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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 establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data.A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender.Differences in the delta band at 34 temporo-posterior source locations (p.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results.Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy.qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.

Background and bbjectives 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. Methods 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. Results 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. Conclusion 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.

Aims 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. Methods 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. Results 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. Conclusion 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.

Introduction 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. Methods 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. Results 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. Conclusion 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.

Introduction 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. Methods 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. Results 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. Conclusion 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.

Background Deep brain stimulation (DBS) is commonly used in the treatment of Parkinson’s disease (PD). Various research groups have reported that DBS is associated with decreased verbal fluency. We investigated the possible confounding effects of patients’ age, disease duration and levodopa equivalent daily dose (LEDD) on verbal fluency performance after DBS in patients with PD. Methods 43 patients with PD and without major psychiatric illness (according to DSM-IV) were enrolled in the study, median age 63.4 years, range 39–76. Subjects were allocated to two groups: the first were scheduled to bilateral DBS-STN (n = 21), the second comprised patients without DBS surgery (n = 22). Verbal fluency performance in both groups was assessed with Controlled Oral Word Association Test at baseline and eight months later. Results Decline in semantic fluency performance was found in the DBS group (p = 0.03). No confounding effect of age, disease Duration and LEDD was found in relation to this decline. Conclusions This restrospective observation confirms previous findings showing a decline in verbal fluency after DBS-STN in PD patients when compared with PD patients without surgery. Verbal fluency decline is unrelated to age, disease duration and LEDD.

Background The decline of olfactory capacity has been identified as an early symptom of Parkinson’s disease (PD) and can precede PD-related motor and non-motor impairment. Applying the olfactory tests in the assessment of PD may increase the accuracy of diagnosis and provide promising markers of the disease progression. Objectives We set the following objectives: (a) to compare olfactory function between samples of PD patients and healthy controls; (b) to check correlations between the olfactory function, clinical features and quantitative EEG; and (c) to check the diagnostic accuracy of the olfactory function. Methods We analyzed 2 samples: PD-sample (n = 54, males 68%), medians: age 68 yr, education 15 yr, MMSE 29, UPDRS-III 18, LEDD 475 mg/day, and control sample (n = 21, males 68%), adjusted by age, education and MMSE. Olfactory function in both samples was assessed with “Sniffin” Sticks®” tool, a set of 12 sticks with a specific odour each (e.g. orange, coffee). The examinees had to inhale the unlabelled odourant and identify it. Sniffing score (SnSc) comprised the number of correct identifications (0–12). Five cognitive tests were applied, and EEG was recorded for each participant. Spectral analyses of the EEGs was performed with MATLAB-based tool and alpha (8–13 Hz)/theta (4–8 Hz) ratio was calculated. We used corrected Wilcoxon and chi-squared tests to compare samples, Spearman rank correlations to check the relation of SnSc with samples” parameters, and ROC-curves to check the PD diagnostics value of SnSc, alpha/theta ratio (ATR) and a combined score of SnSc + ATR. The level of statistical significane set at.05; Bonferroni correction for multiple testing was applied. Results In PD-sample, SnSc was significantly decreased (p Classification in PD and controls: SnSc + ATR(Area under the curve (AUC) 86.5%, spec. 100%, sens. 64.8%), followed by SnSc (AUC 86.1%, spec. 95.2%, sens. 66.7%), and ATR (65.0%, spec. 61.9%, sens. 70.3%). Conclusions Because olfactory decrease in PD correlates with motor impairment (especially items of lower extremities mobility and axial posture), and is independent of cognitive function, the assessment of olfactory function may be a useful additional tool in the detection and follow-up of PD. Cohort studies with larger samples are warranted to identify whether olfactory decline predicts motor severity of PD.

Objective We investigated whether a combination of qEEG variables, in particular signal power in the alpha1 (8–10 Hz) and theta (4–8 Hz) bands, could represent a robust marker for MCI in patients with PD. Background MCI is diagnosed based on the results of a large standardized set of cognitive tests. Certain qEEG parameters are associated with dementia. Previous studies demonstrated a relation between MCI in PD patients and alpha1 power ( Bousleiman et al., 2014 ). Other studies introduced a ratio between alpha and theta powers and demonstrated its association with Alzheimer’s disease ( Schmidt et al., 2013 ). Methods High-resolution 256-channel EEG were recorded in 43 PD patients (MCI/non-MCI: 18/25 ∣age: 68.1 ± 7.9∣ female/male: 16/27). The data was pre-processed semi-automatically and global relative power in alpha1 and theta bands was calculated. Follow-up recordings at four weeks (4W) and six months (6 M) were collected for 32 patients (MCI/non-MCI: 13/19) to test the stability over time. Results were compared between groups using permutation tests on t-statistics to correct for multiple comparisons. Effect sizes (ES) and intra-class correlation (ICC) were calculated. Results An increase ( p = 0.017; ES = 0.789) in the theta and a decrease ( p = 0.042; ES = 0.782) in the alpha1 signal power were associated with MCI in PD patients. The ratio alpha1/theta showed a more robust negative association ( p = 0.012; ES = 1.04) than those calculated for each variable separately. Moreover, the ratio was stable over time (4 W: p = 0.002; ES = 1.082 – 6 M: p = 0.002; ES = 1.084 – ICC = 0.76). Patients whose baseline positive MCI diagnosis did not change at 6 M exhibited a higher ratio than those with a negative MCI diagnosis at 6 M. However, the difference was not statistically significant ( p = 0.1178). Conclusions Reduction of the alpha1/theta ratio is reliably associated with MCI in PD patients. This finding might be used as a robust marker for screening PD patients for early cognitive deficits.

Objective To explore network characteristics of functional connectomes in MS patients over time by graph analysis. Methods 80 RRMS patients (median EDSS: 2.0, IQR: 1.5–3.0) and 44 normal controls had a 256 channel EEG at baseline, years 1 and 2 (MS: n = 65 and n = 63, NC: n = 35). Subjects were assessed by EDSS, SDMT and a fatigue scale (FSMC). Graph measures (normalized cluster coefficient and path length, small-world-index, degree correlation and diversity, efficiency, transitivity) were calculated in theta-, alpha1-, alpha2- and beta-band connectomes based on the phase lag index in signal-space using TAPEEG. Result Patients had reduced cognitive processing speed (SDMT: 56.2 vs. 63.8, p p p p p Conclusions Beta-band graph measures of network efficiency are weakly but consistently altered in MS showing significant correlations to cognitive processing speed at year 2. The small effect size may be due to low disability in the MS group.

Objective To investigate the risks of DBS in Parkinson's disease patients of relatively old age (older than 60years). Methods A retrospective sample of 29 non-demented patients with idiopathic PD (age: median 64.0years±7.0; 11 females) was investigated (Basel group). The patients underwent DBS in Basel and Bern, Switzerland. Mean period of follow-up after surgery was 23.8months. Serious adverse events (SAE) were defined as any event leading to death, disability, prolonged or new hospitalization, according to the Medical Dictionary for Regulatory Activities, Version 14.1. We compared the outcome in the Basel group with the DBS group of EARLYSTIM clinical trial (Schuepbach et al., 2013) with a neurostimulation group (age: median 52.9±6.6years) and a best medical treatment group (age: median 52.2±6.1years). Chi-square test was used for statistical analysis. Results A total of 17 patients (58.6%) in the Basel group had at least one SAE. There were no suicides in the Basel group, but four of the patients deceased during the follow up period. SAE related to psychosis/hallucination ( p p =0.004) were significantly different between the Basel group and the EARLYSTIM groups with the result that overall difference of SAE was also different between the groups ( p =0.011). In addition there were more motor fluctuations in the Basel group ( p =0.02) than in both EARLYSTIM groups. Conclusion While the gross profile of SAE is similar in older and younger patients treated with DBS, the incidence of psychosis, depression and motor fluctuation is higher in older patients. Older patients require increased attention to risk factors for neuropsychiatric consequences of DBS.

Objective To examine the hypothesis that apathy in patients with PD is related to frontal and temporal changes in EEG frequencies. We expected apathy to correlate with slowing of general EEG background activity, with decrease of alpha power and with increase of theta power in frontal and temporal regions of the brain. Methods 37 non-demented patients with idiopathic PD were recruited from the Movement Disorder Outpatient Clinic Basel (age: Median 69y; from 50y to 84y; 14 females). The Apathy Evaluation Scale (AES) (Marin, 1991) in informant version (AES-I) were completed by relatives of the patients.256-channel EEGs with quantitative semi-automatic analyses were used to detect alpha total-frequencies alpha 1-, alpha 2- and theta-in frontal and temporal regions. In addition, slowing of EEG was measured with Median Peak frequency. For statistics, a general linear model with backward elimination procedure was conducted. We controlled for confounding factors: age, gender, education, severity of motor symptoms, levodopa equivalent dose, depression and cognition. Results In this sample, the patients were only slightly affected by apathy ( Median =24; from 18 to 39; cut off value: 38). The resulting model was significant ( R 2 = 0.39 ; p b =−3.74; p =0.08). Relevant variables in the resulting model were alpha total, temporal right ( b =76.493; p b =−58.37; p b =−13.07; p r =−0.40), whereas in females there was no correlation between alpha total and the number of apathy symptoms ( r =0.04). Conclusions Slowing of EEG is correlated with apathy in patients with PD. This correlation is significant even in PD patients with little or no depression. This fact helps to separate the two neuropsychiatric entities. In accordance with our hypothesis, beginning apathy in PD might be related to an alpha 1 decrease the frontal left part of the brain. In contrast, alpha total of the right hemisphere positively correlates with apathy. In addition, however, the results in male gender are consistent with our expectation, but have to be replicated in a larger sample of PD patients with more severe apathy.

Introduction EEG microstates and brain network analyses are known to differentiate patients with Alzheimer’s disease (AD) and healthy controls (HC) and are potential biomarkers of AD. Microstates correspond to defined states of brain activity, and connectivity patterns may change accordingly. To our knowledge, the two methods were not yet combined. Furthermore, little is known about their alterations in early AD. Objective/aims To compare brain network connectivities between early AD and age-matched HC in microstates. Methods 32 outpatients with early AD (mean age 77 ± 7, 47% male, MMS 24–30) and 32 HC were matched for age, gender and education. Diagnosis of AD was made after comprehensive neuropsychological and clinical examinations, and only patients with cognitive decline over 30 months were included. Resting state EEG was recorded with 256 electrodes and analyzed using Matlab®. Five microstates were defined and individual data fitted. After phase transformation, the phase lag index (PLI) was calculated for continuous data and for the five microstates in every subject. Networks were reduced to 22 nodes for statistical analysis. Results After correction for multiple comparisons, no significant differences in connectivities using continuous data were found. Using connectivities from microstates, significant differences in theta and alpha1 band were detected. Connectivities were higher in the AD than in the HC group. Conclusions The combination of microstates and connectivity analyses differentiates between HC and AD patients at the earliest stage of disease (MMS ⩾ 24). Adding microstates information to connectivity analysis may increase sensitivity of quantitative EEG characterization of AD.

Background Functional connectivity is a promising tool to characterize and analyze alterations of oscillating brain activity. In MS the significance of such alterations is not well known. Objective To explore the dynamics of functional connectivity in MS over time. Methods 57 RRMS-patients (76% female; median age: 38.4 yrs, interquartile range (IQR): 33.2–44.5 yrs; median EDSS: 2.0; IQR: 1.5–3.0) and 35 normal controls (80% female; median age: 38.4 yrs, IQR: 30.4–43.2 yrs) received a 256 channel EEG at baseline and at years 1 and 2, and were assessed by the EDSS, the symbol digit modalities test (SDMT) and the fatigue scale for motor and cognitive functions (FSMC, Penner et al., 2009). Functional connectivity between 22 regions was determined by the phase lag index (PLI, Stam et al., 2007) using TAPEEG (Hatz et al., 2014). The connectomes and the average connectivity of each region were calculated in four frequency bands (theta, lower and upper alpha, beta) and compared between groups and over time by permutation t -tests and ANOVA. Result Compared with normal controls, patients had reduced cognitive processing speed (SDMT: 56.2 vs. 63.8, p0.1). Connectivity in the beta-band was reduced in MS patients, most significantly over left temporo-parieto-occipital regions (p). Conclusions Reduced connectivity over the left temporo-parieto-occipital regions in the beta-band discriminates between groups of MS patients and healthy controls, but does not change over time, whereas connectivity over the right centro-parietal regions in the upper alpha band may be a more dynamic marker. However, on average, patients neither experience a change in clinical status, cognitive function nor fatigue during the observation period. Thus it remains unclear whether reduced beta-connectivity represents a marker of state or of trait. Disclosure The study has been financially supported by grants of the Swiss National Science Foundation (grants # 33CM30-124115/1; # 33CM30–140338; # 326030–128775/1) and the Swiss Multiple Sclerosis Society.