Discriminant analysis of longitudinal cortical thickness changes in Alzheimer's disease using dynamic and network features

Abstract

Neuroimage measures from magnetic resonance (MR) imaging, such as cortical thickness, have been playing an increasingly important role in searching for biomarkers of Alzheimer's disease (AD). Recent studies show that, AD, mild cognitive impairment (MCI) and normal control (NC) can be distinguished with relatively high accuracy using the baseline cortical thickness. With the increasing availability of large longitudinal datasets, it also becomes possible to study the longitudinal changes of cortical thickness and their correlation with the development of pathology in AD. In this study, the longitudinal cortical thickness changes of 152 subjects from 4 clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) are measured by our recently developed 4 D (spatial+temporal) thickness measuring algorithm. It is found that the 4 clinical groups demonstrate very similar spatial distribution of grey matter (GM) loss on cortex. To fully utilize the longitudinal information and better discriminate the subjects from 4 groups, especially between Stable-MCI and Progressive-MCI, 3 different categories of features are extracted for each subject, i.e., (1) static cortical thickness measures computed from the baseline and endline, (2) cortex thinning dynamics, such as the thinning speed (mm/year) and the thinning ratio (endline/baseline), and (3) network features computed from the brain network constructed based on the correlation between the longitudinal thickness changes of different regions of interest (ROIs). By combining the complementary information provided by features from the 3 categories, 2 classifiers are trained to diagnose AD and to predict the conversion to AD in MCI subjects, respectively. In the leave-one-out cross-validation, the proposed method can distinguish AD patients from NC at an accuracy of 96.1%, and can detect 81.7% (AUC = 0.875) of the MCI converters 6 months ahead of their conversions to AD. Also, by analyzing the brain network built via longitudinal cortical thickness changes, a significant decrease (p < 0.02) of the network clustering coefficient (associated with the development of AD pathology) is found in the Progressive-MCI group, which indicates the degenerated wiring efficiency of the brain network due to AD. More interestingly, the decreasing of network clustering coefficient of the olfactory cortex region was also found in the AD patients, which suggests olfactory dysfunction. Although the smell identification test is not performed in ADNI, this finding is consistent with other AD-related olfactory studies.

Figure 1

An example of generating the spatial-adaptive ROIs for baseline thickness. (a) Correlation map between the baseline thickness and the classification labels; (b) The ROIs obtained via watershed segmentation corresponding to (a); (c) The ROIs generated for baseline thickness shown in different coronal slices.

Figure 2

Two different methods for thickness-based brain network construction. (a) In the population network, the pairwise connection between two ROIs is established, if the thickness measures from different subjects show significant correlation between these two ROIs; (b) In the individual network, the pairwise connection between two ROIs is established, if the thickness measures from different time-points of the same subject show significant correlation between these two ROIs.

Figure 3

Average correlation between thickness and CDR-SOB scores within the P-MCI subjects (each has 5 time-points). The higher correlation implies the improved accuracy using the 4D thickness measurement (Li et al., 2010). From left to right: the left, inferior, superior and right views, respectively. The bottom shows the T-score (p < 0.05, FDR corrected) where 4D method gives significant high correlation than the 3D method.

Figure 4

Cortical thinning patterns in different clinical groups. The fourth row shows the location where significant (p < 0.05, FDR corrected) cortex thinning (by comparing baseline and endline) is detected.

Figure 5

Cortical areas with higher thinning speed in P-MCI group than in S-MCI group. From left to right, T-scores (p < 0.05, FDR-corrected) are shown in left, inferior, superior and right views, respectively.

Figure 6

Discriminative power of five different types of features in distinguishing NC and AD. High correlation indicates high discrimination power.

Figure 7

Discriminative power of five different types of features in distinguishing P-MCI and S-MCI. High correlation indicates high discrimination power.

Figure 8

Receiver Operation Curve (ROC) of different feature combinations.

Figure 9

Decrease of the average clustering coefficient (on 90 nodes) observed in 4 clinical groups.