title: Different multivariate techniques for automated classification of MRI data in Alzheimer's disease and mild cognitive impairment.
abstract: Automated structural magnetic resonance imaging (MRI) processing pipelines and different multivariate techniques are gaining popularity for Alzheimer's disease (AD) research. We used four supervised learning methods to classify AD patients and controls (CTL) and to prospectively predict the conversion of mild cognitive impairment (MCI) to AD from baseline MRI data. A total of 345 participants from the AddNeuroMed cohort were included in this study; 116 AD patients, 119 MCI patients and 110 CTL individuals. High resolution sagittal 3D MP-RAGE datasets were acquired and MRI data were processed using FreeSurfer. We explored the classification ability of orthogonal projections to latent structures (OPLS), decision trees (Trees), artificial neural networks (ANN) and support vector machines (SVM). Applying 10-fold cross-validation demonstrated that SVM and OPLS were slightly superior to Trees and ANN, although not statistically significant for distinguishing between AD and CTL. The classification experiments resulted in up to 83% sensitivity and 87% specificity for the best techniques. For the prediction of conversion of MCI patients at baseline to AD at 1-year follow-up, we obtained an accuracy of up to 86%. The value of the multivariate models derived from the classification of AD vs. CTL was shown to be robust and efficient in the identification of MCI converters.
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. carlos.aguilar@ki.se
authors.forename: Carlos
authors.initials: C
authors.lastname: Aguilar
authors.forename: Eric
authors.initials: E
authors.lastname: Westman
authors.forename: J-Sebastian
authors.initials: JS
authors.lastname: Muehlboeck
authors.forename: Patrizia
authors.initials: P
authors.lastname: Mecocci
authors.forename: Bruno
authors.initials: B
authors.lastname: Vellas
authors.forename: Magda
authors.initials: M
authors.lastname: Tsolaki
authors.forename: Iwona
authors.initials: I
authors.lastname: Kloszewska
authors.forename: Hilkka
authors.initials: H
authors.lastname: Soininen
authors.forename: Simon
authors.initials: S
authors.lastname: Lovestone
authors.forename: Christian
authors.initials: C
authors.lastname: Spenger
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
authors.forename: Lars-Olof
authors.initials: LO
authors.lastname: Wahlund
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000235:genetics / Q000473:pathology*; D001057:Apolipoproteins E / Q000235:genetics; D019540:Area Under Curve; D060825:Cognitive Dysfunction / Q000235:genetics / Q000473:pathology*; D018450:Disease Progression; D004522:Educational Status; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000145:classification; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D060388:Support Vector Machine
chemical_list: D001057:Apolipoproteins E