Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach

School of Design, Communication & Information Technology, University of Newcastle, Callaghan, NSW, Australia.
NeuroImage (Impact Factor: 6.36). 08/2011; 59(2):1209-17. DOI: 10.1016/j.neuroimage.2011.08.013
Source: PubMed

ABSTRACT Amnestic mild cognitive impairment (aMCI) is a syndrome widely considered to be prodromal Alzheimer's disease. Accurate diagnosis of aMCI would enable earlier treatment, and could thus help minimize the prevalence of Alzheimer's disease. The aim of the present study was to evaluate a magnetic resonance imaging-based automated classification schema for identifying aMCI. This was carried out in a sample of community-dwelling adults aged 70-90 years old: 79 with a clinical diagnosis of aMCI and 204 who were cognitively normal. Our schema was novel in using measures of both spatial atrophy, derived from T1-weighted images, and white matter alterations, assessed with diffusion tensor imaging (DTI) tract-based spatial statistics (TBSS). Subcortical volumetric features were extracted using a FreeSurfer-initialized Large Deformation Diffeomorphic Metric Mapping (FS+LDDMM) segmentation approach, and fractional anisotropy (FA) values obtained for white matter regions of interest. Features were ranked by their ability to discriminate between aMCI and normal cognition, and a support vector machine (SVM) selected an optimal feature subset that was used to train SVM classifiers. As evaluated via 10-fold cross-validation, the classification performance characteristics achieved by our schema were: accuracy, 71.09%; sensitivity, 51.96%; specificity, 78.40%; and area under the curve, 0.7003. Additionally, we identified numerous socio-demographic, lifestyle, health and other factors potentially implicated in the misclassification of individuals by our schema and those previously used by others. Given its high level of performance, our classification schema could facilitate the early detection of aMCI in community-dwelling elderly adults.

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    • "machines (SVMs), a machine learning algorithm for classification, to learn from training data and then classify a separate test set. Cui et al. (2012) used SVMs to classify amnestic mild cognitive impairment (aMCI) based on features indexing anatomical atrophy through segmentations of T1-weighted MRI and fraction anisotropy values from diffusion images using tract-based spatial statistics (TBSS). They ranked the features using Fisher scores, and selected the best performing subset using cross-validation. "
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