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

Abstract

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.

Predicting cognitive abilities across individuals using sparse EEG connectivity

“…Due to this, critics have suggested that fMRI functional connectivity fingerprinting could be driven by individual differences in brain structure or vasculature, rather than meaningful differences in brain function (Dubois & Adolphs, 2016;Llera et al, 2019). They additionally suggest that identification and behavioral prediction could be driven by trait-like head motion, which is a challenging confound in fMRI (Nentwich et al, 2020;Siegel et al, 2017;Xifra-Porxas et al, 2020). Our EEG-based connectome models complement this work by controlling for and ruling out these potential confounds.…”

“…This hypothesis has not been directly tested. However, studies relating cognitive abilities to brain connectivity patterns measured with electrical or magnetic signals (i.e., using electroencephalography [EEG] or magnetoencephalography [MEG]; Burgess & Ali, 2002;Damaševičius et al, 2018;Fellrath et al, 2016;Karamzadeh et al, 2013;Nentwich et al, 2020;Palva et al, 2010)-which often include fewer than one percent of the spatial resolution typically available to fMRI functional connectivity models-suggest that cognitively meaningful variability in brain function may be reflected in sparse, high-frequency neural signals. In this paper, we directly test the hypothesis that dense functional networks are uniquely informative of behavior.…”

Assessing the Relationship between Verbal and Nonverbal Cognitive Abilities Using Resting-State EEG Functional Connectivity

“…The present results are also consistent with structural brain connectivity data [ 62 ], which showed that high density and low modularity of white matter fibers are associated with higher fluid intelligence. Note, however, that anatomical and functional connectivity results should be compared with caution [ 38 ]. Previously, it has been shown that brain integration increases during cognitive load [ 14 ].…”

“…These results are also in line with a recent fast fMRI study [ 37 ] showing that different fMRI frequency bands (0.01–0.15 Hz, 0.15–0.37 Hz, 0.37–0.53 Hz, and 0.53–0.7 Hz) demonstrate band-specific shifts of the brain-wide neural coherence. The study by Nentwich et al [ 38 ], however, showed that EEG functional connectivity patterns differ from fMRI connectivity patterns. These differences may be explained by the physiological origin of these two signals and the methods used to calculate functional connectivity.…”

Oscillation-based connectivity architecture is dominated by an intrinsic spatial organization, not cognitive state or frequency

“…It is therefore unclear how these findings relate to trial-based oscillatory FC commonly investigated in cognitive neuroscience. A more recent scalp EEG study showed that phase coupling in reconstructed source-space is consistent across tasks (resting state, video viewing, and flashing gratings), with FC clusters that are reproducible across frequency bands (Nentwich et al 2020). However, while promising the latter two studies must be interpreted with care due to the methodological limitations imposed by EEG recorded over the scalp, which may lead to spurious FC even in source space .…”