Abstract
Current statistical methods in neuroimaging identify effects of neurodegenerative diseases on the brain structure by detecting group differences. Results are detailed maps showing population-wide effects. Although useful for better understanding the disease, these maps provide little subject-specific information. Furthermore, since group assignments have to be known prior to analysis, resulting maps have limited diagnostic value for new subjects. This article proposes a method to construct subject- and disease-specific effect maps prior to diagnosis. The method combines techniques from binary classification and image restoration to identify the effects of a disease of interest on the measurements. Experimental evaluation is carried out with synthetically generated data and real data selected from the ADNI cohort. Results demonstrate the capability of the proposed method in generating subject-specific effect maps.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
see http://www.fil.ion.ucl.ac.uk/spm/ or https://freesurfer.net to this end.
References
Ashburner, J., Friston, K.J.: Why voxel-based morphometry should be used. Neuroimage 14(6), 1238–1243 (2001)
Greve, D.N.: An absolute beginner’s guide to surface-and voxel-based morphometric analysis. Proc. Intl. Soc. Mag. Reson. Med. 19, 33 (2011)
Fischl, B.: Freesurfer. Neuroimage 62(2), 774–781 (2012)
Thompson, P.M., et al.: Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb. Cortex 11(1), 1–16 (2001)
Rosas, H., et al.: Regional and progressive thinning of the cortical ribbon in huntington’s disease. Neurology 58(5), 695–701 (2002)
Burton, E.J., et al.: Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with lewy bodies and controls. Brain 127(4), 791–800 (2004)
Krishnan, A., et al.: Partial least squares (PLS) methods for neuroimaging: a tutorial and review. Neuroimage 56(2), 455–475 (2011)
Worsley, K.J., et al.: Characterizing the response of PET and fMRI data using multivariate linear models. Neuroimage 6(4), 305–319 (1997)
Gaonkar, B., Davatzikos, C.: Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification. Neuroimage 78, 270–283 (2013)
Mwangi, B., Tian, T.S., Soares, J.C.: A review of feature reduction techniques in neuroimaging. Neuroinformatics 12(2), 229–244 (2014)
Rahim, M., Thirion, B., Abraham, A., Eickenberg, M., Dohmatob, E., Comtat, C., Varoquaux, G.: Integrating multimodal priors in predictive models for the functional characterization of Alzheimer’s disease. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 207–214. Springer, Cham (2015). doi:10.1007/978-3-319-24553-9_26
Ganz, M., et al.: Relevant feature set estimation with a knock-out strategy and random forests. Neuroimage 122, 131–148 (2015)
Maumet, C., Maurel, P., Ferré, J.C., Barillot, C.: An a contrario approach for the detection of patient-specific brain perfusion abnormalities with arterial spin labelling. Neuroimage 134, 424–433 (2016)
Tomas-Fernandez, X., Warfield, S.K.: A model of population and subject (MOPS) intensities with application to multiple sclerosis lesion segmentation. IEEE Trans. Med. Imaging 34(6), 1349–1361 (2015)
Van Leemput, K., Maes, F., Vandermeulen, D., Colchester, A., Suetens, P.: Automated segmentation of multiple sclerosis lesions by model outlier detection. IEEE Trans. Med. Imaging 20(8), 677–688 (2001)
Prastawa, M.: A brain tumor segmentation framework based on outlier detection*1. Med. Image Anal. 8(3), 275–283 (2004)
Zeng, K., Erus, G., Sotiras, A., Shinohara, R.T., Davatzikos, C.: Abnormality detection via iterative deformable registration and basis-pursuit decomposition. IEEE Trans. Med. Imaging PP(99), 1 (2016)
Iqbal, K.: Subgroups of Alzheimer’s disease based on cerebrospinal fluid molecular markers. Ann. Neurol. 58(5), 748–757 (2005)
Kiebel, S., Holmes, P.: The General Linear Model. Academic Press, London (2003)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Good, P.I.: Permutation, Parametric and Bootstrap Tests of Hypotheses. Springer, Heidelberg (2005)
Sabuncu, M.R., Konukoglu, E.: Clinical prediction from structural brain MRI scans: a large-scale empirical study. Neuroinformatics 13(1), 31–46 (2015)
Dickerson, B.C., et al.: The cortical signature of alzheimer’s disease: regionally specific cortical thinning relates to symptom severity in very mild to mild ad dementia and is detectable in asymptomatic amyloid-positive individuals. Cereb. Cortex 19(3), 497–510 (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Konukoglu, E., Glocker, B. (2017). Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases. In: Müller, H., et al. Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. BAMBI MCV 2016 2016. Lecture Notes in Computer Science(), vol 10081. Springer, Cham. https://doi.org/10.1007/978-3-319-61188-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-61188-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61187-7
Online ISBN: 978-3-319-61188-4
eBook Packages: Computer ScienceComputer Science (R0)