Laney Jonathan, Westlake Kelly P, Ma Sai, Woytowicz Elizabeth, Calhoun Vince D, Adalı Tülay
University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
University of Maryland School of Medicine, Baltimore, MD 21201, USA.
J Neurosci Methods. 2015 May 30;247:32-40. doi: 10.1016/j.jneumeth.2015.03.019. Epub 2015 Mar 20.
Recent studies using simulated functional magnetic resonance imaging (fMRI) data show that independent vector analysis (IVA) is a superior solution for capturing spatial subject variability when compared with the widely used group independent component analysis (GICA). Retaining such variability is of fundamental importance for identifying spatially localized group differences in intrinsic brain networks.
Few studies on capturing subject variability and order selection have evaluated real fMRI data. Comparison of multivariate components generated by multiple algorithms is not straightforward. The main difficulties are finding concise methods to extract meaningful features and comparing multiple components despite lack of a ground truth. In this paper, we present a graph-theoretical (GT) approach to effectively compare the ability of multiple multivariate algorithms to capture subject variability for real fMRI data for effective group comparisons. The GT approach is applied to components generated from fMRI data, collected from individuals with stroke, before and after a rehabilitation intervention.
IVA is compared with widely used GICA for the purpose of group discrimination in terms of GT features. In addition, masks are applied for motor related components generated by both algorithms.
Results show that IVA better captures subject variability producing more activated voxels and generating components with less mutual information in the spatial domain than Group ICA. IVA-generated components result in smaller p-values and clearer trends in GT features.
最近使用模拟功能磁共振成像（fMRI）数据的研究表明，与广泛使用的组独立成分分析（GICA）相比，独立向量分析（IVA）在捕获空间个体变异性方面是一种更优的方法。保留这种变异性对于识别内在脑网络中空间定位的组间差异至关重要。
很少有关于捕获个体变异性和顺序选择的研究评估真实的fMRI数据。比较多种算法生成的多变量成分并非易事。主要困难在于找到简洁的方法来提取有意义的特征，以及在缺乏真实对照的情况下比较多个成分。在本文中，我们提出一种基于图论（GT）的方法，以有效比较多种多变量算法捕获真实fMRI数据个体变异性的能力，从而进行有效的组间比较。该GT方法应用于从患有中风的个体在康复干预前后收集的fMRI数据生成的成分。
就GT特征而言，将IVA与广泛使用的GICA进行组间区分比较。此外，对两种算法生成的与运动相关的成分应用掩码。
结果表明，与组独立成分分析相比，IVA能更好地捕获个体变异性，在空间域中产生更多激活的体素，生成的成分具有更少的互信息。IVA生成的成分导致更小的p值和更清晰的GT特征趋势。
