Search

Abstract Anxiety is a common non-motor symptom in Parkinson’s disease (PD) occurring in up to 31% of the patients and affecting their quality of life. Despite the high prevalence, anxiety symptoms in PD are often underdiagnosed and, therefore, undertreated. To date, functional and structural neuroimaging studies have contributed to our understanding of the motor and cognitive symptomatology of PD. Yet, the underlying pathophysiology of anxiety symptoms in PD remains largely unknown and studies on their neural correlates are missing. Here, we used resting-state electroencephalography (RS-EEG) of 68 non-demented PD patients with or without clinically-defined anxiety and 25 healthy controls (HC) to assess spectral and functional connectivity fingerprints characterizing the PD-related anxiety. When comparing the brain activity of the PD anxious group (PD-A, N = 18) to both PD non-anxious (PD-NA, N = 50) and HC groups (N = 25) at baseline, our results showed increased fronto-parietal delta power and decreased frontal beta power depicting the PD-A group. Results also revealed hyper-connectivity networks predominating in delta, theta and gamma bands against prominent hypo-connectivity networks in alpha and beta bands as network signatures of anxiety in PD where the frontal, temporal, limbic and insular lobes exhibited the majority of significant connections. Moreover, the revealed EEG-based electrophysiological signatures were strongly associated with the clinical scores of anxiety and followed their progression trend over the course of the disease. We believe that the identification of the electrophysiological correlates of anxiety in PD using EEG is conducive toward more accurate prognosis and can ultimately support personalized psychiatric follow-up and the development of new therapeutic strategies.

Background and objectivesCognitive decline is an important and common complication in patients with Parkinson’s disease (PD) since it significantly reduces the quality of life. A breakthrough in treating and preventing cognitive decline in PD remains to be achieved. This study aimed to evaluate the effectiveness of high-frequency and intensive multimodal training in improving motor and cognitive function.MethodsTwenty-eight patients diagnosed with idiopathic PD completed a comprehensive neuropsychological test battery and were neurologically examined. The patients of the intervention group (n = 15) underwent 2 weekly sessions of Tai Chi therapy over 4 weeks and participated in an individually tailored training program consisting of two modules (smartphone-based speech training and cognitive training). A matched control group consisted of n = 13 patients with PD who received computer-assisted cognitive training. The data were analyzed with repeated-measures ANOVA.ResultsFour weeks of high-frequency training showed significant effects on verbal and figural episodic memory and visuospatial function in the intervention group.Compared to the control group, the cognitive performance of the intervention group improved significantly in visuospatial function and figural episodic memory. A significant improvement was also shown in the intervention group in the Tinetti Mobility Test and the Epworth Sleepiness Scale. The significant effects in the Tinetti mobility test remained after the 6 months follow-up. After the intervention, the patients reported high motivation and satisfaction with the multimodal training.ConclusionIn patients with PD, a multimodal training program not only improves gait and stability but may also contribute to improving cognition.Clinical trial registrationClinicalTrials.gov Identifier: NCT04103255; https://register.clinicaltrials.gov/prs/app/action/LoginUser?ts=1&cx=-jg9qo4.

Abstract The aim of the study is to identify the dynamic change pattern of EEG to predict cognitive decline in patients with Parkinson’s disease. Here we demonstrate that the quantification of synchrony-pattern changes across the scalp, measured using electroencephalography (EEG), offers an alternative approach of observing an individual’s functional brain organization. This method, called “Time-Between-Phase-Crossing” (TBPC), is based on the same phenomenon as the phase-lag-index (PLI); it also considers intermittent changes in the signals of phase differences between pairs of EEG signals, but additionally analyzes dynamic connectivity changes. We used data from 75 non-demented Parkinson’s disease patients and 72 healthy controls, who were followed over a period of 3 years. Statistics were calculated using connectome-based modeling (CPM) and receiver operating characteristic (ROC). We show that TBPC profiles, via the use of intermittent changes in signals of analytic phase differences of pairs of EEG signals, can be used to predict cognitive decline in Parkinson’s disease (p

ObjectiveAnxiety, depression, and apathy are the most common neuropsychiatric symptoms in Parkinson's disease (PD) patients. They impair cognitive functioning and have a profound impact on quality of life. This follow-up study aims to investigate the predictive value of anxiety, depression, and apathy on the development of Mild Cognitive Impairment (MCI) in PD patients.MethodsTwenty-nine cognitively unimpaired PD patients (mean age 68.2 SD ± 7.12 years; 13 women) participated in this study. At Baseline (BL) levels of apathy (Apathy Evaluation Scale, AES), depression (Beck Depression Inventory, BDI-II), and anxiety (Beck Anxiety Inventory, BAI), were assessed. Cognitive status was reassessed three years later according to MCI/non-MCI status. For statistics, we used binary logistic regression and receiver operating characteristic curve (ROC) analysis to examine anxiety, apathy, and depression at BL as a predictor of MCI status three years later.ResultsEight of the 29 patients developed MCI. Anxiety level at BL was found to predict MCI status at three-year follow-up (OR = 1.20, CI = 1.02–1.41, p = 0.02), while depression (OR = 1.16, CI = 0.93–1.47, p = 0.20) and apathy (OR = 1.06, CI = 0.92–1.23, p = 0.40) did not predict MCI status. The area under the ROC curve (AUC) of BAI for discriminating PD-non-MCI from PD-MCI was 0.79 (CI = 0.61–0.98). The optimal classification threshold yielded a sensitivity of 75.0 % and a specificity of 76.2 %. Neither apathy nor depression at BL discriminated between PD-non-MCI patients from PD-MCI three years later.ConclusionsThis study shows an association between anxiety and the development of MCI in PD patients, although the association between apathy, depression, and MCI did not reach a significant level.

Background:Recent studies suggest movements of speech and gait in patients with Parkinson's Disease (PD) are impaired by a common underlying rhythmic dysfunction. If this being the case, motor deficits in speech and gait should equally benefit from rhythmic interventions regardless of whether it is a speech-specific or step-training-specific approach.Objective:In this intervention trial, we studied the effects of two rhythmic interventions on speech and gait. These rhythmic intervention programs are similar in terms of intensity and frequency (i.e., 3x per week, 45 min-long sessions for 4 weeks in total), but differ regarding therapeutic approach (rhythmic speech vs. rhythmic balance-mobility training).Methods:This study is a cross-over, parallel multi-arms, single blind intervention trial, in which PD patients treated with rhythmic speech-language therapy (rSLT; N = 16), rhythmic balance-mobility training (rBMT; N = 10), or no therapy (NT; N = 18) were compared to healthy controls (HC; N = 17; matched by age, sex, and education: p > 0.82). Velocity and cadence in speech and gait were evaluated at baseline (BL), 4 weeks (4W-T1), and 6 months (6M-T2) and correlated.Results:Parameters in speech and gait (i.e., speaking and walking velocity, as well as speech rhythm with gait cadence) were positively correlated across groups (p < 0.01). Statistical analyses involved repeated measures ANOVA across groups and time, as well as independent and one-samples t-tests for within groups analyses. Statistical analyses were amplified using Reliable Change (RC) and Reliable Change Indexes (RCI) to calculate true clinically significant changes due to the treatment on a patient individual level. Rhythmic intervention groups improved across variables and time (total Mean Difference: 3.07 [SD 1.8]; 95% CI 0.2–11.36]) compared to the NT group, whose performance declined significantly at 6 months (p < 0.01). HC outperformed rBMT and NT groups across variables and time (p < 0.001); the rSLT performed similarly to HC at 4 weeks and 6 months in speech rhythm and respiration.Conclusions:Speech and gait deficits in PD may share a common mechanism in the underlying cortical circuits. Further, rSLT was more beneficial to dysrhythmic PD patients than rBMT, likely because of the nature of the rhythmic cue.

Background: Fatigue is a common non-motor symptom in Parkinson’s disease (PD) and is typically assessed via self-reported questionnaires such as the Parkinson’s Fatigue Scale (PFS). The PFS captures the presence of subjective experience of physical fatigue as well as its impact on daily functioning. Objectives: We aimed to investigate whether different variables (cognition, neuropsychiatric symptoms, disease-related measures) are associated with the experience of physical fatigue in comparison to fatigue affecting daily functioning. Method: Sixty-two non-demented PD patients were evaluated through questionnaires assessing fatigue, daytime sleepiness, apathy, depression, anxiety, and cognition. Items of fatigue were classified and summarized into two index variables measuring either the subjective experience of physical fatigue or the impact of fatigue on daily functioning. Linear regression with a stepwise elimination procedure was conducted to select the significant predictors for each index variable separately. Results: Subjective experience of physical fatigue (Model 1; r2 = 0.46; p 0.05), and lower motor functioning (b = 0.01; p = 0.05). Conclusions: In conclusion, our work supports associations between fatigue and other neuropsychiatric symptoms in PD and extends prior work suggesting that motor disturbances are specifically linked to fatigue-related impairment of daily functioning, but not to the subjective experience of physical fatigue.

Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.

An individual's brain functional organization is unique and can reliably be observed using modalities such as functional magnetic resonance imaging (fMRI). Here we demonstrate that a quantification of the dynamics of functional connectivity (FC) as measured using electroencephalography (EEG) offers an alternative means of observing an individual's brain functional organization. Using data from both healthy individuals as well as from patients with Parkinson's disease (PD) (n = 103 healthy individuals, n = 57 PD patients), we show that “dynamic FC” (DFC) profiles can be used to identify individuals in a large group. Furthermore, we show that DFC profiles predict gender and exhibit characteristics shared both among individuals as well as between both hemispheres. Furthermore, DFC profile characteristics are frequency band specific, indicating that they reflect distinct processes in the brain. Our empirically derived method of DFC demonstrates the potential of studying the dynamics of the functional organization of the brain using EEG.

Objective: To evaluate the sensitivity to change of differently calculated quantitative scores from motor evoked potentials (MEP) in patients with primary progressive multiple sclerosis (PPMS).Methods: Twenty patients with PPMS had MEP to upper and lower limbs at baseline, years 1 and 2 measured in addition to clinical assessment [Expanded Disability Status Scale (EDSS), ambulation score]; a subsample (n = 9) had a nine-hole peg test (NHPT) and a timed 25-foot walk (T25FW). Quantitative MEP scores for upper limbs (qMEP-UL), lower limbs (qMEP-LL), and all limbs (qMEP) were calculated in three different ways, based on z-transformed central motor conduction time (CMCT), shortest corticomuscular latency (CxM-sh), and mean CxM (CxM-mn). Changes in clinical measures and qMEP metrics were analyzed by repeated-measures analysis of variance (rANOVA), and a factor analysis was performed on change in qMEP metrics.Results: Expanded Disability Status Scale and ambulation score progressed in the rANOVA model (p < 0.05; post-hoc comparison baseline–year 2, p < 0.1). Lower limb and combined qMEP scores showed significant deterioration of latency (p < 0.01, MEP-LL_CxM-sh: p < 0.05) and in post-hoc comparisons (baseline–year 2, p < 0.05), qMEP_CxM-mn even over 1 year (p < 0.05). Effect sizes were higher for qMEP scores than for clinical measures, and slightly but consistently higher when based on CxM-mn compared to CxM-sh or CMCT. Subgroup analysis yielded no indication of higher sensitivity of timed clinical measures over qMEP scores. Two independent factors were detected, the first mainly associated with qMEP-LL, the second with qMEP-UL, explaining 65 and 29% of total variability, respectively.Conclusions: Deterioration in qMEP scores occurs earlier than EDSS progression in patients with PPMS. Upper and lower limb qMEP scores contribute independently to measuring change, and qMEP scores based on mean CxM are advantageous. The capability to detect subclinical changes longitudinally is a unique property of EP and complementary to clinical assessment. These features underline the role of EP as candidate biomarkers to measure effects of therapeutic interventions in PPMS.

Objective: We aimed to determine whether the combination of two parameters: (a) score of axial impairment and limb rigidity (SAILR) with (b) EEG global relative median power in the frequency range theta 4–8 Hz (GRMPT) predicted cognitive outcome in patients with Parkinson's disease (PD) better than each of these measures alone.Methods: 47 non-demented patients with PD were examined and re-examined after 3 years. At both time-points, the patients underwent a comprehensive neuropsychological and neurological assessment and EEG in eyes-closed resting-state condition. The results of cognitive tests were normalized and individually summarized to obtain a “global cognitive score” (GCS). Change of GCS was used to represent cognitive changes over time. GRMPT and SAILR was used for further analysis. Linear regression models were calculated.Results: GRMPT and SAILR independently predicted cognitive change. Combination of GRMPT and SAILR improved the significance of the regression model as compared to using each of these measures alone. GRMPT and SAILR only slightly correlate between each other.Conclusion: The combination of axial signs and rigidity with quantitative EEG improves early identification of patients with PD prone to severe cognitive decline. GRMPT and SAILR seem to reflect different disease mechanisms.Significance Combination of EEG and axial motor impairment assessment may be a valuable marker in the cognitive prognosis of PD.

Background: Neuropsychiatric symptoms (NPS) and mild cognitive impairment (MCI) are common in Parkinson disease (PD) and have a negative impact on the patient’s quality of life (QoL). We aim to describe the effect of NPS and MCI on each other and on QoL. Methods: Sixteen MCI and 37 non-MCI PD patients completed different self-assessment questionnaires including the Parkinson’s Disease Questionnaire (PDQ-39), the Beck Depression Inventory (BD), the Beck Anxiety Inventory (BAI), and the Self-Report Manic Inventory (SRMI). Linear regression with stepwise elimination was used to select the significant predictors of QoL and to control for confounding factors. Results: The significant linear regression model (R2 = 0.68, p ≤ 0.01) revealed significant effects of MCI (p = 0.03), BDI (p ≤ 0.01), BAI (p ≤ 0.01), age (p = 0.03), a trend of SRMI (p = 0.06), and disease duration (p = 0.08) on QoL. Conclusions: MCI, anxiety, depression, and age decrease QoL in patients with PD. NPS has the main influence in the prediction of QoL in patients with PD, whereas MCI is only of minor importance.

Background. Parkinson’s disease (PD) is associated with cognitive decline, progressing from subjective memory complaints (SMC) via mild cognitive impairment (MCI) to dementia. SMC are only measurable by an interview and thus rely on individuals reporting a subjectively perceived worsening of cognitive functioning. Cognitive decline is accompanied by a reduction in quality of life (QoL); however, the extent to which SMC manifest a reduction of QoL remains unclear. Objective. To determine the association between SMC and deterioration of QoL in patients suffering from PD. Methods. A total of 46 cognitively unimpaired PD patients (29 men and 17 women) completed PDQ-39, two assessments to measure SMC (Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE) and a Self-Assessment questionnaire), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Multiple regression modelling was conducted to investigate the confounding effect of depression and anxiety. Results. The PDQ-39 domain cognitions, but not the PDQ-39 sum score, correlated significantly with the SMC Self-Assessment questionnaire (r = 0.57; p

Introduction: Deep brain stimulation of the subthalamic nucleus (STN-DBS) ameliorates motor function in patients with Parkinson's disease and allows reducing dopaminergic therapy. Beside effects on motor function STN-DBS influences many non-motor symptoms, among which decline of verbal fluency test performance is most consistently reported. The surgical procedure itself is the likely cause of this decline, while the influence of the electrical stimulation is still controversial. STN-DBS also produces widespread changes of cortical activity as visualized by quantitative EEG. The present study aims to link an alteration in verbal fluency performance by electrical stimulation of the STN to alterations in quantitative EEG.Methods: Sixteen patients with STN-DBS were included. All patients had a high density EEG recording (256 channels) while testing verbal fluency in the stimulator on/off situation. The phonemic, semantic, alternating phonemic and semantic fluency was tested (Regensburger Wortflüssigkeits-Test).Results: On the group level, stimulation of STN did not alter verbal fluency performance. EEG frequency analysis showed an increase of relative alpha2 (10–13 Hz) and beta (13–30 Hz) power in the parieto-occipital region (p ≤ 0.01). On the individual level, changes of verbal fluency induced by stimulation of the STN were disparate and correlated inversely with delta power in the left temporal lobe (p < 0.05).Conclusion: STN stimulation does not alter verbal fluency performance in a systematic way at group level. However, when in individual patients an alteration of verbal fluency performance is produced by electrical stimulation of the STN, it correlates inversely with left temporal delta power.

Spontaneous fluctuations of neuronal activity in large-scale distributed networks are a hallmark of the resting brain. In relapsing-remitting multiple sclerosis (RRMS) several fMRI studies have suggested altered resting-state connectivity patterns. Topographical EEG analysis reveals much faster temporal fluctuations in the tens of milliseconds time range (termed “microstates”), which showed altered properties in a number of neuropsychiatric conditions. We investigated whether these microstates were altered in patients with RRMS, and if the microstates' temporal properties reflected a link to the patients' clinical features. We acquired 256-channel EEG in 53 patients (mean age 37.6 years, 45 females, mean disease duration 9.99 years, Expanded Disability Status Scale ≤4, mean 2.2) and 49 healthy controls (mean age 36.4 years, 33 females). We analyzed segments of a total of 5 min of EEG during resting wakefulness and determined for both groups the four predominant microstates using established clustering methods. We found significant differences in the temporal dynamics of two of the four microstates between healthy controls and patients with RRMS in terms of increased appearance and prolonged duration. Using stepwise multiple linear regression models with 8-fold cross-validation, we found evidence that these electrophysiological measures predicted a patient's total disease duration, annual relapse rate, disability score, as well as depression score, and cognitive fatigue measure. In RRMS patients, microstate analysis captured altered fluctuations of EEG topographies in the sub-second range. This measure of high temporal resolution provided potentially powerful markers of disease activity and neuropsychiatric co-morbidities in RRMS.

Olfactory decline is a frequent and early non-motor symptom in Parkinson’s disease (PD), which is increasingly used for diagnostic purposes. Another early appearing sign of PD consists in electroencephalographic (EEG) alterations. The combination of olfactory and EEG assessment may improve the identification of patients with early stages of PD. We hypothesized that olfactory capacity would be correlated with EEG alterations and motor and cognitive impairment in PD patients. To the best of our knowledge, the mutual influence of both markers of PD—olfactory decrease and EEG changes—was not studied before. We assessed the function of odor identification using olfactory “Screening 12 Test” (“Sniffin’ Sticks®”), between two samples: patients with PD and healthy controls (HC). We analyzed correlations between the olfactory function and demographical parameters, Unified Parkinson’s Disease Rating Scale (UPDRS-III), cognitive task performance, and spectral alpha/theta ratio (α/θ). In addition, we used receiver operating characteristic-curve analysis to check the classification capacity (PD vs HC) of olfactory function, α/θ, and a combined marker (olfaction and α/θ). Olfactory capacity was significantly decreased in PD patients, and correlated with age, disease duration, UPDRS-III, and with items of UPDRS-III related to gait and axial rigidity. In HC, olfaction correlated with age only. No correlation with α/θ was identified in both samples. Combined marker showed the largest area under the curve. In addition to EEG, the assessment of olfactory function may be a useful tool in the early characterization and follow-up of PD.

Introduction: Quantitative electroencephalography (QEEG) brain frequency and network analyses are known to differentiate between disease stages in Parkinson's disease (PD) and are possible biomarkers. They correlate with cognitive decline. Little is known about changes in brain networks in relation to apathy.Objective/Aims: To analyze changes in brain network connectivities related to apathy.Methods: 40 PD patients (14 PD with mild cognitive deficits and 26 PD with normal cognition) were included. All patients had extensive neuropsychological testing; apathy was evaluated using the apathy evaluation score (AES, median 24.5, range 18–39). Resting state EEG was recorded with 256 electrodes and analyzed using fully automated Matlab® code (TAPEEG). For estimation of the connectivities between brain regions, PLI (phase lag index) was used, enhanced by a microstates segmentation.Results: After correction for multiple comparisons, significant correlations were found for single alpha2-band connectivities with the AES (p-values < 0.05). Lower connectivities, mainly involving the left fronto-polar region, were related to higher apathy scores.Conclusions: In our sample of patients with PD, apathy correlates with a network alteration mainly involving the left fronto-polar region. This might be due to dysfunction of the cortico-basal loop, modulating motivation.

Background. Visuospatial dysfunction is among the first cognitive symptoms in Parkinson’s disease (PD) and is often predictive for PD-dementia. Furthermore, cognitive status in PD-patients correlates with quantitative EEG. This cross-sectional study aimed to investigate the correlation between EEG slowing and visuospatial ability in nondemented PD-patients. Methods. Fifty-seven nondemented PD-patients (17 females/40 males) were evaluated with a comprehensive neuropsychological test battery and a high-resolution 256-channel EEG was recorded. A median split was performed for each cognitive test dividing the patients sample into either a normal or lower performance group. The electrodes were split into five areas: frontal, central, temporal, parietal, and occipital. A linear mixed effects model (LME) was used for correlational analyses and to control for confounding factors. Results. Subsequently, for the lower performance, LME analysis showed a significant positive correlation between ROCF score and parietal alpha/theta ratio (b=.59, p=.012) and occipital alpha/theta ratio (b=0.50, p=.030). No correlations were found in the group of patients with normal visuospatial abilities. Conclusion. We conclude that a reduction of the parietal alpha/theta ratio is related to visuospatial impairments in PD-patients. These findings indicate that visuospatial impairment in PD-patients could be influenced by parietal dysfunction.

Objective: We investigated quantitative electroencephalography (qEEG) and clinical parameters as potential risk factors of severe cognitive decline in Parkinson’s disease.Methods: We prospectively investigated 37 patients with Parkinson’s disease at baseline and follow-up (after 3 years). Patients had no severe cognitive impairment at baseline. We used a summary score of cognitive tests as the outcome at follow-up. At baseline we assessed motor, cognitive, and psychiatric factors; qEEG variables (global relative median power spectra) were obtained by a fully automated processing of high-resolution EEG (256-channels). We used linear regression models with calculation of the explained variance to evaluate the relation of baseline parameters with cognitive deterioration.Results: The following baseline parameters significantly predicted severe cognitive decline: global relative median power theta (4-8 Hz), cognitive task performance in executive functions and working memory.Conclusions: Combination of neurocognitive tests and qEEG improves identification of patients with higher risk of cognitive decline in PD.

Cognitive decline is common with the progression of Parkinson’s disease (PD). Different candidate biomarkers are currently studied for the risk of dementia in PD. Several studies have shown that quantitative EEG (QEEG) is a promising predictor of PD-related cognitive decline. In this paper we briefly outline the basics of QEEG analysis and analyze the recent publications addressing the predictive value of QEEG in the context of cognitive decline in PD. The MEDLINE database was searched for relevant publications from January 01, 2005, to March 02, 2015. Twenty-four studies reported QEEG findings in various cognitive states in PD. Spectral and connectivity markers of QEEG could help to discriminate between PD patients with different level of cognitive decline. QEEG variables correlate with tools for cognitive assessment over time and are associated with significant hazard ratios to predict PD-related dementia. QEEG analysis shows high test-retest reliability and avoids learning effects associated with some neuropsychological testing; it is noninvasive and relatively easy to repeat.

Functional connectivity (FC) and graph measures provide powerful means to analyze complex networks. The current study determines the inter-subject-variability using the coefficient of variation (CoV) and long-term test-retest-reliability (TRT) using the intra-class correlation coefficient (ICC) in 44 healthy subjects with 35 having a follow-up at years 1 and 2. FC was estimated from 256-channel-EEG by the phase-lag-index (PLI) and weighted PLI (wPLI) during an eyes-closed resting state condition. PLI quantifies the asymmetry of the distribution of instantaneous phase differences of two time-series and signifies, whether a consistent non-zero phase lag exists. WPLI extends the PLI by additionally accounting for the magnitude of the phase difference. Signal-space global and regional PLI/wPLI and weighted first-order graph measures, i.e. normalized clustering coefficient (gamma), normalized average path length (lambda), and the small-world-index (SWI) were calculated for theta-, alpha1-, alpha2- and beta-frequency bands. Inter-subject variability of global PLI was low to moderate over frequency bands (0.12