TY - GEN
T1 - Feature selection improves the accuracy of classifying Alzheimer disease using diffusion tensor images
AU - Demirhan, Ayse
AU - Nir, Talia M.
AU - Zavaliangos-Petropulu, Artemis
AU - Jack, Clifford R.
AU - Weiner, Michael W.
AU - Bernstein, Matt A.
AU - Thompson, Paul M.
AU - Jahanshad, Neda
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/21
Y1 - 2015/7/21
N2 - Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.
AB - Diffusion tensor imaging (DTI) has recently been added to several large-scale studies of Alzheimer's disease (AD), such as the Alzheimer's Disease Neuroimaging Initiative (ADNI), to investigate white matter (WM) abnormalities not detectable on standard anatomical MRI. Disease effects can be widespread, and the profile of WM abnormalities across tracts is still not fully understood. Here we analyzed image-wide measures from DTI fractional anisotropy (FA) maps to classify AD patients (n=43), mild cognitive impairment (n=114) and cognitively healthy elderly controls (n=70). We used voxelwise maps of FA along with averages in WM regions of interest (ROI) to drive a Support Vector Machine. We further used the ReliefF algorithm to select the most discriminative WM voxels for classification. This improved accuracy for all classification tasks by up to 15%. We found several clusters formed by the ReliefF algorithm, highlighting specific pathways affected in AD but not always captured when analyzing ROIs.
KW - Alzheimer's disease
KW - diffusion tensor imaging
KW - fractional anisotropy
KW - support vector machines
KW - voxel-based analysis
UR - http://www.scopus.com/inward/record.url?scp=84944311785&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84944311785&partnerID=8YFLogxK
U2 - 10.1109/ISBI.2015.7163832
DO - 10.1109/ISBI.2015.7163832
M3 - Conference contribution
AN - SCOPUS:84944311785
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 126
EP - 130
BT - 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PB - IEEE Computer Society
T2 - 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Y2 - 16 April 2015 through 19 April 2015
ER -