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

Wirsich, Jonathan; Amico, Enrico; Guye, Maxime; Ranjeva, Jean‐Philippe; Giraud, Anne-Lise; Goñi, Joaquín; Sadaghiani, Sepideh

doi:10.1101/800797

Cited by 4 publications

(16 citation statements)

References 56 publications

(91 reference statements)

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“…This suggests that part of the EEG FC is distinct from fMRI FC. This observation has also been confirmed by a study analysing phase-coupling ICA networks ( Wirsich et al, 2020 ). It is plausible that phase coupling FC captures networks that are distinct from fMRI, while amplitude coupling FC capture similar networks ( Brookes et al, 2011a , 2011b ; Hiltunen et al, 2014 ; Hipp and Siegel, 2015 ; Liu et al, 2018 , 2017 ; Pasquale et al, 2010 ).…”

Section: Discussionsupporting

confidence: 67%

“…Given the different physiological origin of EEG and fMRI as well as differences in connectivity measures ( Haufe et al, 2018 ; Siems and Siegel, 2020 ; Wirsich et al, 2020 ), it is not surprising that we do not find any similarities between the connectivity matrices. However, it has been shown that the two measures capture complementary information of connectivity ( Wirsich et al, 2017 ).…”

Section: Discussionmentioning

confidence: 80%

“…The differences in FC between several EEG and fMRI measures have been investigated extensively ( Colclough et al, 2016 ; Demuru et al, 2020 ; Siems and Siegel, 2020 ; Wirsich et al, 2020 ). Here wefind that the structure of the FC of EEG differs from that of fMRI ( Fig.…”

Section: Discussionmentioning

confidence: 99%

“…They have been used to characterize neuronal interactions ( Fries, 2005 ), although more recent work shows that spurious interactions remain a concern ( Palva et al, 2018b ). There is some evidence that patterns of phase-coupling are similar to conventional FC in fMRI ( Wirsich et al, 2017 ), but some components of these networks also appear to differ from fMRI ( Wirsich et al, 2020 ). Phase-coupling has also been associated with some phenotypes such as age ( He et al, 2019 ; Lavanga et al, 2018 ), sex ( Fujimoto et al, 2016 ), and ADHD ( Sudre et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Nentwich

Madsen

et al. 2020

NeuroImage

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A variety of psychiatric, behavioral and cognitive phenotypes have been linked to brain “functional connectivity” – the pattern of correlation observed between different brain regions. Most commonly assessed using functional magnetic resonance imaging (fMRI), here, we investigate the connectivity-phenotype associations with functional connectivity measured with electroencephalography (EEG), using phase-coupling. We analyzed data from the publicly available Healthy Brain Network Biobank. This database compiles a growing sample of children and adolescents, currently encompassing 1657 individuals. Among a variety of assessment instruments we focus on ten phenotypic and additional demographic measures that capture most of the variance in this sample. The largest effect sizes are found for age and sex for both fMRI and EEG. We replicate previous findings of an association of Intelligence Quotient (IQ) and Attention Deficit Hyperactivity Disorder (ADHD) with the pattern of fMRI functional connectivity. We also find an association with socioeconomic status, anxiety and the Child Behavior Checklist Score. For EEG we find a significant connectivity-phenotype relationship with IQ. The actual spatial patterns of functional connectivity are quite different between fMRI and source-space EEG. However, within EEG we observe clusters of functional connectivity that are consistent across frequency bands. Additionally we analyzed reproducibility of functional connectivity. We compare connectivity obtained with different tasks, including resting state, a video and a visual flicker task. For both EEG and fMRI the variation between tasks was smaller than the variability observed between subjects. We also found an increase of reliability with increasing frequency of the EEG, and increased sampling duration. We conclude that, while the patterns of functional connectivity are distinct between fMRI and phase-coupling of EEG, they are nonetheless similar in their robustness to the task, and similar in that idiosyncratic patterns of connectivity predict individual phenotypes.

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Section: Discussionsupporting

confidence: 67%

Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Nentwich

Madsen

et al. 2020

NeuroImage

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“…Beyond the static relationship of EEG and fMRI to structure, we also observed crossmodal linked dynamics (1 minute sliding window) (Wirsich et al, 2020b). While most work focused on crossmodal agreement of FC, we have equally shown that the crossmodal connectivity can be split up into a common and complimentary connectivity profile across different timescales (Wirsich et al, 2020a). A crucial open question is whether crossmodal dissimilarities arise mainly from differences in data quality and acquisition setup, or represent differences arising from measuring complementary multimodal aspects of brain functional connectivity (Sadaghiani and Wirsich, 2020).…”

Section: Introductionmentioning

confidence: 99%

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Wirsich

Jorge

Iannotti

et al. 2020

Preprint

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Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite their differences in probing brain activity, both electrophysiology and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage).Here, we investigated the reliability of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reliably correlates across different datasets and that the crossmodal correlation between EEG and fMRI connectivity of r≈0.3 can be reliably extracted in low and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and withing intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship.This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects being organized into reliable ICNs across different timescales. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. Alterations of this coupling could be explored as a potential clinical marker of pathological brain function.

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Exploring MEG brain fingerprints: evaluation, pitfalls, and interpretations

Sareen

Zahar

Ville

et al. 2021

Preprint

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Individual characterization of subjects based on their functional connectome (FC), termed 'FC fingerprinting', has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction and frequency bands. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprints in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identification offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.

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

Cited by 4 publications

References 56 publications

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Exploring MEG brain fingerprints: evaluation, pitfalls, and interpretations

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