Modeling Alzheimer's Disease: a Statistical Approach to Understanding Pathogenesis Across Brain Regions
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Date
2015-11-12
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Abstract
With a rapidly aging U.S. population, Alzheimer’s disease is a growing public health concern. Key to the success of future Alzheimer’s disease psychopharmacological clinical trials is understanding its complex pathogenesis with sophisticated statistical models. Presently, the literature has several models of Alzheimer’s disease pathogenesis that utilize fluctuations in biomarkers as the focal point of understanding progression across symptomatic states. Recent research has implicated regional volumetric changes in specific brain regions as one such biomarker of interest. However, the principal models are fundamentally theoretical, and these models are not derived purely from samples of clinical populations within a sophisticated statistical framework. Thus, these theoretical models are yet to be completely validated. Additionally, although some models have analyzed volumetric data from specific brain regions, no such model has assessed volumetric changes cross-regionally to document Alzheimer’s disease pathogenesis relative to healthy controls in a large, robust sample.
This model analyzes the relationship among four crucial brain regions impacted by Alzheimer’s disease—the hippocampus, entorhinal cortex, fusiform gyrus, and the middle temporal gyrus— using cross-sectional MRI data from a large study of Alzheimer’s disease patients and controls.
Results demonstrate a sequence through which we can understand where along the Alzheimer’s disease symptom spectrum each brain region becomes pathological relative to healthy controls. Understanding the neurodegenerative sequence of the hippocampus, entorhinal cortex, fusiform gyrus, and middle temporal gyrus is critical to our understanding of Alzheimer’s disease pathogenesis. Clinical implications include earlier and more accurate differential diagnosis, elucidation of disease subtypes, and future directions for pharmacological trials.
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Alzheimer's disease, statistical model