Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: A combined spatial atrophy and white matter alteration approach
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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ABSTRACT: We compare a variety of different anatomic connectivity measures, including several novel ones, that may help in distinguishing Alzheimer's disease (AD) patients from controls. We studied diffusion-weighted magnetic resonance imaging from 200 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative. We first evaluated measures derived from connectivity matrices based on whole-brain tractography; next, we studied additional network measures based on a novel flow-based measure of brain connectivity, computed on a dense 3-dimensional lattice. Based on these 2 kinds of connectivity matrices, we computed a variety of network measures. We evaluated the measures' ability to discriminate disease with a repeated, stratified 10-fold cross-validated classifier, using support vector machines, a supervised learning algorithm. We tested the relative importance of different combinations of features based on the accuracy, sensitivity, specificity, and feature ranking of the classification of 200 people into normal healthy controls and people with early or late mild cognitive impairment or AD.Neurobiology of Aging 08/2014; 36. DOI:10.1016/j.neurobiolaging.2014.04.037 · 4.85 Impact Factor
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ABSTRACT: Background: Amnestic mild cognitive impairment (aMCI) is considered to be the transitional stage between healthy aging and Alzheimer’s disease (AD). Moreover, aMCI individuals with additional impairment in one or more non-memory cognitive domains are at higher risk of conversion to AD. Hence accurate identification of the sub-types of aMCI would enable earlier detection of individuals progressing to AD. Methods: We examine the group differences in cortical thickness between single-domain and multiple-domain sub-types of aMCI, and as well as with respect to age-matched controls in a well-balanced cohort from the Sydney Memory and Aging Study. In addition, the diagnostic value of cortical thickness in the sub-classification of aMCI as well as from normal controls using support vector machine (SVM) classifier is evaluated, using a novel cross-validation technique that can handle class-imbalance. Results: This study revealed an increased, as well as a wider spread, of cortical thinning in multiple-domain aMCI compared to single-domain aMCI. The best performances of the classifier for the pairs (1) single-domain aMCI and normal controls, (2) multiple-domain aMCI and normal controls, and (3) single and multiple-domain aMCI were AUC = 0.52, 0.66, and 0.54, respectively. The accuracy of the classifier for the three pairs was just over 50% exhibiting low specificity (44–60%) and similar sensitivity (53–68%). Conclusion: Analysis of group differences added evidence to the hypothesis that multiple-domain aMCI is a later stage of AD compared to single-domain aMCI. The classification results show that discrimination among single, multiple-domain sub-types of aMCI and normal controls is limited using baseline cortical thickness measures.Frontiers in Neurology 05/2014; 5:76. DOI:10.3389/fneur.2014.00076
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ABSTRACT: Machine learning algorithms and multivariate data analysis methods have been widely utilized in the field of Alzheimer's disease (AD) research in recent years. Advances in medical imaging and medical image analysis have provided a means to generate and extract valuable neuroimaging information. Automatic classification techniques provide tools to analyze this information and observe inherent disease-related patterns in the data. In particular, these classifiers have been used to discriminate AD patients from healthy control subjects and to predict conversion from mild cognitive impairment to AD. In this paper, recent studies are reviewed that have used machine learning and multivariate analysis in the field of AD research. The main focus is on studies that used structural magnetic resonance imaging (MRI), but studies that included positron emission tomography and cerebrospinal fluid biomarkers in addition to MRI are also considered. A wide variety of materials and methods has been employed in different studies, resulting in a range of different outcomes. Influential factors such as classifiers, feature extraction algorithms, feature selection methods, validation approaches, and cohort properties are reviewed, as well as key MRI-based and multi-modal based studies. Current and future trends are discussed.Journal of Alzheimer's disease: JAD 04/2014; 41(3). DOI:10.3233/JAD-131928 · 3.61 Impact Factor