Combining DTI and MRI for the automated detection of Alzheimer's disease using a large European multicenter dataset

Martin Dyrba, Michael Ewers, Martin Wegrzyn, Ingo Kilimann, Claudia Plant, Annahita Oswald, Thomas Meindl, Michela Pievani, Arun L W Bokde, Andreas Fellgiebel, Massimo Filippi, Harald Hampel, Stefan Klöppel, Karlheinz Hauenstein, Thomas Kirste, Stefan J. Teipel

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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% was reached for GMD data alone and for multimodal classification when GMD was included. This high accuracy remained stable across each of the approaches. As our sample consisted of mildly to moderately affected patients, cortical atrophy may be far progressed so that the decline in structural network connectivity derived from DTI may not add additional information relevant for the SVM classification. This may be different for predementia stages of AD. Further research will focus on multimodal detection of AD in predementia stages of AD, e.g. in amnestic mild cognitive impairment (aMCI), and on evaluating the classification performance when adding other modalities, e.g. functional MRI or FDG-PET.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages18-28
Number of pages11
Volume7509 LNCS
DOIs
Publication statusPublished - 2012
Event2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 1 2012Oct 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7509 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/1/1210/5/12

Keywords

  • Alzheimer's disease
  • combining classifiers
  • Diffusion Tensor Imaging
  • Magnetic Resonance Imaging
  • multicenter study
  • multimodal analysis
  • Support Vector Machine

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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