Dominant frequencies of resting human brain activity as measured by the electrocorticogram

Abstract

The brain’s spontaneous, intrinsic activity is increasingly being shown to reveal brain function, delineate large scale brain networks, and diagnose brain disorders. One of the most studied and clinically utilized types of intrinsic brain activity are oscillations in the electrocorticogram (ECoG), a relatively localized measure of cortical synaptic activity. Here we objectively characterize the types of ECoG oscillations commonly observed over particular cortical areas when an individual is awake and immobile with eyes closed, using a surface-based cortical atlas and cluster analysis. Both methods show that [1] there is generally substantial variability in the dominant frequencies of cortical regions and substantial overlap in dominant frequencies across the areas sampled (primarily lateral central, temporal, and frontal areas), [2] theta (4–8 Hz) is the most dominant type of oscillation in the areas sampled with a mode around 7 Hz, [3] alpha (8–13 Hz) is largely limited to parietal and occipital regions, and [4] beta (13–30 Hz) is prominent peri-Rolandically, over the middle frontal gyrus, and the pars opercularis. In addition, the cluster analysis revealed seven types of ECoG spectral power densities (SPDs). Six of these have peaks at 3, 5, 7 (narrow), 7 (broad), 10, and 17 Hz, while the remaining cluster is broadly distributed with less pronounced peaks at 8, 19, and 42 Hz. These categories largely corroborate conventional sub-gamma frequency band distinctions (delta, theta, alpha, and beta) and suggest multiple sub-types of theta. Finally, we note that gamma/high gamma activity (30+ Hz) was at times prominently observed, but was too infrequent and variable across individuals to be reliably characterized. These results should help identify abnormal patterns of ECoG oscillations, inform the interpretation of EEG/MEG intrinsic activity, and provide insight into the functions of these different oscillations and the networks that produce them. Specifically, our results support theories of the importance of theta oscillations in general cortical function, suggest that alpha activity is primarily related to sensory processing/attention, and demonstrate that beta networks extend far beyond primary sensorimotor regions.

Neural generators of psychogenic seizures: Evidence from intracranial and extracranial brain recordings

“…All patients were confronted with stressogenic experiences, in concordance with previous studies suggesting stress as an important risk factor for the development of PSs. Analysis of intracranial recordings of patients with epilepsy in the resting state showed that a cluster at the theta range characterizes resting-state activity at the 'default-mode network' in which the posterior parietal cortex plays a key role [12]. This is in concordance with previous resting-state fMRI findings showing stronger connectivity values within Fig.…”

“…The posterior parietal cortex is related to self-processing [15] and is implicated in autobiographical thinking about past and future events [16]. We speculate that PSs, reflecting conflicts related to one's past and current self-referenced dilemmas, are related to a dysfunction of the default-mode network [12] as reflected here by changes in theta band activity in the posterior parietal cortex, a central hub of the default-mode network, which embodies emotional and autobiographical memory functions.…”

Directional patterns of cross frequency phase and amplitude coupling within the resting state mimic patterns of fMRI functional connectivity

“…However, Foster and Parvizi's (2012) contribution raises the suggestion that the low-frequency entrainment dynamics in the resting state may in part be defined by the characteristic idling rhythm inherent to different functional systems of the cortex (c.f. Groppe et al, 2013) as been for example seen with theta-gamma PAC within the hippocampus (Lisman and Jenson, 2013). Sensorimotor systems are classically defined as owning both ‘idling’ rhythms at both the Beta range (12–30 Hz) as well as the alpha ‘Mu’ range (8–12 Hz) (Mackay, 1997).…”

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

“…Accordingly, microstates have typically been regarded as broad-band phenomena. Microstates may thus take into account the broad range of frequency components observable in the human brain (Groppe et al, 2013). Furthermore, the typical features extracted from microstate analyses, such as mean durations and percentages of time covered, are independent of signal amplitudes.…”