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.

Fig. 1

[Left] Cortical surface of a single participant, S1, with electrode locations represented as black disks. Different colors indicate different cortical areas according to the Desikan-Killiany atlas. [Middle] One second of resting state activity from a single electrode over S1’s left postcentral gyrus, LGd64. The raw time series is shown in gray under the whitened version of the time series. [Right] Trimmed mean spectral power density (SPD) of S1’s LGd64 channel before and after whitening. SPD is normalized to unit power across the frequencies of interest before being log transformed. Dashed lines indicate peaks in the whitened SPD. Note that SPD values from 51 to 64 Hz and from 117 to 123 Hz were ignored due to line noise; linear interpolations across those bands are shown.

Fig. 2

Histogram of SPD peaks of whitened data found across all electrodes and participants. Note that Hertz is scaled logarithmically.

Fig. 3

Mean SPDs of nonwhitened [A] and whitened [B] data per cortical area across all participants. SPDs are color coded according to cortical area (shown in the middle). Dashed lines indicate conventional delta, theta, alpha, and beta boundaries. Only data from areas with more than three subjects are shown for comparison with Fig. 4.

Fig. 4

Mean frequency that exhibits maximal power in whitened data SPDs across three frequency bands. Errors bars indicate 95% confidence intervals (t-distribution assumed, no correction for multiple comparisons). [A] Dashed lines indicate conventional delta, theta, and alpha boundaries. Asterisks below error bars indicate that mean peak frequency is significantly (p_adj < 0.05 FDR corrected) above delta. No error bars are significantly below alpha after FDR adjustment. [B–C] Solid lines indicate lower boundary of frequency range; no deviation from this line indicates an absence of peaks. Asterisks above error bars indicate significant peaks. Bars are color coded according to cortical area [D] and organized from most anterior to most posterior area.

Fig. 5

Mean clustering disagreement between independent applications of k-means to split-halves of all SPDs. A disagreement of 0 means that perfectly compatible clustering solutions were derived. A disagreement of 1 means that completely incompatible solutions were derived. Raw error is normalized by dividing it by the disagreement expected by chance. Error bars are 95% confidence intervals (t-distribution assumed).

Fig. 6

SPD centroids of two [A] and seven [B] cluster solutions of all 1208 SPDs. Dashed lines indicate conventional delta, theta, alpha, beta, gamma, and high gamma boundaries. Note that Hertz is logarithmically scaled.

Fig. 7

Locations of all 1208 electrodes on average cortical surface. Electrodes are color coded to indicate cluster membership (see Fig. 6: B).

Fig. 8

Proportions of electrodes belonging to each cluster in various cortical areas (see Fig. 4: D) averaged across participants. Clusters are color coded according to Figs. 6: B & 7. Error bars indicate 68% confidence intervals (Zar, 1999), equivalent to standard error for normally distributed variables. * indicates that some clusters occurred significantly more frequently than others in that area (Chi² test, p_adj < 0.05 FDR corrected for 25 comparisons–Benjamini and Hochberg, 1995). Only areas covered by more than one electrode (out of all 1208) were included in this analysis.