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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Detecting biological heterogeneity patterns in ADNI amnestic mild cognitive impairment based on volumetric MRI
Ali Ezzati1,2, Andrea R Zammit3, Christian Habeck4
1Department of Neurology, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY, 10461, USA. aezzati@einstein.yu.edu.
Abstract:
There is substantial biological heterogeneity among older adults with amnestic mild cognitive impairment (aMCI). We hypothesized that this heterogeneity can be detected solely based on volumetric MRI measures, which potentially have clinical implications and can improve our ability to predict clinical outcomes. We used latent class analysis (LCA) to identify subgroups among persons with aMCI (n = 696) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI), based on baseline volumetric MRI measures. We used volumetric measures of 10 different brain regions. The subgroups were validated with respect to demographics, cognitive performance, and other AD biomarkers. The subgroups were compared with each other and with normal and Alzheimer's disease (AD) groups with respect to baseline cognitive function and longitudinal rate of conversion. Four aMCI subgroups emerged with distinct MRI patterns: The first subgroup (n = 404), most similar to normal controls in volumetric characteristics and cognitive function, had the lowest incidence of AD. The second subgroup (n = 230) had the most similar MRI profile to early AD, along with poor performance in memory and executive function domains. The third subgroup (n = 36) had the highest global atrophy, very small hippocampus and worst overall cognitive performance. The fourth subgroup (n = 26) had the least amount of atrophy, however still had poor cognitive function specifically in in the executive function domain. Individuals with aMCI who were clinically categorized within one group other showed substantial heterogeneity based on MRI volumetric measures.
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