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 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 Graph Analysis of EEG Functional Connectivity Networks During a Letter-Speech Sound Binding Task in Adult Dyslexics

2021 ◽  
Vol 12 ◽  
Author(s):  
Gorka Fraga-González ◽  
Dirk J. A. Smit ◽  
Melle J. W. Van der Molen ◽  
Jurgen Tijms ◽  
Cornelis J. Stam ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Minimum Spanning Tree ◽  
Speech Sound ◽  
Graph Analysis ◽  
Phase Lag ◽  
New Associations ◽  
Degree Correlation ◽  
Accuracy Increase ◽  
And Performance ◽  
Eeg Functional Connectivity

We performed an EEG graph analysis on data from 31 typical readers (22.27 ± 2.53 y/o) and 24 dyslexics (22.99 ± 2.29 y/o), recorded while they were engaged in an audiovisual task and during resting-state. The task simulates reading acquisition as participants learned new letter-sound mappings via feedback. EEG data was filtered for the delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), and beta (13–30 Hz) bands. We computed the Phase Lag Index (PLI) to provide an estimate of the functional connectivity between all pairs of electrodes per band. Then, networks were constructed using a Minimum Spanning Tree (MST), a unique sub-graph connecting all nodes (electrodes) without loops, aimed at minimizing bias in between groups and conditions comparisons. Both groups showed a comparable accuracy increase during task blocks, indicating that they correctly learned the new associations. The EEG results revealed lower task-specific theta connectivity, and lower theta degree correlation over both rest and task recordings, indicating less network integration in dyslexics compared to typical readers. This pattern suggests a role of theta oscillations in dyslexia and may reflect differences in task engagement between the groups, although robust correlations between MST metrics and performance indices were lacking.

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

2019 ◽  
Vol 64 ◽  
pp. S167
Author(s):  
L.S. Imperatori ◽  
J. Cataldi ◽  
M. Betta ◽  
E. Ricciardi ◽  
R. Ince ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Combined Use ◽  
Eeg Functional Connectivity

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 A Machine Learning Approach Involving Functional Connectivity Features to Classify Rest-EEG Psychogenic Non-Epileptic Seizures from Healthy Controls

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 129
Author(s):  
Giuseppe Varone ◽  
Wadii Boulila ◽  
Michele Lo Lo Giudice ◽  
Bilel Benjdira ◽  
Nadia Mammone ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Epileptic Seizures ◽  
Brain Regions ◽  
Support Vector ◽  
Phase Lag ◽  
Healthy Controls ◽  
Distributed Information ◽  
Linear Discriminant ◽  
And Gender ◽  
Mlp Classifier

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

Sign in / Sign up

Export Citation Format

Share Document