An MRI-derived definition of MCI-to-AD conversion for long-term, automatic prognosis of MCI patients

Abstract

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are of great current research interest. While there is no consensus on whether MCIs actually "convert" to AD, this concept is widely applied. Thus, the more important question is not whether MCIs convert, but what is the best such definition. We focus on automatic prognostication, nominally using only a baseline brain image, of whether an MCI will convert within a multi-year period following the initial clinical visit. This is not a traditional supervised learning problem since, in ADNI, there are no definitive labeled conversion examples. It is not unsupervised, either, since there are (labeled) ADs and Controls, as well as cognitive scores for MCIs. Prior works have defined MCI subclasses based on whether or not clinical scores significantly change from baseline. There are concerns with these definitions, however, since, e.g., most MCIs (and ADs) do not change from a baseline CDR = 0.5 at any subsequent visit in ADNI, even while physiological changes may be occurring. These works ignore rich phenotypical information in an MCI patient's brain scan and labeled AD and Control examples, in defining conversion. We propose an innovative definition, wherein an MCI is a converter if any of the patient's brain scans are classified "AD" by a Control-AD classifier. This definition bootstraps design of a second classifier, specifically trained to predict whether or not MCIs will convert. We thus predict whether an AD-Control classifier will predict that a patient has AD. Our results demonstrate that this definition leads not only to much higher prognostic accuracy than by-CDR conversion, but also to subpopulations more consistent with known AD biomarkers (including CSF markers). We also identify key prognostic brain region biomarkers.

Figure 1. AD-Control SVM score trajectories for MCI subjects.

(a) Nonconverters-by-CDR. (b) Converters-by-CDR.

Figure 2. A population of 298 MCI subjects in ADNI is shown here, broken up according to the two criteria discussed in Sec. 3.2.1: (a) by-CDR criterion, (b) by-trajectory criterion; (c) overlap shown.

Figure 3. Test set accuracy comparison of by-CDR and by-trajectory classification.

(a) Training/test set selection for by-CDR classification. (b) Training/test set selection for by-trajectory classification. (c) By-trajectory. Left: nonconverters; Right: converters. (d) By-CDR. Left: nonconverters; Right: converters.

Figure 4. Training/test set selection for by-trajectory, considering a larger training set size ( per class) and features.

Figure 5. For the hippocampus, by-trajectory (red) has larger histogram separation between converter (dashed line) and nonconverter (solid line) groups than by-CDR (blue).

To illustrate this more clearly, also shown is the Gaussian curve for each of these four subject groups (plotted based on group mean (m) and standard deviation (s) indicated in the figure legend with the same 0.001 scaling as the x-axis).

Figure 6. Test set misclassification rate during the course of feature elimination for: (a) the AD-Control classifier and (b) the CT-NT classifier.

Figure 7. Sorted retained voxel percentages for initial regions used to select final regions (Sec. 3.2.3): (a) AD-Control; b) CT-NT.