Karas Marta, Brzyski Damian, Dzemidzic Mario, Goñi Joaquín, Kareken David A, Randolph Timothy W, Harezlak Jaroslaw
615 N. Wolfe Street, Suite E3039, Baltimore, MD 21205, Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health.
1025 E. 7th Street, Suite E112, Bloomington, IN 47405, Department of Epidemiology and Biostatistics, Indiana University Bloomington.
Stat Biosci. 2019 Apr;11(1):47-90. doi: 10.1007/s12561-017-9208-x. Epub 2017 Dec 6.
One of the challenging problems in brain imaging research is a principled incorporation of information from different imaging modalities. Frequently, each modality is analyzed separately using, for instance, dimensionality reduction techniques, which result in a loss of mutual information. We propose a novel regularization-method to estimate the association between the brain structure features and a scalar outcome within the linear regression framework. Our regularization technique provides a principled approach to use external information from the structural brain connectivity and inform the estimation of the regression coefficients. Our proposal extends the classical Tikhonov regularization framework by defining a penalty term based on the structural connectivity-derived Laplacian matrix. Here, we address both theoretical and computational issues. The approach is first illustrated using simulated data and compared with other penalized regression methods. We then apply our regularization method to study the associations between the alcoholism phenotypes and brain cortical thickness using a diffusion imaging derived measure of structural connectivity. Using the proposed methodology in 148 young male subjects with a risk for alcoholism, we found a negative associations between cortical thickness and drinks per drinking day in bilateral caudal anterior cingulate cortex, left lateral OFC and left precentral gyrus.
脑成像研究中具有挑战性的问题之一是如何有原则地整合来自不同成像模态的信息。通常，每种模态会分别使用例如降维技术进行分析，这会导致互信息的损失。我们提出了一种新颖的正则化方法，用于在线性回归框架内估计脑结构特征与标量结果之间的关联。我们的正则化技术提供了一种有原则的方法，可利用来自脑结构连通性的外部信息来为回归系数的估计提供信息。我们的提议通过基于结构连通性导出的拉普拉斯矩阵定义惩罚项，扩展了经典的蒂霍诺夫正则化框架。在此，我们解决了理论和计算问题。该方法首先使用模拟数据进行说明，并与其他惩罚回归方法进行比较。然后，我们应用我们的正则化方法，使用扩散成像导出的结构连通性测量值，研究酒精中毒表型与脑皮质厚度之间的关联。在148名有酒精中毒风险的年轻男性受试者中使用所提出的方法，我们发现双侧尾侧前扣带回皮质、左侧外侧眶额皮质和左侧中央前回的皮质厚度与每日饮酒量之间存在负相关。
