Alzheimer's disease diagnosis in individual subjects using structural MR images: validation studies

Abstract

Objective: To develop and validate a tool for Alzheimer's disease (AD) diagnosis in individual subjects using support vector machine (SVM)-based classification of structural MR (sMR) images.

Background: Libraries of sMR scans of clinically well characterized subjects can be harnessed for the purpose of diagnosing new incoming subjects.

Methods: One hundred ninety patients with probable AD were age- and gender-matched with 190 cognitively normal (CN) subjects. Three different classification models were implemented: Model I uses tissue densities obtained from sMR scans to give STructural Abnormality iNDex (STAND)-score; and Models II and III use tissue densities as well as covariates (demographics and Apolipoprotein E genotype) to give adjusted-STAND (aSTAND)-score. Data from 140 AD and 140 CN were used for training. The SVM parameter optimization and training were done by four-fold cross validation (CV). The remaining independent sample of 50 AD and 50 CN was used to obtain a minimally biased estimate of the generalization error of the algorithm.

Results: The CV accuracy of Model II and Model III aSTAND-scores was 88.5% and 89.3%, respectively, and the developed models generalized well on the independent test data sets. Anatomic patterns best differentiating the groups were consistent with the known distribution of neurofibrillary AD pathology.

Conclusions: This paper presents preliminary evidence that application of SVM-based classification of an individual sMR scan relative to a library of scans can provide useful information in individual subjects for diagnosis of AD. Including demographic and genetic information in the classification algorithm slightly improves diagnostic accuracy.

Fig. 1

(a) Plot of Hippocampal and Ventricular volume of 20 AD and 20 Controls. (b) Linear SVM and (c) Third degree polynomial kernel SVM to separate both the classes shown in Fig 1(a). (d) SVM output for the polynomial kernel. The circle data points represent control and square data points represent AD patients. The filled symbols are the support vectors. The decision boundary is marked as 0.

Fig. 2

(a): Flow chart of the SVM based classification algorithm of AD and CN. Hierarchical representation of the three models evaluated in this paper. (b) Division of data for the algorithm training and then testing on independent samples of subjects. The numbering in the flow diagram indicates the order of operations.

Fig. 3

Four-fold CV for Model I: (a) Sensitivity and (b) Specificity as a function of C and threshold t on the feature rank.

Fig 4

Interpolated weight vectors for GM, WM and CSF (before threshold is applied) overlaid on the corresponding custom template. Color scale indicates the weight i.e. the importance of the voxel location for classification. For display purposes only the scale from 0.1 to 1 was used to display the non-zero voxels in the custom template.

Fig 5

Anatomic patterns with maximum discriminative power between AD and controls are overlaid on the corresponding custom T1 template. Color scale used to indicate the occurrence of the voxel in multiple tissue maps. Yellow: voxel location used in all three tissues (GM, WM and CSF); orange: voxel location used in at least two tissues and red: voxel location used in one tissue only.

Fig 6

(a) Model III aSTAND-Score. The circle data points represent CN and square data points represent AD patients. (b) ROC curve for the test data for all the three models.

Fig 7

SVM weights of tissue voxels of each model plotted as a function of SVM weights of Model I. The blue circles indicate Model I, red diamonds Model II and black plus signs Model III weights at the same tissue voxel.

Fig 8

Output of the SVM categorized according to the number of APOE alleles for (a) Model II (b) Model III. The blue circles indicate the test CN and red squares indicate the test AD patients. The squares below the line zero indicate AD misclassified as CN and circles above zero indicate CN misclassified as AD.