Frequency-specific electrophysiologic correlates of resting state fMRI networks

Abstract

Resting state functional MRI (R-fMRI) studies have shown that slow (< 0.1 Hz), intrinsic fluctuations of the blood oxygen level dependent (BOLD) signal are temporally correlated within hierarchically organized functional systems known as resting state networks (RSNs) (Doucet et al., 2011). Most broadly, this hierarchy exhibits a dichotomy between two opposed systems (Fox et al., 2005). One system engages with the environment and includes the visual, auditory, and sensorimotor (SMN) networks as well as the dorsal attention network (DAN), which controls spatial attention. The other system includes the default mode network (DMN) and the fronto-parietal control system (FPC), RSNs that instantiate episodic memory and executive control, respectively. Here, we test the hypothesis, based on the spectral specificity of electrophysiologic responses to perceptual vs. memory tasks (Klimesch, 1999; Pfurtscheller and Lopes da Silva, 1999), that these two large-scale neural systems also manifest frequency specificity in the resting state. We measured the spatial correspondence between electrocorticographic (ECoG) band-limited power (BLP) and R-fMRI correlation patterns in awake, resting, human subjects. Our results show that, while gamma BLP correspondence was common throughout the brain, theta (4–8 Hz) BLP correspondence was stronger in the DMN and FPC, whereas alpha (8–12 Hz) correspondence was stronger in the SMN and DAN. Thus, the human brain, at rest, exhibits frequency specific electrophysiology, respecting both the spectral structure of task responses and the hierarchical organization of RSNs.

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

“…We focused here on network nodes where electrode coverage was available, often only within a few sites per subject (especially in depth electrode cases). Consistent with previous studies (He et al, 2008, Keller et al, 2013, Ramot et al, 2013, Foster et al, 2015, Hacker et al, 2017, Kucyi et al, 2018a, we found that the spatial topography of functional connectivity patterns remained similar across task, rest and sleep states and between iEEG and within-individual resting state fMRI. We also found that even when dPPC-PMC and dAIC-PMC anticorrelations were not found, inter-network correlations between these pairs were typically within the lowest percentiles compared with all other region pairs.…”

“…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).…”

Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics

“…Beyond the signal-to-noise limitations in human concurrent EEG-fMRI, there is evidence that γ-and β-band connectivity may contain information complementary to fMRI-derived connectivity. A weaker relation to fMRI connectivity has been reported for β and low-γ compared to other bands in intracranial electrophysiological recordings in humans (38,58) and in animals (59,60). The weaker relationship for β-and low γ-bands likely reflects a general property of the electrophysiology-fMRI relationship.…”

Natural Grouping of Neural Responses Reveals Spatially Segregated Clusters in Prearcuate Cortex

“…Traditionally, parcellation of cortex has depended heavily on anatomical techniques that cannot be applied in live subjects: cyto-, myelo-, and chemoarchitectonic markers, anterograde and retrograde tracers, and electron microscopy (Amir et al, 1993; Gerbella et al, 2007; Levitt et al, 1993; Rockland and Lund, 1983). More recently, technical advances have enabled cortical parcellation based on fMRI BOLD responses (Power et al, 2011; Vincent et al, 2007), optical imaging, diffusion weighted imaging, and electrocorticography (ECoG) (Hacker et al, 2012; He et al, 2008) in living subjects. To our knowledge, however, our study is first to do so based on spiking activity in association cortex.…”

“…Interestingly, this large-scale parcellation based on BOLD seems to reflect large-scale electrophysiological properties as well, since the BOLD response fluctuations are closely related to local field potentials and spiking activity within each voxel (Logothetis et al, 2001; Scholvinck et al, 2010). Moreover, the BOLD response correlations across voxels can be mapped to the correlation of the slow cortical potentials in the corresponding locations, as evidenced by electrocorticography (ECoG) (Hacker et al, 2012; He et al, 2008). …”