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: The first (primacy region) and last (recency region) items of a word list are generally better memorized than items from the middle region. The recency effect depends on short-term memory (STM) and the primacy effect on long-term memory (LTM), where verbal information is transferred from STM into LTM by maintenance rehearsal. We compared the serial position effects (SPE) between patients with mild cognitive impairment (MCI) due to Parkinson’s disease (PD), i.e., PD-MCI, and patients with MCI due to Alzheimer’s disease (AD-MCI), and evaluated the influence of SPE and frontostriatal deficits on verbal memory recall. Methods: Four similar groups of subjects participated in the study: 26 PD-MCI patients, 26 cognitively normal patients with PD (PD-CN), 26 AD-MCI patients, and 26 normal controls (NC). Verbal episodic memory, verbal span, attentional capacity, executive functions, and verbal working memory performance were assessed. Measures for primacy and recency regions were defined at the first trial of a 16-items word list. Hierarchical regression models were used to investigate the contribution of frontostriatal deficits beyond SPE on verbal memory recall performance (“long-delay free recall”) in PD and AD patients. Results: Primacy effects were significantly diminished in both PD-MCI and AD-MCI patients relative to NC and PD-CN (all p < 0.01). Compared to PD-MCI patients, AD-MCI patients exhibited significantly worse “delayed-recall ‘savings’.” Reduced primacy effect was predictive for decreased recall performance in PD and AD. The conducted hierarchical regression model revealed that in PD, but not in AD patients, performance of attention and executive function significantly increased the prediction of free recalled words. Conclusions: Reduced recall performance is likely due to impaired transition of newly learned material from STM into LTM in AD and in PD. Whereas AD-MCI patients suffer from a storage deficit, the similarly reduced recall performance found in patients with PD-MCI may additionally be related to deficient attentional and executive capacity.

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.

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.

May 6, 2019April 9, 2019Free AccessComparison of EEG Connectivity measures and Frequency analysis for identification of patients with Mild Cognitive Impairment in Parkinson’s disease – a Random Forest Analysis (P2.8-043)Menorca Chaturvedi, Jan Guy Bogaarts, Florian Hatz, Vitali Cozac, Antonia Meyer, Ute Gschwandtner, Peter Fuhr, and Volker RothAuthors Info & AffiliationsApril 9, 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.P2.8-043 Letters to the Editor

Objectives: To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients. Methods: We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains). Results: PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI. Conclusion: PLI is an effective quantitative EEG measure to identify PD patients with MCI. Significance: We identified quantitative EEG measures which are important for early identification of cognitive decline in PD. (C) 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.

Quantitative EEG and motor assessment tools are among the techniques investigated as biomarkers of dementia in Parkinson’s disease (PD) (Aarsland et al., 2017). It is assumed that a combination of various markers has a better predictive capacity of dementia than a single technique. We aimed to check if items of Unified Parkinson’s Disease Rating Scale (UPDRS-III), related to axial symptoms, and EEG power spectra predict cognitive outcome in a three-years-cohort of patients with Parkinson’s disease. We analyzed a group of patients with PD without dementia (n = 47, males 60%) at baseline and after 3 years. On inclusion: median age 66 [47, 80] years. At both time-points, the patients underwent a comprehensive neuropsychological assessment (14 cognitive tests) and neurological examination with UPDRS-III, EEG with 214 active electrodes were recorded in eyes-closed resting-state condition. The results of cognitive tests were scaled to a normative database (Berres et al., 2000) and averaged to obtain an ‘overall cognitive score’ (OCS). To assess the changes over time, reliable change index (RCI) of OCS was calculated according to (Jacobson and Truax, 1991). Global relative median power (GRMP) in the frequency range theta 4–8 Hz was calculated, and logarithmic transformed. A sum of UPDRS-III items: speech, rigidity (neck and all limbs), postural stability and gait, was calculated as ‘score of axial impairment’ (SAI), as mentioned in Bejjani et al., 2000. To investigate the influence of age, sex, GRMP theta, SAI, education, and disease duration on changes of cognition we used general linear regression models with RCI as dependent variable. We checked if baseline parameters correlate between each other with Spearman rank correlation test. Only GRMP theta and SAI significantly predicted RCI. Combination (sum) of these two parameters improved the significance of the model. No significant correlation between these two parameters was identified. The assessment of axial signs in combination with quantitative EEG may improve early identification of PD patients prone to severe cognitive decline. These parameters do not correlate between each other, probably covering different information aspects in the process of assessment. Larger cohorts with longer observation and various assessment tools are warranted.

The phase-lag-index (PLI) (Stam et al., 2007) is a commonly used method to quantify functional connectivity (FC) in EEG/MEG data. When calculated for short epochs in the order of several seconds, PLI varies considerably from epoch to epoch. Using resting-state EEG data from 105 healthy subjects, we demonstrate that the pattern of correlations between PLI time-series is characteristic for an individual’s brain. In addition to acting as an identifying fingerprint, this pattern is also highly gender-specific and bears characteristics that are shared among people and also between hemispheres. Furthermore, optimal performance is achieved using an epoch length of 250 ms which is in the same range as typical durations of phase-synchronisation (Varela et al., 2001). This observation hints at the timing-relation between phase-synchronisation events being the underlying source of information captured by correlation between PLI time-series. In conclusion, our work reveals a novel way of extracting meaningful information about the brain’s functional organisation from EEG data.

Background Models have been constructed to estimate individual risk for global cognitive impairment in Parkinson’s disease (PD) using a small set of clinical predictor variables (age at disease onset, sex, education, MMSE, motor impairment, depression) ( Liu et al., 2017 ). The prediction algorithm accurately forecast cognitive decline with a predefined cut-off score. Slowing of the electroencephalogram (EEG) is frequent in PD and as it is a predictive biomarker for dementia in PD (PDD), it is likely that adding information about EEG frequency might increase predictive accuracy of cognitive decline. Objective The present study aims at (1) investigating whether quantitative EEG (qEEG) measures could identify differences between PD patients at high risk and PD patients at low risk of cognitive decline and at (2) analysing whether the inclusion of qEEG parameters improve predictive accuracy of cognitive decline within 3 years. Methods In a total of 44 non-demented PD patients (disease duration: median = 2 years), a prediction algorithm for cognitive decline developed by Liu et al. (2017) was applied. At baseline, according to the defined cut-off score by Liu et al. (2017) , n = 23 patients were identified at high risk and n = 21 patients at low risk of cognitive decline. Resting state EEG was recorded from 256 electrodes. Relative power spectra and median frequency (4–14 Hz) were compared between groups using ANOVA. Receiver-operator-characteristic (ROC) was used to demonstrate prediction of global cognitive decline after 3 years (dementia vs. non dementia) using clinical risk score only and in combination with qEEG variable. Results At baseline after correction for multiple comparisons, differences in global theta power and theta power in all brain regions (p Conclusion PD patients at high risk of cognitive decline are characterized by pronounced slowing as compared to PD patients at low risk. Even at a very short time span, cognitive risk scores are indicative of dementia in PD patients. Adding information about qEEG enhances prediction. Combined marker (qEEG and clinical-only risk score) may help to improve prediction of cognitive decline in PWD patients.

To find the best EEG parameters to discriminate between Parkinson’s disease (PD) and Parkinson’s Disease Dementia (PDD) patients and to evaluate the significance of Phase Lag Index as a parameter for classification of PD and PDD patients, in contrast to the use of frequency-band power measures alone. The study also deals with the challenge of handling imbalanced data for classification. EEG data for a group of 81 PD patients and 19 PDD patients were collected from three centres and analysed using automated segmentation and Inverse Solution post-processing. The PD group was a mix of MCI, Non MCI and unclassified early stage PD patients. 63 Frequency measures and 216 Phase Lag Index measures were obtained for all patients. To overcome the problem of imbalanced data, Random Forest was applied with stratified sampling, in which equal numbers of patients (19) were taken from both the groups for training. This process was repeated 100 times and average AUC measures were obtained. Classification models were built using frequency measures, PLI measures and frequency combined with PLI measures respectively. Using 63 frequency measures for classification gave a ROC curve with average AUC value of 0.68. The AUC value increased to 0.75 when using PLI measures alone, which further increased to 0.8 when combining PLI and frequency measures. Further analysis revealed many more PLI measures than frequency measures to be amongst the top features distinguishing the two groups accurately. Phase Lag Index measures may contain more information and can be a more accurate way to distinguish PD patients from PDD rather than using EEG band-power measures alone. Furthermore, band-power and PLI measures contain non-redundant information.

The aims of this study are to investigate the usage of Phase Lag Index and frequency-band power measures as parameters for classification of PD and PDD patients, and dealing with the challenge of handling imbalanced data for classification. EEG data for a group of 81 PD patients and 19 PDD patients were collected from three centres and analysed using automated segmentation and Inverse Solution post-processing. The PD group was a mix of MCI, Non MCI and unclassified early stage PD patients. 63 Frequency measures and 216 Phase Lag Index measures were obtained for all patients. To overcome the problem of imbalanced data, Random Forest algorithm was applied to the data and compared with Random Forest using cost-sensitive learning as well as Random Forest with stratified sampling. Classification models were built using frequency measures, PLI measures and frequency combined with PLI measures respectively. Applying cost-sensitive learning or stratified sampling to Random Forest increased the predictive performance of the model, in comparison to using Random Forest alone. In the case of stratified sampling, using 63 frequency measures for classification gave a ROC curve with average AUC value of 0.68. The AUC value increased to 0.75 when using PLI measures alone, which further increased to 0.8 when combining PLI and frequency measures. Further analysis revealed many more PLI measures than frequency measures to be amongst the top features distinguishing the two groups accurately. Phase Lag Index measures may contain more information than EEG-band power measures and can be useful in distinguishing PD patients from PDD. Furthermore, band-power and PLI measures contain non-redundant information.

Quantitative EEG analyses, especially connectivity and graph analyses, are promising biomarkers in neurodegenerative disorders such as Alzheimer’s disease. To develop reliable and valid biomarkers, many aspects in pre- and post-processing of EEG data are important. Artifact detection and segment selection are done by visual inspection or automated routines, both with specific advantages and difficulties. Inverse solution is an elegant way of solving the issue of common source but may be somewhat imprecise in the absence of defined graphoelements such as epileptiform discharges. Connectivity analyses allow a description of interactions between brain regions, which may represent one of the earliest signs of cognitive decline.

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.

studies have shown that quantitative EEG (QEEG) and neuropsychological parameters are associated with Parkinson’s disease (PD). We investigated the differences between PD patients and healthy controls (HC) in high-resolution QEEG measures, and analyzed the prediction accuracy. We also wanted to see if a combination of QEEG and neuropsychological factors could increase the prediction accuracy of the model in comparison with QEEG parameters alone. high-resolution 256-channel EEG were recorded in 66 PD patients and 59 HC. Neuropsychological assessment of the patients covered five cognitive domains: attention, working memory, executive functions, memory and visuo-spatial functions (18 cognitive tests). An average score for each domain was calculated along with an overall cognitive score, resulting in 6 additional scores. EEG data were processed to calculate the relative power in alpha, theta, delta, beta frequency bands across 10 regions of the brain. Alpha1/theta ratios were also calculated, resulting in a total of 77 QEEG frequency measures. Random Forest algorithm was applied to the data to check for change in prediction accuracy. using the QEEG measures alone for classification, Area-under-the-Curve (AUC) value of 0.819 was obtained along with Positive and Negative predictive values (PPV, NPV) of 0.736 and 0.754, respectively. The 6 neuropsychological domain scores, when used alone, resulted in an AUC of 0.82, PPV of 0.71 and NPV of 0.8. On combining the QEEG measures and the 6 neuropsychological scores, an AUC value of 0.859 was obtained along with a PPV of 0.729 and NPV of 0.76. A slight increase in the AUC was observed on combining the QEEG and 6 neuropsychological measures, in comparison to using them alone while the PPV and NPV values did not have much difference. However, on combining the QEEG measures with all 24 available neuropsychological scores instead of using the average domain scores and overall cognitive scores alone, the AUC value increased to 0.88 while the PPV and NPV values increased to 0.785 and 0.8. QEEG measures can be useful in distinguishing Parkinson’s disease patients from healthy controls with a considerable accuracy. This accuracy can be significantly improved by combining the QEEG measures with distinct neuropsychological test scores.

Reliability of assessment of evoked potentials (EP) critically depends on experience of the neurophysiologist. Latencies are the most robust parameters of EP. This pilot-study aims to establish a fully automated detection of P100 latency in visual EP (VEP) with a standard 10–20 EEG-system. We hypothesized that this latency is reliable over at least 2 years in healthy controls (HC).

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.

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.