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Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks
Betzel, Richard F.1,2; Fukushima, Makoto2; He, Ye4,5; Zuo, Xi-Nian4,5; Sporns, Olaf2,3
2016-02-15
发表期刊NEUROIMAGE
ISSN1053-8119
文章类型Article
卷号127期号:0页码:287-297
摘要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. (C) 2015 The Authors. Published by Elsevier Inc.
关键词Networks Dynamic functional connectivity Modularity
DOI10.1016/j.neuroimage.2015.12.001
收录类别SCI
语种英语
项目资助者National Science Foundation/Integrative Graduate Education ; Research Traineeship Training Program in the Dynamics of Brain-Body-Environment Systems at the Indiana University ; NIH(1 R01 AT009036-01) ; Uehara Memorial Foundation ; National Basic Research Program (973)(2015CB35170) ; Major Joint Fund for International Cooperation and Exchange of the National Natural Science Foundation(81220108014) ; Chinese Academy of Sciences K.C. Wong Education Foundation
WOS研究方向Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号WOS:000369952900026
WOS标题词Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]CONNECTIVITY DYNAMICS ; INTERHEMISPHERIC COORDINATION ; COMMUNITY STRUCTURE ; MOTION ARTIFACT ; FMRI ; ORGANIZATION ; EEG ; CONNECTOMICS ; REPERTOIRE ; EMERGENCE
引用统计
文献类型期刊论文
条目标识符http://ir.psych.ac.cn/handle/311026/19579
专题中国科学院行为科学重点实验室
作者单位1.Univ Penn, Dept Bioengn, Sch Engn & Appl Sci, Philadelphia, PA 19104 USA
2.Indiana Univ, Psychol & Brain Sci, Bloomington, IN 47405 USA
3.Indiana Univ, Network Sci Inst, Bloomington, IN 47405 USA
4.Chinese Acad Sci, Key Lab Behav Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China
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GB/T 7714
Betzel, Richard F.,Fukushima, Makoto,He, Ye,et al. Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks[J]. NEUROIMAGE,2016,127(0):287-297.
APA Betzel, Richard F.,Fukushima, Makoto,He, Ye,Zuo, Xi-Nian,&Sporns, Olaf.(2016).Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks.NEUROIMAGE,127(0),287-297.
MLA Betzel, Richard F.,et al."Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks".NEUROIMAGE 127.0(2016):287-297.
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