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Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1427–1443 | Cite as

Joint representation of connectome-scale structural and functional profiles for identification of consistent cortical landmarks in macaque brain

  • Shu Zhang
  • Xi Jiang
  • Wei Zhang
  • Tuo Zhang
  • Hanbo Chen
  • Yu Zhao
  • Jinglei Lv
  • Lei Guo
  • Brittany R. Howell
  • Mar M. SanchezEmail author
  • Xiaoping HuEmail author
  • Tianming LiuEmail author
Original Research
  • 104 Downloads

Abstract

Discovery and representation of common structural and functional cortical architectures has been a significant yet challenging problem for years. Due to the remarkable variability of structural and functional cortical architectures in human brain, it is challenging to jointly represent a common cortical architecture which can comprehensively encode both structure and function characteristics. In order to better understand this challenge and considering that macaque monkey brain has much less variability in structure and function compared with human brain, in this paper, we propose a novel computational framework to apply our DICCCOL (Dense Individualized and Common Connectivity-based Cortical Landmarks) and HAFNI (Holistic Atlases of Functional Networks and Interactions) frameworks on macaque brains, in order to jointly represent structural and functional connectome-scale profiles for identification of a set of consistent and common cortical landmarks across different macaque brains based on multimodal DTI and resting state fMRI (rsfMRI) data. Experimental results demonstrate that 100 consistent and common cortical landmarks are successfully identified via the proposed framework, each of which has reasonably accurate anatomical, structural fiber connection pattern, and functional correspondences across different macaque brains. This set of 100 landmarks offer novel insights into the structural and functional cortical architectures in macaque brains.

Keywords

Cortical landmarks DTI Resting state fMRI Joint representation of structure and function Rhesus monkey 

Notes

Funding

This work was supported by National Institutes of Health (DA033393, AG042599, MH078105, MH078105-04S1, HD055255), National Science Foundation (IIS-1149260, CBET-1302089, BCS-1439051 and DBI-1564736), and Office of Research Infrastructure Programs/OD grant OD11132 (YNPRC Base grant, formerly RR000165). We want to thank Anne Glenn, Christine Marsteller, Dora Guzman, and the staff at the Yerkes National Primate Research Center (YNPRC) Field Station and Imaging Center for the excellent technical support and animal care provided during these studies. The YNPRC is fully accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC), International.

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 macaque 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.

References

  1. Aboitiz, F., & Garcıa, R. (1997). The evolutionary origin of the language areas in the human brain. A neuroanatomical perspective. Brain Research Reviews, 25(3), 381–396.CrossRefGoogle Scholar
  2. Abolghasemi, V., Ferdowsi, S., & Sanei, S. (2015). Fast and incoherent dictionary learning algorithms with application to fMRI. Signal, Image and Video Processing., 9(1), 147–158.CrossRefGoogle Scholar
  3. Armstrong, E., Zilles, K., Curtis, M., & Schleicher, A. (1991). Cortical folding, the lunate sulcus and the evolution of the human brain. Journal of Human Evolution, 20(4), 341–348.CrossRefGoogle Scholar
  4. Baaré, W. F. C., et al. (2001). Quantitative genetic modeling of variation in human brain morphology. Cerebral Cortex, 11(9), 816–824.CrossRefGoogle Scholar
  5. Baylis, G. C., Rolls, E. T., & Leonard, C. M. (1987). Functional subdivisions of the temporal lobe neocortex. The Journal of Neuroscience., 7(2), 330–342.CrossRefGoogle Scholar
  6. Calabrese, E., Badea, A., Coe, C. L., Lubach, G. R., Shi, Y., Styner, M. A., & Johnson, G. A. (2015). A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. NeuroImage, 117, 408–416.CrossRefGoogle Scholar
  7. Chen, H., Zhang, T., Guo, L., Li, K., Yu, X., Li, L., Hu, X., Han, J., Hu, X., & Liu, T. (2012). Coevolution of gyral folding and structural connection patterns in primate brains. Cerebral Cortex, 23(5), 1208–1217.CrossRefGoogle Scholar
  8. Chen H, Zhang T, Liu T. 2013. Identifying Group-Wise Consistent White Matter Landmarks via Novel Fiber Shape Descriptor. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013. Springer Berlin Heidelberg. 66–73.Google Scholar
  9. Dehaene, S., Hauser, M. D., Duhamel, J. R., & Rizzolatti, G. (Eds.) (2005). From monkey brain to human brain: A Fyssen foundation symposium. Cambridge: MIT Press.Google Scholar
  10. Felleman, D. J., & Van Essen, D. C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral cortex., 1(1), 1–47.CrossRefGoogle Scholar
  11. Ferry, A. T., Öngür, D., An, X., & Price, J. L. (2000). Prefrontal cortical projections to the striatum in macaque monkeys: Evidence for an organization related to prefrontal networks. Journal of Comparative Neurology., 425(3), 447–470.CrossRefGoogle Scholar
  12. Galletti, C., Fattori, P., Gamberini, M., & Kutz, D. F. (1999). The cortical visual area V6: Brain location and visual topography. European Journal of Neuroscience., 11(11), 3922–3936.CrossRefGoogle Scholar
  13. Gorski, K. M., Hivon, E., Banday, A. J., Wandelt, B. D., Hansen, F. K., Reinecke, M., & Bartelmann, M. (2005). HEALPix: A framework for high-resolution discretization and fast analysis of data distributed on the sphere. The Astrophysical Journal, 622(2), 759–771.CrossRefGoogle Scholar
  14. Howell, B. R., McCormack, K. M., Grand, A. P., Sawyer, N. T., Zhang, X., Maestripieri, D., Hu, X., & Sanchez, M. M. (2013). Brain white matter microstructure alterations in adolescent rhesus monkeys exposed to early life stress: Associations with high cortisol during infancy. Biol Mood Anxiety Disord, 3(1), 21.CrossRefGoogle Scholar
  15. Howell, B. R., McMurray, M. S., Guzman, D. B., Nair, G., Shi, Y., McCormack, K. M., Hu, X., Styner, M. A., & Sanchez, M. M. (2017). Maternal buffering beyond glucocorticoids: impact of early life stress on corticolimbic circuits that control infant responses to novelty. Social Neuroscience, 12(1), 50–64.CrossRefGoogle Scholar
  16. Jiang, X., Zhu, D., Li, K., Zhang, T., Wang, L., Shen, D., Guo, L., & Liu, T. (2014a). Predictive models of resting state networks for assessment of altered functional connectivity in mild cognitive impairment. Brain Imaging and Behavior, 8(4), 542–557.CrossRefGoogle Scholar
  17. Jiang, X., Zhang, X., & Zhu, D. (2014b). Intrinsic functional component analysis via sparse representation on Alzheimer's disease neuroimaging initiative database. Brain connectivity, 4(8), 575–586.CrossRefGoogle Scholar
  18. Jiang, X., Li, X., Lv, J., Zhang, T., Zhang, S., Guo, L., & Liu, T. (2015a). Sparse representation of HCP grayordinate data reveals novel functional architecture of cerebral cortex. Human brain mapping, 36(12), 5301–5319.CrossRefGoogle Scholar
  19. Jiang, X., Zhang, T., Zhu, D., Li, K., Chen, H., Lv, J., Hu, X., Han, J., Shen, D., Guo, L., & Liu, T. (2015b). Anatomy-guided dense individualized and common connectivity-based cortical landmarks (A-DICCCOL). Biomedical Engineering, IEEE Transactions on., 62(4), 1108–1119.CrossRefGoogle Scholar
  20. Jiang, X., Zhang, T., Zhao, Q., Lu, J., Guo, L., & Liu, T. (2015c). Fiber connection pattern-guided structured sparse representation of whole-brain FMRI signals for functional network inference. Medical Image Computing and Computer-Assisted Intervention., 9349, 133–141.Google Scholar
  21. Khachaturian, M. H. (2010). A 4-channel 3 tesla phased array receive coil for awake rhesus monkey fMRI and diffusion MRI experiments. Journal of biomedical science and engineering., 3(11), 1085–1092.CrossRefGoogle Scholar
  22. Kolster, H., Mandeville, J. B., Arsenault, J. T., Ekstrom, L. B., Wald, L. L., & Vanduffel, W. (2009). Visual field map clusters in macaque extrastriate visual cortex. The Journal of Neuroscience., 29(21), 7031–7039.CrossRefGoogle Scholar
  23. Lee, L., Harrison, L. M., & Mechelli, A. (2003). A report of the functional connectivity workshop, Dusseldorf 2002. NeuroImage, 19(2), 457–465.CrossRefGoogle Scholar
  24. Lee, K., Tak, S., & Ye, J. C. (2011). A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion. IEEE Transactions on Medical Imaging, 30(5), 1076–1089.CrossRefGoogle Scholar
  25. Li, C., Zhang, X., Komery, A., Li, Y., Novembre, F. J., & Herndon, J. G. (2011). Longitudinal diffusion tensor imaging and perfusion MRI investigation in a macaque model of neuro-AIDS: A preliminary study. NeuroImage, 58(1), 286–292.CrossRefGoogle Scholar
  26. Li, K., Guo, L., Faracoc, C., Zhu, D., Chen, H., Yuan, Y., Lv, J., Deng, F., Jiang, X., Zhang, T., Hu, X., Zhang, D., Miller, S., & Liu, T. (2012). Visual analytics of brain networks. NeuroImage, 61(1), 82–97.CrossRefGoogle Scholar
  27. Li, K., Zhu, D., Guo, L., Li, Z., Lynch, M. E., Coles, C., Hu, X., & Liu, T. (2013). Connectomics signatures of prenatal cocaine exposure affected adolescent brains. Human brain mapping., 34(10), 2494–2510.CrossRefGoogle Scholar
  28. Li X, Chen H, Zhang T, Yu, X., Jiang X, Li K., Li K, Razavi MJ, Wang X, Hu X, Han J, Guo L, Hu X, Liu T. 2016. Commonly preserved and species-specific gyral folding patterns across primate brains. Brain structure and function, 1-15.Google Scholar
  29. Liu, T. (2011). A few thoughts on brain ROIs. Brain imaging and behavior., 5(3), 189–202.CrossRefGoogle Scholar
  30. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453(7197), 869–878.CrossRefGoogle Scholar
  31. Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., Chen, H., Zhang, T., Hu, X., Han, J., Ye, J., Guo, L., & Liu, T. (2015a). Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Transactions on Biomedical Engineering., 62(4), 1120–1131.CrossRefGoogle Scholar
  32. Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., Zhang, S., Hu, X., Han, J., Huang, H., Zhang, J., Guo, L., & Liu, T. (2015b). Sparse representation of whole-brain FMRI signals for identification of functional networks. Medical image analysis., 20(1), 112–134.CrossRefGoogle Scholar
  33. Lv, J., Jiang, X., Li, X., Zhu, D., Zhao, S., Zhang, T., Hu, X., Han, J., Guo, L., Li, Z., Coles, C., Hu, X., & Liu, T. (2015c). Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data. Psychiatry Research, 233, 254–268.CrossRefGoogle Scholar
  34. Lyon, D. C., & Kaas, J. H. (2002). Connectional evidence for dorsal and ventral V3, and other extrastriate areas in the prosimian primate, Galago garnetti. Brain, behavior and evolution., 59(3), 114–129.CrossRefGoogle Scholar
  35. Mairal, J., Bach, F., Ponce, J., & Sapiro, G. (2010). Online learning for matrix factorization and sparse coding. The Journal of Machine Learning Research., 11, 19–60.Google Scholar
  36. Mantini, D., Gerits, A., Nelissen, K., Durand, J. B., Joly, O., Simone, L., Sawamura, H., Wardak, C., Orban, G. A., Buckner, R. L., & Vanduffel, W. (2011). Default mode of brain function in monkeys. The Journal of Neuroscience., 31(36), 2954–12962.CrossRefGoogle Scholar
  37. Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., et al. (2012). A weighted and directed interareal connectivity matrix for macaque cerebral cortex. Cerebral Cortex, 24(1), 17–36.CrossRefGoogle Scholar
  38. McCormack, K., Howell, B. R., Guzman, D., Villongco, C., Pears, K., Kim, H., Gunnar, M. R., & Sanchez, M. M. (2015). The development of an instrument to measure global dimensions of maternal care in rhesus macaques (Macaca mulatta). American Journal of Primatology, 77(1), 20–33.CrossRefGoogle Scholar
  39. Mori, S., & Zhang, J. (2006). Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron, 51(5), 527–539.CrossRefGoogle Scholar
  40. Oikonomou, V. P., Blekas, K., & Astrakas, L. (2012). A sparse and spatially constrained generative regression model for fMRI data analysis. IEEE Transactions on Biomedical Engineering., 59(1), 58–67.CrossRefGoogle Scholar
  41. Passingham, R. (2009). How good is the macaque monkey model of the human brain? Current Opinion in Neurobiology, 19(1), 6–11.CrossRefGoogle Scholar
  42. Passingham, R. E., Stephan, K. E., & Kötter, R. (2002). The anatomical basis of functional localization in the cortex. Nature Reviews Neuroscience., 3(8), 606–616.CrossRefGoogle Scholar
  43. Paxinos, G., & Franklin, K. B. J. (2004). The mouse brain in stereotaxic coordinates. Gulf Professional Publishing.Google Scholar
  44. Preuss, T. M., & Goldman-Rakic, P. S. (1991). Architectonics of the parietal and temporal association cortex in the strepsirhine primate Galago compared to the anthropoid primate Macaca. Journal of Comparative Neurology., 310(4), 475–506.CrossRefGoogle Scholar
  45. Rohlfing, T., Kroenke, C. D., Sullivan, E. V., Dubach, M. F., Bowden, D. M., Grant, K. A., & Pfefferbaum, A. (2012). The INIA19 template and NeuroMaps atlas for primate brain image parcellation and spatial normalization. Frontiers in Neuroinformatics, 6, 27.CrossRefGoogle Scholar
  46. Schoenemann PT. 2006. Evolution of the size and functional areas of the human brain. Annu. Rev. Anthropol.. Oct 21;35:379-406.Google Scholar
  47. Sereno MI, Tootell RB. 2005. From monkeys to humans: What do we now know about brain homologies?. Current opinion in neurobiology. Apr 30;15(2):135-44.Google Scholar
  48. Shi, Y., Budin, F., Yapuncich, E., Rumple, A., Young, J. T., Payne, C., Zhang, X., Hu, X., Godfrey, J., Howell, B., Sanchez, M. M., & Styler, M. A. (2017). UNC-Emory infant atlases for macaque brain image analysis: Postnatal brain development through 12 months. Frontiers in Neuroscience, 10, 617.CrossRefGoogle Scholar
  49. Van Essen, D. C. (2004). Surface-based approaches to spatial localization and registration in primate cerebral cortex. NeuroImage, 23, S97–S107.CrossRefGoogle Scholar
  50. Van Essen, D. C., Lewis, J. W., Drury, H. A., Hadjikhani, N., Tootell, R. B., Bakircioglu, M., & Miller, M. I. (2001). Mapping visual cortex in monkeys and humans using surface-based atlases. Vision Research, 41(10), 1359–1378.CrossRefGoogle Scholar
  51. Van Essen DC, Glasser MF, Dierker DL, Harwell J. 2011. Cortical parcellations of the macaque monkey analyzed on surface-based atlases. Cerebral cortex. bhr290.Google Scholar
  52. Yuan, Y., Jiang, X., Zhu, D., Chen, H., Li, K., Lv, P., Yu, X., Li, X., Zhang, S., Zhang, T., Hu, X., Han, J., Guo, L., & Liu, T. (2013). Meta-analysis of functional roles of DICCCOLs. Neuroinformatics, 11(1), 47–63.CrossRefGoogle Scholar
  53. Zhang, X., & Li, C. (2013). Quantitative MRI measures in SIV-infected macaque brains. Journal of Clinical & Cellular Immunology.  https://doi.org/10.4172/2155-9899.S7-005.
  54. Zhang, T., Guo, L., Li, G., Nie, J., & Liu, T. (2009). Parametric representation of cortical surface folding based on polynomials. Medical Image Computing and Computer-Assisted Intervention–MICCAI, 2009, 184–191.Google Scholar
  55. Zhang, D., Guo, L., Zhu, D., Li, K., Li, L., Chen, H., Zhao, Q., Hu, X., & Liu, T. (2013a). Diffusion tensor imaging reveals evolution of primate brain architectures. Brain Structure and Function., 218(6), 1429–1450.CrossRefGoogle Scholar
  56. Zhang, S., Li, X., Lv, J., et al. (2013b). Sparse representation of higher-order functional interaction patterns in task-based FMRI data. In International conference on medical image computing and computer-assisted intervention. Springer, berlin, Heidelberg (pp. 626–634).Google Scholar
  57. Zhang, S., Li, X., Lv, J., Jiang, X., Guo, L., & Liu, T. (2016a). Characterizing and differentiating task-based and resting state fMRI signals via two-stage sparse representations. Brain imaging and behavior., 10(1), 21–32.CrossRefGoogle Scholar
  58. Zhang, T., Zhu, D., Jiang, X., Zhang, S., Kou, Z., Guo, L., & Liu, T. (2016b). Group-wise consistent cortical parcellation based on connectional profiles. Medical image analysis., 32, 32–45.CrossRefGoogle Scholar
  59. Zhao, S., Han, J., Lv, J., Jiang, X., Hu, X., Zhao, Y., Ge, B., Guo, L., & Liu, T. (2015). Supervised dictionary learning for inferring concurrent brain networks. IEEE transactions on medical imaging., 34(10), 2036–2045.CrossRefGoogle Scholar
  60. Zhu D, Li K, Faraco C, Deng F, Zhu D, Jiang X, Chen H, Guo L, Miller S, Liu T. 2011. Discovering dense and consistent landmarks in the brain. Information Processing in Medical Imaging. Springer Berlin Heidelberg. 97–110.Google Scholar
  61. Zhu D, Li K, Guo L, Jiang X, Zhang T, Zhang D, Chen H, Deng F, Faraco C, Jin C, Wee CY, Yuan Y, Lv P, Yin Y, Hu X, Duan L, Hu X, Han J, Wang L, Shen D, Miller S, Li L, Liu T. 2012 DICCCOL: Dense individualized and common connectivity-based cortical landmarks. Cerebral cortex. bhs072Google Scholar
  62. Zilles K, Armstrong E, Schleicher A, Kretschmann HJ. 1988. The human pattern of gyrification in the cerebral cortex. Anatomy and Embryology. Nov 1;179(2):173-179.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Shu Zhang
    • 1
  • Xi Jiang
    • 1
  • Wei Zhang
    • 1
  • Tuo Zhang
    • 2
    • 1
  • Hanbo Chen
    • 1
  • Yu Zhao
    • 1
  • Jinglei Lv
    • 2
    • 1
  • Lei Guo
    • 2
  • Brittany R. Howell
    • 4
    • 5
    • 6
  • Mar M. Sanchez
    • 4
    • 5
    Email author
  • Xiaoping Hu
    • 3
    Email author
  • Tianming Liu
    • 1
    Email author
  1. 1.Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensUSA
  2. 2.School of AutomationNorthwestern Polytechnical UniversityXi’anPeople’s Republic of China
  3. 3.Department of BioengineeringUC RiversideRiversideUSA
  4. 4.Department of Psychiatry & Behavioral SciencesEmory University School of MedicineAtlantaUSA
  5. 5.Yerkes National Primate Research CenterEmory UniversityAtlantaUSA
  6. 6.Institute of Child DevelopmentUniversity of MinnesotaMinneapolisUSA

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