Abstract
White matter hyperintensities (WMH) and atrophy are common findings in neurodegenerative diseases as well as healthy aging. However, it is not clear whether their co-occurrence is due to shared risk factors. Previous work has analyzed univariate associations between individual brain regions but not joint patterns over multiple regions. We propose a new method that jointly analyzes all the regions to discover spatial association patterns between WMH and atrophy. Univariate analyses typically correct for shared risk factors at the level of individual WMH and atrophy variables. Our method incorporates a novel correction strategy at the level of the entire pattern over multiple regions. Furthermore, we enforce sparsity to yield interpretable results. Results in a cohort of 703 participants from the Rhineland Study reveal two consistent spatial association patterns. Correction of individual variables did not yield qualitatively different patterns. Our proposed multi-variate correction strategy yielded different patterns thus, suggesting that it might be more appropriate for multi-variate analysis.
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Sanroma, G. et al. (2018). SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy. In: Shi, Y., Suk, HI., Liu, M. (eds) Machine Learning in Medical Imaging. MLMI 2018. Lecture Notes in Computer Science(), vol 11046. Springer, Cham. https://doi.org/10.1007/978-3-030-00919-9_10
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DOI: https://doi.org/10.1007/978-3-030-00919-9_10
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