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 bio-markers 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 four clinical groups (AD, NC, Progressive-MCI and Stable-MCI) selected from Alzheimer's Disease Neuroimaging Initiative (ADNI) are measured by our recently-developed 4D (spatial+temporal) thickness measuring algorithm. It is found that the four clinical groups demonstrate very similar spatial distribution of GM loss on cortex. To fully utilizing the longitudinal information and better discriminate the subjects from four groups, especially between Stable-MCI and Progressive-MCI, three 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 ROIs. By combining the complementary information provided by features from all three different categories, two 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 at 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

Directed Progression Brain Networks in Alzheimer's Disease: Properties and Classification

“…In a sense, our classification results are not competitive with Li et al (2012), who attained over 95% accuracy; however, they used much more information in their classification. Not only did Li et al (2012) use five timepoints and over 200 features, but perhaps even more importantly, they used the raw thinning data directly in their discriminant analysis, which allowed their algorithm to use locational information about which specific region was thinning.…”

“…However, to compare thicknesses among different subjects, this procedure required normalization by personal characteristics, such as age and gender (Barnes et al, 2010;Sowell et al, 2007), as this affects baseline thicknesses, complicating the process. For longitudinal data, personalized (by subject) networks have been constructed using temporal correlations between regions (Li et al, 2012), which we discuss below.…”

A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer's Disease

“…In contrast to recent studies on the ADNI data that rely on combinations of complex features [7], [33], [53], [54], [55] or integrations of multiple modalities [5], [56], [14], the proposed biomarker is a single MRI-based interpretable feature. When the grading value is close to −1, it indicates that the subject is more characteristic of PMCI than SMCI and has a high possibility to convert to AD within 3 years while the grading value 1 means that this MCI subject will possibly remain stable within this period.…”

Intra-Rater and Inter-Rater Reliability of Brain Surface Intensity Model (BSIM)-Based Cortical Thickness Analysis Using 3T MRI

“…Regional cortical thinning has been extensively studied using cortical thickness analysis, and is a valuable imaging marker for AD, frontotemporal lobar degeneration, Lewy body disease, and other neurodegenerative diseases (7,(18)(19)(20)(21)(22)(23)(24). Longitudinal evaluation of cortical thinning is a potential tool for treatment response evaluation and for monitoring clinical progression of AD (25,26). Nevertheless, cortical thickness analysis methods have not been incorporated into radiological practice, primarily because the researchoriented Freesurfer method is time consuming and may need manual editing despite its fully automatic nature (7,8).…”