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

Jonathan Wirsich,Anne-Lise Giraud,Enrico Amico,Joaquín Goñi,Sepideh Sadaghiani

doi:10.1162/netn_a_00135

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 FCEEG to second range of FCfMRI. 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.

Highlights

The advances in neuroimaging in the last decades have brought new important insights on brain functioning by using both electroencephalography (EEG) data and functional magnetic resonance imaging data extracted from human brain
Figure 3) we observed two to four stable reoccurring components in the main dataset, whereas two stable components reoccurred in the generalization dataset
Two stable components were appearing in both datasets (Figure 3, Supporting Information Figures S1 and S2): 1. We observed an Intrinsic Connectivity Network-Frequency-General component (ICNFG) that captures all the main within-network connectivity in the intrinsic connectivity networks (ICNs) described by Yeo et al (2011) (Figure 4 and Figure 5)

Summary

Introduction

The advances in neuroimaging in the last decades have brought new important insights on brain functioning by using both electroencephalography (EEG) data and functional magnetic resonance imaging (fMRI) data extracted from human brain. EEG provides a direct measure of neuronal activity with high temporal resolution but is limited by the spatial coverage of electrodes on the scalp and by volume conductance. FMRI provides a high-resolution estimate of brain function with whole-brain coverage, with limited temporal reliability restricted by the slow filter of the hemodynamic response (Logothetis et al, 2001) and the time needed to acquire a whole-brain image (typically in the range of 1–3 seconds). When looking at the whole-brain functional connectivity of the human brain, a stable pattern of interconnected intrinsic connectivity networks (ICNs) arise that closely resemble co-activation patterns observed during cognitive tasks (Damoiseaux et al, 2006; Fox et al, 2005; Yeo et al, 2011)

Methods

Results

Conclusion