Liang Xiaoyun, Vaughan David N, Connelly Alan, Calamante Fernando
The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia.
Department of Neurology, Austin Health, Melbourne, VIC, Australia.
Brain Topogr. 2018 May;31(3):364-379. doi: 10.1007/s10548-017-0615-6. Epub 2017 Dec 29.
The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.
估计功能网络的传统方法主要基于皮尔逊相关性以及经典的费舍尔Z检验。一般来说，网络通常在个体层面进行计算，随后进行汇总以获得组层面的网络。然而，这种估计的网络不可避免地受到个体间固有巨大变异性的影响。最近有研究表明，一种带有稳定性选择的联合图形模型（JGMSS）方法可以通过从一组数据中同时估计个体层面的网络，有效降低主要由混杂变异引起的个体间变异性。然而，当比较两组数据时，其优势可能会受到影响，因为JGMSS在应用于从给定组估计网络时对其他组是盲目的。我们提出了一种新的方法，即使用组融合多重图形套索结合稳定性选择，从两组数据中稳健地估计网络，称为GMGLASS。具体而言，通过同时估计组内相似网络和组间差异，可以解决估计的个体网络中与现有方法（如费舍尔Z检验）固有相关的个体间变异性问题，以及JGMSS在组间比较中忽略组间信息的相关问题。为了评估GMGLASS在一些关键网络指标方面的性能，以及与JGMSS和费舍尔Z检验进行比较，我们将它们应用于模拟数据和体内数据。作为一种针对组间比较研究的方法，我们的研究在每个案例中涉及两组，即正常对照组和患者组；对于体内数据，我们关注一组右侧颞叶内侧癫痫患者。
