title: Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study.
abstract: 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 (EO) 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.
authors.affiliation: The Mind Research Network Albuquerque, NM, USA.
authors.forename: Qingbao
authors.initials: Q
authors.lastname: Yu
authors.affiliation: The Mind Research Network Albuquerque, NM, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Wu
authors.affiliation: The Mind Research Network Albuquerque, NM, USA.
authors.forename: David A
authors.initials: DA
authors.lastname: Bridwell
authors.affiliation: Department of Mathematics and Statistics, University of New Mexico Albuquerque, NM, USA.
authors.forename: Erik B
authors.initials: EB
authors.lastname: Erhardt
authors.affiliation: The Mind Research NetworkAlbuquerque, NM, USA; School of Information and Communication Engineering, North University of ChinaTaiyuan, China.
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Du
authors.affiliation: Department of Electrical and Computer Engineering, University of New Mexico Albuquerque, NM, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: He
authors.affiliation: The Mind Research Network Albuquerque, NM, USA.
authors.forename: Jiayu
authors.initials: J
authors.lastname: Chen
authors.affiliation: The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Life Science Research Center, School of Life Sciences and Technology, Xidian UniversityShanxi, China.
authors.forename: Peng
authors.initials: P
authors.lastname: Liu
authors.affiliation: The Mind Research NetworkAlbuquerque, NM, USA; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.
authors.forename: Jing
authors.initials: J
authors.lastname: Sui
authors.affiliation: Olin Neuropsychiatry Research CenterHartford, CT, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA; Department of Neurobiology, Yale UniversityNew Haven, CT, USA.
authors.forename: Godfrey
authors.initials: G
authors.lastname: Pearlson
authors.affiliation: The Mind Research NetworkAlbuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New MexicoAlbuquerque, NM, USA; Department of Psychiatry, Yale UniversityNew Haven, CT, USA.
authors.forename: Vince D
authors.initials: VD
authors.lastname: Calhoun
keywords: EEG-fMRI; ICA; brain graph; dynamic; multi-modal