Mean phase coherence1/1/2024 ![]() ![]() In the last decades, systems neuroscience has made it clear that brain functioning requires the cooperation of several spatially separated brain regions to allow for integrative functions (e.g., vision, audition), as well as for higher order functions (e.g., understanding of actions), for a review see e.g., Rizzolatti et al. In conclusion, we believe that the field of MEG functional connectivity has made substantial steps forward in the recent years and is now ready for bringing the study of brain networks to a more mechanistic understanding. Additionally, the relation of these functional brain circuits to anatomy and to behavior highlights the usefulness of MEG phase coupling in systems neuroscience studies. Overall, the results of the studies provide evidence for frequency specific and/or cross-frequency brain circuits which partially overlap with brain networks as identified by hemodynamic-based imaging techniques, such as functional Magnetic Resonance (fMRI). In the second part of this review, we present examples of the use of specific phase methods on real MEG data in the context of resting state, visuospatial attention and working memory. We focus on phase coupling metrics given that phase coupling of neuronal oscillations is a putative mechanism for inter-areal communication, and that MEG is an ideal tool to non-invasively detect such coupling. The different metrics are presented according to their characteristics: same-frequency or cross-frequency, univariate or multivariate, directed or undirected. In the first part of this review, we describe, in a unified mathematical framework, a large set of metrics used to estimate MEG functional connectivity at the same or at different frequencies. Magnetoencephalography has gained an increasing importance in systems neuroscience thanks to the possibility it offers of unraveling brain networks at time-scales relevant to behavior, i.e., frequencies in the 1–100 Hz range, with sufficient spatial resolution. 2Institute for Advanced Biomedical Technologies, University of Chieti-Pescara, Chieti, Italy.1Imaging and Clinical Sciences, Department of Neuroscience, University of Chieti-Pescara, Chieti, Italy.All rights reserved.Laura Marzetti 1,2 * Alessio Basti 1,2 Federico Chella 1,2 Antea D'Andrea 1,2 Jaakko Syrjälä 1,2 Vittorio Pizzella 1,2 The experimental results demonstrated the reliability of the presented method to be generalized in the field of automated MS diagnosis systems.īivariate empirical mode decomposition Electroencephalography Mean phase coherence Multiple sclerosis Phase-synchrony Visual task reliefF.Ĭopyright © 2019 Elsevier Ltd. In the validation phase, the leave-one-subject-out cross-validation (LOOCV) method was used to assess the validity of the proposed algorithm. The results revealed a higher level of network synchronization in the posterior regions of the brain and desynchronization in the anterior regions among the MS group as compared with the normal subjects. Finally, the best distinguishing features were input to a k-nearest neighbor (KNN) classifier. After the construction of the feature space using MPC values, the ReliefF algorithm was applied for dimension reduction. The phases of these IMFs were then obtained using the Hilbert transform to be utilized in the mean phase coherence (MPC), a measure for phase-synchrony calculation. Initially, the bivariate empirical mode decomposition (BEMD) was applied to extract the intrinsic mode functions (IMFs). This method is based on calculating the pairwise phase-synchrony of EEG recordings during a visual task. To address these issues, in this work, we proposed a robust pattern recognition algorithm as a computer-aided diagnosis system. Moreover, there has always been a great need to automate the MS diagnosis procedure to eliminate the evaluation errors thereby improving its consistency and reliability. Despite the widespread prevalence of Multiple Sclerosis (MS), the study of brain interactions is still poorly understood. ![]()
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