title: 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.
abstract: 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.
authors: German Center for Neurodegenerative Diseases, Rostock, Germany.
authors: Martin
authors: M
authors: Dyrba
authors: Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands.
authors: Frederik
authors: F
authors: Barkhof
authors: Department of Psychiatry, University Medical Center Mainz, Mainz, Germany.
authors: Andreas
authors: A
authors: Fellgiebel
authors: Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, Scientific Institute and University Vita-Salute San Raffaele, Milan, Italy.
authors: Massimo
authors: M
authors: Filippi
authors: Department of Geriatric Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
authors: Lucrezia
authors: L
authors: Hausner
authors: Department of Radiology, University Medicine Rostock, Rostock, Germany.
authors: Karlheinz
authors: K
authors: Hauenstein
authors: Mobile Multimedia Information Systems Group, University of Rostock, Rostock, Germany.
authors: Thomas
authors: T
authors: Kirste
authors: German Center for Neurodegenerative Diseases, Rostock, Germany.|Clinic for Psychosomatic and Psychotherapeutic Medicine, University Medicine Rostock, Rostock, Germany.
authors: Stefan J
authors: SJ
authors: Teipel
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000209:etiology / Q000473:pathology*; D060825:Cognitive Dysfunction / Q000150:complications / Q000473:pathology*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D000069550:Machine Learning; D008297:Male; D064847:Multimodal Imaging / Q000379:methods*; D062706:Prodromal Symptoms; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Alzheimer's disease (AD); diffusion tensor imaging (DTI); mild cognitive impairment (MCI); multicenter study; multiple kernels Support Vector Machine (MK-SVM)