Functional connectivity change as shared signal dynamics.
Functional connectivity change as shared signal dynamics.
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DOI：
10.1016/j.jneumeth.2015.11.011
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发表时间：
2016-02-01
影响因子：
3
通讯作者：
Anticevic A
中科院分区：
文献类型：
--
作者：
Cole MW;Yang GJ;Murray JD;Repovš G;Anticevic A
越来越多的神经科学研究通过关注功能连接的差异--群体、个体、时间窗或任务条件之间的差异--来获得见解。我们发现使用模拟，可以通过放弃方差归一化来获得对这种差异的额外见解，这是大多数功能连接性测量所使用的程序。模拟表明，这些功能连接性测量对时间序列中的独立波动（非共享信号）的增加敏感，从而一致地降低功能连接性估计（例如，相关性），即使这样的变化与那些时间序列之间的对应波动（共享信号）无关。这与功能连通性作为区域间互动量的共同概念不一致。模拟显示，没有方差归一化的相关性版本-协方差-能够隔离共享信号的差异，增加观察到的功能连接变化的可解释性。模拟还揭示了非标准化方法的问题，导致“协方差结合”方法结合了标准化和非标准化方法的优点。我们发现，协方差和协方差结合方法可以检测各种任务的功能连接变化，并在临床和非临床功能MRI数据集中休息。我们验证了使用各种任务和休息在临床和非临床功能性MRI数据集，它的问题，在实践中是否使用相关性，协方差，或协方差结合方法。这些结果证明了分离共享信号变化的实际和理论效用，提高了解释观察到的功能连接变化的能力。
An increasing number of neuroscientific studies gain insights by focusing on differences in functional connectivity – between groups, individuals, temporal windows, or task conditions. We found using simulations that additional insights into such differences can be gained by forgoing variance normalization, a procedure used by most functional connectivity measures. Simulations indicated that these functional connectivity measures are sensitive to increases in independent fluctuations (unshared signal) in time series, consistently reducing functional connectivity estimates (e.g., correlations) even though such changes are unrelated to corresponding fluctuations (shared signal) between those time series. This is inconsistent with the common notion of functional connectivity as the amount of inter-region interaction. Simulations revealed that a version of correlation without variance normalization – covariance – was able to isolate differences in shared signal, increasing interpretability of observed functional connectivity change. Simulations also revealed cases problematic for non-normalized methods, leading to a “covariance conjunction” method combining the benefits of both normalized and non-normalized approaches. We found that covariance and covariance conjunction methods can detect functional connectivity changes across a variety of tasks and rest in both clinical and non-clinical functional MRI datasets. We verified using a variety of tasks and rest in both clinical and non-clinical functional MRI datasets that it matters in practice whether correlation, covariance, or covariance conjunction methods are used. These results demonstrate the practical and theoretical utility of isolating changes in shared signal, improving the ability to interpret observed functional connectivity change.