Structural and functional biomarkers of prodromal Alzheimer's disease: a high-dimensional pattern classification study

Abstract

This work builds upon previous studies that reported high sensitivity and specificity in classifying individuals with mild cognitive impairment (MCI), which is often a prodromal phase of Alzheimer's disease (AD), via pattern classification of MRI scans. The current study integrates MRI and PET (15)O water scans from 30 participants in the Baltimore Longitudinal Study of Aging, and tests the hypothesis that joint evaluation of structure and function can yield higher classification accuracy than either alone. Classification rates of up to 100% accuracy were achieved via leave-one-out cross-validation, whereas conservative estimates of generalization performance in new scans, evaluated via bagging cross-validation, yielded an area under the receiver operating characteristic (ROC) curve equal to 0.978 (97.8%), indicating excellent diagnostic accuracy. Spatial maps of regions determined to contribute the most to the classification implicated many temporal, prefrontal, orbitofrontal, and parietal regions. Detecting complex patterns of brain abnormality in early stages of cognitive impairment has pivotal importance for the detection and management of AD.

Figure 1

The classification rate as a function of the number of regions used in classification.

Figure 2

Spatial patterns of structural and functional brain abnormality associated with MCI detected by the classification method. Brain regions that collectively contributed to the classification are overlaid on the template image. Images are displayed in radiological convention. The relatively most consistently used regions are shown in red or yellow.

Figure 3

Top left: Axial sections of effect size maps of group differences in GM. Top right and middle row: Tri-planar sections of effect size maps of group difference in GM. Bottom row: Axial sections of effect size maps of group differences in WM and PET. The color scales indicate CN>MCI. Images are in radiology convention.

Figure 4

Left: Distribution of SVM scores calculated via the leave-one-out bagging procedure described in the text. Right: ROC curve of the ensemble classifiers. Numbers around the curve are the correct classification rates (%) corresponding to different sensitivities and specificities. The bold face point on the curve corresponds to the classification rate with zero as the classification threshold.