title: AddNeuroMed and ADNI: similar patterns of Alzheimer's atrophy and automated MRI classification accuracy in Europe and North America.
abstract: The European Union AddNeuroMed program and the US-based Alzheimer Disease Neuroimaging Initiative (ADNI) are two large multi-center initiatives designed to collect and validate biomarker data for Alzheimer's disease (AD). Both initiatives use the same MRI data acquisition scheme. The current study aims to compare and combine magnetic resonance imaging (MRI) data from the two study cohorts using an automated image analysis pipeline and a multivariate data analysis approach. We hypothesized that the two cohorts would show similar patterns of atrophy, despite demographic differences and could therefore be combined. MRI scans were analyzed from a total of 1074 subjects (AD=295, MCI=444 and controls=335) using Freesurfer, an automated segmentation scheme which generates regional volume and regional cortical thickness measures which were subsequently used for multivariate analysis (orthogonal partial least squares to latent structures (OPLS)). OPLS models were created for the individual cohorts and for the combined cohort to discriminate between AD patients and controls. The ADNI cohort was used as a replication dataset to validate the model created for the AddNeuroMed cohort and vice versa. The combined cohort model was used to predict conversion to AD at baseline of MCI subjects at 1 year follow-up. The AddNeuroMed, the ADNI and the combined cohort showed similar patterns of atrophy and the predictive power was similar (between 80 and 90%). The combined model also showed potential in predicting conversion from MCI to AD, resulting in 71% of the MCI converters (MCI-c) from both cohorts classified as AD-like and 60% of the stable MCI subjects (MCI-s) classified as control-like. This demonstrates that the methods used are robust and that large data sets can be combined if MRI imaging protocols are carefully aligned.
authors.affiliation: Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden. eric.westman@ki.se
authors.forename: Eric
authors.initials: E
authors.lastname: Westman
authors.forename: Andrew
authors.initials: A
authors.lastname: Simmons
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: Kłoszewska
authors.forename: Hilkka
authors.initials: H
authors.lastname: Soininen
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.forename: Simon
authors.initials: S
authors.lastname: Lovestone
authors.forename: Christian
authors.initials: C
authors.lastname: Spenger
authors.forename: Lars-Olof
authors.initials: LO
authors.lastname: Wahlund
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000145:classification / Q000473:pathology*; D001284:Atrophy; D001921:Brain / Q000473:pathology*; D005060:Europe; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008297:Male; D008875:Middle Aged; D009656:North America; D011237:Predictive Value of Tests