scholarly journals Bilateral Representation of Sensorimotor Responses in Benign Adult Familial Myoclonus Epilepsy: An MEG Study

2021 ◽  
Vol 12 ◽  
Author(s):  
Teppei Matsubara ◽  
Seppo P. Ahlfors ◽  
Tatsuya Mima ◽  
Koichi Hagiwara ◽  
Hiroshi Shigeto ◽  
...  
Keyword(s):  
Giant Component ◽  
Phase Lag ◽  
Time Frequency ◽  
Functional Regions ◽  
Unilateral Stimulation ◽  
Interhemispheric Connectivity ◽  
Myoclonus Epilepsy ◽  
Primary Sensorimotor Cortex ◽  
Weighted Phase Lag Index ◽  
Sensorimotor Responses

Patients with cortical reflex myoclonus manifest typical neurophysiologic characteristics due to primary sensorimotor cortex (S1/M1) hyperexcitability, namely, contralateral giant somatosensory-evoked potentials/fields and a C-reflex (CR) in the stimulated arm. Some patients show a CR in both arms in response to unilateral stimulation, with about 10-ms delay in the non-stimulated compared with the stimulated arm. This bilateral C-reflex (BCR) may reflect strong involvement of bilateral S1/M1. However, the significance and exact pathophysiology of BCR within 50 ms are yet to be established because it is difficult to identify a true ipsilateral response in the presence of the giant component in the contralateral hemisphere. We hypothesized that in patients with BCR, bilateral S1/M1 activity will be detected using MEG source localization and interhemispheric connectivity will be stronger than in healthy controls (HCs) between S1/M1 cortices. We recruited five patients with cortical reflex myoclonus with BCR and 15 HCs. All patients had benign adult familial myoclonus epilepsy. The median nerve was electrically stimulated unilaterally. Ipsilateral activity was investigated in functional regions of interest that were determined by the N20m response to contralateral stimulation. Functional connectivity was investigated using weighted phase-lag index (wPLI) in the time-frequency window of 30–50 ms and 30–100 Hz. Among seven of the 10 arms of the patients who showed BCR, the average onset-to-onset delay between the stimulated and the non-stimulated arm was 8.4 ms. Ipsilateral S1/M1 activity was prominent in patients. The average time difference between bilateral cortical activities was 9.4 ms. The average wPLI was significantly higher in the patients compared with HCs in specific cortico-cortical connections. These connections included precentral-precentral, postcentral-precentral, inferior parietal (IP)-precentral, and IP-postcentral cortices interhemispherically (contralateral region-ipsilateral region), and precentral-IP and postcentral-IP intrahemispherically (contralateral region-contralateral region). The ipsilateral response in patients with BCR may be a pathologically enhanced motor response homologous to the giant component, which was too weak to be reliably detected in HCs. Bilateral representation of sensorimotor responses is associated with disinhibition of the transcallosal inhibitory pathway within homologous motor cortices, which is mediated by the IP. IP may play a role in suppressing the inappropriate movements seen in cortical myoclonus.

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 Time-frequency analysis methods and their application in developmental EEG data

2021 ◽  
Author(s):  
Santiago Morales ◽  
Maureen Bowers
Keyword(s):  
Brain Activity ◽  
Event Related Potentials ◽  
Phase Lag ◽  
Phase Synchrony ◽  
Time Frequency ◽  
Use Of Time ◽  
Eeg Data ◽  
Related Potentials ◽  
Neurophysiological Mechanisms ◽  
The Fourier Transform

EEG provides a rich measure of brain activity that can be characterized as neuronal oscillations. However, most developmental EEG work to date has focused on analyzing EEG data as Event-Related Potentials (ERPs) or power based on the Fourier transform. While these measures have been productive, they do not leverage all the information contained within the EEG signal. Namely, ERP analyses ignore non-phase-locked signals and Fourier-based power analyses ignore temporal information. Time-frequency analyses can better characterize the oscillations contained in the EEG data. By separating power and phase information across different frequencies, time-frequency measures provide a closer interpretation of the neurophysiological mechanisms, facilitate translation across neurophysiology disciplines, and capture processes not observed by ERP or Fourier-based analyses (e.g., connectivity). Despite their unique contributions, a literature review of this journal reveals that time-frequency analyses of EEG are yet to be embraced by the developmental cognitive neuroscience field. This manuscript presents a conceptual introduction to time-frequency analyses for developmental researchers. To facilitate the use of time-frequency analyses, we include a tutorial of accessible scripts, based on Cohen (2014), to calculate time-frequency power (signal strength), inter-trial phase synchrony (signal consistency), and two types of phase-based connectivity (inter-channel phase synchrony and weighted phase lag index).

scholarly journals Objective Recognition of Tinnitus Location Using Electroencephalography Connectivity Features

2022 ◽  
Vol 15 ◽  
Author(s):  
Zhaobo Li ◽  
Xinzui Wang ◽  
Weidong Shen ◽  
Shiming Yang ◽  
David Y. Zhao ◽  
...  
Keyword(s):  
Resting State ◽  
Phase Locking ◽  
Pearson Correlation ◽  
Computing Time ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Phase Lag ◽  
Eeg Signals ◽  
Time Frequency ◽  
Phase Locking Value

Purpose: Tinnitus is a common but obscure auditory disease to be studied. This study will determine whether the connectivity features in electroencephalography (EEG) signals can be used as the biomarkers for an efficient and fast diagnosis method for chronic tinnitus.Methods: In this study, the resting-state EEG signals of tinnitus patients with different tinnitus locations were recorded. Four connectivity features [including the Phase-locking value (PLV), Phase lag index (PLI), Pearson correlation coefficient (PCC), and Transfer entropy (TE)] and two time-frequency domain features in the EEG signals were extracted, and four machine learning algorithms, included two support vector machine models (SVM), a multi-layer perception network (MLP) and a convolutional neural network (CNN), were used based on the selected features to classify different possible tinnitus sources.Results: Classification accuracy was highest when the SVM algorithm or the MLP algorithm was applied to the PCC feature sets, achieving final average classification accuracies of 99.42 or 99.1%, respectively. And based on the PLV feature, the classification result was also particularly good. And MLP ran the fastest, with an average computing time of only 4.2 s, which was more suitable than other methods when a real-time diagnosis was required.Conclusion: Connectivity features of the resting-state EEG signals could characterize the differentiation of tinnitus location. The connectivity features (PCC and PLV) were more suitable as the biomarkers for the objective diagnosing of tinnitus. And the results were helpful for clinicians in the initial diagnosis of tinnitus.

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 Implication between Geophysical Events and the Variation of Seasonal Signal Determined in GNSS Position Time Series

2021 ◽  
Vol 13 (17) ◽  
pp. 3478
Author(s):  
Sorin Nistor ◽  
Norbert-Szabolcs Suba ◽  
Ahmed El-Mowafy ◽  
Michal Apollo ◽  
Zinovy Malkin ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite System ◽  
Phase Lag ◽  
Time Frequency ◽  
Richter Scale ◽  
Different Types ◽  
Before And After ◽  
Position Time Series ◽  
Global Navigation Satellite ◽  
Seasonal Signal

The seasonal signal determined by the Global Navigation Satellite System (GNSS), which is captured in the coordinate time series, exhibits annual and semi-annual periods. This signal is frequently modelled by two periodic signals with constant amplitude and phase-lag. The purpose of this study is to explore the implication of different types of geophysical events on the seasonal signal in three stages—in the time span that contains the geophysical events, before and after the geophysical event, but also the stationarity phenomena, which is analysed on approximately 200 reference stations from the EPN network since 1995. The novelty of the article is demonstrated by correlating three different types of geophysical events, such as earthquakes with a magnitude greater than 6° on the Richter scale, landslides, and volcanic activity, and analysing the variation in amplitude of the seasonal signal. The geophysical events situated within a radius of 30 km from the epicentre showed a higher seasonal value than when the timespan did not contain a geophysical event. The presence of flicker and random walk noise was computed using overlapping Hadamard variance (OHVAR) and the non-stationary behaviour of the time series of the CORS coordinates in the time frequency analysis was done using continuous wavelet transform (CWT).

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