Intracranial Electrophysiology Reveals Reproducible Intrinsic Functional Connectivity within Human Brain Networks

Abstract

Evidence for intrinsic functional connectivity (FC) within the human brain is largely from neuroimaging studies of hemodynamic activity. Data are lacking from anatomically precise electrophysiological recordings in the most widely studied nodes of human brain networks. Here we used a combination of fMRI and electrocorticography (ECoG) in five human neurosurgical patients with electrodes in the canonical "default" (medial prefrontal and posteromedial cortex), "dorsal attention" (frontal eye fields and superior parietal lobule), and "frontoparietal control" (inferior parietal lobule and dorsolateral prefrontal cortex) networks. In this unique cohort, simultaneous intracranial recordings within these networks were anatomically matched across different individuals. Within each network and for each individual, we found a positive, and reproducible, spatial correlation for FC measures obtained from resting-state fMRI and separately recorded ECoG in the same brains. This relationship was reliably identified for electrophysiological FC based on slow (<1 Hz) fluctuations of high-frequency broadband (70-170 Hz) power, both during wakeful rest and sleep. A similar FC organization was often recovered when using lower-frequency (1-70 Hz) power, but anatomical specificity and consistency were greatest for the high-frequency broadband range. An interfrequency comparison of fluctuations in FC revealed that high and low-frequency ranges often temporally diverged from one another, suggesting that multiple neurophysiological sources may underlie variations in FC. Together, our work offers a generalizable electrophysiological basis for intrinsic FC and its dynamics across individuals, brain networks, and behavioral states. The study of human brain networks during wakeful "rest", largely with fMRI, is now a major focus in both cognitive and clinical neuroscience. However, little is known about the neurophysiology of these networks and their dynamics. We studied neural activity during wakeful rest and sleep within neurosurgical patients with directly implanted electrodes. We found that network activity patterns showed striking similarities between fMRI and direct recordings in the same brains. With improved resolution of direct recordings, we also found that networks were best characterized with specific activity frequencies and that different frequencies show different profiles of within-network activity over time. Our work clarifies how networks spontaneously organize themselves across individuals, brain networks, and behavioral states.

Resting-state “Physiological Networks”

“…In human studies, the existence of spontaneous, task-free FC among distributed brain regions have also been confirmed by alternative neuroimaging modalities such as electrocorticography (He BJ et al 2008;Miller KJ et al 2009;Kucyi A et al 2018) and magnetoencephalography (Brookes MJ et al 2011;Hipp JF et al 2012;Baker AP et al 2014). Therefore, these correlations in hemodynamic fluctuations appear to in part reflect correlations in the underlying neuronal activity.…”

Anticorrelated inter-network electrophysiological activity varies dynamically with attentional performance and behavioral states

“…A handful of iEEG studies involving recordings from putative DMN and DAN nodes have shown task-evoked HFB responses that resemble the antagonistic inter-network patterns observed in fMRI (Ossandon et al, 2011, Ramot et al, 2012, Raccah et al, 2018. In addition, resting state iEEG has revealed correlates of the DMN and DAN (Foster et al, 2015, Hacker et al, 2017, Kucyi et al, 2018a and that a subset of region pairs with resting BOLD anticorrelations exhibit weaker, but significant anticorrelations of slow (0.1-1 Hz) HFB activity (Keller et al, 2013).…”

“…*p<0.05; **p<0.01; ***p<0.001. It is well-established that the spatial topography of intrinsic functional connectivity shows a high degree of stability across task, rest and sleep states in fMRI (Cole et al, 2014, Gratton et al, 2018 and iEEG (He et al, 2008, Ramot et al, 2013, Foster et al, 2015, Kucyi et al, 2018a).…”

“…We therefore repeated our analyses with unfiltered, 0.1-1 Hz filtered, and <0.1 Hz filtered HFB signals. The 0.1-1 Hz filter range was determined on the basis of our own and others' prior work linking BOLD functional connectivity with HFB power amplitude in this frequency range (Nir et al, 2008, Keller et al, 2013, Foster et al, 2015, Kucyi et al, 2018a. We computed both zero-lag correlations and lag-minimum correlations, defined as the maximum anticorrelation between regions for time series that could be shifted from -2 to +2 seconds ( Figure 8A).…”

Predicting dysfunctional age-related task activations from resting-state network alterations

“…Formalized in the subfield of resting-state functional connectivity (restFC), such approaches have primarily been applied to human functional magnetic resonance imaging (fMRI) data. As testament to the reproducibility of restFC, convergent network topologies have been recovered across different regional atlases (Power et al, 2011;Ji et al, 2019), non-fMRI human imaging modalities (Brookes et al, 2011;Kucyi et al, 2018) and non-human animals (Wang et al, 2013;Stafford et al, 2014). Whilst these findings highlight that restFC is reliably observed, debate persists over its cognitive relevance, given that the experimentally unconstrained nature of rest raises practical difficulties in separating signal from noise (Power et al, 2012;Laumann et al, 2016), as well as theoretical difficulties in moving from an exploratory to explanatory understanding of brain network function .…”