Abstract
This work investigates the possibility of predicting future onset of dementia in subjects who are cognitively normal, using hippocampal shape and volume information extracted from MRI scans. A group of 47 subjects who were non-demented normal at the time of the MRI acquisition, but were diagnosed with dementia during a 9 year follow-up period, was selected from a large population based cohort study. 47 Age and gender matched subjects who stayed cognitively intact were selected from the same cohort study as a control group. The hippocampi were automatically segmented and all segmentations were inspected and, if necessary, manually corrected by a trained observer. From this data a statistical model of hippocampal shape was constructed, using an entropy-based particle system. This shape model provided the input for a Support Vector Machine classifier to predict dementia. Cross validation experiments showed that shape information can predict future onset of dementia in this dataset with an accuracy of 70%. By incorporating both shape and volume information into the classifier, the accuracy increased to 74%.
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References
Plassman, B.L., Langa, K.M., Fisher, G.G., Heeringa, S.G., Weir, D.R., Ofstedal, M.B., Burke, J.R., Hurd, M.D., Potter, G.G., Rodgers, W.L., Steffens, D.C., Willis, R.J., Wallace, R.B.: Prevalence of dementia in the united states: the aging, demographics, and memory study. Neuroepidemiology 29(1-2), 125–132 (2007)
Association, A.: alzheimer’s disease facts and figures. Alzheimers & Dementia 5, 234–270 (May 2009)
Scarpini, E., Scheltens, P., Feldman, H.: Treatment of Alzheimer’s disease: current status and new perspectives. Lancet Neurol. 2(9), 539–547 (2003)
Jack, C.R., Petersen, R.C., Xu, Y.C., O’Brien, P.C., Smith, G.E., Ivnik, R.J., Boeve, B.F., Waring, S.C., Tangalos, E.G., Kokmen, E.: Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52(7), 1397–1403 (1999)
Scheltens, P., Fox, N., Barkhof, F., Carli, C.D.: Structural magnetic resonance imaging in the practical assessment of dementia: beyond exclusion. Lancet Neurol. 1(1), 13–21 (2002)
den Heijer, T., Geerlings, M.I., Hoebeek, F.E., Hofman, A., Koudstaal, P.J., Breteler, M.M.B.: Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people. Arch. Gen. Psychiatry 63(1), 57–62 (2006)
West, M.J., Coleman, P.D., Flood, D.G., Troncoso, J.C.: Differences in the pattern of hippocampal neuronal loss in normal ageing and alzheimer’s disease. Lancet 344(8925), 769–772 (1994)
Simi, G., Kostovi, I., Winblad, B., Bogdanovi, N.: Volume and number of neurons of the human hippocampal formation in normal aging and alzheimer’s disease. J. Comp. Neurol. 379(4), 482–494 (1997)
Csernansky, J.G., Wang, L., Joshi, S., Miller, J.P., Gado, M., Kido, D., McKeel, D., Morris, J.C., Miller, M.I.: Early dat is distinguished from aging by high-dimensional mapping of the hippocampus. dementia of the alzheimer type. Neurology 55(11), 1636–1643 (2000)
Scher, A.I., Xu, Y., Korf, E.S.C., White, L.R., Scheltens, P., Toga, A.W., Thompson, P.M., Hartley, S.W., Witter, M.P., Valentino, D.J., Launer, L.J.: Hippocampal shape analysis in alzheimer’s disease: a population-based study. Neuroimage 36(1), 8–18 (2007)
Ferrarini, L., Frisoni, G.B., Pievani, M., Reiber, J.H.C., Ganzola, R., Milles, J.: Morphological hippocampal markers for automated detection of alzheimer’s disease and mild cognitive impairment converters in magnetic resonance images. J. Alzheimers Dis. 17(3), 643–659 (2009)
West, M.J., Kawas, C.H., Stewart, W.F., Rudow, G.L., Troncoso, J.C.: Hippocampal neurons in pre-clinical alzheimer’s disease. Neurobiol. Aging 25(9), 1205–1212 (2004)
Heijer, d.T., Vermeer, S., Clarke, R., Oudkerk, M., Koudstaal, P., Hofman, A., Breteler, M.: Homocysteine and brain atrophy on mri of non-demented elderly. Brain. A journal of neurology 126, 170–175 (2003)
van der Lijn, F., den Heijer, T., Breteler, M.M.B., Niessen, W.J.: Hippocampus segmentation in mr images using atlas registration, voxel classification, and graph cuts. Neuroimage 43(4), 708–720 (2008)
Cates, J.E., Fletcher, P.T., Styner, M.A., Shenton, M.E., Whitaker, R.T.: Shape modeling and analysis with entropy-based particle systems. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 333–345. Springer, Heidelberg (2007)
Goodall, C.: Procrustes methods in the statistical analysis of shape. Journal of the Royal Statistical Society. Series B (Methodological) 53(2), 285–339 (1991)
Duin, R., Juszczak, P., de Ridder, D., PaclÃk, P., Pekalska, E., Tax, D.M.J.: PR-Tools (2004), http://prtools.org
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001) Software, available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
McNemar, Q.: Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika 12(2), 152–157 (1947)
Csernansky, J.G., Wang, L., Swank, J., Miller, J.P., Gado, M., McKeel, D., Miller, M.I., Morris, J.C.: Preclinical detection of alzheimer’s disease: hippocampal shape and volume predict dementia onset in the elderly. Neuroimage 25(3), 783–792 (2005)
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Achterberg, H.C. et al. (2010). Prediction of Dementia by Hippocampal Shape Analysis. In: Wang, F., Yan, P., Suzuki, K., Shen, D. (eds) Machine Learning in Medical Imaging. MLMI 2010. Lecture Notes in Computer Science, vol 6357. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15948-0_6
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DOI: https://doi.org/10.1007/978-3-642-15948-0_6
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