Towards an Atlas of Human Neocortical Oscillations

The electroencephalogram (EEG) is one of the most popular measures of human brain function and is currently the most clinically utilized. The EEG primarily reflects the synaptic activity of patches of neocortical pyramidal cells (at minimum

Fig. 1: Single EEG channels illustrating different types of oscillations

Fig. 1: Single EEG channels illustrating different types of oscillations

probably around the order of 25 mm2–Baillet, Mosher, & Leahy, 2001) and its most salient feature is intermittent oscillations at various frequencies (Fig. 1), which vary according to brain state (e.g., awake vs. slow wave sleep) and cortical area/scalp location. The most conventional frequency bands are: delta [1–4 Hz], theta [4– 8 Hz], alpha/mu [8–13 Hz], beta [13–30 Hz], gamma [30–80 Hz], and high gamma [80–150 Hz]. However, the boundaries between bands are not well defined and can vary somewhat depending on whom you ask.

Since these oscillations are often larger when an individual is relatively inactive (e.g., eyes closed) some have interpreted them as the product of idling neurons with no functional importance. However, there is increasing evidence that these oscillations serve an important role in functions such as mediating communication between brain areas and encoding neural representations. Moreover, these rhythms are useful for identifying macroscale brain networks and are clinically useful for detecting brain abnormalities (e.g., tumors), which can disrupt them.


Fig. 2: [Left] Eyes-open resting state EEG spectral power density (SPD) averaged across data from 7 neurotypical individuals (unpublished data). Each line is the SPD at a different electrode. [Right] The topography of the PSD at 10.5 Hz.


Fig. 3: Same as Fig. 2 but topography is the SPD at 5.4 Hz.

Decades of research have well characterized typical oscillatory activity at the scalp. The EEG power spectrum follows a roughly 1/f distribution with a peak in the alpha band around 10 Hz that is largest over the back of the head (Fig 2; Fig 4-bottom left) but apparent at all standard EEG electrode scalp locations. Note that alpha band oscillations over sensory-motor regions, called “mu” rhythms, are present but are dwarfed by the much stronger posterior alpha oscillations. A second, much smaller peak in the theta range is also observed at frontal-midline electrodes (Fig 3; Fig 4-upper right). Peaks are generally not clearly observed in the other frequency ranges. Delta power is broadly distributed but greatest medially and centrally (Fig 4-upper left) and beta power is even more uniformly distributed (Fig 4-bottom right).


Fig. 4: Topographies of resting EEG spectral amplitude density averaged across 43 individuals in delta [upper left], theta [upper right], alpha [lower left], and beta [lower right] frequency bands (Maurer & Dierks, 1991). It was actually surprisingly hard for me to find a published figure illustrating this.

For medical reasons, EEG is also sometimes recorded intracranially with electrodes below the dura mater or with penetrating “depth” electrodes. Although intracranial EEG (iEEG) oscillations have been studied since the early 20th century (e.g., Penfield & Jasper, 1954), there has been little quantitative research that characterizes the types and distribution of iEEG oscillations. This is most likely due to the difficulty of precisely identifying the location of iEEG electrodes and mapping those locations to a standard brain, which enables combining data across individuals. Contemporary neuroimaging has solved these problems, and our research group recently attempted to develop a quantitative atlas of iEEG oscillations using data obtained from patients undergoing evaluation for epilepsy surgery (Groppe et al., 2013). We found some surprising differences with what is observed in scalp EEG.


Fig. 5: Histogram of SPD peaks of resting state intracranial electrode data collapsed across all electrodes and participants. Note that Hertz is scaled logarithmically and the high proportion of peaks above 40 Hz is due to the flattening of the SPD distribution, which makes it difficult to distinguish true peaks from noise.


Fig. 6: Mean SPDs of seven clusters.


Fig. 7: Proportion of electrodes in each cortical area (as defined by the Desikan-Killiany atlas) belonging to each cluster. Electrodes from both hemispheres have been combined to increase the number of electrodes per area. Only areas covered by more than one electrode (out of all 1208) are colored. All other areas are shown in gray.

The first difference is that alpha oscillations are not the most dominant type of rhythm. If you simply look at what frequencies exhibit peaks in each electrode’s power spectrum, you find that theta peaks are the most common with a mode at 7 Hz (Fig. 5). Less frequent modes are also apparent at 3, 9, and 15 Hz. To get a sense of the cortical topography of these oscillations, we first performed k-means cluster analysis and found that there were seven types of spectral power densities (Fig. 6) and then computed the proportion of electrodes in each of 35 cortical areas that belonged to each cluster (Fig. 7). The 3 Hz delta cluster is mostly frontal and temporal, including the temporal-parietal junction. The three theta clusters have somewhat complementary distributions: 5 Hz is mostly frontal and basal temporal, the strongly peaked 7 Hz cluster is mostly temporal and lateral parietal, and the weakly peaked 7 Hz cluster is occipital and medial parietal. The alpha 10 Hz cluster is largely limited to parietal and occipital areas. The beta cluster, as expected is strongest over the peri-central gyri. Somewhat surprisingly, it also extends frontally across the middle frontal gyrus and pars opercularis. The last cluster that lacks strong spectral peaks is primarily found over basal temporal and medial areas. Finally, it is worth noting that although no cluster emerged with peaks in the high gamma range (80–150 Hz), a handful of channels in a few patients did exhibit high gamma peaks. There has been some debate as to whether or not high gamma activity reflects true oscillations or simply a general elevation in the 1/f distribution (Crone, Korzeniewska, & Franaszczuk, 2011). These few channels suggest that some brain areas can elicit oscillatory activity in this frequency band, though this activity is uncommon.


Fig. 8: Locations of all 1208 electrodes on average cortical surface. Electrodes are color coded to indicate cluster membership (see Fig. 6).

In addition to the average distribution of these oscillations, it is important to consider how much variation there is within cortical areas and across subjects. Figure 8 shows the location of electrodes collapsed across all patients and color coded according to power spectrum type. Although some spectrum types clearly cluster together in areas, there is considerable variation even in the most homogenous regions (e.g., the pre- and post-central gyri). This is mostly due to individual variation in oscillatory activity. Some individual variation is surely due to factors such as drowsiness, medications, and age that are difficult to control for when acquiring intracranial EEG data. However, I suspect that most of the variation reflects individual differences in functional architecture, which we know from non-invasive imaging of neuro-typical individuals is substantial (e.g., Mueller et al., 2013). If I’m correct, understanding this individual variability will be key to understanding both the underlying processes that generate these oscillations and their importance for brain function.

-David Groppe

Baillet, S., Mosher, J., & Leahy, R. (2001). Electromagnetic brain mapping IEEE Signal Processing Magazine, 18 (6), 14-30 DOI: 10.1109/79.962275

Crone NE, Korzeniewska A, & Franaszczuk PJ (2011). Cortical γ responses: searching high and low. International Journal of Psychophysiology, 79 (1), 9-15 PMID: 21081143

Groppe DM, Bickel S, Keller CJ, Jain SK, Hwang ST, Harden C, & Mehta AD (2013). Dominant frequencies of resting human brain activity as measured by the electrocorticogram. NeuroImage, 79, 223-33 PMID: 23639261

Maurer, K.; Dierks, T. (1991). Atlas of Brain Mapping Springer

Mueller, S., Wang, D., Fox, M., Yeo, B., Sepulcre, J., Sabuncu, M., Shafee, R., Lu, J., & Liu, H. (2013). Individual Variability in Functional Connectivity Architecture of the Human Brain Neuron, 77 (3), 586-595 DOI: 10.1016/j.neuron.2012.12.028

Penfield, W., & Jasper, H. (1954). Epilepsy and the Functional Anatomy of the Human Brain Boston: Little, Brown., 47 (7) DOI: 10.1097/00007611-195407000-00024


~ by eeging on December 24, 2014.

2 Responses to “Towards an Atlas of Human Neocortical Oscillations”

  1. Fascinating stuff. What do you think explains the discrepancy between EEG and iEEG in terms of the power spectra? Is it just a reflection of the alertness state of patients? Or could it be due to differences in synchronization across locations (such that certain frequencies are more likely to survive the spatial smearing that occurs with EEG)? Might we expect better correspondence between iEEG and MEG? Sorry that’s a lot of questions!!

    • I suspect that alterness and patient medications have little to do with the discrepancy since these factors do not drastically slow scalp alpha activity. I believe that the main culprit is, as you mention, that alpha is more synchronized across locations and thus conducts better to the scalp. Simultaneous EEG and iEEG recordings should be able to answer that question. As for iEEG and MEG, MEG is also dominated by alpha activity (although less so at anterior scalp locations than EEG) so the alpha-theta discrepancy is similar. See the following for those data:

      Srinivasan, R., Winter, W.R., Nunez, P.L., 2006. Source analysis of EEG oscillations using
      high-resolution EEG and MEG. Prog. Brain Res. 159, 29–42.

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