Abstract
The paper presents a novel statistical framework for cortical folding pattern analysis that relies on a rich multivariate descriptor of folding patterns in a region of interest (ROI). The ROI-based approach avoids problems faced by spatial-normalization-based approaches stemming from the severe deficiency of homologous features between typical human cerebral cortices. Unlike typical ROI-based methods that summarize folding complexity or shape by a single number, the proposed descriptor unifies complexity and shape of the surface in a high-dimensional space. In this way, the proposed framework couples the reliability of ROI-based analysis with the richness of the novel cortical folding pattern descriptor. Furthermore, the descriptor can easily incorporate additional variables, e.g. cortical thickness. The paper proposes a novel application of a nonparametric permutation-based approach for statistical hypothesis testing for any multivariate high-dimensional descriptor. While the proposed framework has a rigorous theoretical underpinning, it is straightforward to implement. The framework is validated via simulated and clinical data. The paper is the first to quantitatively evaluate cortical folding in neonates with complex congenital heart disease.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Probability Density Function
- Cortical Surface
- Hypoplastic Left Heart Syndrome
- Surface Patch
- Folding Pattern
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Thompson, P., Lee, A., Dutton, R., Geaga, J., Hayashi, K., Eckert, M., Bellugi, U., Galaburda, A., Korenberg, J., Mills, D., Toga, A., Reiss, A.: Abnormal cortical complexity and thickness profiles mapped in Williams syndrome. J. Neuroscience 25(16), 4146–4158 (2005)
Nordahl, C., Dierker, D., Mostafavi, I., Schumann, C., Rivera, S., Amaral, D., Van-Essen, D.: Cortical folding abnormalities in autism revealed by surface-based morphometry. Journal of Neuroscience 27(43), 11725–11735 (2007)
Batchelor, P., Castellano-Smith, A., Hill, D., Hawkes, D., Cox, T., Dean, A.: Measures of folding applied to the development of the human fetal brain. IEEE Trans. Med. Imaging 21(8), 953–965 (2002)
Yu, P., Grant, P.E., Qi, Y., Han, X., Segonne, F., Pienaar, R., Busa, E., Pacheco, J., Makris, N., Buckner, R.L., Golland, P., Fischl, B.: Cortical surface shape analysis based on spherical wavelets. IEEE Trans. Med. Imaging 26(4), 582–597 (2007)
Pienaar, R., Fischl, B., Caviness, V., Makris, N., Grant, P.E.: A methodology for analyzing curvature in the developing brain from preterm to adult. Int. J. Imaging Systems Technology 18(1), 42–68 (2008)
Chen, C., Zimmerman, R., Faro, S., Parrish, B., Wang, Z., Bilaniuk, L., Chou, T.: MR of the cerebral operculum: abnormal opercular formation in infants and children. American Journal of Neuroradiology 17(7), 1303–1311 (1996)
Childs, A., Ramenghi, L., Cornette, L., Tanner, S., Arthur, R., Martinez, D., Levene, M.: Cerebral maturation in premature infants: Quantitative assessment using MR imaging. Amer. J. of Neuroradiology 22, 1577–1582 (2001)
Miller, S., McQuillen, P., Hamrick, S., Xu, D., Glidden, D., Charlton, N., Karl, T., Azakie, A., Ferriero, D., Barkovich, J., Vigneron, D.: Abnormal brain development in newborns with congenital heart disease. New Eng. J. Med. 357, 1928–1938 (2007)
Licht, D., Shera, D., Clancy, R., Wernovsky, G., Montenegro, L., Nicolson, S., Zimmerman, R., Spray, T., Gaynor, W., Vossough, A.: Brain maturation is delayed in infants with complex congenital heart defects. J. Thorac. Cardiovasc. Surg. 137, 529–537 (2009)
Tosun, D., Duchesne, S., Rolland, Y., Toga, A., Verin, M., Barillot, C.: 3D analysis of cortical morphometry in differential diagnosis of Parkinson’s Plus Syndromes. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 891–899. Springer, Heidelberg (2007)
Van-Essen, D., Dierker, D.: Surface-based and probabilistic atlases of primate cerebral cortex. Neuron 56, 209–225 (2007)
Mangin, J., Riviere, D., Cachia, A., Duchesnay, E., Cointepas, Y., Papadopoulos-Orfanos, D., Scifo, P., Ochiai, T., Brunelle, F., Regis, J.: A framework to study the cortical folding patterns. NeuroImage 23(1), S129–S138 (2004)
Lyttelton, O., Boucher, M., Robbins, S., Evans, A.: An unbiased iterative group registration template for cortical surface analysis. NeuroImage 34, 1535–1544 (2007)
Thompson, P., Schwartz, C., Lin, R., Khan, A., Toga, A.: Three-dimensional statistical analysis of sulcal variability in the human brain. J. Neurosci. 16(13), 4261–4274 (1996)
Van-Essen, D., Drury, H.: Structural and functional analyses of human cerebral cortex using a surface-based atlas. J. Neuroscience 17(18), 7079–7102 (1997)
Awate, S.P., Win, L., Yushkevich, P., Schultz, R.T., Gee, J.C.: 3D cerebral cortical morphometry in autism: Increased folding in children and adolescents in frontal, parietal, and temporal lobes. In: Proc. Int. Conf. Med. Image Comput. Comp. Assist. Interv., vol. 1, pp. 559–567 (2008)
Griffin, L.: The intrinsic geometry of the cerebral cortex. J. Theor. Biol. 166(3), 261–273 (1994)
Zilles, K., Armstrong, E., Schleicher, A., Kretschmann, H.: The human pattern of gyrification in the cerebral cortex. Anat. Embryol. 179, 173–179 (1988)
Koenderink, J., van Doorn, A.: Surface shape and curvature scales. Image and Vision Computing 10(8), 557–565 (1992)
Akgul, C., Sankur, B., Schmitt, F., Yemez, Y.: Multivariate density-based 3D shape descriptors. In: Int. Conf. Shape Modeling and Appl., pp. 3–12 (2007)
Papoulis, A., Pillai, S.U.: Probability, Random Variables, and Stochastic Processes, 4th edn. McGraw-Hill, New York (2001)
Lu, Z., Chen, X.: Spatial kernel regression estimation: weak consistency. Stat. and Prob. Letters 68(2), 125–136 (2004)
Chow, Y., Geman, S., Wu, L.: Consistant cross-validated density estimation. Annals of Statistics 11(1), 25–38 (1983)
Osher, S., Paragios, N.: Geometric Level Set Methods in Imaging, Vision, and Graphics. Springer, Heidelberg (2003)
Nichols, T., Holmes, A.: Nonparametric permutation tests for functional neuroimaging: a primer with examples. Human Brain Mapping 15(1), 1–25 (2002)
Aubert-Broche, B., Collins, D., Evans, A.: Twenty new digital brain phantoms for creation of validation image data bases. IEEE Trans. Med. Imag. 25(11), 1410–1416 (2006)
Styner, M., Gerig, G.: Correction scheme for multiple correlated statistical tests in local shape analysis. In: SPIE Medical Imaging, pp. 233–240 (2003)
Avants, B., Gee, J.: Geodesic estimation for large deformation anatomical shape averaging and interpolation. Neuroimage 23(1), 139–150 (2004)
Song, Z., Awate, S.P., Licht, D., Gee, J.: Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors. In: Proc. Med. Image Comput. Comp. Assist. Interv., vol. 1, pp. 883–890 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Awate, S.P., Yushkevich, P., Song, Z., Licht, D., Gee, J.C. (2009). Multivariate High-Dimensional Cortical Folding Analysis, Combining Complexity and Shape, in Neonates with Congenital Heart Disease. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_46
Download citation
DOI: https://doi.org/10.1007/978-3-642-02498-6_46
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02497-9
Online ISBN: 978-3-642-02498-6
eBook Packages: Computer ScienceComputer Science (R0)