Abstract
Alzheimer’s disease (AD) is the most common cause of dementia. Early detection of AD is important since treatment is more efficacious if introduced earlier. Mild cognitive impairment (MCI) is often a precursory stage of AD, so is considered to be a good target for early detection of AD. However, MCI is not easy to diagnose due to the subtlety of cognitive impairment. In this study, we developed a method to automate the diagnosis of AD and MCI using support vector machines (SVMs) and diffusion tensor imaging (DTI) data. We implemented two SVM models: one for classifying AD and MCI and another for classifying AD and normal control (NC). In both SVM models, the fractional anisotropy (FA) and the mode of anisotropy (MO) values of DTI were used as features. MO values resulted in a better performance than FA values in both models. In independent testing, the AD-MCI classifier showed a sensitivity of 69.2 %, a specificity of 100 % and an accuracy of 89.7 %, and the AD-NC classifier showed a sensitivity of 84.6 %, a specificity of 90.9 % and an accuracy of 87.5 %. These results are encouraging and suggest that SVM-based classification of DTI data is potentially powerful in early detection of MCI and AD.
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References
Vetrivel, K.S., Thinakaran, G.: Amyloidogenic processing of beta-amyloid precursor protein in intracellular compartments. Neurology 66(2 Suppl. 1), S69–S73 (2006)
Jonsson, L., Lindgren, P., Wimo, A., Jonsson, B., Winblad, B.: The cost-effectiveness of donepezil therapy in Swedish patients with Alzheimer’s disease: a Markov model. Clin. Ther. 21(7), 1230–1240 (1999)
Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., Iwatsubo, T., Jack, Jr., C.R., Kaye, J., Montine, T.J., Park, D.C., Reiman, E.M., Rowe, C.C., Siemers, E., Stern, Y., Yaffe, K., Carrillo, M.C., Thies, B., Morrison-Bogorad, M., Wagster, M.V., Phelps, C.H.: Toward defining the preclinical stages of Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement. 7(3), 280–292 (2011)
Kloppel, S., Stonnington, C.M., Chu, C., Draganski, B., Scahill, R.I., Rohrer, J.D., Fox, N.C., Jack, Jr., C.R., Ashburner, J., Frackowiak, R.S.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(Pt. 3), 681–689 (2008)
Magnin, B., Mesrob, L., Kinkingnehun, S., Pelegrini-Issac, M., Colliot, O., Sarazin, M., Dubois, B., Lehericy, S., Benali, H.: Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI. Neuroradiology 51(2), 73–83 (2009)
Bischkopf, J., Busse, A., Angermeyer, M.C.: Mild cognitive impairment—a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr. Scand. 106, 403–414 (2002)
Medina, D.A., Gaviria, M.: Diffusion tensor imaging investigations in Alzheimer’s disease: the resurgence of white matter compromise in the cortical dysfunction of the aging brain. Neuropsychiatr. Dis. Treat. 4(4), 737–742 (2008)
Oishi, K., Mielke, M.M., Albert, M., Lyketsos, C.G., Mori, S.: DTI analyses and clinical applications in Alzheimer’s disease. J. Alzheimers Dis. 26(Suppl. 3), 287–296 (2011)
Hess, C.P.: Update on diffusion tensor imaging in Alzheimer’s disease. Magn. Reson. Imaging Clin. N. Am. 17(2), 215–224 (2009)
Bastin, M.E., Le Roux, P.: On the application of a non-CPMG single-shot fast spin-echo sequence to diffusion tensor MRI of the human brain. Magn. Reson. Med. 48(1), 6–14 (2002)
Ito, R., Mori, S., Melhem, E.R.: Diffusion tensor brain imaging and tractography. Neuroimaging Clin. N. Am. 12(1), 1–19 (2002)
Melhem, E.R., Itoh, R., Jones, L., Barker, P.B.: Diffusion tensor MR imaging of the brain: effect of diffusion weighting on trace and anisotropy measurements. AJNR Am. J. Neuroradiol. 21(10), 1813–1820 (2000)
Pierpaoli, C., Jezzard, P., Basser, P.J., Barnett, A., Di Chiro, G.: Diffusion tensor MR imaging of the human brain. Radiology 201(3), 637–648 (1996)
Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31(4), 1487–1505 (2006)
Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. J. Magn. Reson. 213(2), 560–570 (2011)
Ennis, D.B., Kindlmann, G.: Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images. Magn. Reson. Med. 55(1), 136–146 (2006)
Douaud, G., Jbabdi, S., Behrens, T.E., Menke, R.A., Gass, A., Monsch, A.U., Rao, A., Whitcher, B., Kindlmann, G., Matthews, P.M., Smith, S.: DTI measures in crossing-fiber areas: increased diffusion anisotropy reveals early white matter alteration in MCI and mild Alzheimer’s disease. Neuroimage 55(3), 880–890 (2011)
Noble, W.S.: What is a support vector machine? Nat. Biotechnol. 24(12), 1565–1567 (2006)
Robnik-Šikonja, M., Kononenko, I.: Theoretical and empirical analysis of ReliefF and RReliefF. Mach. Learn. 53(1–2), 23–69 (2003)
Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)
Smith, S.M.: Fast robust automated brain extraction. Hum. Brain Mapp. 17(3), 143–155 (2002)
Smith, S.M., Jenkinson, M., Woolrich, M.W., Beckmann, C.F., Behrens, T.E., Johansen-Berg, H., Bannister, P.R., De Luca, M., Drobnjak, I., Flitney, D.E., Niazy, R.K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J.M., Matthews, P.M.: Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl. 1), S208–S219 (2004)
Haller, S., Nguyen, D., Rodriguez, C., Emch, J., Gold, G., Bartsch, A., Lovblad, K.O., Giannakopoulos, P.: Individual prediction of cognitive decline in mild cognitive impairment using support vector machine-based analysis of diffusion tensor imaging data. J. Alzheimers Dis. 22(1), 315–327 (2010)
O’Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., Tanner, C., Mazoyer, B., O’Neill, D., Bartley, M., Collins, D.R., Coughlan, T., Prvulovic, D., Hampel, H.: Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS ONE 7(2), e32441 (2012)
Scholkopf, B., Sung, K.K., Burges, C.J.C., Girosi, F., Niyogi, P., Poggio, T., Vapnik, V.: Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans. Sig. Process. 45(11), 2758–2765 (1997)
Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H.: Data mining in bioinformatics using Weka. Bioinformatics 20(15), 2479–2481 (2004)
Behrens, T.E., Berg, H.J., Jbabdi, S., Rushworth, M.F., Woolrich, M.W.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007)
Acknowledgments
This research was supported by the Ministry of Education (2010-0020163) and in part by the Basic Science Research Program through the National Research Foundation (NRF) funded by the Ministry of Science, ICT & Future Planning (2015R1A1A3A04001243).
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Lee, W., Park, B., Han, K. (2015). SVM-Based Classification of Diffusion Tensor Imaging Data for Diagnosing Alzheimer’s Disease and Mild Cognitive Impairment. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_49
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DOI: https://doi.org/10.1007/978-3-319-22186-1_49
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