Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment
PLoS ONE. 2012-02-23; 7(2): e32441
DOI: 10.1371/journal.pone.0032441
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1. PLoS One. 2012;7(2):e32441. doi: 10.1371/journal.pone.0032441. Epub 2012 Feb 23.
Using support vector machines with multiple indices of diffusion for automated
classification of mild cognitive impairment.
O’Dwyer L(1), Lamberton F, Bokde AL, Ewers M, Faluyi YO, Tanner C, Mazoyer B,
O’Neill D, Bartley M, Collins DR, Coughlan T, Prvulovic D, Hampel H.
Author information:
(1)Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe
University, Frankfurt, Germany.
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.
DOI: 10.1371/journal.pone.0032441
PMCID: PMC3285682
PMID: 22384251 [Indexed for MEDLINE]