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Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study
Yu, Qingbao1; Wu, Lei1; Bridwell, David A.1; Erhardt, Erik B.2; Du, Yuhui1,3; He, Hao4; Chen, Jiayu1; Liu, Peng1,4,5; Sui, Jing1,6,7; Pearlson, Godfrey8,9,10; Calhoun, Vince D.1,4,9
2016-09-28
发表期刊FRONTIERS IN HUMAN NEUROSCIENCE
卷号10
文章类型Article
摘要The topological architecture of brain connectivity has been well characterized by graph theory based analysis. However, previous studies have primarily built brain graphs based on a single modality of brain imaging data. Here we develop a framework to construct multi-modal brain graphs using concurrent EEG-fMRI data which are simultaneously collected during eyes open (EC)) and eyes closed (EC) resting states. FMRI data are decomposed into independent components with associated time courses by group independent component analysis (ICA). EEG time series are segmented, and then spectral power time courses are computed and averaged within 5 frequency bands (delta; theta; alpha; beta; low gamma). EEG-fMRI brain graphs, with EEG electrodes and fMRI brain components serving as nodes, are built by computing correlations within and between fMRI ICA time courses and EEG spectral power time courses. Dynamic EEG-fMRI graphs are built using a sliding window method, versus static ones treating the entire time course as stationary. In global level, static graph measures and properties of dynamic graph measures are different across frequency bands and are mainly showing higher values in eyes closed than eyes open. Nodal level graph measures of a few brain components are also showing higher values during eyes closed in specific frequency bands. Overall, these findings incorporate fMRI spatial localization and EEG frequency information which could not be obtained by examining only one modality. This work provides a new approach to examine EEG-fMRI associations within a graph theoretic framework with potential application to many topics.
关键词Eeg-fmri Dynamic Multi-modal Brain Graph Ica
WOS标题词Science & Technology ; Social Sciences ; Life Sciences & Biomedicine
DOI10.3389/fnhum.2016.00476
关键词[WOS]RESTING-STATE FMRI ; FUNCTIONAL CONNECTIVITY ANALYSIS ; TIME-VARYING CONNECTIVITY ; ALPHA-RHYTHM ; MEMORY TASK ; COMMUNITY STRUCTURE ; COGNITIVE CONTROL ; NETWORK STRUCTURE ; HUMAN CONNECTOME ; GROUP PICA
收录类别SCI
语种英语
项目资助者National Institutes of Health (NIH)(P20GM103472 ; "100 Talents Plan" of Chinese Academy of Sciences ; state high-tech development plan of China (863)(2015AA020513) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060005) ; Chinese NSF(81471367 ; natural science foundation of Shanxi(2016021077) ; R01EB005846 ; 81471738) ; 1R01EB006841 ; 1R01DA040487 ; REB020407 ; EB000840 ; 5P20RR021938 ; R37 M1143775)
WOS研究方向Neurosciences & Neurology ; Psychology
WOS类目Neurosciences ; Psychology
WOS记录号WOS:000384153200001
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12662
专题脑网络组研究中心
作者单位1.Mind Res Network, Albuquerque, NM 87131 USA
2.Univ New Mexico, Dept Math & Stat, Albuquerque, NM 87131 USA
3.North Univ China, Sch Informat & Commun Engn, Taiyuan, Peoples R China
4.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
5.Xidian Univ, Sch Life Sci & Technol, Life Sci Res Ctr, Xian, Shanxi, Peoples R China
6.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
8.Olin Neuropsychiat Res Ctr, Hartford, CT USA
9.Yale Univ, Dept Psychiat, New Haven, CT 06520 USA
10.Yale Univ, Dept Neurobiol, New Haven, CT USA
推荐引用方式
GB/T 7714
Yu, Qingbao,Wu, Lei,Bridwell, David A.,et al. Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study[J]. FRONTIERS IN HUMAN NEUROSCIENCE,2016,10.
APA Yu, Qingbao.,Wu, Lei.,Bridwell, David A..,Erhardt, Erik B..,Du, Yuhui.,...&Calhoun, Vince D..(2016).Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study.FRONTIERS IN HUMAN NEUROSCIENCE,10.
MLA Yu, Qingbao,et al."Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study".FRONTIERS IN HUMAN NEUROSCIENCE 10(2016).
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