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 Resting-State SEEG May Help Localize Epileptogenic Brain Regions

Neurosurgery ◽  
2019 ◽  
Vol 86 (6) ◽  
pp. 792-801 ◽  
Author(s):  
Sarah E Goodale ◽  
Hernán F J González ◽  
Graham W Johnson ◽  
Kanupriya Gupta ◽  
William J Rodriguez ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Focal Epilepsy ◽  
Area Under The Curve ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Alpha Band ◽  
Connectivity Model ◽  
Connectivity Patterns ◽  
Neurosurgical Treatment

Abstract BACKGROUND Stereotactic electroencephalography (SEEG) is a minimally invasive neurosurgical method to localize epileptogenic brain regions in epilepsy but requires days in the hospital with interventions to trigger several seizures. OBJECTIVE To make initial progress in the development of network analysis methods to identify epileptogenic brain regions using brief, resting-state SEEG data segments, without requiring seizure recordings. METHODS In a cohort of 15 adult focal epilepsy patients undergoing SEEG, we evaluated functional connectivity (alpha-band imaginary coherence) across sampled regions using brief (2 min) resting-state data segments. Bootstrapped logistic regression was used to generate a model to predict epileptogenicity of individual regions. RESULTS Compared to nonepileptogenic structures, we found increased functional connectivity within epileptogenic regions (P < .05) and between epileptogenic areas and other structures (P < .01, paired t-tests, corrected). Epileptogenic areas also demonstrated higher clustering coefficient (P < .01) and betweenness centrality (P < .01), and greater decay of functional connectivity with distance (P < .05, paired t-tests, corrected). Our functional connectivity model to predict epileptogenicity of individual regions demonstrated an area under the curve of 0.78 and accuracy of 80.4%. CONCLUSION Our study represents a preliminary step towards defining resting-state SEEG functional connectivity patterns to help localize epileptogenic brain regions ahead of neurosurgical treatment without requiring seizure recordings.

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 EEG Resting State Functional Connectivity in Adult Dyslexics Using Phase Lag Index and Graph Analysis

2018 ◽  
Vol 12 ◽  
Author(s):  
Gorka Fraga González ◽  
Dirk J. A. Smit ◽  
Melle J. W. van der Molen ◽  
Jurgen Tijms ◽  
Cornelis Jan Stam ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Graph Analysis ◽  
Phase Lag ◽  
Resting State Functional Connectivity

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.

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.

Prediction of Depression Severity Scores Based on Functional Connectivity and Complexity of the EEG Signal

2020 ◽  
Vol 52 (1) ◽  
pp. 52-60
Author(s):  
Yousef Mohammadi ◽  
Mohammad Hassan Moradi
Keyword(s):  
Functional Connectivity ◽  
Regression Model ◽  
Brain Activity ◽  
Clustering Coefficient ◽  
Eeg Signal ◽  
Alpha Band ◽  
Depression Severity ◽  
Severity Scores ◽  
Severity Prediction ◽  
Severity Of Depression

Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.

scholarly journals Decreased resting-state alpha-band activation and functional connectivity after sleep deprivation

2020 ◽  
Author(s):  
Jintao Wu ◽  
Qianxiang Zhou ◽  
Jiaxuan Li ◽  
Yang Chen ◽  
Shuyu Shao ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Power Spectrum ◽  
Sleep Deprivation ◽  
Resting State ◽  
Cingulate Cortex ◽  
Posterior Cingulate Cortex ◽  
Alpha Band ◽  
Posterior Cingulate ◽  
Recovery Sleep ◽  
Normal Sleep

Abstract BackgroundCognitive abilities are impaired by sleep deprivation and can be recovered when sufficient sleep is obtained. Changes in alpha-band oscillations are considered to be highly related to sleep deprivation. The effect of sleep deprivation on brain activation and functional connectivity in the resting-state alpha band remains unclear. The purpose of this study was to investigate how sleep deprivation and recovery sleep could change resting-state alpha-band neural oscillations.MethodsIn this study, thirty young, healthy participants obtained approximately 8 h of normal sleep, followed by 36 h of sleep deprivation. On the following recovery night, subjects underwent recovery sleep. Resting-state EEG after normal sleep, sleep deprivation and recovery sleep was recorded. Power spectrum, source localization and functional connectivity analyses were used to investigate the changes in resting-state alpha-band activity after normal sleep, sleep deprivation and recovery sleep.ResultsThe results showed that the global alpha power spectrum decreased and source activation was notably reduced in the precuneus, posterior cingulate cortex, cingulate gyrus, and paracentral lobule after sleep deprivation. Functional connectivity analysis after sleep deprivation showed a weakened functional connectivity pattern in a widespread network with the precuneus and posterior cingulate cortex as the key nodes. Furthermore, the changes caused by sleep deprivation were reversed to a certain extent but not significantly after one night of sleep recovery, which may be due to inadequate time for recovery sleep.ConclusionsIn conclusion, large-scale resting-state alpha-band activation and functional connectivity were weakened after sleep deprivation, and the inhibition of default mode network function with the precuneus and posterior cingulate cortex as the pivotal nodes may be an important cause of cognitive impairment. These findings provide new insight into the physiological response of sleep deprivation and determine how sleep deprivation disrupts brain alpha-band oscillations.

scholarly journals Abnormal Spontaneous Brain Activity and Functional Connectivity of the Default Mode Network in Patients with Bipolar Disorder

2021 ◽  
Author(s):  
Lei Zhao ◽  
Qijing Bo ◽  
Zhifang Zhang ◽  
Feng Li ◽  
Yuan Zhou ◽  
...  
Keyword(s):  
Bipolar Disorder ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Resting State ◽  
Brain Activity ◽  
Seed Region ◽  
Potential Candidate ◽  
Neural Basis ◽  
Default Mode ◽  
Spontaneous Brain Activity

Abstract Background: No consistent evidence on the specific brain regions is available in the default mode network (DMN), which show abnormal spontaneous activity in bipolar disorder (BD). We aim to identify this region that is particularly impaired in patients with BD by using several different indices measuring spontaneous brain activity and then investigate its functional connectivity (FC).Methods: A total of 56 patients with BD and 71 healthy controls (HC) underwent resting-state functional magnetic resonance imaging. Three commonly used functional indices were used to identify the brain region showing abnormal spontaneous brain activity in BD. Then, this region served as the seed region for resting-state FC analysis to identify its functional networks altered in BD.Results: The BD group exhibited decreased fALFF, ReHo, and DC values in the left precuneus. The BD group had decreased rsFC within the DMN, indicated by decreased resting-state FC within the left precuneus and between the left precuneus and the medial prefrontal cortex. The BD group had decreased negative connectivity between the left precuneus and the left putamen, extending to the left insula.Conclusions: The findings provide convergent evidence for the abnormalities in the DMN of BD, particularly located in the left precuneus. Decreased FC within the DMN and the disruptive anticorrelation between the DMN and the salience network are found in BD. These findings suggest that the DMN is a key aspect for understanding the neural basis of BD, and the altered functional patterns of DMN may be a potential candidate biomarker of BD.

scholarly journals Reorganization of Brain Functional Connectivity Network and Vision Restoration Following Combined tACS-tDCS Treatment After Occipital Stroke

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahua Xu ◽  
Zheng Wu ◽  
Andreas Nürnberger ◽  
Bernhard A. Sabel
Keyword(s):  
Visual Field ◽  
Functional Connectivity ◽  
Temporal Lobe ◽  
Brain Stimulation ◽  
Occipital Lobe ◽  
Clustering Coefficient ◽  
Alpha Band ◽  
Delta Band ◽  
Brain Functional Connectivity

Objective: Non-invasive brain stimulation (NIBS) is already known to improve visual field functions in patients with optic nerve damage and partially restores the organization of brain functional connectivity networks (FCNs). However, because little is known if NIBS is effective also following brain damage, we now studied the correlation between visual field recovery and FCN reorganization in patients with stroke of the central visual pathway.Method: In a controlled, exploratory trial, 24 patients with hemianopia were randomly assigned to one of three brain stimulation groups: transcranial direct current stimulation (tDCS)/transcranial alternating current stimulation (tACS) (ACDC); sham tDCS/tACS (AC); sham tDCS/sham tACS (Sham), which were compared to age-matched controls (n = 24). Resting-state electroencephalogram (EEG) was collected at baseline, after 10 days stimulation and at 2 months follow-up. EEG recordings were analyzed for FCN measures using graph theory parameters, and FCN small worldness of the network and long pairwise coherence parameter alterations were then correlated with visual field performance.Result: ACDC enhanced alpha-band FCN strength in the superior occipital lobe of the lesioned hemisphere at follow-up. A negative correlation (r = −0.80) was found between the intact visual field size and characteristic path length (CPL) after ACDC with a trend of decreased alpha-band centrality of the intact middle occipital cortex. ACDC also significantly decreased delta band coherence between the lesion and the intact occipital lobe, and coherence was enhanced between occipital and temporal lobe of the intact hemisphere in the low beta band. Responders showed significantly higher strength in the low alpha band at follow-up in the intact lingual and calcarine cortex and in the superior occipital region of the lesioned hemisphere.Conclusion: While ACDC decreases delta band coherence between intact and damaged occipital brain areas indicating inhibition of low-frequency neural oscillations, ACDC increases FCN connectivity between the occipital and temporal lobe in the intact hemisphere. When taken together with the lower global clustering coefficient in responders, these findings suggest that FCN reorganization (here induced by NIBS) is adaptive in stroke. It leads to greater efficiency of neural processing, where the FCN requires fewer connections for visual processing.

scholarly journals Modeling the Switching behavior of Functional Connectivity Microstates (FCμstates) as a Novel Biomarker for Mild Cognitive Impairment

2019 ◽  
Author(s):  
SI Dimitriadis ◽  
María Eugenia López ◽  
Fernando Maestu ◽  
Ernesto Pereda
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Resting State ◽  
External Validation ◽  
Clinical Importance ◽  
Switching Behavior ◽  
Phase Lag ◽  
Dynamic Functional Connectivity ◽  
Frequency Coupling

AbstractIt is evident the need for designing and validating novel biomarkers for the detection of mild cognitive impairment (MCI). MCI patients have a high risk of developing Alzheimer’s disease (AD), and for that reason the introduction of novel and reliable biomarkers is of significant clinical importance. Motivated by recent findings about the rich information of dynamic functional connectivity graphs (DFCGs) about brain (dys)function, we introduced a novel approach of identifying MCI based on magnetoencephalographic (MEG) resting state recordings.The activity of different brain rhythms {δ, θ, α1, α2, β1, β2, γ1, γ2} was first beamformed with linear constrained minimum norm variance in the MEG data to determine ninety anatomical regions of interest (ROIs). A dynamic functional connectivity graph (DFCG) was then estimated using the imaginary part of phase lag value (iPLV) for both intra-frequency coupling (8) and also cross-frequency coupling pairs (28). We analyzed DFCG profiles of neuromagnetic resting state recordings of 18 Mild Cognitive Impairment (MCI) patients and 20 healthy controls. We followed our model of identifying the dominant intrinsic coupling mode (DICM) across MEG sources and temporal segments that further leads to the construction of an integrated DFCG (iDFCG). We then filtered statistically and topologically every snapshot of the iDFCG with data-driven approaches. Estimation of the normalized Laplacian transformation for every temporal segment of the iDFCG and the related eigenvalues created a 2D map based on the network metric time series of the eigenvalues (NMTSeigs). NMTSeigs preserves the non-stationarity of the fluctuated synchronizability of iDCFG for each subject. Employing the initial set of 20 healthy elders and 20 MCI patients, as training set, we built an overcomplete dictionary set of network microstates (nμstates). Afterward, we tested the whole procedure in an extra blind set of 20 subjects for external validation.We succeeded a high classification accuracy on the blind dataset (85 %) which further supports the proposed Markovian modeling of the evolution of brain states. The adaptation of appropriate neuroinformatic tools that combine advanced signal processing and network neuroscience tools could manipulate properly the non-stationarity of time-resolved FC patterns revealing a robust biomarker for MCI.

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