title: Structural imaging biomarkers of Alzheimer's disease: predicting disease progression.
abstract: Optimized magnetic resonance imaging (MRI)-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined prediction of the disease. In addition, they could serve as valuable tools when designing therapeutic studies of individuals at risk of AD. In this study, we combine (1) a novel method for grading medial temporal lobe structures with (2) robust cortical thickness measurements to predict AD among subjects with mild cognitive impairment (MCI) from a single T1-weighted MRI scan. Using AD and cognitively normal individuals, we generate a set of features potentially discriminating between MCI subjects who convert to AD and those who remain stable over a period of 3 years. Using mutual information-based feature selection, we identify 5 key features optimizing the classification of MCI converters. These features are the left and right hippocampi gradings and cortical thicknesses of the left precuneus, left superior temporal sulcus, and right anterior part of the parahippocampal gyrus. We show that these features are highly stable in cross-validation and enable a prediction accuracy of 72% using a simple linear discriminant classifier, the highest prediction accuracy obtained on the baseline Alzheimer's Disease Neuroimaging Initiative first phase cohort to date. The proposed structural features are consistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.
authors.affiliation: Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark. Electronic address: seskildsen@cfin.au.dk.
authors.forename: Simon F
authors.initials: SF
authors.lastname: Eskildsen
authors.affiliation: Laboratoire Bordelais de Recherche en Informatique, Unité Mixte de Recherche CNRS (UMR 5800), PICTURA Group, Bordeaux, France.
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.affiliation: McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
authors.forename: Vladimir S
authors.initials: VS
authors.lastname: Fonov
authors.affiliation: Department of Psychiatry, McGill University, Montreal, Canada; Department of Neurology and Neurosurgery, McGill University, Montreal, Canada.
authors.forename: Jens C
authors.initials: JC
authors.lastname: Pruessner
authors.affiliation: McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
authors.forename: D Louis
authors.initials: DL
authors.lastname: Collins
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology*; D001284:Atrophy; D015415:Biomarkers; D015331:Cohort Studies; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D018450:Disease Progression; D005260:Female; D005544:Forecasting; D066128:Gray Matter / Q000473:pathology; D006624:Hippocampus / Q000473:pathology; D006801:Humans; D008297:Male; D019579:Neocortex / Q000473:pathology*; D059906:Neuroimaging / Q000379:methods*; D012680:Sensitivity and Specificity
chemical_list: D015415:Biomarkers
keywords: Alzheimer; Cortical thickness; Early detection; Fast Accurate Cortex Extraction; Hippocampus; MCI; MRI; Prediction; SNIPE