Abstract
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Highlights
Many neurodegenerative diseases, such as Alzheimer (AD), are characterized by macroscopic changes in the brain morphology, which can be visualized using magnetic resonance (MR) imaging
Cross-sectional studies aim at extracting brain changes that are specific for certain subgroups within a large heterogeneous set of brain MR images by analyzing and comparing the brain MR images of these different subgroups [1]
The subgroups of brain MR images are defined in advance, based on clinical prior knowledge
Summary
Many neurodegenerative diseases, such as Alzheimer (AD), are characterized by macroscopic changes in the brain morphology, which can be visualized using magnetic resonance (MR) imaging. Cross-sectional studies aim at extracting brain changes that are specific for certain subgroups within a large heterogeneous set of brain MR images (e.g. healthy, multiple diseases, disease-stages, etc.) by analyzing and comparing the brain MR images of these different subgroups [1]. As such, they can deliver reliable and measurable image features (biomarkers) indicative for a specific disease and/or disease stage that can contribute to early diagnosis and to the study of the neurodegenerative disease. We present a probabilistic framework for cross-sectional analysis that simultaneously determines the subgroups and the subgroup-specific image features for a heterogeneous data set, based on the morphology of the images
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