- Title
- Detection of Cross-Frequency Coupling Between Brain Areas: An Extension of Phase Linearity Measurement
- Creator
- Sorrentino, Pierpaolo; Ambrosanio, Michele; Rucco, Rosaria; Cabral, Joana; Gollo, Leonardo L.; Breakspear, Michael; Baselice, Fabio
- Relation
- Frontiers in Neuroscience Vol. 16, no. 846623
- Publisher Link
- http://dx.doi.org/10.3389/fnins.2022.846623
- Publisher
- Frontiers Research Foundation
- Resource Type
- journal article
- Date
- 2022
- Description
- The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity. Results show that the method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies.
- Subject
- cross frequency coupling; brain network; brain functional connectivity; phase coupling; phase linearity measurement; PLM
- Identifier
- http://hdl.handle.net/1959.13/1488539
- Identifier
- uon:52477
- Identifier
- ISSN:1662-4548
- Rights
- © 2022 Sorrentino, Ambrosanio, Rucco, Cabral, Gollo, Breakspear and Baselice. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY), (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
- Language
- eng
- Full Text
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