Intrinsic frequency biases and profiles across human cortex

Abstract

Recent findings in monkeys suggest that intrinsic periodic spiking activity in selective cortical areas occurs at timescales that follow a sensory or lower order-to-higher order processing hierarchy (Murray JD, Bernacchia A, Freedman DJ, Romo R, Wallis JD, Cai X, Padoa-Schioppa C, Pasternak T, Seo H, Lee D, Wang XJ. Nat Neurosci 17: 1661-1663, 2014). It has not yet been fully explored if a similar timescale hierarchy is present in humans. Additionally, these measures in the monkey studies have not addressed findings that rhythmic activity within a brain area can occur at multiple frequencies. In this study we investigate in humans if regions may be biased toward particular frequencies of intrinsic activity and if a full cortical mapping still reveals an organization that follows this hierarchy. We examined the spectral power in multiple frequency bands (0.5-150 Hz) from task-independent data using magnetoencephalography (MEG). We compared standardized power across bands to find regional frequency biases. Our results demonstrate a mix of lower and higher frequency biases across sensory and higher order regions. Thus they suggest a more complex cortical organization that does not simply follow this hierarchy. Additionally, some regions do not display a bias for a single band, and a data-driven clustering analysis reveals a regional organization with high standardized power in multiple bands. Specifically, theta and beta are both high in dorsal frontal cortex, whereas delta and gamma are high in ventral frontal cortex and temporal cortex. Occipital and parietal regions are biased more narrowly toward alpha power, and ventral temporal lobe displays specific biases toward gamma. Thus intrinsic rhythmic neural activity displays a regional organization but one that is not necessarily hierarchical.NEW & NOTEWORTHY The organization of rhythmic neural activity is not well understood. Whereas it has been postulated that rhythms are organized in a hierarchical manner across brain regions, our novel analysis allows comparison of full cortical maps across different frequency bands, which demonstrate that the rhythmic organization is more complex. Additionally, data-driven methods show that rhythms of multiple frequencies or timescales occur within a particular region and that this nonhierarchical organization is widespread.

Trial registration: ClinicalTrials.gov NCT00001360.

Keywords: EEG/MEG; clustering; cortical rhythms; spectral analysis.

Copyright © 2017 the American Physiological Society.

Figures

Fig. 1.

Standardized power maps for each frequency band. Spectral power was standardized from spatial measures of power separately for each band, effectively creating minimal-maximal (min-max) maps in units of standard deviation; therefore, color bars are in units of z scores and do not correspond across frequency bands so that locations of max and min can be shown separately for each band. Bands are divided into delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–14 Hz), beta low (beta-L; 14–20 Hz), beta high (beta-H; 20–30 Hz), low gamma (gamma-L; 30–50 Hz), high gamma 1 (gamma-H1; 50–100 Hz), and high gamma 2 (gamma-H2; 100–150 Hz) and are shown by Destrieux atlas ROIs on pial surface maps of the fsaverage brain.

Fig. 2.

Interindividual variability maps. Interindividual variability of standardized power is demonstrated through the standard deviation of z scores across subjects for each frequency band. Maps for each band are scaled separately to the max and min standard deviations.

Fig. 3.

Dominant frequency maps. A: cortical regions showing a strong bias (defined as a frequency with maximal standardized power > standardized power of all other frequencies, P < 0.05, FDR corrected) toward the dominant frequency. Dominant frequency for an ROI is color coded according to the legend at left. B: cortical regions showing a weak bias (frequency with maximal standardized power > standardized power of at least one other frequency, P < 0.05, FDR corrected) toward the dominant frequency. C: for each ROI, 7 post hoc paired, one-sided t-tests were performed to compare frequencies. The histogram shows the number of ROIs passing the stated number of t-tests. Only about half of the ROIs show a strong bias (all 7 tests passed at α < 0.05, FDR corrected), suggesting many regions do not have strong timescale biases.

Fig. 4.

Clustering reveals several spectral profiles across the cortex. A: ROIs were clustered by spectral features using K-means analysis for k = 2 through 10 clusters. The percent variance explained by each cluster solution is graphed. B: nonparametric statistics demonstrated clustering consistency across subjects (i.e., whether each ROI consistently assigned the same cluster spectral profile across all subjects). The blue line in the graph plots the number of ROIs that were not consistently clustered for each solution of k. The green line plots the number of ROIs that were consistently clustered across subjects but have a plurality (largest subset), not a majority, of subjects picking the same spectral profile for a given ROI. As k increases, the number of possible clusters assigned to an ROI increases, making it harder to achieve a majority of at least 16/30 subjects. Both graphs helped us determine that k = 3, 4, and 5 were solutions fitting a good trade-off of model reliability and complexity. C: cortical maps of the k = 3 cluster solution (first column) where colors index the spectral profiles (second column). White ROIs on cortical surface maps did not pass the permutation test after FDR correction. The similarity matrix (third column) shows reasonably tight clustering across the 3 clusters. The multidimensional scaling (MDS) plot of ROIs (fourth column) also demonstrates tight clustering with this solution. D: the k = 4 cluster solution. E: the k = 5 cluster solution.