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SCCA-Ref: Novel Sparse Canonical Correlation Analysis with Reference to Discover Independent Spatial Associations Between White Matter Hyperintensities and Atrophy

机译:SCCA-Ref:新颖的稀疏典范相关性分析,以发现白质高信号和萎缩之间的独立空间关联

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White matter hyperintensities (WMH) and atrophy are com-mon 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.
机译:在神经退行性疾病以及健康衰老中,白质高信号(WMH)和萎缩是常见现象。但是,尚不清楚它们的共现是否归因于共同的风险因素。先前的工作分析了单个大脑区域之间的单变量关联,但没有分析多个区域的关节模式。我们提出了一种新方法,可以联合分析所有区域以发现WMH与萎缩之间的空间关联模式。单变量分析通常在单个WMH和萎缩变量的水平上校正共享的风险因素。我们的方法在多个区域的整个图案级别上采用了一种新颖的校正策略。此外,我们实行稀疏性以产生可解释的结果。来自莱茵兰研究的703名参与者的研究结果揭示了两种一致的空间关联模式。各个变量的校正不会产生本质上不同的模式。我们提出的多变量校正策略产生了不同的模式,因此表明它可能更适合多变量分析。

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