%0 Conference Paper
%A Dyrba, Martin
%A Wegrzyn, Martin
%A Kilimann, Ingo
%A Teipel, Stefan
%T Combining DTI and MRI for the automated detection of Alzheimer's disease using a large European multicenter dataset
%M DZNE-2020-00931
%D 2012
%X Diffusion tensor imaging (DTI) allows assessing neuronal fiber tract integrity in vivo to support the diagnosis of Alzheimer’s disease (AD). It is an open research question to which extent combinations of different neuroimaging techniques increase the detection of AD. In this study we examined different methods to combine DTI data and structural T 1-weighted magnetic resonance imaging (MRI) data. Further, we applied machine learning techniques for automated detection of AD. We used a sample of 137 patients with clinically probable AD (MMSE 20.6 ±5.3) and 143 healthy elderly controls, scanned in nine different scanners, obtained from the recently created framework of the European DTI study on Dementia (EDSD). For diagnostic classification we used the DTI derived indices fractional anisotropy (FA) and mean diffusivity (MD) as well as grey matter density (GMD) and white matter density (WMD) maps from anatomical MRI. We performed voxel-based classification using a Support Vector Machine (SVM) classifier with tenfold cross validation. We compared the results from each single modality with those from different approaches to combine the modalities. For our sample, combining modalities did not increase the detection rates of AD. An accuracy of approximately 89
%B MBIA 2012
%C 1 Oct 2012 - 5 Oct 2012, Nice (France)
Y2 1 Oct 2012 - 5 Oct 2012
M2 Nice, France
%F PUB:(DE-HGF)6
%9 Conference Presentation
%U https://pub.dzne.de/record/145601