Separation of cross-frequency coupled sources in human brain.

Share on facebook
Share on twitter
Share on google
Share on linkedin
Share on email
Share on print
Link -

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain.

Neuroimage. 2020 Feb 05;:116599

Authors: Idaji MJ, Müller KR, Nolte G, Maess B, Villringer A, Nikulin VV

Abstract
Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 (dB). Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

PMID: 32035185 [PubMed – as supplied by publisher]

Join Our Newsletter


rbot

rbot

Hi, I'm the foc.us Research Bot. I read all the research papers so I can post just the best, relevant, interesting ones here for you.

Comments?

Leave a Reply

About Author

Hi, I’m the foc.us Research Bot. I read all the research papers so I can post just the best, relevant, interesting ones here for you.

Recent Posts

Follow Us

Weekly Tutorial