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Journal of Medical Systems
Published in: Journal of Medical Systems 5/2011

01-10-2011 | Original Paper

Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities

Authors: Ayman El-Baz, Ahmed Elnakib, Manuel F. Casanova, Georgy Gimel’farb, Andrew E. Switala, Desha Jordan, Sabrina Rainey

Published in: Journal of Medical Systems | Issue 5/2011

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Abstract

The importance of accurate early diagnostics of autism that severely affects personal behavior and communication skills cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the Corpus Callosum (CC) in autistic brains. We propose a new approach to quantitative analysis of three-dimensional (3D) magnetic resonance images (MRI) of the brain that ensures a more accurate quantification of anatomical differences between the CC of autistic and normal subjects. It consists of three main processing steps: (i) segmenting the CC from a given 3D MRI using the learned CC shape and visual appearance; (ii) extracting a centerline of the CC; and (iii) cylindrical mapping of the CC surface for its comparative analysis. Our experiments revealed significant differences (at the 95% confidence level) between 17 normal and 17 autistic subjects in four anatomical divisions, i.e. splenium, rostrum, genu and body of their CCs.
Footnotes
1
To the best of our knowledge, we are the first authors who introduced an analytical form to estimate Gibbs potentials [36].
 
Literature
1.
Brambilla, P., Hardan, A., and Nemi, S., Brain anatomy and development in autism: Review of MRI studies. Brain Res. Bull. 61:557–569, 2003.CrossRef
2.
Minshew, N., and Payton, J., New perspectives in autism, part i. the clinical spectrum of autism. Curr. Probl. Pediatr. 18:561–610, 1988.
3.
Stevens, M., Fein, D., Dunn, M., Allen, D., Waterhouse, L. H., Feinstein, C., and Rapin, I., Subgroups of children with autism by cluster analysis: A longitudinal examination. J. Am. Acad. Child Adolesc. Psychiatry 39:346–352, 2000.CrossRef
4.
Kanner, L., Autistic disturbances of affective contact. Nerv. Child 2:250–250, 1943.
5.
Aylward, E., Minshew, N., Field, K., Sparks, B., and Singh, N., Effects of age on brain volume and head circumference in autism. Neurology 59(2):175–183, 2002.
6.
Courchesne, R., Carper, R., and Akshoomoff, N., Evidence of brain overgrowth in the first year of life in autism. JAMA 290:337–344, 2003.CrossRef
7.
Casanova, M. F., White matter volume increases and minicolumns in autism. Ann. Neurol. 56(3):453, 2004.CrossRef
8.
Casanova, M. F., van Kooten, I. A., Switala, A., van Engeland, H., Heinsen, H., Steinbusch, H., Hof, P. R., Trippe, J., Stone, J., and Schmitz, C., Minicolumnar abnormalities in autism. Acta Neuropathological, 2006.
9.
Mountcastle, V. B., Perpetual Neuroscience: The Cerebral Cortex. Harvard University Press, Cambridge, 1988.
10.
Calvin, W., How Brains Think. Basic Books, New York, 1996.
11.
Buxhoeveden, D., and Casanova, M. F., Encephalization, minicolumns, and hominid evolution. In: Casanova, M. F. (Ed.), Neocortical Modularity and the Cell Minicolumn. Nova Biomedical, New York, pp. 117–136, 2005.
12.
Gressens, P., and Evrard, P., The glial fascicle: An ontogenic and phylogenic unit guiding, supplying and distributing mammalian cortical neurons. Brain Res. Dev. Brain Res. 76:272–277, 1993.CrossRef
13.
Fahmi, R., El-Baz, A., Hassan, H., Farag, A., and Casanova, M. F., Classification Techniques for Autistic Vs. Typically Developing Brain Using MRI Data. Proc. of IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI’07), Arlington, Virginia, USA 1348–1351, 2007.
14.
Casanova, M. F., Farag, A., El-Baz, A., Mott, M., Hassan, H., Fahmi, R., and Switala, A. E., Abnormalities of the gyral window in autism: A macroscopic correlate to a putative minicolumnopathy. J. Spec. Educ. Rehabil. 1:85–101, 2007.CrossRef
15.
Abell, F., Krams, M., Ashburner, J., Passingham, R., Friston, K., Frackowiak, R., Happe, F., Frith, C., and Frith, U., The neuroanatomy of autism: A voxel-based whole brain analysis of structural scans. NeuroReport 10(8):647–1651, 1999.CrossRef
16.
Waiter, G., Williams, J., Murray, A., Gilchrist, A., Perrett, D., and Whiten, A., A voxel-based investigation of brain structure in male adolescents with autistic spectrum disorder. Neuroimage 22:619–625, 2004.CrossRef
17.
Rojas, D. C., Peterson, E., Winterrowd, E., Reite, M. L., Rogers, S. J., and Tregellas, J. R., Regional gray matter volumetric changes in autism associated with social and repetitive behavior symptoms. BMC Psychiatry 6(56), 2006.
18.
Boddaert, N., Chabane, N., Gervais, H., Good, C. D., Bourgeois, M., Plumet, M. H., Barthelemy, C., Mouren, M. C., Artiges, E., Samson, Y., Brunelle, F., Frackowiak, R. S. J., and Zilbovicius, M., Superior temporal sulcus anatomical abnormalities in childhood autism: a voxel-based morphometry MRI study. Neuroimage 23:364–369, 2004.CrossRef
19.
20.
Herbert, M. R., Ziegler, D. A., Deutsch, C. K., O’Brien, L. M., Lange, N., Bakardjiev, A., Hodgson, J., Adrien, K. T., Steele, S., Makris, N., Kennedy, D., Harris, G. J., and Caviness, V. S., Jr., Dissociations of cerebral cortex, subcortical and cerebral white matter volumes in autistic boys. Brain 126:1182–1192, 2003.CrossRef
21.
Waiter, G. D., Williams, J. H., Murray, A. D., Gilchrist, A., Perrett, D. I., and Whiten, A., Structural white matter deficits in high-functioning individuals with autistic spectrum disorder: A voxel-based investigation. Neuroimage 24(2):455–461, 2005.CrossRef
22.
Barnea-Goraly, N., Kwon, H., Menon, V., Eliez, S., Lotspeich, L., and Reiss, A. L., White matter structure in autism: Preliminary evidence from diffusion tensor imaging. Biol. Psychiatry 55:323–328, 2004.CrossRef
23.
El-Baz, A., Casanova, M. F., Gimel’farb, G., Mott, M., and Switala, A. E., Autism Diagnostics by 3D Texture Analysis of Cerebral White Matter Gyrifications. Proc. of International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’07), Brisbane, Australia 235–243, 2007.
24.
Egaas, B., Courchesne, E., and Saitoh, O., Reduced size of corpus callosum in autism. Arch. Neurol. 52(8):794–801, 1995.CrossRef
25.
Piven, J., Bailey, J., Ranson, B. J., and Arndt, S., An MRI study of the corpus callosum in autism. Am. J. Psychiatry 154(8):1051–1056, 1997.
26.
Manes, F., Piven, J., Vrancic, D., Nanclares, V., Plebst, C., and Starkstein, S., An MRI study of the corpus callosum and cerebellum in mentally retarded autistic individuals. J. Neuropsychiatry Clin. Neurosci. 11(4):470–474, 1999.
27.
Hardan, A. Y., Minshew, N. J., and Keshavan, M. S., Corpus callosum size in autism. Neurology 55:1033–1036, 2000.
28.
Chung, M. K., Dalton, K. M., Alexander, A. L., and Davidson, R. J., Less white matter concentration in autism: 2D voxel-based morphometry. Neuroimage 23:242–251, 2004.CrossRef
29.
He, Q., Duan, Y., Miles, J., and Takahashi, N., Statistical Shape Analysis of the Corpus Callosum in Subtypes of Autism, Proc. 7th IEEE Int. Conf. BIBE 1087–1091, 2007.
30.
He, Q., Karsch, K., and Duan, Y., Abnormalities in MRI traits of Corpus Callosum in Autism Subtype. in Proc. 30th IEEE Int. Conf. of EMBS pp. 3900–3903, 2008.
31.
Vidal, C. N., Nicolson, R., DeVito, T. J., Hayashi, K. M., Geaga, J. A., Drost, D. J., Williamson, P. C., Rajakumar, N., Sui, Y., Dutton, R. A., Toga, A. W., and Thompson, P. M., Mapping corpus callosum deficits in autism: An index of aberrant cortical connectivity. Biol. Psychiatry 60(3):218–225, 2006.CrossRef
32.
Schinzinger, R., Conformal Mapping: Methods and Applications, Courier Dover Publications, 2003.
33.
Wang, S., Wang, Y., Jin, M., Gu, X., and Samaras, D., Conformal geometry and its applications on 3D shape matching, recognition and stitching. IEEE Trans. Pattern Anal. Mach. Intell. 29(7):1029–1220, 2007.CrossRef
34.
Hong, W., Gu, X., Qiu, F., Jin, M., and Kaufman, A., Conformal virtual colon flattening. Proc. ACM Symp. Solid and Physical Modeling, Wales, UK:85–93, 2006.
35.
El-Baz, A., and Gimel’farb G., Image segmentation with a parametric deformable model using shape and appearance priors, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Anchorage, AL, USA pp. 1–8, 2008.
36.
Farag, A., El-Baz, A., and Gimel’farb, G., Precise segmentation of multi-modal images. IEEE Trans. Image Process. 15(4):952–968, 2006.CrossRef
37.
Gimel’farb, G., Image Textures and Gibbs Random Fields. Kluwer Academic, Dordrecht, 1999.CrossRefMATH
38.
El-Baz, A., and Gimel’farb, G., EM based approximation of empirical distributions with linear combinations of discrete Gaussians. Proc. IEEE Int. Conference on Image Processing, San Antonio, Texas, USA 4:373–376, 2007.
39.
Viola, P., and Wells, W. M., Alignment by maximization of mutual information. Proc. 5th Int. Conference on Computer Vision16–23, 1995.
40.
Cohen, L., and Kimmel, R., Global minimum for active contour models: A minimal path approach. Int. J. Comput. Vis. 24(1):57–78, 1997.CrossRef
41.
42.
Hassouna, M., and Farag, A., Robust Centerline Extraction Framework Using Level Sets, Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, CA, USA 458–465, 2005.
43.
Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. E., and Willsky, A., A shape based approach to the segmentation of medical imagery using level sets. IEEE Trans. Med. Imaging 22:137–154, 2003.CrossRef
44.
Cootes, T., and Taylor, C., A mixture model for representing shape variation. Image Vis. Comput. 17(8):567–574, 1999.CrossRef
45.
Frazier, T. W., and Harden, Y. H., A meta-analysis of the corpus callosum in Autism. Biol. Psychiatry 66(10):935–941, 2009. Nov 15.CrossRef
46.
Just, M. A., Cherkassky, V. L., Keller, T. A., Kana, R. K., and Minshew, N. J., Functional and anatomical cortical underconnectivity in autism: Evidence from an fMRI study of an executive function task and corpus callosum morphometry. Cereb. Cortex 17:951–961, 2007.CrossRef
47.
Boger-Megiddo, I., Shaw, D. W., Friedman, S. D., Sparks, B. F., Artru, A. A., Giedd, J. N., Dawson, G., and Dager, S. R., Corpus callosum morphometrics in young children with autism spectrum disorder. J. Autism Dev. Disord. 36:733–739, 2006.CrossRef
48.
Rice, S. A., Bigler, E. D., Cleavinger, H. B., Tate, J., Sayer, D. F., McMahon, W., Ozonoff, S., Lu, J., and Lainhart, J. E., Macrocephaly, corpus callosum morphology, and autism. J. Child Neurol. 20:34–41, 2005.CrossRef
49.
Elia, M., Ferri, R., Musumeci, S. A., Panerai, S., Bottitta, M., and Scuderi, C., Clinical correlates of brain morphometric features of subjects with low-functioning autistic disorder. J. Child Neurol. 15:504–508, 2000.CrossRef
50.
Gaffney, G. R., Kuperman, S., Tsai, L. Y., Minchin, S., and Hassanein, K. M., Midsaggital magnetic resonance imaging of autism. Br. J. Psychiatry 151:831–833, 1987.CrossRef
Metadata
Title
Accurate Automated Detection of Autism Related Corpus Callosum Abnormalities
Authors
Ayman El-Baz
Ahmed Elnakib
Manuel F. Casanova
Georgy Gimel’farb
Andrew E. Switala
Desha Jordan
Sabrina Rainey
Publication date
01-10-2011
Publisher
Springer US
Published in
Journal of Medical Systems / Issue 5/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-010-9510-3

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