Abstract
We propose a method to analyze the relationship between the shape of functional regions of the cortex and cognitive measures, such as reading ability and vocabulary knowledge. Functional regions on the cortical surface can vary not only in size and shape but also in topology and position relative to neighboring regions. Standard diffeomorphism-based shape analysis tools do not work well here because diffeomorphisms are unable to capture these topological differences, which include region splitting and merging across subjects. State-of-the-art cortical surface shape analyses compute derived regional properties (scalars), such as regional volume, cortical thickness, curvature, and gyrification index. However, these methods cannot compare the full extent of topological or shape differences in cortical regions. We propose icosahedral spatial pyramid matching (ISPM) of region borders computed on the surface of a sphere to capture this variation in regional topology, position, and shape. We then analyze how this variation corresponds to measures of cognitive performance. We compare our method to other approaches and find that it is indeed informative to consider aspects of shape beyond the standard approaches. Analysis is performed using a subset of 27 test/retest subjects from the Human Connectome Project in order to understand both the effectiveness and reproducibility of this method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vision 61(2), 139–157 (2005)
Duffau, H.: A two-level model of interindividual anatomo-functional variability of the brain and its implications for neurosurgery. Cortex 86, 303–313 (2017)
Glasser, M.F., et al.: A multi-modal parcellation of human cerebral cortex. Nature 536(7615), 171–178 (2016)
Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1458–1465. IEEE (2005)
Lazebnik, S., Schmid, C., Ponce, J.: Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 2, pp. 2169–2178. IEEE (2006)
Leventon, M.E., Grimson, W.E.L., Faugeras, O.: Statistical shape influence in geodesic active contours. In: 5th IEEE EMBS International Summer School on Biomedical Imaging. IEEE (2002)
Lyons, R.: Distance covariance in metric spaces. Annal. Probab. 41(5), 3284–3305 (2013)
Marcus, D., et al.: Informatics and data mining tools and strategies for the human connectome project. Front. Neuroinformatics 5, 4 (2011)
Mémoli, F.: Gromov-Wasserstein distances and the metric approach to object matching. Found. Comput. Math. 11(4), 417–487 (2011)
Oguz, I., et al.: Entropy-based particle correspondence for shape populations. Int. J. Comput. Assist. Radiol. Surg. 11(7), 1221–1232 (2016)
Székely, G.J., Rizzo, M.L., Bakirov, N.K.: Measuring and testing dependence by correlation of distances. Annal. Stat. 35(6), 2769–2794 (2007)
Yeo, B.T.T., Sabuncu, M., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast surface registration. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008. LNCS, vol. 5241, pp. 745–753. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85988-8_89
Zhu, P., Awate, S.P., Gerber, S., Whitaker, R.: Fast shape-based nearest-neighbor search for brain mris using hierarchical feature matching. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011. LNCS, vol. 6892, pp. 484–491. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23629-7_59
Acknowledgements
Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Campbell, K.M., Anderson, J.S., Fletcher, P.T. (2019). Surface-Based Spatial Pyramid Matching of Cortical Regions for Analysis of Cognitive Performance. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11767. Springer, Cham. https://doi.org/10.1007/978-3-030-32251-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-030-32251-9_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32250-2
Online ISBN: 978-3-030-32251-9
eBook Packages: Computer ScienceComputer Science (R0)
