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

Abstract : Optimized MRI-based biomarkers of Alzheimer's disease (AD) may allow earlier detection and refined pre-diction 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 (i) a novel method for grading medial temporal lobe structures with (ii) robust cortical thickness measurements to predict AD among subjects with mild cogni-tive 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 that remain stable over a period of three years. Using mutual information based feature selection we iden-tify five key features optimizing the classification of MCI converters. These features are the left and right hippocampus grading 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 valida-tion and enable a prediction accuracy of 72% using a simple linear discriminant classifier; the highest pre-diction accuracy obtained on the baseline ADNI1 cohort to date. The proposed structural features are con-sistent with Braak stages and previously reported atrophic patterns in AD and are easy to transfer to new cohorts and to clinical practice.
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Simon Eskildsen, Pierrick Coupé, Vladimir Fonov, Jens Pruessner, Louis Collins. Structural imaging biomarkers of Alzheimer's disease: predicting disease progression. Neurobiology of Aging, Elsevier, 2014, <10.1016/j.neurobiolaging.2014.04.034>. <hal-01060331>

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