Disclosing Brain Functional Connectivity From Electrophysiological Signals With Phase Slope Based Metrics

Basti, Alessio; Pizzella, Vittorio; Nolte, Guido; Chella, Federico; Marzetti, Laura

doi:10.24874/jsscm.2017.11.02.05

Cited by 7 publications

(3 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The sign of MPSI IJ gives information about the directionality of the phase coupling. Specifically, a positive sign, which means that the phase differences between the components of I and J are more prone to increase with increasing frequency, implies that the average time lag between the two parcels is positive and thus that parcel I is the leading source; conversely, a negative value implies the opposite [37,54].…”

Section: Multivariate Phase Slope Indexmentioning

confidence: 99%

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

et al. 2021

Self Cite

View full text Add to dashboard Cite

Objective. The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals. Approach. We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability. Main results. The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%). Significance. Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.

show abstract

Section: Multivariate Phase Slope Indexmentioning

confidence: 99%

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…Similarly, here we focused on the performance of undirected PC methods and, thus, we did not consider e.g. amplitudebased methods (O'Neill et al 2015), cross-frequency methods (Palva et al 2005, Chella et al 2016, and directed PC methods, such as phase slope index (Nolte et al 2008, Basti et al 2017 and phase transfer entropy (Lobier et al 2014).…”

Section: Discussionmentioning

confidence: 99%

Looking through the windows: a study about the dependency of phase-coupling estimates on the data length

et al. 2022

Self Cite

View full text Add to dashboard Cite

Objective. Being able to characterize functional connectivity (FC) state dynamics in a real-time setting, such as in brain–computer interface, neurofeedback or closed-loop neurostimulation frameworks, requires the rapid detection of the statistical dependencies that quantify FC in short windows of data. The aim of this study is to characterize, through extensive realistic simulations, the reliability of FC estimation as a function of the data length. In particular, we focused on FC as measured by phase-coupling (PC) of neuronal oscillations, one of the most functionally relevant neural coupling modes. Approach. We generated synthetic data corresponding to different scenarios by varying the data length, the signal-to-noise ratio, the phase difference value, the spectral analysis approach (Hilbert or Fourier) and the fractional bandwidth. We compared seven PC metrics, i.e. imaginary part of phase locking value (PLV), PLV of orthogonalized signals, phase lag index (PLI), debiased weighted PLI, imaginary part of coherency, coherence of orthogonalized signals and lagged coherence. Main results. Our findings show that, for a signal-to-noise-ratio of at least 10 dB, a data window that contains 5 to 8 cycles of the oscillation of interest (e.g. a 500-800ms window at 10Hz) is generally required to achieve reliable PC estimates. In general, Hilbert-based approaches were associated with higher performance than Fourier-based approaches. Furthermore, the results suggest that, when the analysis is performed in a narrow frequency range, a larger window is required. Significance. The achieved results pave the way to the introduction of best-practice guidelines to be followed when a real-time frequency-specific PC assessment is at target.

show abstract

“…PSI is essentially a weighted average of the slope of the phase difference Δθ ij ( f + df ) − Δθ ij ( f ) across F , whose sign provides a measure of the directionality. In particular, a positive value indicates that the signal i precedes (thus leads) the signal j , while a negative value indicates the opposite (Basti et al, 2017). Similarly to ImCoh, PSI requires a phase slope between signals to be non-vanishing, and thus it does not lead to artifactual detection of directed connectivity due to artificial zero-lag correlations.…”

Section: Methods To Assess Brain Connectivity Based On Phase Couplingmentioning

confidence: 99%

Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography

et al. 2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Disclosing Brain Functional Connectivity From Electrophysiological Signals With Phase Slope Based Metrics

Cited by 7 publications

References 26 publications

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

Looking through the windows: a study about the dependency of phase-coupling estimates on the data length

Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography

Contact Info

Product

Resources

About