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Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment

By Laurence O’Dwyer, Franck Lamberton, Arun L. W. Bokde, Michael Ewers, Yetunde O. Faluyi, Colby Tanner, Bernard M. Mazoyer, Desmond O’Neill, Máiréad Bartley, D. Rónán Collins, Tara Coughlan, David Prvulovic and Harald Hampel


Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy older subjects and subjects with mild cognitive impairment (MCI). Here we apply DTI to 40 healthy older subjects and 33 MCI subjects in order to derive values for multiple indices of diffusion within the white matter voxels of each subject. DTI measures were then used together with support vector machines (SVMs) to classify control and MCI subjects. Greater than 90% sensitivity and specificity was achieved using this method, demonstrating the potential of a joint DTI and SVM pipeline for fast, objective classification of healthy older and MCI subjects. Such tools may be useful for large scale drug trials in Alzheimer’s disease where the early identification of subjects with MCI is critical

Topics: ddc:610
Year: 2012
OAI identifier: oai:publikationen.ub.uni-frankfurt.de:23737

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