Abstract
A hierarchical clustering algorithm was applied to magnetic resonance images (MRI) of a cohort of 751 subjects having a mild cognitive impairment (MCI), 282 subjects having received Alzheimer's disease (AD) diagnosis, and 428 normal controls (NC). MRIs were preprocessed to gray matter density maps and registered to a stereotactic space. By first rendering the gray matter density maps comparable by regressing out age, gender, and years of education, and then performing the hierarchical clustering, we found clusters displaying structural features of typical AD, cortically-driven atypical AD, limbic-predominant AD, and early-onset AD (EOAD). Among these clusters, EOAD subjects displayed marked cortical gray matter atrophy and atrophy of the precuneus. Furthermore, EOAD subjects had the highest progression rates as measured with ADAS slopes during the longitudinal follow-up of 36 months. Striking heterogeneities in brain atrophy patterns were observed with MCI subjects. We found clusters of stable MCI, clusters of diffuse brain atrophy with fast progression, and MCI subjects displaying similar atrophy patterns as the typical or atypical AD subjects. Bidirectional differences in structural phenotypes were found with MCI subjects involving the anterior cerebellum and the frontal cortex. The diversity of the MCI subjects suggests that the structural phenotypes of MCI subjects would deserve a more detailed investigation with a significantly larger cohort. Our results demonstrate that the hierarchical agglomerative clustering method is an efficient tool in dividing a cohort of subjects with gray matter atrophy into coherent clusters manifesting different structural phenotypes.
Highlights
Alzheimer’s disease (AD) is the most common neurodegenerative disease and cause of dementia [1]
The cluster category was decided by dividing the interval from 1 to 3 into 3 wide subintervals, i.e., a cluster was an mild cognitive impairment (MCI) cluster when the average diagnosis was between 1.667 and 2.333; normal controls (NC) cluster if the average diagnosis was at most 1.666; and AD cluster when the average diagnosis was greater than 2.333. This categorization is possible as the distribution of numerically coded diagnoses within clusters never was bimodal, i.e., there were no clusters characterized by the absence of MCI subjects and containing both NC and AD subjects
The clustering algorithm was based on a voxel by voxel distances between gray densities of the magnetic resonance images (MRI) normalized to stereotactic space
Summary
Alzheimer’s disease (AD) is the most common neurodegenerative disease and cause of dementia [1]. Genetic variation and different environmental exposures lead to heterogeneities in neurodegenerative patterns. Finding and classifying these patterns (clusters) using sophisticated computer-aided tools and thereby grouping the subjects to more homogeneous groups can be clinically useful [1, 5,6,7]. Data clustering methods from applied mathematics have found increasing applications in neuroscience [8] The goal of these methods is to group or cluster the subjects by maximizing a certain similarity condition, which is typically a numerical metric
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