Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses.
Using Dual Regression to Investigate Network Shape and Amplitude in Functional Connectivity Analyses.
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DOI：
10.3389/fnins.2017.00115
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发表时间：
2017
影响因子：
4.3
通讯作者：
Beckmann CF
中科院分区：
文献类型：
--
作者：
Nickerson LD;Smith SM;Öngür D;Beckmann CF
独立成分分析（伊卡）是静息态功能磁共振成像数据分析中最流行的技术之一，因为它具有许多优点时，与其他技术相比。最值得注意的是，与传统的基于种子的相关性分析相比，它是无模型的和多变量的，因此将重点从评估先验识别的单个大脑区域的功能连接转换为评估同时参与振荡活动的所有大脑静息状态网络（RSN）的大脑连接。此外，典型的基于种子的分析表征RSN的空间分布模式的相关性（通常通过简单的皮尔逊系数），从而混淆在一起的振荡活动和噪声的振幅信息。另一方面，伊卡和其他回归技术保留了幅度信息，因此可以对相关性的空间分布性质（空间模式或“形状”的差异）以及网络活动的幅度的变化敏感。此外，运动可以模拟振幅效应，因此使用保留此类信息的技术以确保准确定位连接差异至关重要。在这项工作中，我们研究的双重回归方法，经常与组伊卡评估组之间的差异，在静息状态下的功能连接的大脑网络。我们将展示如何忽略振幅效应以及过度运动如何破坏连接图并导致虚假的连接差异。我们还展示了如何实现二元回归来保留幅度信息，以及如何使用二元回归输出来识别潜在的运动效应。两个关键的发现是，使用一种保留震级信息的技术，在静息状态连接性分析中，双重回归和使用严格的运动标准对于分别控制网络振幅和运动相关振幅效应都是至关重要的。我们说明这些概念，使用现实的模拟静息状态的功能磁共振成像数据和在健康受试者和双相情感障碍和精神分裂症患者体内获得的数据。
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 in vivo data acquired in healthy subjects and patients with bipolar disorder and schizophrenia.