V. Cozac

Older Candidates for Subthalamic Deep Brain Stimulation in Parkinson's Disease Have a Higher Incidence of Psychiatric Serious Adverse Events.

Objective: To investigate the incidence of serious adverse events (SAE) of subthalamic deep brain stimulation (STN-DBS) in elderly patients with Parkinsons disease (PD). Methods: We investigated a group of 26 patients with PD who underwent STN-DBS at mean age 63.2 ± 3.3 years. The operated patients from the EARLYSTIM study (mean age 52.9 ± 6.6) were used as a comparison group. Incidences of SAE were compared between these groups. Results: A higher incidence of psychosis and hallucinations was found in these elderly patients compared to the younger patients in the EARLYSTIM study (p < 0.01). Conclusions: The higher incidence of STN-DBS-related psychiatric complications underscores the need for comprehensive psychiatric pre- and postoperative assessment in older DBS candidates. However, these psychiatric SAE were transient, and the benefits of DBS clearly outweighed its adverse effects.

Quantitative EEG and Cognitive Decline in Parkinson’s Disease

Cognitive decline is common with the progression of Parkinsons 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.

Increase of EEG Spectral Theta Power Indicates Higher Risk of the Development of Severe Cognitive Decline in Parkinson's Disease after 3 Years.

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 (GRMP) 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: GRMP 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.

P30. A novel application of the Phase-lag-Index in functional connectivity research

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.

F67. Distinguishing Parkinson’s Disease Dementia (PDD) patients from Parkinson’s Disease (PD) patients using EEG frequency and connectivity measures

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.

P76. Axial impairment and EEG slowing are independent predictors of cognitive outcome in a three-year cohort of PD patients

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.

P77. Prognosis of cognitive decline in Parkinsons disease: a combined marker of quantitative EEG and clinical variables improves prediction

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.

P78. Can Phase Lag Index (PLI) be beneficial in distinguishing Parkinsons disease Dementia (PDD) patients from Parkinsons disease (PD) patients

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.

P 129 Quantitative EEG and neuropsychological tests to differentiate between Parkinson’s disease patients and healthy controls with Random Forest algorithm

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.

P180. Serious adverse effects of deep brain stimulation (DBS) in patients with Parkinson’s disease (PD) of relatively old age in comparison with the EARLYSTIM study

Objective To investigate the risks of DBS in Parkinsons 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.