Structural imaging biomarkers of Alzheimer's disease: predicting disease progression.

TitleStructural imaging biomarkers of Alzheimer's disease: predicting disease progression.
Publication TypeJournal Article
Year of Publication2015
AuthorsEskildsen SF, Coupé P, Fonov VS, Pruessner JC, D Collins L
Corporate AuthorsAlzheimer's Disease Neuroimaging Initiative
JournalNeurobiol Aging
Volume36 Suppl 1
PaginationS23-31
Date Published2015 Jan
ISSN1558-1497
KeywordsAged, Aged, 80 and over, Alzheimer Disease, Atrophy, Biomarkers, Cohort Studies, Diffusion Magnetic Resonance Imaging, Disease Progression, Female, Forecasting, Gray Matter, Hippocampus, Humans, Male, Neocortex, Neuroimaging, Sensitivity and Specificity
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.

DOI10.1016/j.neurobiolaging.2014.04.034
Alternate JournalNeurobiol. Aging
PubMed ID25260851
Grant ListMOP-111169 / / Canadian Institutes of Health Research / Canada
U01 AG024904 / AG / NIA NIH HHS / United States