Skip to main content

Construction of Spatiotemporal Infant Cortical Surface Functional Templates

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12267))

Abstract

Infant cortical surface templates play an essential role in spatial normalization of cortical surfaces across individuals in pediatric neuroimaging analysis. However, existing infant surface templates have two major limitations in functional MRI analysis. First, they are constructed by co-registration of cortical surfaces based on structural attributes, which cannot lead to accurate functional alignment, due to the highly variable relationship between cortical folds and functions. Second, they are constructed by simply averaging co-registered cortical attributes, which is sensitive to registration errors and lead to blurred attribute patterns on templates, thus deteriorating the accuracy in spatial normalization. Therefore, construction of infant cortical functional templates encoding sharp functional architectures is critical for infant fMRI analysis. To this end, we construct the first set of spatiotemporal infant cortical surface functional templates using Wasserstein barycenter and a state-of-the-art functional feature, namely the gradient density of functional connectivity. To address the first issue, we leverage functional gradient density to drive surface registration to improve inter-individual functional correspondences. To address the second issue, we compute templates based on the Wasserstein barycenter of functional gradient density maps across individuals. The motivation is that Wasserstein barycenter represents a meaningful mean under the Wasserstein distance metric, which takes into account the alignment of local spatial distribution of cortical attributes and thus is robust to registration errors, leading to sharp and detailed patterns on templates. Experiments on a dataset with 207 fMRI scans between 0 and 2 years of age show the validity and accuracy of our constructed infant cortical functional templates.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Li, G., Wang, L., Yap, P.-T., Wang, F., Wu, Z., Meng, Y., Dong, P., Kim, J., Shi, F., Rekik, I.: Computational neuroanatomy of baby brains: a review. NeuroImage 185, 906–925 (2019)

    Article  Google Scholar 

  2. Oishi, K., Chang, L., Huang, H.: Baby brain atlases. NeuroImage 185, 865–880 (2019)

    Article  Google Scholar 

  3. Wu, Z., Wang, L., Lin, W., Gilmore, J.H., Li, G., Shen, D.: Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation. Hum. Brain Mapp. 40(13), 3860–3880 (2019)

    Google Scholar 

  4. Bozek, J., Makropoulos, A., Schuh, A., Fitzgibbon, S., Wright, R., Glasser, M.F., Coalson, T.S., O’Muircheartaigh, J., Hutter, J., Price, A.N.: Construction of a neonatal cortical surface atlas using multimodal surface matching in the developing Human Connectome Project. NeuroImage 179, 11–29 (2018)

    Article  Google Scholar 

  5. Li, G., Wang, L., Shi, F., Lin, W., Shen, D.: Constructing 4D infant cortical surface atlases based on dynamic developmental trajectories of the cortex. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 89–96 (2014)

    Google Scholar 

  6. Wang, F., Lian, C., Xia, J., Wu, Z., Duan, D., Wang, L., Shen, D., Li, G.: Construction of spatiotemporal infant cortical surface atlas of rhesus macaque. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 704–707 (2018)

    Google Scholar 

  7. Wright, R., Makropoulos, A., Kyriakopoulou, V., Patkee, P.A., Koch, L.M., Rutherford, M.A., Hajnal, J.V., Rueckert, D., Aljabar, P.: Construction of a fetal spatio-temporal cortical surface atlas from in utero MRI: application of spectral surface matching. NeuroImage 120, 467–480 (2015)

    Article  Google Scholar 

  8. Wu, Z., Li, G., Wang, L., Lin, W., Gilmore, J.H., Shen, D.: Construction of spatiotemporal neonatal cortical surface atlases using a large-scale dataset. In: 2018 IEEE 15th International Symposium on Biomedical Imaging, pp. 1056–1059 (2018)

    Google Scholar 

  9. Xia, J., Wang, F., Benkarim, O.M., Sanroma, G., Piella, G., González Ballester, M.A., Hahner, N., Eixarch, E., Zhang, C., Shen, D.: Fetal cortical surface atlas parcellation based on growth patterns. Hum. Brain Mapp. 40(13), 3881–3899 (2019)

    Google Scholar 

  10. Conroy, B., Singer, B., Haxby, J., Ramadge, P.J.: fMRI-based inter-subject cortical alignment using functional connectivity. In: Advances in Neural Information Processing Systems, pp. 378–386 (2009)

    Google Scholar 

  11. Jiang, D., Du, Y., Cheng, H., Jiang, T., Fan, Y.: Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns. Neuroimage 82, 355–372 (2013)

    Article  Google Scholar 

  12. Sabuncu, M.R., Singer, B.D., Conroy, B., Bryan, R.E., Ramadge, P.J., Haxby, J.V.: Function-based intersubject alignment of human cortical anatomy. Cereb. Cortex 20(1), 130–140 (2010)

    Article  Google Scholar 

  13. Yeo, B.T., Krienen, F.M., Sepulcre, J., Sabuncu, M.R., Lashkari, D., Hollinshead, M., Roffman, J.L., Smoller, J.W., Zöllei, L., Polimeni, J.R.: The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J. Neurophysiol. (2011)

    Google Scholar 

  14. Wig, G.S., Laumann, T.O., Cohen, A.L., Power, J.D., Nelson, S.M., Glasser, M.F., Miezin, F.M., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Parcellating an individual subject’s cortical and subcortical brain structures using snowball sampling of resting-state correlations. Cereb. Cortex 24(8), 2036–2054 (2014)

    Article  Google Scholar 

  15. Glasser, M.F., Coalson, T.S., Robinson, E.C., Hacker, C.D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C.F., Jenkinson, M.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)

    Article  Google Scholar 

  16. Laumann, T.O., Gordon, E.M., Adeyemo, B., Snyder, A.Z., Joo, S.J., Chen, M.-Y., Gilmore, A.W., McDermott, K.B., Nelson, S.M., Dosenbach, N.U.: Functional system and areal organization of a highly sampled individual human brain. Neuron 87(3), 657–670 (2015)

    Article  Google Scholar 

  17. Chizat, L., Peyré, G., Schmitzer, B., Vialard, F.-X.: Unbalanced optimal transport: Dynamic and Kantorovich formulations. J. Funct. Anal. 274(11), 3090–3123 (2018)

    Article  MathSciNet  Google Scholar 

  18. Chizat, L., Peyré, G., Schmitzer, B., Vialard, F.-X.: Scaling algorithms for unbalanced optimal transport problems. Math. Comput. 87(314), 2563–2609 (2018)

    Article  MathSciNet  Google Scholar 

  19. Janati, H., Cuturi, M., Gramfort, A.: Wasserstein regularization for sparse multi-task regression. In: The 22nd International Conference on Artificial Intelligence and Statistics, pp. 1407–1416 (2018)

    Google Scholar 

  20. Howell, B.R., Styner, M.A., Gao, W., Yap, P.-T., Wang, L., Baluyot, K., Yacoub, E., Chen, G., Potts, T., Salzwedel, A.: The UNC/UMN baby connectome project (BCP): an overview of the study design and protocol development. NeuroImage 185, 891–905 (2019)

    Article  Google Scholar 

  21. Li, G., Nie, J., Wang, L., Shi, F., Gilmore, J.H., Lin, W., Shen, D.: Measuring the dynamic longitudinal cortex development in infants by reconstruction of temporally consistent cortical surfaces. Neuroimage 90, 266–279 (2014)

    Article  Google Scholar 

  22. Li, G., Wang, L., Shi, F., Gilmore, J.H., Lin, W., Shen, D.: Construction of 4D high-definition cortical surface atlases of infants: methods and applications. Med. Image Anal. 25(1), 22–36 (2015)

    Article  Google Scholar 

  23. Wang, F., Lian, C., Wu, Z., Zhang, H., Li, T., Meng, Y., Wang, L., Lin, W., Shen, D., Li, G.: Developmental topography of cortical thickness during infancy. Proc. Natl. Acad. Sci. 116(32), 15855–15860 (2019)

    Article  Google Scholar 

  24. Van Essen, D.C., Glasser, M.F., Dierker, D.L., Harwell, J., Coalson, T.: Parcellations and hemispheric asymmetries of human cerebral cortex analyzed on surface-based atlases. Cereb. Cortex 22(10), 2241–2262 (2012)

    Article  Google Scholar 

  25. Yeo, B.T., Sabuncu, M.R., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans. Med. Imaging 29(3), 650–668 (2009)

    Article  Google Scholar 

  26. Glasser, M.F., Sotiropoulos, S.N., Wilson, J.A., Coalson, T.S., Fischl, B., Andersson, J.L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J.R.: The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage 80, 105–124 (2013)

    Article  Google Scholar 

  27. Gordon, E.M., Laumann, T.O., Adeyemo, B., Huckins, J.F., Kelley, W.M., Petersen, S.E.: Generation and evaluation of a cortical area parcellation from resting-state correlations. Cereb. Cortex 26(1), 288–303 (2016)

    Article  Google Scholar 

  28. Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Advances in neural information processing systems, pp. 2292–2300 (2013)

    Google Scholar 

Download references

Acknowledgments

This work was partially supported by NIH grants (MH116225 and MH117943). This work also utilizes approaches developed by an NIH grant (1U01MH110274) and the efforts of the UNC/UMN Baby Connectome Project Consortium.

Author information

Authors and Affiliations

Authors

Consortia

Corresponding author

Correspondence to Gang Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Huang, Y. et al. (2020). Construction of Spatiotemporal Infant Cortical Surface Functional Templates. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12267. Springer, Cham. https://doi.org/10.1007/978-3-030-59728-3_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59728-3_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59727-6

  • Online ISBN: 978-3-030-59728-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics