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.

Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band

“…Eyes-open resting state networks derived using MEG also have greater test-retest reliability than eyes-closed derived networks (Jin SH et al 2011). While some MEG/EEG studies do find differences between the two conditions (Jin SH et al 2011;Tan B et al 2013;Tagliazucchi E and H Laufs 2014;Miraglia F et al 2016;Yu Q et al 2016), overall, the differences between the eyes closed and eyes open conditions in all of these studies were small.…”

Maturation trajectories of cortical resting-state networks depend on the mediating frequency band

“…Eyes-open resting state networks derived using MEG also have greater test-retest reliability than eyes-closed derived networks (Jin et al, 2011). While some MEG/EEG studies do find differences between the two conditions (Jin et al, 2011; Tan et al, 2013; Tagliazucchi and Laufs, 2014; Miraglia et al, 2016; Yu et al, 2016), overall, the differences between the eyes closed and eyes open conditions in all of these studies were small. Another limitation of the proposed study is that we only had IQ measures available for a subset of the sample (N = 68), and no other behavioral measures uniformly across the sample.…”

From eyes‐closed to eyes‐open: Role of cholinergic projections in EC‐to‐EO alpha reactivity revealed by combining EEG and MRI

“…Interestingly, there was a moderate association between alpha reactivity and LA load along the BNM to ACC fiber tracts, although the fiber tracts linking BNM with visual cortex and that linking BNM with ACC belong to different major cholinergic pathways (Selden et al, ), further suggesting that BNM‐ACC interactions have implications for alpha reactivity. Other evidence linking ACC and alpha oscillations includes: Resting state BOLD activities in ACC was found to be negatively correlated with posterior alpha power (Laufs et al, ) and posterior alpha power difference between EC and EO during resting state was negatively correlated with hemodynamic responses and inter‐regional functional connectivity in ACC (Wu, Eichele, & Calhoun, ; Yu, Shi, Han, Gao, & Dan, ).…”