Alzheimer's disease diagnosis in individual subjects using structural MR images: Validation studies
Introduction
Diagnostic criteria for Alzheimer's disease (AD) are currently based on clinical and psychometric assessment. As the search for effective therapies to arrest or slow the progression of Alzheimer's disease (AD) intensifies, there is a need to develop better diagnostic tools. Development of such tools should also aid in measuring the efficacy of new therapies. Neuroimaging and specifically magnetic resonance imaging (MRI) have been shown to be a surrogate for early diagnosis of AD, particularly in subjects clinically classified as amnestic mild cognitive impairment (aMCI) which in most patients is a precursor to AD (Petersen et al., 1995). Degenerative histological changes occur long before the disease is clinically detectable (Gomez-Isla et al., 1996), perhaps decades, and thus imaging can be expected to help identify the early onset.
Studies demonstrating cross sectional inter-group differences associated with AD and MCI are now common place. Algorithmic complexity varies from conceptually simple measurement of volumes in manually or semi-manually delineated a priori regions of interest (ROI) (Barnes et al., 2004, Jack et al., 1999, Testa et al., 2004), to mathematically complex voxel-wise modeling of tissue density changes, e.g. voxel or tensor-based morphometry (VBM, TBM) (Bozzali et al., 2006, Freeborough and Fox, 1998a, Hirata et al., 2005, Thompson et al., 2007). By their nature, ROI-based analyses are spatially limited and do not make use of all the available information contained in a 3-dimensional image data set. The most widely used voxel-based analytic techniques (Ashburner and Friston, 2000) are agnostic of disease-specific information. They are also designed to perform only group-wise comparisons and thus are unsuitable for evaluating the disease state of an individual subject. Because of these disadvantages, investigators have recently turned their attention toward multivariate analysis and machine learning-based algorithms for distinguishing patients from cognitively normal (CN) subjects (Alexander and Moeller, 1994, Csernansky et al., 2005, Davatzikos et al., 2005, Fan et al., 2005, Freeborough and Fox, 1998b, Lao et al., 2004). These techniques use the entire 3D MRI data to form a disease model against which individual subjects may be compared.
Machine learning and computer-aided diagnostics (CAD) have been of growing interest in the field of medical imaging (Metz, 1999). Broadly speaking, a supervised machine learning algorithm is “trained” to produce a desired output from a set of input (training) data. As such, the trained algorithm may be treated as a “black box” encapsulating knowledge gleaned from the training data whose inputs are useful for producing the expected outcome. The supervised machine learning algorithm used in this paper is the support vector machine (SVM) (Vapnik, 1998).
The data used in this paper for each patient include a structural MR (sMR) scan, ApolipoproteinE (APOE) genotype information, and commonly available demographic details: age and gender. Demographic information was included because brain volume varies with both age and gender (Jack et al., 1997, Lemaitre et al., 2005, Shiino et al., 2006, Smith et al., 2006). In addition, old age is the strongest known risk factor for typical late onset AD (Evans et al., 1989). The information about APOE was added because there is a well established positive risk for AD associated with the presence of the ε4 allele while ε2 is protective (Bickeboller et al., 1997, Craft et al., 1998, Farrer et al., 1997). Various imaging studies have shown that APOE genotype, age, and gender co-vary with imaging measures of brain morphology (Bigler et al., 2000, Fleisher et al., 2005).
The aims of the paper were (1) to optimize and train a model for AD vs. CN classification based on a library of MRI scans from 280 clinically well-characterized subjects as well as demographic and genetic information and (2) to measure the diagnostic sensitivity and specificity of the SVM algorithm in an independent test set of 50 AD and 50 CN. A hierarchical model construction to classify AD and CN was followed, starting with sMR and then adding demographics and APOE information in a stepwise fashion. Our overall goal was preliminary validation of this algorithmic approach for the diagnosis of AD.
Section snippets
SVM classifier
Consider a set of m training data sets {xk, yk} (k = 1, 2, …, m in our case m = 280), where each training vector xk has p input features and the desired output is yk ∈ {− 1, + 1} where + 1 corresponds to AD and − 1 corresponds to CN. A simple two dimensional example for our application with real data for 20 CN and 20 AD patients taken from the Mayo Clinic Alzheimer's Disease Research Center study is plotted in Fig. 1(a). The figure shows the expected difference between the two classes where AD patients
Subjects
One hundred ninety subjects that fulfilled clinical criteria for probable AD (McKhann et al., 1984) were age- and gender-matched to 190 CN subjects. By design there was no difference in age or gender distribution across subject groups. All subjects had been prospectively recruited into the Mayo Clinic Alzheimer's Disease Research Center (ADRC), or the Alzheimer's disease Patient Registry (ADPR), and were identified from the ADRC/ADPR database. These longitudinal studies include independent
Model I: MRI-based tissue densities
Model I can roughly be divided into four main steps: First, the features of each image (i.e. tissue densities) were extracted for each individual structural MR scan. Second, a linear SVM was used for feature reduction to choose a subset of the voxel locations or patterns which most significantly differentiate AD from CN. The tissue density values at the subset of voxel locations were subsequently input to the third step in which SVM parameters are optimized. Optimization is such that the two
Model II: reduced set of tissue densities and demographic variables
The input to Model II is XkMRD = {XkMR(W˜ > 0.66);s1xk,age;s2xk,gender}, where xk,age and xk,gender represent the normalized age and gender information (xk,gender = 1 for male and xk,gender = − 1 for female) respectively for subject k and XkMR(W˜ > 0.66) indicates the tissue densities at the voxels that were obtained after thresholding in Model I. When heterogeneous inputs are input into SVM, optimal relative weighting for each type of input features is likely to improve the prediction accuracy (Palvidis
Model III: reduced set of tissue densities, demographic variables, and APOE
The input to Model III is XkALL = {XkMRD;s3xk,APOE}, where xk,APOE represents the vector containing both the APOE allele information for each subject k, i.e. (2,2); (2,3); (3,3); (2,4); (3,4); (4,4). The linear kernel reinforces the well established fact that the risk of AD increases with the numeric value of the allele, i.e. 4 confers greater risk than 3 while 2 confers less risk than 3 (Bickeboller et al., 1997, Craft et al., 1998, Farrer et al., 1997). The relative scaling of APOE (s3) with
Model I
The components of the neighborhood weight vector W˜ in GM, WM, and CSF in 280 training data sets are shown in Fig. 4. Warm colors in Fig. 4 indicate greater vector weights. Note that the weight vector images are quite sensible biologically. Large GM weight vectors (indicating GM loss) are concentrated on the medial temporal lobe, temporal-parietal association cortex, posterior cingulate/precuneus, and insula. WM loss is concentrated in temporal lobe (particularly in the WM of the
Discussion and conclusions
Classification algorithms for the diagnosis of AD in single subjects using structural MR images are presented in this paper. Model I generates a structural abnormality index (STAND)-score based on a linear SVM that can be summarized as a weighted sum of volumes in voxel locations that are more affected in AD than in CN in a manner consistent with disease related brain atrophy, i.e. brain shrinkage and CSF expansion. Unlike traditional ROI-based volumetric techniques where predefined volumes of
Acknowledgments
This study was supported by grants P50 AG16574, R01 AG11378, and R01 AG15866 from the National Institute on Aging, Bethesda MD, RR24151 K12 CTSA Mentored Career Development Program, the support of the Robert H. and Clarice Smith, and Abigail Van Buren Alzheimers Disease Research Program of the Mayo Foundation, U.S.A. The authors would like to thank Stephen D. Weigand and Scott Przybelski, Department of Health Science Research at Mayo Clinic, for their statistical assistance in identifying
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