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Background: Cross-frequency interactions between distinct brain areas have been observed in connection with a variety of cognitive tasks. With electro- and magnetoencephalography (EEG/MEG) data, typical connectivity measures between two brain regions analyze a single quantity from each region within a specific frequency band; given the wideband nature of EEG/MEG signals, many statistical tests may be required to identify true coupling. Furthermore, because of the poor spatial resolution of activity reconstructed from EEG/MEG, some interactions may actually be due to the linear mixing of brain sources., New Method: In the present work, a method for the detection of cross-frequency functional connectivity in MEG data using canonical correlation analysis (CCA) is described. We demonstrate that CCA identifies correlated signals and also the frequencies that cause the correlation. We also implement a procedure to deal with linear mixing based on symmetry properties of cross-covariance matrices., Results: Our tests with both simulated and real MEG data demonstrate that CCA is able to detect interacting locations and the frequencies that cause them, while accurately discarding spurious coupling., Comparison With Existing Methods: Recent techniques look at time delays in the activity between two locations to discard spurious interactions, while we propose a linear mixing model and demonstrate its relationship with symmetry aspects of cross-covariance matrices., Conclusions: Our tests indicate the benefits of the CCA approach in connectivity studies, as it allows the simultaneous evaluation of several possible combinations of cross-frequency interactions in a single statistical test., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Cross-frequency phase-amplitude coupling (PAC) within large neuronal populations is hypothesized to play a functional role in information processing in a range of cognitive tasks. The goal of our study was to examine the putative role of PAC in the brain networks that mediate continuous visuomotor control. We estimated the cortical activity that mediates visuomotor control via magnetoencephalography (MEG) recordings in 15 healthy volunteers. We extracted the cortical signal amplitudes and phases at the frequencies of interest by means of band-pass filtering followed by Hilbert transforms. To quantify task-related changes of PAC, we implemented a technique based on the Kullback-Leibler divergence. The choice of this technique among others was based on the results of comparisons performed on simulations of coupled sources in various noise conditions. The application of PAC to the MEG data revealed a significant task-related increase in coupling between the phase of delta (2-5 Hz) and the amplitude of high-gamma (60-90 Hz) oscillations in the occipital and parietal cortices as well as in the cerebellum. Remarkably, when comparing PAC in the early trials to PAC recorded towards the end of the experiment we found a significant increase in delta-high-gamma coupling over time in the superior parietal lobule, possibly reflecting visuomotor adaptation processes. Our results suggest that, in addition to power modulations, cross-frequency interactions play a key role in visuomotor behavior.

We describe a method to detect brain activation in cortically constrained maps of current density computed from magnetoencephalography (MEG) data using multivariate statistical inference. We apply time-frequency (wavelet) analysis to individual epochs to produce dynamic images of brain signal power on the cerebral cortex in multiple time-frequency bands. We form vector observations by concatenating the power in each frequency band, and fit them into separate multivariate linear models for each time band and cortical location with experimental conditions as predictor variables. The resulting Roy's maximum root statistic maps are thresholded for significance using permutation tests and the maximum statistic approach. A source is considered significant if it exceeds a statistical threshold, which is chosen to control the familywise error rate, or the probability of at least one false positive, across the cortical surface. We compare and evaluate the multivariate approach with existing univariate approaches to time-frequency MEG signal analysis, both on simulated data and experimental data from an MEG visuomotor task study. Our results indicate that the multivariate method is more powerful than the univariate approach in detecting experimental effects when correlations exist between power across frequency bands. We further describe protected F-tests and linear discriminant analysis to identify individual frequencies that contribute significantly to experimental effects., ((c) 2009 Wiley-Liss, Inc.)