Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data.
Background: Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI).Methods: We applied a Support Vector Machine (SVM)...
| Published in: | Journal of Neuroimaging Vol. 25; no. 5; pp. 738 - 748 |
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| Main Authors: | , , , , , , , |
| Format: | Journal Article |
| Published: |
Wiley-Blackwell
Sep2015
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109622120&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109622120 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10512284 L2W jtl: Journal of Neuroimaging issn: 10512284 maglogo: Y pubinfo: dt: Sep2015 vid: 25 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109622120 NLM25644739 2013130403 10.1111/jon.12214 NLM25644739 109622120 ppf: 738 ppct: 10 formats: tig: atl: Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. aug: au: Dyrba, Martin Barkhof, Frederik Fellgiebel, Andreas Filippi, Massimo Hausner, Lucrezia Hauenstein, Karlheinz Kirste, Thomas Teipel, Stefan J sug: ab: Background: Alzheimer's disease (AD) patients show early changes in white matter (WM) structural integrity. We studied the use of diffusion tensor imaging (DTI) in assessing WM alterations in the predementia stage of mild cognitive impairment (MCI).Methods: We applied a Support Vector Machine (SVM) classifier to DTI and volumetric magnetic resonance imaging data from 35 amyloid-β42 negative MCI subjects (MCI-Aβ42-), 35 positive MCI subjects (MCI-Aβ42+), and 25 healthy controls (HC) retrieved from the European DTI Study on Dementia. The SVM was applied to DTI-derived fractional anisotropy, mean diffusivity (MD), and mode of anisotropy (MO) maps. For comparison, we studied classification based on gray matter (GM) and WM volume.Results: We obtained accuracies of up to 68% for MO and 63% for GM volume when it came to distinguishing between MCI-Aβ42- and MCI-Aβ42+. When it came to separating MCI-Aβ42+ from HC we achieved an accuracy of up to 77% for MD and a significantly lower accuracy of 68% for GM volume. The accuracy of multimodal classification was not higher than the accuracy of the best single modality.Conclusions: Our results suggest that DTI data provide better prediction accuracy than GM volume in predementia AD. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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