Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography.
paper
Cited
Public
- Authors
- Marzetti, Laura; Basti, Alessio; Chella, Federico; D'Andrea, Antea; SyrjΓ€lΓ€, Jaakko; Pizzella, Vittorio
- Year
- 2019
- Journal
- Frontiers in neuroscience
- PMID
- 31572116
- DOI
- 10.3389/fnins.2019.00964
- PMCID
- PMC6751382
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. 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. The different metrics are presented according to their characteristics: same-frequency or cross-frequency, univariate or multivariate, directed or undirected. 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. 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. 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). Additionally, the relation of these functional brain circuits to anatomy and to behavior highlights the usefulness of MEG phase coupling in systems neuroscience studies. 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.
Effects of source leakage on connectivity estimates. (A) Cortical map for the absolute value of the Point Spread Function (PSF) of source 1. (B) Ratio between the estimated functional connectivity values and the true value (i.e., the one obtained directly from the generated source time sources) as a function of the distance between sources 1 and 2.
Attend Left and Attend Right conjunction map of aBic modulation vs. Baseline in the alpha-beta band with respect to the right frontal cortex seed (indicated by the black dot). Modified from D'Andrea et al. (2019) (CCBY license).
Major brain rhythms as classified by their frequency span.
Distribution of the circular phase difference across signal realizations for phase-coupled signals (A) and independent signals (B). Top plots: an illustrative example of signal realization. Bottom plots: polar histogram for the distribution of the circular phase difference. A narrow peak in the circular phase difference distribution can be observed in the case of phase-coupled signals, as opposed to the case of independent signals.
Effects of source leakage on different connectivity measures. Functional connectivity was estimated by using: coherence (C; top left), phase locking value (PLV; bottom left), imaginary part of coherency (ImCoh; top center), imaginary part of phase locking value (iPLV; bottom center), lagged coherence (Ο2; top right), weighted phase lag index (wPLI; bottom right). For each connectivity measure, the ratio between the estimated functional connectivity values and the true value is plotted as a function of the distance between sources 1 and 2. Artificially inflated connectivity estimates (ratio values larger than 1) can be observed for coherence and phase locking value, but not for other measures which were specifically designed to handle the effects of source leakage.
Comparison of brain networks obtained using ICA independently on MEG and fMRI data. Modified from Brookes et al. (2011b) (CCBY license). (A) Top row: fMRI derived Default Mode Network (DMN); bottom row MEG derived DMN. (B) Top row: fMRI derived Right lateral FrontoParietal Network; bottom row MEG derived Right lateral FrontoParietal Network. (C) Top row: fMRI derived Left lateral FrontoParietal Network; bottom row MEG derived Left lateral FrontoParietal Network. (D) Top row: fMRI derived Sensori-Motor Network; bottom row MEG derived Sensori-Motor Network.
(Left) Topography of the phase synchronization between the major nodes of the Dorsal Attention Network (DAN) in the left hemisphere, i.e., left Frontal Eye Field (lFEF), left posterior Inferior Parietal Sulcus (lpIPS) and the homologous DAN nodes in the alpha band (right FEF and right pIPS). (Right) Frequency specificity of the coupling shown on the left. Modified from Marzetti et al. (2013).
Group averaged map in the alpha band between the primary visual cortex (V1) and all other locations over the cortex obtained by MPSI. The cortical locations in red exert an influence on V1 while V1 exerts an influence on the regions in blue. The dots, in color according to the legend, represent resting state network nodes which overlap areas which lead or follow V1. Modified from Basti et al. (2018) (CCBY license).
Cartoon of the interregional phase coherence modulated by attention in Siegel et al. (2008), displayed on a flattened cortex. Circles indicate cortical areas in which phase coupling was observed to be modulated, namely: posterior intraparietal sulcus (pIPS), middle temporal (MT+), and frontal eye field (FEF). Panels on the left show the cortical areas between which attention significantly reduced phase coherence in the hemisphere ipsilateral as compared to contralateral to the attended hemifield. Panels on the right highlight the corresponding attentional enhancement of phase coherence. The different frequency bands (alpha, beta, low gamma, and high gamma) involved are indicated by different colors.
Grand average MIM functional connectivity modulation (mMIM) with respect to the left superior occipital cortex (indicated by the black dot) for the βAttend Leftβ condition vs. Baseline in the alpha band. Modified from D'Andrea et al. (2019) (CCBY license).
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|---|---|---|
| 40 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | To improve the robustness of PLI with respect to correlated and uncorrelated noise, as well as toβ¦ |
| 41 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | where the weights are equal to the imaginary part of the cross-spectra computed within realizations. |
| 42 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | The effects of source leakage on these different connectivity measures is shown in Figure 4.β¦ |
| 43 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | value as an effect of source leakage. Of note, this effect is not limited to close by sources but isβ¦ |
| 44 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | The following two methods are conceived to investigate the directionality of phase coupling.β¦ |
| 45 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | where df is an incremental step in the frequency domain, and F denotes the frequency band ofβ¦ |
| 46 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | Lobier et al. (2014) applied the concept of transfer entropy (Schreiber, 2000), developed in theβ¦ |
| 47 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | In the above notation, ΞΈj(F, t) denotes the instantaneous phase signal obtained from, e.g., Hilbertβ¦ |
| 48 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Univariate Methods | The above described methods are univariate methods since they are designed to assess connectivityβ¦ |
| 49 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | Let xI(t)=(xi1(t),β¦,xiN(t))T and xJ(t)=(xj1(t),β¦,xjM(t))T be the multivariate time series forβ¦ |
| 50 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | where (β)H denotes the Hermitian conjugate of a matrix. The elements of SIJ(f) are theβ¦ |
| 51 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | The multivariate interaction measure (MIM, Ewald et al., 2012) is an index of the total phaseβ¦ |
| 52 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | where the superscripts β and β denote the real and imaginary part of the complex-valuedβ¦ |
| 53 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | The generalization of the lagged coherence to the multivariate case will be here referred to as theβ¦ |
| 54 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | where det(β) denotes the determinant of a matrix, and 0 is a M Γ N matrix of zeros. In theβ¦ |
| 55 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | To assess the directionality from pairs of vector-signals, Basti et al. (2018) generalized theβ¦ |
| 56 | Methods to Assess Brain Connectivity Based on Phase Coupling β Frequency-Specific Phase Coupling Methods β Multivariate Methods | where df and F denote an incremental step in the frequency domain and the frequency band ofβ¦ |
| 57 | Methods to Assess Brain Connectivity Based on Phase Coupling β Cross-Frequency Phase Coupling Methods | In section Frequency-specific phase coupling methods, we introduced a large set of connectivityβ¦ |
| 58 | Methods to Assess Brain Connectivity Based on Phase Coupling β Cross-Frequency Phase Coupling Methods | In this review, consistently with what already presented for coupling at the same frequency, weβ¦ |
| 59 | Methods to Assess Brain Connectivity Based on Phase Coupling β Cross-Frequency Phase Coupling Methods | A popular cross-frequency measure in this framework is the n:m synchronization index (Rosenblum etβ¦ |
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In this knowledge base
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| Alcohol use disorder is associated with altered frontomedial phase-amplitude coupling strength during resting state. | 2026 | 41657495 |
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