Nickerson Lisa D, Smith Stephen M, Öngür Döst, Beckmann Christian F
Applied Neuroimaging Statistics Lab, McLean HospitalBelmont, MA, USA; Department of Psychiatry, Harvard Medical School, Harvard UniversityBoston, MA, USA.
Nuffield Department of Clinical Neurosciences, Oxford University Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of Oxford Oxford, UK.
Front Neurosci. 2017 Mar 13;11:115. doi: 10.3389/fnins.2017.00115. eCollection 2017.
Independent Component Analysis (ICA) is one of the most popular techniques for the analysis of resting state FMRI data because it has several advantageous properties when compared with other techniques. Most notably, in contrast to a conventional seed-based correlation analysis, it is model-free and multivariate, thus switching the focus from evaluating the functional connectivity of single brain regions identified a priori to evaluating brain connectivity in terms of all brain resting state networks (RSNs) that simultaneously engage in oscillatory activity. Furthermore, typical seed-based analysis characterizes RSNs in terms of spatially distributed patterns of correlation (typically by means of simple Pearson's coefficients) and thereby confounds together amplitude information of oscillatory activity and noise. ICA and other regression techniques, on the other hand, retain magnitude information and therefore can be sensitive to both changes in the spatially distributed nature of correlations (differences in the spatial pattern or "shape") as well as the amplitude of the network activity. Furthermore, motion can mimic amplitude effects so it is crucial to use a technique that retains such information to ensure that connectivity differences are accurately localized. In this work, we investigate the dual regression approach that is frequently applied with group ICA to assess group differences in resting state functional connectivity of brain networks. We show how ignoring amplitude effects and how excessive motion corrupts connectivity maps and results in spurious connectivity differences. We also show how to implement the dual regression to retain amplitude information and how to use dual regression outputs to identify potential motion effects. Two key findings are that using a technique that retains magnitude information, e.g., dual regression, and using strict motion criteria are crucial for controlling both network amplitude and motion-related amplitude effects, respectively, in resting state connectivity analyses. We illustrate these concepts using realistic simulated resting state FMRI data and data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.
独立成分分析（ICA）是静息态功能磁共振成像（fMRI）数据分析中最常用的技术之一，因为与其他技术相比，它具有几个优势特性。最显著的是，与传统的基于种子点的相关性分析不同，它是无模型且多变量的，因此将重点从评估先验确定的单个脑区的功能连接性，转变为根据同时参与振荡活动的所有脑静息态网络（RSN）来评估脑连接性。此外，典型的基于种子点的分析是根据相关性的空间分布模式（通常通过简单的皮尔逊系数）来表征RSN，从而将振荡活动的幅度信息和噪声混淆在一起。另一方面，ICA和其他回归技术保留了幅度信息，因此对相关性的空间分布性质的变化（空间模式或“形状”的差异）以及网络活动的幅度都可能敏感。此外，运动可以模拟幅度效应，因此使用一种保留此类信息的技术至关重要，以确保连接性差异能够准确地定位。在这项工作中，我们研究了经常与组ICA一起应用的双重回归方法，以评估脑网络静息态功能连接性的组间差异。我们展示了忽略幅度效应以及过度运动会如何破坏连接性图谱并导致虚假的连接性差异。我们还展示了如何实施双重回归以保留幅度信息，以及如何使用双重回归输出识别潜在的运动效应。两个关键发现是，在静息态连接性分析中，使用保留幅度信息的技术（例如双重回归）以及使用严格的运动标准分别对于控制网络幅度和与运动相关的幅度效应至关重要。我们使用逼真的模拟静息态fMRI数据以及在健康受试者、双相情感障碍患者和精神分裂症患者中获取的数据来说明这些概念。
