Identifying EEG Responses Modulated by Working Memory Loads from Weighted Phase Lag Index Based Functional Connectivity Microstates

2019 ◽  
pp. 441-449
Author(s):  
Li Zhang ◽  
Bo Shi ◽  
Mingna Cao ◽  
Sai Zhang ◽  
Yiming Dai ◽  
...  
Keyword(s):  
Working Memory ◽  
Functional Connectivity ◽  
Phase Lag ◽  
Weighted Phase Lag Index

scholarly journals Dynamic Cortical Connectivity during General Anesthesia in Surgical Patients

2019 ◽  
Vol 130 (6) ◽  
pp. 885-897 ◽  
Author(s):  
Phillip E. Vlisides ◽  
Duan Li ◽  
Mackenzie Zierau ◽  
Andrew P. Lapointe ◽  
Ka I. Ip ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
General Anesthesia ◽  
Temporal Dynamics ◽  
Transition Probabilities ◽  
Surgical Patients ◽  
Phase Lag ◽  
Cortical Connectivity ◽  
Single Measure ◽  
Connectivity Patterns ◽  
Weighted Phase Lag Index

Abstract Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Functional connectivity across the cortex has been posited to be important for consciousness and anesthesia, but functional connectivity patterns during the course of surgery and general anesthesia are unknown. The authors tested the hypothesis that disrupted cortical connectivity patterns would correlate with surgical anesthesia. Methods Surgical patients (n = 53) were recruited for study participation. Whole-scalp (16-channel) wireless electroencephalographic data were prospectively collected throughout the perioperative period. Functional connectivity was assessed using weighted phase lag index. During anesthetic maintenance, the temporal dynamics of connectivity states were characterized via Markov chain analysis, and state transition probabilities were quantified. Results Compared to baseline (weighted phase lag index, 0.163, ± 0.091), alpha frontal–parietal connectivity was not significantly different across the remaining anesthetic and perioperative epochs, ranging from 0.100 (± 0.041) to 0.218 (± 0.136) (P > 0.05 for all time periods). In contrast, there were significant increases in alpha prefrontal–frontal connectivity (peak = 0.201 [0.154, 0.248]; P < 0.001), theta prefrontal–frontal connectivity (peak = 0.137 [0.091, 0.182]; P < 0.001), and theta frontal–parietal connectivity (peak = 0.128 [0.084, 0.173]; P < 0.001) during anesthetic maintenance. Additionally, shifts occurred between states of high prefrontal–frontal connectivity (alpha, beta) with suppressed frontal–parietal connectivity, and high frontal–parietal connectivity (alpha, theta) with reduced prefrontal–frontal connectivity. These shifts occurred in a nonrandom manner (P < 0.05 compared to random transitions), suggesting structured transitions of connectivity during general anesthesia. Conclusions Functional connectivity patterns dynamically shift during surgery and general anesthesia but do so in a structured way. Thus, a single measure of functional connectivity will likely not be a reliable correlate of surgical anesthesia.

scholarly journals Weighted Phase Lag Index and Graph Analysis: Preliminary Investigation of Functional Connectivity during Resting State in Children

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Erick Ortiz ◽  
Krunoslav Stingl ◽  
Jana Münßinger ◽  
Christoph Braun ◽  
Hubert Preissl ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Power Distribution ◽  
Clustering Coefficient ◽  
Graph Analysis ◽  
Seed Region ◽  
Phase Lag ◽  
Alpha Band ◽  
Occipital Area ◽  
Weighted Phase Lag Index

Resting state functional connectivity of MEG data was studied in 29 children (9-10 years old). The weighted phase lag index (WPLI) was employed for estimating connectivity and compared to coherence. To further evaluate the network structure, a graph analysis based on WPLI was used to determine clustering coefficient (C) and betweenness centrality (BC) as local coefficients as well as the characteristic path length (L) as a parameter for global interconnectedness. The network’s modular structure was also calculated to estimate functional segregation. A seed region was identified in the central occipital area based on the power distribution at the sensor level in the alpha band. WPLI reveals a specific connectivity map different from power and coherence. BC and modularity show a strong level of connectedness in the occipital area between lateral and central sensors.Cshows different isolated areas of occipital sensors. Globally, a network with the shortestLis detected in the alpha band, consistently with the local results. Our results are in agreement with findings in adults, indicating a similar functional network in children at this age in the alpha band. The integrated use of WPLI and graph analysis can help to gain a better description of resting state networks.

scholarly journals Cross-participant prediction of vigilance stages through the combined use of wPLI and wSMI EEG functional connectivity metrics

SLEEP ◽  
2020 ◽  
Author(s):  
Laura Sophie Imperatori ◽  
Jacinthe Cataldi ◽  
Monica Betta ◽  
Emiliano Ricciardi ◽  
Robin A A Ince ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Rem Sleep ◽  
Phase Lag ◽  
Linear Discriminant ◽  
Functional Differences ◽  
Combined Use ◽  
Discriminant Analyses ◽  
Eeg Functional Connectivity ◽  
Weighted Phase Lag Index ◽  
Healthy Participants

Abstract Functional connectivity (FC) metrics describe brain inter-regional interactions and may complement information provided by common power-based analyses. Here, we investigated whether the FC-metrics weighted Phase Lag Index (wPLI) and weighted Symbolic Mutual Information (wSMI) may unveil functional differences across four stages of vigilance—wakefulness (W), NREM-N2, NREM-N3, and REM sleep—with respect to each other and to power-based features. Moreover, we explored their possible contribution in identifying differences between stages characterized by distinct levels of consciousness (REM+W vs. N2+N3) or sensory disconnection (REM vs. W). Overnight sleep and resting-state wakefulness recordings from 24 healthy participants (27 ± 6 years, 13F) were analyzed to extract power and FC-based features in six classical frequency bands. Cross-validated linear discriminant analyses (LDA) were applied to investigate the ability of extracted features to discriminate (1) the four vigilance stages, (2) W+REM vs. N2+N3, and (3) W vs. REM. For the four-way vigilance stages classification, combining features based on power and both connectivity metrics significantly increased accuracy relative to considering only power, wPLI, or wSMI features. Delta-power and connectivity (0.5–4 Hz) represented the most relevant features for all the tested classifications, in line with a possible involvement of slow waves in consciousness and sensory disconnection. Sigma-FC, but not sigma-power (12–16 Hz), was found to strongly contribute to the differentiation between states characterized by higher (W+REM) and lower (N2+N3) probabilities of conscious experiences. Finally, alpha-FC resulted as the most relevant FC-feature for distinguishing among wakefulness and REM sleep and may thus reflect the level of disconnection from the external environment.

scholarly journals Reproducibility of Functional Connectivity and Graph Measures Based on the Phase Lag Index (PLI) and Weighted Phase Lag Index (wPLI) Derived from High Resolution EEG

PLoS ONE ◽  
2014 ◽  
Vol 9 (10) ◽  
pp. e108648 ◽  
Author(s):  
Martin Hardmeier ◽  
Florian Hatz ◽  
Habib Bousleiman ◽  
Christian Schindler ◽  
Cornelis Jan Stam ◽  
...  
Keyword(s):  
High Resolution ◽  
Functional Connectivity ◽  
Phase Lag ◽  
Weighted Phase Lag Index ◽  
High Resolution Eeg

scholarly journals Cortical functional connectivity indexes arousal state during sleep and anesthesia

2019 ◽  
Author(s):  
Matthew I. Banks ◽  
Bryan M. Krause ◽  
Christopher M. Endemann ◽  
Declan I. Campbell ◽  
Christopher K. Kovach ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sensory Processing ◽  
Phase Lag ◽  
Alpha Band ◽  
Cortical Connectivity ◽  
Arousal State ◽  
Neurosurgical Patients ◽  
Underlying Mechanisms ◽  
Intracranial Recordings ◽  
Weighted Phase Lag Index

AbstractDisruption of cortical connectivity likely contributes to loss of consciousness (LOC) during both sleep and general anesthesia, but the degree of overlap in the underlying mechanisms is unclear. Both sleep and anesthesia comprise states of varying levels of arousal and consciousness, including states of largely maintained consciousness (sleep: N1, REM; anesthesia: sedated but responsive) as well as states of substantially reduced consciousness (sleep: N2/N3; anesthesia: unresponsive). Here, we tested the hypotheses that (1) cortical connectivity will reflect clear changes when transitioning into states of reduced consciousness, and (2) these changes are similar for arousal states of comparable levels of consciousness during sleep and anesthesia. Using intracranial recordings from five neurosurgical patients, we compared resting state cortical functional connectivity (as measured by weighted phase lag index) in the same subjects across arousal states during natural sleep [wake (WS), N1, N2, N3, REM] and propofol anesthesia [pre-drug wake (WA), sedated/responsive (S) and unresponsive (U)]. In wake states WS and WA, alpha-band connectivity within and between temporal, parietal and occipital regions was dominant. This pattern was largely unchanged in N1, REM and S. Transitions into states of reduced consciousness N2, N3 and U were characterized by dramatic and strikingly similar changes in connectivity, with dominant connections shifting to frontal cortex. We suggest that shifts from temporo-parieto-occipital to frontal cortical connectivity may reflect impaired sensory processing in states of reduced consciousness. The data indicate that functional connectivity can serve as a biomarker of arousal state and suggest common mechanisms of LOC in sleep and anesthesia.

scholarly journals Effect of Zolpidem in the Aftermath of Traumatic Brain Injury: An MEG Study

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Praveen Sripad ◽  
Jessica Rosenberg ◽  
Frank Boers ◽  
Christian P. Filss ◽  
Norbert Galldiks ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Connectivity ◽  
Brain Activity ◽  
Brain Regions ◽  
Phase Lag ◽  
Before And After ◽  
Source Level ◽  
Weighted Phase Lag Index

In the past two decades, many studies have shown the paradoxical efficacy of zolpidem, a hypnotic used to induce sleep, in transiently alleviating various disorders of consciousness such as traumatic brain injury (TBI), dystonia, and Parkinson’s disease. The mechanism of action of this effect of zolpidem is of great research interest. In this case study, we use magnetoencephalography (MEG) to investigate a fully conscious, ex-coma patient who suffered from neurological difficulties for a few years due to traumatic brain injury. For a few years after injury, the patient was under medication with zolpidem that drastically improved his symptoms. MEG recordings taken before and after zolpidem showed a reduction in power in the theta-alpha (4–12 Hz) and lower beta (15–20 Hz) frequency bands. An increase in power after zolpidem intake was found in the higher beta/lower gamma (20–43 Hz) frequency band. Source level functional connectivity measured using weighted-phase lag index showed changes after zolpidem intake. Stronger connectivity between left frontal and temporal brain regions was observed. We report that zolpidem induces a change in MEG resting power and functional connectivity in the patient. MEG is an informative and sensitive tool to detect changes in brain activity for TBI.

scholarly journals Test–Retest Reliability of Magnetoencephalography Resting-State Functional Connectivity in Schizophrenia

2020 ◽  
Vol 11 ◽  
Author(s):  
Felicha T. Candelaria-Cook ◽  
Julia M. Stephen
Keyword(s):  
Functional Connectivity ◽  
Cognitive Control ◽  
Resting State ◽  
Phase Lag ◽  
Source Modeling ◽  
Resting State Functional Connectivity ◽  
Whole Brain ◽  
Default Mode ◽  
Eyes Closed ◽  
Weighted Phase Lag Index

The reliability of magnetoencephalography (MEG) resting-state functional connectivity in schizophrenia (SZ) is unknown as previous research has focused on healthy controls (HC). Here, we examined reliability in 26 participants (13-SZ, 13-HC). Eyes opened and eyes closed resting-state data were collected on 4 separate occasions during 2 visits, 1 week apart. For source modeling, we used minimum norm software to apply dynamic statistical parametric mapping. Source analyses compared the following functional connectivity metrics from each data run: coherence (coh), imaginary coherence (imcoh), pairwise phase consistency (ppc), phase-locking value (plv), phase lag index (pli), weighted phase lag index (wpli), and weighted phase lag index debiased (wpli2). Intraclass correlation coefficients (ICCs) were calculated for whole brain, network, and network pair averages. For reliability, ICCs above 0.75 = excellent, above 0.60 = good, above 0.40 = fair, and below 0.40 = poor reliability. We found the reliability of these metrics varied greatly depending on frequency band, network, network pair, and participant group examined. Broadband (1–58 Hz) whole brain averages in both HC and SZ showed excellent reliability for wpli2, and good to fair reliability for ppc, plv, and coh. Broadband network averages showed excellent to good reliability across 1 hour and 1 week for coh, imcoh, ppc, plv, wpli within default mode, cognitive control, and visual networks in HC, while the same metrics had excellent to fair reliability in SZ. Regional network pair averages showed good to fair reliability for coh, ppc, plv within default mode, cognitive control and visual network pairs in HC and SZ. In general, HC had higher reliability compared to SZ, and the default mode, cognitive control, and visual networks had higher reliability compared to somatosensory and auditory networks. Similar reliability levels occurred for both eyes opened and eyes closed resting-states for most metrics. The functional connectivity metrics of coh, ppc, and plv performed best across 1 hour and 1 week in HC and SZ. We also found that SZ had reduced coh, plv, and ppc in the dmn average and pair values indicating dysconnectivity in SZ. These findings encourage collecting both eyes opened and eyes closed resting-state MEG, while demonstrating that clinical populations may differ in reliability.

Functional Connectivity Alterations Based on the Weighted Phase Lag Index: An Exploratory Electroencephalography Study on Alzheimer’s Disease

2021 ◽  
Vol 18 ◽  
Author(s):  
Yi Yan ◽  
Aonan Zhao ◽  
Yinghui Qiu ◽  
Yanfei Ding ◽  
Ying Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Phase Synchronization ◽  
Significant Negative Correlation ◽  
Phase Lag ◽  
Connection Strength ◽  
Theta Band ◽  
Delta Band ◽  
Weighted Phase Lag Index

Objectives: Numerous electroencephalography (EEG) studies focus on the alteration of electrical activity in patients with Alzheimer’s Disease (AD), but there are no consistent results es- pecially regarding functional connectivity. We supposed that the weighted Phase Lag Index (w- PLI), as phase-based measures of functional connectivity, may be used as an auxiliary diagnostic method for AD. Methods: We enrolled 30 patients with AD, 30 patients with Mild Cognitive Impairment (MCI), and 30 Healthy Controls (HC). EEGs were recorded in all participants at baseline during relaxed wakefulness. Following EEG preprocessing, Power Spectral Density (PSD) and wPLI parameters were determined to further analyze whether they were correlated to cognitive scores. Results: In the patients with AD, the increased PSD in theta band was presented compared with MCI and HC groups, which was associated with disturbances of the directional, computational, and delayed memory capacity. Furthermore, the wPLI revealed a distinctly lower connection strength between frontal and distant areas in the delta band and a higher connection strength of the central and temporo-occipital region in the theta band for AD patients. Moreover,we found a significant negative correlation between theta functional connectivity and cognitive scores. Conclusions: Increased theta PSD and decreased delta wPLI may be one of the earliest changes in AD and associated with disease severity. The parameter wPLI is a novel measurement of phase synchronization and has potentials in understanding underlying functional connectivity and aiding in the diagnostics of AD.

scholarly journals Emotional Information in News Reporting on Audience Cognitive Processing in the Age of Posttruth: An Electroencephalogram and Functional Connectivity Approach

2021 ◽  
Vol 12 ◽  
Author(s):  
Ya Yang ◽  
Lichao Xiu ◽  
Guoming Yu
Keyword(s):  
Functional Connectivity ◽  
Cognitive Processing ◽  
Emotional Expression ◽  
Brain Activity ◽  
Cognitive Resources ◽  
Phase Lag ◽  
Emotional Information ◽  
News Reporting ◽  
Brain Functional Connectivity ◽  
Weighted Phase Lag Index

The purpose of the present study is to explore how the emotionalized expression of news content in the posttruth era affects the cognitive processing of the audiences. One news that was text-written with two different expression types (emotional expression vs. neutral expression) was adopted as an experiment material in the study, and changes in cortical activity during news reporting reading tasks were examined with electroencephalograms, sampled from nine sites and four channels and analyzed with weighted phase lag index (wPLI) based on brain functional connectivity (FC) method. The results show that emotional discourses caused a stronger cortical brain activity and more robust brain FC (beta oscillations); besides, reading emotionalized expression consumed more attention resources but fewer cognitive resources, which may impede further rational thinking of the audiences.

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