Multi-timescale hybrid components of the functional brain connectome: A bimodal EEG-fMRI decomposition

Abstract

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FC_EEG to second range of FC_fMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals.

Keywords: Brain connectivity; Concurrent EEG-fMRI; Human connectome; ICA.

Figure 1.

Construction of EEG and fMRI connectomes. EEG and fMRI data were parcellated into the 148 cortical regions of the Destrieux atlas as follows. EEG was source reconstructed to a fine-grained grid, and the time courses of the solution points were averaged per region and per subject. For fMRI, the BOLD signal time course was averaged over the voxels in each region for each subject. Pearson correlation of the mean fMRI-BOLD time courses and imaginary part of the coherency of averaged EEG source signals were used to build functional connectivity matrices/connectomes (FC_EEG and FC_fMRI) for each subject.

Figure 2.

The upper triangular of each individual fMRI functional connectivity (FC_fMRI) matrix (left, top) and lower triangular of each correspondent EEG functional connectivity (FC_EEG) matrix (left, bottom) are added to a matrix where rows are the subjects times the EEG frequency bands and columns are their vectorized hybrid (fMRI-EEG) connectivity patterns; the fMRI vector is repeated and concatenated with each of the five canonical frequency bands (middle). The ICA algorithm extracts the n independent components (i.e., ICs) associated to the whole population and their relative ICA mixing weights across the subjects times the frequency bands (right).

Figure 3.

Impact of the choice of free parameters on the consistency across datasets. Matrices visualize the intercorrelation between the IC strengths of the two datasets by using different parameter sets, shown separately for the EEG and fMRI parts of the ICN-FG and VIS-FS components (A–D). We tested all combinations, keeping principal components with 75–90% of the variance, followed by an independent component analysis keeping 5–20 ICs (e.g., IC5PCA75 depicts an analysis using 5 ICs and keeping all PCs that cumulatively explain 75% of the data variance). Rows with consistent correlation of zero mean that in one of the datasets, the preceding separate ICC procedure had found no component passing stability requirements for this parameter combination. There is significant (p <10⁻¹⁰⁰) spatial correlation of IC matrices between datasets for all parameters for which stable ICs had been identified (average correlation for the ICN-FG component: fMRI part r = 0.27; EEG part r = 0.35; and for the VIS-FS component: fMRI part r = 0.45; EEG part r = 0.44).

Figure 4.

(A) Subject- and band-specific mixing weights of the hybrid EEG-fMRI ICN-FG component. (B) fMRI part of the ICN-FG component. (C) EEG part of the ICN-FG component. Note that as we stacked up all frequency-specific EEG connectomes for each subject (cf. Figure 2), we obtained a single EEG component part (C) associated with an ICA mixing weight for each subject and frequency (represented by one bar each in A). (D–F) These panels visualize equivalent aspects for the VIS-FS component. All panels represent the main dataset (for generalization data see Supporting Information Figure S3. Colorbars have been saturated at 95th and 5th percentile for better comparison with Figures 5 and 6). VIS = visual; SM = somatomotor; DA = dorsal attention; VA = ventral attention; L = limbic; FP = fronto-parietal; DMN = default mode network.

Figure 5.

Connections with highest nodal strength (99th percentile) for the ICN-FG component: circle graphs show all connections between different ICN networks for fMRI (A) and EEG (D); matrices summarize the number of connections falling into each ICN-ICN pair for fMRI (B) and EEG (E); brain renderings show strongest connections of the components on a canonical reconstructed cortical surface for the fMRI (C) and EEG (F) part of the hybrid component. Data correspond to the main dataset; for the results of the generalization dataset see Supporting Information Figure S4. VIS = visual; SM = somatomotor; DA = dorsal attention; VA = ventral attention; L = limbic; FP = fronto-parietal; DMN = default mode network.

Figure 6.

Connections with highest nodal strength (99th percentile) for the VIS-FS component: circle graphs show all connections between different ICN networks for fMRI (A) and EEG (D); matrices summarize the number of connections falling into each ICN-ICN pair for fMRI (B) and EEG (E); brain renderings show strongest connections of the components on a canonical reconstructed cortical surface for the fMRI (C) and EEG (F) part of the hybrid component. Data correspond to the main dataset; for the results of the generalization dataset see Supporting Information Figure S5. VIS = visual; SM = somatomotor; DA = dorsal attention; VA = ventral attention; L = limbic; FP = fronto-parietal; DMN = default mode network.