Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks.
Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks.
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DOI：
10.1016/j.neuroimage.2015.12.001
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发表时间：
2016-02-15
期刊：
影响因子：
5.7
通讯作者：
Sporns O
中科院分区：
文献类型：
--
作者：
Betzel RF;Fukushima M;He Y;Zuo XN;Sporns O
我们研究了长时间内估计的静息态 fMRI 功能连接与较短时间间隔内估计的时变功能连接之间的关系。我们表明，使用皮尔逊相关性来估计功能连接意味着功能连接在短时间尺度上的波动范围受到其在较长尺度上的连接强度所施加的统计约束。我们提出了一种估计随时间变化的功能连接的方法，旨在缓解这个问题，并允许我们识别功能连接意外强或弱的事件。我们将此方法应用于 N = 80 名参与者记录的数据，结果表明，意外的强/弱连接的数量会随着时间的推移而波动，并且这些变化与时变功能连接中高和低模块化的间歇期相一致。我们还发现，在相对静止期间，与默认模式网络相关的区域倾向于加入具有注意力、控制和初级感觉系统的社区。相反，在许多连接意外强/弱的时期，默认模式区域会分离并形成不同的模块。最后，我们继续表明，虽然所有功能连接有时会比预期表现得更强（更正相关）或更弱（更负相关），但少数连接（主要在视觉和躯体运动网络中）这样做的次数不成比例。我们的统计方法可以根据长期平均值检测出比预期波动更大或更少的功能连接，并且可以在未来的研究中用于表征随时间变化的功能连接的时空模式。
We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson’s correlation to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are unexpectedly strong or weak. We apply this method to data recorded from N = 80 participants, and show that the number of unexpectedly strong/weak connections fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with default mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form distinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of time-varying functional connectivity.