Can spurious indications for phase synchronization due to superimposed signals be avoided?

Porz, Stephan; Kiel, Matthäus; Lehnertz, Klaus

doi:10.1063/1.4890568

Cited by 41 publications

(29 citation statements)

References 81 publications

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“…First, functional connectivity was estimated using the phase lag index (PLI), which measures the asymmetry in the distribution of instantaneous phase differences between two time series. It ranges between zero (flat distribution due to noise or no connectivity, or a phase difference of zero modulus pi due to volume conduction) and one (full synchronization), and is robust against the effects of volume conduction and field spread . Using the PLI, the minimum spanning tree (MST) was constructed, which forms the backbone of the original network, where the ROIs served as nodes and the inverted PLI values (1/PLI) as edge weights.…”

Section: Methodsmentioning

confidence: 99%

“…It ranges between zero (flat distribution due to noise or no connectivity, or a phase difference of zero modulus pi due to volume conduction) and one (full synchronization), and is robust against the effects of volume conduction and field spread. 39 Using the PLI, the minimum spanning tree (MST) was constructed, which forms the backbone of the original network, where the ROIs served as nodes and the inverted PLI values (1/PLI) as edge weights. The MST is formed by starting with an empty network, and by subsequently adding the edges with the smallest weights (i.e., with the highest PLI), until all nodes in the network are connected, while discarding edges that form loops.…”

Section: Functional Networkmentioning

confidence: 99%

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Identifying the epileptogenic zone in interictal resting‐stateMEGsource‐space networks

et al. 2016

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Objective: In one third of patients, seizures remain after epilepsy surgery, meaning that improved preoperative evaluation methods are needed to identify the epilepto-genic zone. A potential framework for such a method is network theory, as it can be applied to noninvasive recordings, even in the absence of epileptiform activity. Our aim was to identify the epileptogenic zone on the basis of hub status of local brain areas in interictal magnetoencephalography (MEG) networks. Methods: Preoperative eyes-closed resting-state MEG recordings were retrospectively analyzed in 22 patients with refractory epilepsy, of whom 14 were seizure-free 1 year after surgery. Beamformer-based time series were reconstructed for 90 corti-cal and subcortical automated anatomic labeling (AAL) regions of interest (ROIs). Broadband functional connectivity was estimated using the phase lag index in artifact-free epochs without interictal epileptiform abnormalities. A minimum spanning tree was generated to represent the network, and the hub status of each ROI was calculated using betweenness centrality, which indicates the centrality of a node in a network. The correspondence of resection cavity to hub values was evaluated on four levels: resection cavity, lobar, hemisphere, and temporal versus extratemporal areas. Results: Hubs were localized within the resection cavity in 8 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (57% sensitivity, 100% specificity, 73% accuracy). Hubs were localized in the lobe of resection in 9 of 14 seizure-free patients and in zero of 8 patients who were not seizure-free (64% sensitivity, 100% specificity, 77% accuracy). For the other two levels, the true negatives are unknown; hence, only sensitivity could be determined: hubs coincided with both the resection hemisphere and the resection location (temporal versus extratemporal) in 11 of 14 seizure free patients (79% sensitivity). Significance: Identifying hubs noninvasively before surgery is a valuable approach with the potential of indicating the epileptogenic zone in patients without interictal abnormalities.

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Section: Methodsmentioning

confidence: 99%

Section: Functional Networkmentioning

confidence: 99%

Identifying the epileptogenic zone in interictal resting‐stateMEGsource‐space networks

et al. 2016

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“…The advantage of the PLI is that it is less likely to be contaminated by volume conduction (Porz et al, 2014; Stam et al, 2007). The PLI ranges between 0 (no phase synchronization) and 1 (complete phase synchronization).…”

Section: Graph Analysismentioning

confidence: 99%

Meditation is associated with increased brain network integration

et al. 2017

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Introduction This study aims to identify novel quantitative EEG measures associated with mindfulness meditation. As there is some evidence that meditation is associated with higher integration of brain networks, we focused on EEG measures of network integration. Methods Sixteen novice meditators and sixteen experienced meditators participated in the study. Novice meditators performed a basic meditation practice that supported effortless awareness, which is an important quality of experience related to mindfulness practices, while their EEG was recorded. Experienced meditators performed a self-selected meditation practice that supported effortless awareness. Network integration was analyzed with maximum betweenness centrality and leaf fraction (which both correlate positively with network integration) as well as with diameter and average eccentricity (which both correlate negatively with network integration), based on a phase-lag index (PLI) and minimum spanning tree (MST) approach. Differences between groups were assessed using repeated-measures ANOVA for the theta (4–8 Hz), alpha (8–13 Hz) and lower beta (13–20 Hz) frequency bands. Results Maximum betweenness centrality was significantly higher in experienced meditators than in novices (P=0.012) in the alpha band. In the same frequency band, leaf fraction showed a trend toward being significantly higher in experienced meditators than in novices (P=0.056), while diameter and average eccentricity were significantly lower in experienced meditators than in novices (P=0.016 and P=0.028 respectively). No significant differences between groups were observed for the theta and beta frequency bands. Conclusion These results show that alpha band functional network topology is better integrated in experienced meditators than in novice meditators during meditation. This novel finding provides the rationale to investigate the temporal relation between measures of functional connectivity network integration and meditation quality, for example using neurophenomenology experiments.

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“…Thus, by analysing both functional brain networks (the “true” one derived from PLI and the one derived from PLV, which may be affected by volume conduction effects, but includes all type of connections), we can be sure to gather most of the information available from our data in the form of functional connectivity patterns. Indeed, recent results comparing the application of PLV to PS indices robust to volume conduction, clearly suggest [96, 97] that PLV may provide additional information not present in other indices, whereas the use of advanced pre-processing methods such as the Laplacian to work with current source densities instead of voltages does not solve the volume conduction problem either [98]. …”

Section: Discussionmentioning

confidence: 99%

Electrical Brain Responses to an Auditory Illusion and the Impact of Musical Expertise

et al. 2015

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The presentation of two sinusoidal tones, one to each ear, with a slight frequency mismatch yields an auditory illusion of a beating frequency equal to the frequency difference between the two tones; this is known as binaural beat (BB). The effect of brief BB stimulation on scalp EEG is not conclusively demonstrated. Further, no studies have examined the impact of musical training associated with BB stimulation, yet musicians' brains are often associated with enhanced auditory processing. In this study, we analysed EEG brain responses from two groups, musicians and non-musicians, when stimulated by short presentation (1 min) of binaural beats with beat frequency varying from 1 Hz to 48 Hz. We focused our analysis on alpha and gamma band EEG signals, and they were analysed in terms of spectral power, and functional connectivity as measured by two phase synchrony based measures, phase locking value and phase lag index. Finally, these measures were used to characterize the degree of centrality, segregation and integration of the functional brain network. We found that beat frequencies belonging to alpha band produced the most significant steady-state responses across groups. Further, processing of low frequency (delta, theta, alpha) binaural beats had significant impact on cortical network patterns in the alpha band oscillations. Altogether these results provide a neurophysiological account of cortical responses to BB stimulation at varying frequencies, and demonstrate a modulation of cortico-cortical connectivity in musicians' brains, and further suggest a kind of neuronal entrainment of a linear and nonlinear relationship to the beating frequencies.

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Can spurious indications for phase synchronization due to superimposed signals be avoided?

Cited by 41 publications

References 81 publications

Identifying the epileptogenic zone in interictal resting‐stateMEGsource‐space networks

Identifying the epileptogenic zone in interictal resting‐stateMEGsource‐space networks

Meditation is associated with increased brain network integration

Electrical Brain Responses to an Auditory Illusion and the Impact of Musical Expertise

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