MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 132-140 | Cite as
Genetic, Structural and Functional Imaging Biomarkers for Early Detection of Conversion from MCI to AD
Abstract
With the advent of advanced imaging techniques, genotyping, and methods to assess clinical and biological progression, there is a growing need for a unified framework that could exploit information available from multiple sources to aid diagnosis and the identification of early signs of Alzheimer’s disease (AD). We propose a modeling strategy using supervised feature extraction to optimally combine high-dimensional imaging modalities with several other low-dimensional disease risk factors. The motivation is to discover new imaging biomarkers and use them in conjunction with other known biomarkers for prognosis of individuals at high risk of developing AD. Our framework also has the ability to assess the relative importance of imaging modalities for predicting AD conversion. We evaluate the proposed methodology on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to predict conversion of individuals with Mild Cognitive Impairment (MCI) to AD, only using information available at baseline.
Keywords
Positron Emission Tomography Mild Cognitive Impairment Partial Little Square Linear Discriminant Analysis Quadratic Discriminant AnalysisReferences
- 1.Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiology of Aging 32(12), 2322.e19–2322.e27 (2011)CrossRefGoogle Scholar
- 2.Lemoine, B., Rayburn, S., Benton, R.: Data Fusion and Feature Selection for Alzheimer’s Diagnosis. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds.) BI 2010. LNCS, vol. 6334, pp. 320–327. Springer, Heidelberg (2010)CrossRefGoogle Scholar
- 3.Weiner, M.W., et al.: The Alzheimers Disease Neuroimaging Initiative: A review of papers published since its inception. Alzheimer’s and Dementia, S1–S68 (2012)Google Scholar
- 4.Kohannim, O., et al.: Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiology of Aging 31(8), 1429–1442 (2010)CrossRefGoogle Scholar
- 5.Younes, L., Arrate, F., Miller, M.: Evolutions equations in computational anatomy. NeuroImage 45(1S1), 40–50 (2009)CrossRefGoogle Scholar
- 6.Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23, 151–160 (2004)CrossRefGoogle Scholar
- 7.Vialard, F.X., et al.: Diffeomorphic 3D image registration via geodesic shooting using an efficient adjoint calculation. IJCV, 1–13 (2011)Google Scholar
- 8.Minoshima, S., Frey, K.A., Koeppe, R.A., Foster, N.L., Kuhl, D.E.: A diagnostic approach in Alzheimers disease using three-dimensional stereotactic surface projections of Fluorine-18-FDG PET. J. of Nuclear Medicine 36(7), 1238–1248 (1995)Google Scholar
- 9.Bookstein, F.L.: Partial Least Squares: A dose-response model for measurement in the behavioral and brain sciences. Psycoloquy 5(23) (1994) (revised)Google Scholar
- 10.Singh, N., Fletcher, P.T., Preston, J.S., Ha, L., King, R., Marron, J.S., Wiener, M., Joshi, S.: Multivariate Statistical Analysis of Deformation Momenta Relating Anatomical Shape to Neuropsychological Measures. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part III. LNCS, vol. 6363, pp. 529–537. Springer, Heidelberg (2010)CrossRefGoogle Scholar