Despite being the focus of a thriving field of research, the biological mechanisms that underlie information integration in the brain are not yet fully understood. A theory that has gained a lot of traction in recent years suggests that multi-scale integration is regulated by a hierarchy of mutually interacting neural oscillations. In particular, there is accumulating evidence that phase-amplitude coupling (PAC), a specific form of cross-frequency interaction, plays a key role in numerous cognitive processes.
View Article and Find Full Text PDFBackground: 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.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
August 2013
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.
View Article and Find Full Text PDFWe 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.
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