NeuroImage, 152, 94–107. Scholar
  31. Marcus, D. S., Harms, M. P., Snyder, A. Z., Jenkinson, M., Wilson, J. A., Glasser, M. F., et al. (2013). Human Connectome project informatics: Quality control, database services, and data visualization. NeuroImage, 80, 202–219. Scholar
  32. Mitelman, S. A., Shihabuddin, L., Brickman, A. M., Hazlett, E. A., & Buchsbaum, M. S. (2003). MRI assessment of gray and white matter distribution in Brodmann’s areas of the cortex in patients with schizophrenia with good and poor outcomes. American Journal of Psychiatry, 160(12), 2154–2168.CrossRefGoogle Scholar
  33. Mueller, S., Wang, D. H., Fox, M. D., Yeo, B. T. T., Sepulcre, J., Sabuncu, M. R., et al. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron, 77(3), 586–595. Scholar
  34. Nelson, S. M., Cohen, A. L., Power, J. D., Wig, G. S., Miezin, F. M., Wheeler, M. E., et al. (2010). A parcellation scheme for human left lateral parietal cortex. Neuron, 67(1), 156–170.CrossRefGoogle Scholar
  35. Parisot, S., Arslan, S., Passerat-Palmbach, J., Wells 3rd, W. M., & Rueckert, D. (2016). Group-wise parcellation of the cortex through multi-scale spectral clustering. NeuroImage, 136, 68–83. Scholar
  36. Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65.CrossRefGoogle Scholar
  37. Seghier, M. L. (2018). Clustering of fMRI data: The elusive optimal number of clusters. PeerJ, 6, e5416. Scholar
  38. Sethian, J. A. (1996). A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Sciences, 93(4), 1591–1595.CrossRefGoogle Scholar
  39. Sethian, J. A. (1999). Fast marching methods. Siam Review, 41(2), 199–235. Scholar
  40. Shen, X., Tokoglu, F., Papademetris, X., & Constable, R. T. (2013). Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. NeuroImage, 82, 403–415. Scholar
  41. Shi, J. B., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905. Scholar
  42. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., et al. (2009). Correspondence of the brain's functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045. Scholar
  43. Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., et al. (2013). Resting-state fMRI in the human connectome project. NeuroImage, 80(20), 144–168.CrossRefGoogle Scholar
  44. Thirion, B., Varoquaux, G., Dohmatob, E., & Poline, J. B. (2014). Which fMRI clustering gives good brain parcellations? Frontiers in Neuroscience, 8.
  45. Tong, T., Aganj, I., Ge, T., Polimeni, J. R., & Fischl, B. (2017). Functional density and edge maps: Characterizing functional architecture in individuals and improving cross-subject registration. NeuroImage, 158, 346–355. Scholar
  46. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. Scholar
  47. Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., et al. (2012). The human Connectome project: A data acquisition perspective. NeuroImage, 62(4), 2222–2231. Scholar
  48. Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., Ugurbil, K., et al. (2013). The WU-Minn human Connectome project: An overview. NeuroImage, 80, 62–79. Scholar
  49. Wang, J., & Wang, H. (2016). A supervoxel-based method for groupwise whole brain Parcellation with resting-state fMRI data. Frontiers in Human Neuroscience, 10, 659. Scholar
  50. Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., et al. (2015). Parcellating cortical functional networks in individuals. Nature Neuroscience, 18(12), 1853–1860. Scholar
  51. Wang, C., Ng, B., & Garbi, R. (2018). Multimodal brain parcellation based on functional and anatomical connectivity. Brain Connectivity, 8(10), 604–617.Google Scholar
  52. Wang, J., Hu, Z., & Wang, H. (2016). Parcellating whole brain for individuals by simple linear iterative clustering. In International Conference on Neural Information Processing (pp. 131–139). Cham: Springer.Google Scholar
  53. Weiner, K. S., Golarai, G., Caspers, J., Chuapoco, M. R., Mohlberg, H., Zilles, K., et al. (2014). The mid-fusiform sulcus: A landmark identifying both cytoarchitectonic and functional divisions of human ventral temporal cortex. NeuroImage, 84, 453–465. Scholar
  54. Wig, G. S., Laumann, T. O., Cohen, A. L., Power, J. D., Nelson, S. M., Glasser, M. F., et al. (2014a). Parcellating an individual subject’s cortical and subcortical brain structures using snowball sampling of resting-state correlations. Cerebral Cortex, 24(8), 2036–2054.CrossRefGoogle Scholar
  55. Wig, G. S., Laumann, T. O., & Petersen, S. E. (2014b). An approach for parcellating human cortical areas using resting-state correlations. NeuroImage, 93(Pt 2), 276–291. Scholar
  56. Yeo, B. T., & Eickhoff, S. B. (2016). Systems neuroscience: A modern map of the human cerebral cortex. Nature, 536(7615), 152–154.CrossRefGoogle Scholar
  57. Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. Scholar
  58. Zilles, K., Palomero-Gallagher, N., & Amunts, K. (2013). Development of cortical folding during evolution and ontogeny. Trends in Neurosciences, 36(5), 275–284. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Psychology, Center for Studies of Psychological Application, Institute of Cognitive NeuroscienceSouth China Normal UniversityGuangzhouChina

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