Habib Bousleiman

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.

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<CoV<0.28), higher for wPLI (0.25<CoV<0.55) and very low for gamma, lambda and SWI (CoV<0.048). TRT was good to excellent for global PLI/wPLI (0.68<ICC<0.80), regional PLI/wPLI (0.58<ICC<0.77), and fair to good for graph measures (0.32<ICC<0.73) except wPLI-based lambda in alpha1 (ICC = 0.12). Inter-electrode distance correlated very weakly with inter-electrode PLI (−0.06<rho<0) and weakly with inter-electrode wPLI (−0.22<rho<−0.18). Global PLI/wPLI and topographic connectivity patterns differed between frequency bands, and all individual networks showed a small-world-configuration. PLI/wPLI based network characterization derived from high-resolution EEG has apparently good reliability, which is one important requirement for longitudinal studies exploring the effects of chronic brain diseases over several years.

Reliability of fully automated versus visually controlled pre- and post-processing of resting-state EEG

OBJECTIVE To compare the reliability of a newly developed Matlab® toolbox for the fully automated, pre- and post-processing of resting state EEG (automated analysis, AA) with the reliability of analysis involving visually controlled pre- and post-processing (VA). METHODS 34 healthy volunteers (age: median 38.2 (20-49), 82% female) had three consecutive 256-channel resting-state EEG at one year intervals. Results of frequency analysis of AA and VA were compared with Pearson correlation coefficients, and reliability over time was assessed with intraclass correlation coefficients (ICC). RESULTS Mean correlation coefficient between AA and VA was 0.94±0.07, mean ICC for AA 0.83±0.05 and for VA 0.84±0.07. CONCLUSION AA and VA yield very similar results for spectral EEG analysis and are equally reliable. AA is less time-consuming, completely standardized, and independent of raters and their training. SIGNIFICANCE Automated processing of EEG facilitates workflow in quantitative EEG analysis.

Apathy in Parkinson's disease is related to executive function, gender and age but not to depression.

Deficits in executive functions occur in up to 93% of patients with Parkinsons disease (PD). Apathy, a reduction of motivation and goal-directed behavior is an important part of the syndrome; affecting both the patients as well as their social environment. Executive functions can be subdivided into three different processes: initiation, shifting and inhibition. We examined the hypotheses, (1) that apathy in patients with Parkinsons disease is only related to initiation and not to shifting and inhibition, and (2) that depression and severity of motor signs correlate with apathy. Fifty-one non-demented patients (19 = female) with PD were evaluated for apathy, depression and executive functions. Executive function variables were summarized with an index variable according to the defined executive processes. Linear regression with stepwise elimination procedure was used to select significant predictors. The significant model (R2 = 0.41; p < 0.01) revealed influences of initiation (b = −0.79; p < 0.01), gender (b = −7.75; p < 0.01), age (b = −0.07; p < 0.05) and an age by gender interaction (b = 0.12; p < 0.01) on apathy in Parkinsons disease. Motor signs, depression and level of education did not influence the relation. These results support an association of apathy and deficits of executive function in PD. Initiation strongly correlates with apathy, whereas depression does not. We conclude, that initiation dysfunction in a patient with Parkinsons disease heralds apathy. Apathy and depression can be dissociated. Additionally, apathy is influenced by age and gender: older age correlates with apathy in men, whereas in women it seems to protect against it.

Slowing of EEG background activity in Parkinson's and Alzheimer's disease with early cognitive dysfunction

Background: Slowing of the electroencephalogram (EEG) is frequent in Parkinson’s (PD) and Alzheimer’s disease (AD) and correlates with cognitive decline. As overlap pathology plays a role in the pathogenesis of dementia, it is likely that demented patients in PD show similar physiological alterations as in AD. Objective: To analyze distinctive quantitative EEG characteristics in early cognitive dysfunction in PD and AD. Methods: Forty patients (20 PD- and 20 AD patients with early cognitive impairment) and 20 normal controls (NC) were matched for gender, age, and education. Resting state EEG was recorded from 256 electrodes. Relative power spectra, median frequency (4–14 Hz), and neuropsychological outcome were compared between groups. Results: Relative theta power in left temporal region and median frequency separated the three groups significantly (p = 0.002 and p < 0.001). Relative theta power was increased and median frequency reduced in patients with both diseases compared to NC. Median frequency was higher in AD than in PD and classified groups significantly (p = 0.02). Conclusion: Increase of theta power in the left temporal region and a reduction of median frequency were associated with presence of AD or PD. PD patients are characterized by a pronounced slowing as compared to AD patients. Therefore, in both disorders EEG slowing might be a useful biomarker for beginning cognitive decline.

Power spectra for screening parkinsonian patients for mild cognitive impairment

Mild cognitive impairment in Parkinsons disease (PD‐MCI) is diagnosed based on the results of a standardized set of cognitive tests. We investigate whether quantitative EEG (qEEG) measures could identify differences between cognitively normal PD (PD‐CogNL) and PD‐MCI patients.

Correlation of EEG Slowing with Cognitive Domains in Nondemented Patients with Parkinson's Disease

Background: Cognitive deficits in Parkinsons disease (PD) are heterogeneous and can be classified into cognitive domains. Quantitative EEG is related to and predictive of cognitive status in PD. In this cross-sectional study, the relationship of cognitive domains and EEG slowing in PD patients without dementia is investigated. Methods: A total of 48 patients with idiopathic PD were neuropsychologically tested. Cognitive domain scores were calculated combining Z-scores of test variables. Slowing of EEG was measured with median EEG frequency. Linear regression was used for correlational analyses and to control for confounding factors. Results: EEG median frequency was significantly correlated to cognitive performance in most domains (episodic long-term memory, rho = 0.54; overall cognitive score, rho = 0.47; fluency, rho = 0.39; attention, rho = 0.37; executive function, rho = 0.34), but not to visuospatial functions and working memory. Conclusion: Global EEG slowing is a marker for overall cognitive impairment in PD and correlates with impairment in the domains attention, executive function, verbal fluency, and episodic long-term memory, but not with working memory and visuospatial functions. These disparate effects warrant further investigations.

Reliability of Functional Connectivity of Electroencephalography Applying Microstate-Segmented Versus Classical Calculation of Phase Lag Index.

Abstract Connectivity analysis characterizes normal and altered brain function, for example, using the phase lag index (PLI), which is based on phase relations. However, reliability of PLI over time is limited, especially for single- or regional-link analysis. One possible cause is repeated changes of network configuration during registration. These network changes may be associated with changes of the surface potential fields, which can be characterized by microstate analysis. Microstate analysis describes repeating periods of quasistable surface potential fields lasting in the subsecond time range. This study aims to describe a novel combination of PLI with microstate analysis (microstate-segmented PLI = msPLI) and to determine its impact on the reliability of single links, regional links, and derived graph measures. msPLI was calculated in a cohort of 34 healthy controls three times over 2 years. A fully automated processing of electroencephalography was used. Resulting connectomes were compared using ...Connectivity analysis characterizes normal and altered brain function, for example, using the phase lag index (PLI), which is based on phase relations. However, reliability of PLI over time is limited, especially for single- or regional-link analysis. One possible cause is repeated changes of network configuration during registration. These network changes may be associated with changes of the surface potential fields, which can be characterized by microstate analysis. Microstate analysis describes repeating periods of quasistable surface potential fields lasting in the subsecond time range. This study aims to describe a novel combination of PLI with microstate analysis (microstate-segmented PLI = msPLI) and to determine its impact on the reliability of single links, regional links, and derived graph measures. msPLI was calculated in a cohort of 34 healthy controls three times over 2 years. A fully automated processing of electroencephalography was used. Resulting connectomes were compared using Pearson correlation, and test-retest reliability (TRT reliability) was assessed using the intraclass correlation coefficient. msPLI resulted in higher TRT reliability than classical PLI analysis for single or regional links, average clustering coefficient, average shortest path length, and degree diversity. Combination of microstates and phase-derived connectivity measures such as PLI improves reliability of single-link, regional-link, and graph analysis.

Reliability of functional connectivity of EEG applying microstates-segmented versus classical calculation of phase lag index

Abstract Connectivity analysis characterizes normal and altered brain function, for example, using the phase lag index (PLI), which is based on phase relations. However, reliability of PLI over time is limited, especially for single- or regional-link analysis. One possible cause is repeated changes of network configuration during registration. These network changes may be associated with changes of the surface potential fields, which can be characterized by microstate analysis. Microstate analysis describes repeating periods of quasistable surface potential fields lasting in the subsecond time range. This study aims to describe a novel combination of PLI with microstate analysis (microstate-segmented PLI = msPLI) and to determine its impact on the reliability of single links, regional links, and derived graph measures. msPLI was calculated in a cohort of 34 healthy controls three times over 2 years. A fully automated processing of electroencephalography was used. Resulting connectomes were compared using ...Connectivity analysis characterizes normal and altered brain function, for example, using the phase lag index (PLI), which is based on phase relations. However, reliability of PLI over time is limited, especially for single- or regional-link analysis. One possible cause is repeated changes of network configuration during registration. These network changes may be associated with changes of the surface potential fields, which can be characterized by microstate analysis. Microstate analysis describes repeating periods of quasistable surface potential fields lasting in the subsecond time range. This study aims to describe a novel combination of PLI with microstate analysis (microstate-segmented PLI = msPLI) and to determine its impact on the reliability of single links, regional links, and derived graph measures. msPLI was calculated in a cohort of 34 healthy controls three times over 2 years. A fully automated processing of electroencephalography was used. Resulting connectomes were compared using Pearson correlation, and test-retest reliability (TRT reliability) was assessed using the intraclass correlation coefficient. msPLI resulted in higher TRT reliability than classical PLI analysis for single or regional links, average clustering coefficient, average shortest path length, and degree diversity. Combination of microstates and phase-derived connectivity measures such as PLI improves reliability of single-link, regional-link, and graph analysis.

T85. Sensory and motor evoked potentials in a multicenter setting: Definition of significant change in repeated measurements in healthy subjects on individual level

Introduction Sensory and motor evoked potentials (SEP; MEP) can be used to measure quantitatively the extent of delayed signal conduction in multiple sclerosis (MS). They may be useful to monitor disease course and even serve as biomarkers in clinical trials (Hardmeier et al., 2017). Here we evaluate physiological and rater-related variability to determine the minimal significant change between two measurements intra-individually. Methods 15 healthy subjects were evaluated twice within 30 days with median and tibial SEP and upper (UL) and lower limb (LL) MEP in three centers1–3 according to a common standardized protocol in keeping with IFCN recommendations. Four neurophysiologists (FJ, MH, PA, PF) independently marked all curves blinded to their previous ratings using a web-based tool (EPMark; HB, MH, PF). In SEP, N13, N20 and N22, P40, in MEP, cortico-muscular- (CML) and spinal-muscular-latencies were marked. N20, P40, shortest and mean CML, and central (motor) conduction times (CCT; CMCT) were analyzed. Mixed effect models were calculated using results of (a) 1st and 2nd rating of identical baseline curves (model 1), and (b) 1st baseline and follow-up rating (model 2) as combined outcomes. Based on model 2, confidence intervals (CI) were calculated for the difference of a repeated measurement of the same curve. Results Intra-class-correlation coefficient (ICC) for intra-and inter-rater reliability (model 1) was very high in median SEP (N20: 0.97; CCT: 0.85) and tibial SEP (P40: 0.95; CCT: 0.89). In MEP, ICC was higher when mean CML (UL: 0.94; LL: 0.90) or mean CMCT (UL: 0.88; LL: 0.91) was used instead of shortest CML (UL: 0.80; LL: 0.78) or shortest CMCT (UL: 0.65; LL: 0.81). As CCT and CMCT showed lower ICC, only CML, N20 and P40 were further analyzed. Total variance in model 2 ranged from 0.9 to 6.4 ms (N20: 0.9, P40: 6.4; CML-UL [shortest/mean]: 4.8/4.1; CML-LL: 5.8/3.7), mainly accounted for by inter-subject variability (64–79%); estimation of center effects was unreliable. 80%-CI ranged from 0.4 to 1.5 ms (N20: 0.4, P40: 1.3; CML-UL: 1.5/1.1; CML-LL: 1.1/0.9), 95%-CI from 0.7 to 3.0 ms (N20: 0.7, P40: 2.6; CML-UL: 3.0/2.2; CML-LL: 2.2/1.7). Conclusion Main SEP components (N20, P40) and MEP cortico-muscular latencies show higher reliability than central conduction times. Mean instead of shortest CML further improves reliability. Intra-subject differences in individual tracts exceeding 0.4 to 3.0 ms (depending on test and CI level) most likely reflect true underlying changes. These numbers may be used to define responders to remyelinating therapies in MS. However, these confidence intervals may be higher in patients and have to be validated in a larger sample. In group level analyses, variability is much less important as over- and underestimation counterbalance each other. Supported by an unconditional research grant from Biogen Inc. MA, USA , which had no influence in planning the study or data analysis.

F107. Sensory and motor evoked potentials in a multicenter setting: Estimation of detectable group differences at varying sample sizes

Introduction Sensory and motor evoked potentials (SEP; MEP) can be used to measure quantitatively the extent of delayed signal conduction in multiple sclerosis (MS). They may be useful to monitor disease course and even serve as biomarkers in clinical trials (Hardmeier et al., 2017). Here we estimate the detectable differences in group means and in longitudinal group changes for different sample sizes accounting for physiological and rater-related variability. Methods 15 healthy subjects were evaluated twice within 30 days with median and tibial SEP and upper (UL) and lower limb (LL) MEP in three centers 1-3 according to a common standardized protocol in keeping with IFCN recommendations. Four neurophysiologists (FJ, MH, PA, PF) independently marked all curves blinded to their previous ratings using a web-based tool (EPMark; HB, MH, PF). N20, P40, shortest and mean cortico-muscular-latencies (CML) were analyzed; central (motor) conduction times were omitted due to lower test-retest-reliability. In addition, the sum of the z-transformed results from each test was divided by number of tests to yield a quantitative EP-score (qEPS). Mixed effect models using results of time-points 1 and 2 as combined outcomes were employed to calculate the standard error (SE) of a group mean of cross-sectional measurements or longitudinal changes. The respective SE formulas were used to calculate the difference d detectable with a power of 90% [ d  = (1.96 + 1.28) ∗ sqrt( SE 1 2 + SE 2 2 ) -2CoV] as a function of group size assuming a central reading center. Covariance between group means by common influence of raters on both groups was ignored for a more conservative estimate. Results For a sample size of n  = 60, cross-sectional group mean difference (d1) is estimated to range from 0.6 to 2.1 ms (N20: 0.6, P40: 1.5; CML-UL [shortest/ mean]: 1.4/1.2; CML-LL: 1.7/2.1), and longitudinal mean group difference (d2) from 0.4 to 2.7 ms (N20: 0.4, P40: 1.0; CML-UL: 1.1/0.8; CML-LL: 1.6/2.7). For a sample size of n  = 100, d1 ranged from 0.5 to 2.0 ms (N20: 0.5, P40: 1.2; CML-UL: 1.1/1.0; CML-LL: 1.4/2.0), and d2 from 0.4 to 2.6 ms (N20: 0.4, P40: 0.8; CML-UL: 0.9/0.7; CML-LL: 1.5/2.6). For qEPS [including shortest or mean CML], d1 was 0.5/0.4 ms for n  = 60, and 0.4/0.3 ms for n  = 100; d2 was 0.3/0.2 ms for n  = 60 and 0.2/0.2 for n  = 100. Conclusion Quantitative evaluation of combined SEP and MEP is possible in a multicenter setting with an independent reading center and allows reliable detection of significant differences in small groups of subjects. When evaluating an intervention, significant changes of EP results at an individual level help to identify responders, while significant changes at a group level may help to determine the biological effectiveness of an intervention. However, variability may be higher in patients and thus, consecutively, estimates of sample size. Supported by an unconditional research grant from Biogen Inc. MA, USA, which had no influence in planning the study or data analysis.