Search

Background DBS is commonly used to treat Parkinson's disease (PD). DBS is not considered to cause major cognitive side effects, but some research groups have reported that it can cause decreased verbal fluency. The influence of age on DBS cognitive outcome is unclear. We investigated the possible influence of patients' age, level of education, disease duration, disease progression, depression, and levodopa equivalent dose (LED) on verbal fluency performance in patients with PD who underwent DBS of the subthalamic nucleus (STN-DBS). In this article, we investigated the influence of demographic and clinical parameters, especially age, on cognitive performance post-DBS in PD patients. Methods Forty-three patients with PD and without major psychiatric illness (according to Diagnostic and Statistical Manual of Mental Disroders, Fourth Edition) were enrolled in the study. Median age was 64.0 years (range, 46–77). In 21 patients, the indication for DBS was established on clinical grounds in keeping with international guidelines; these patients underwent STN-DBS, and the remaining 22 did not. Cognitive performance in both groups was assessed by standard neuropsychological test batteries at baseline and after median follow-up of 7 months. Results A statistically significant decline in the semantic category of verbal fluency task was found in the STN-DBS group (P < 0.01). Linear regression model revealed an influence of age (P < 0.01) and disease duration (P < 0.01) in relation to this decline. Conclusions This study confirms previous findings that verbal fluency declines after STN-DBS in PD patients in comparison to PD patients without DBS. This decline is related to age and disease duration.

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.

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.

International audience; Objective: To investigate the incidence of serious adverse events (SAE) of subthalamic deep brain stimulation (STN-DBS) in elderly patients with Parkinson's 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.

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.

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.

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.

Psychotic disorders in Parkinson's disease (PDPD) are common and significantly influence the quality of life and disability level. The pathogenesis of PDPD is complex and not yet fully understood. Taking into consideration the features of the Parkinson's disease (usually older patients with a risk of cognitive decline), and the pharmacodynamics of the antiparkinsonian and traditional antipsychotic drugs, the management of PDPD is a challenging issue of clinical neurology and psychiatry. In this systematic review, scientific publications for the period 2014-2016 were analyzed within two bibliographic databases: MEDLINE/PubMed and eLIBRARY.RU. Additionally, the guidelines of the International Parkinson and Movement Disorders Society, American Academy of Neurology and European Academy of Neurology were included in the analysis. Clozapine is recommended to use in the treatment of PDPD, quetiapine is possible to use, pimavanserin will probably become a remedy of choice. Nonpharmacological approaches have positive effects on the general condition of the patients with PDPD, however the efficacy of such approaches to treat psychosis is unclear.Психотические расстройства при болезни Паркинсона (ПРБП) отмечаются у большого числа пациентов. Они оказывают значительное влияние на качество жизни и уровень инвалидизации. Патогенез ПРБП является сложным и до конца не изученным. Принимая во внимание специфику БП (как правило, пациенты пожилого возраста с риском появления когнитивных нарушений), а также фармакодинамические особенности антипаркинсонических и классических антипсихотических средств, лечение ПРБП требует особого внимания неврологов и психиатров. Представлен систематический обзор, в котором обобщены результаты научных публикаций за период 2014-2016 гг. в двух библиографических базах данных: MEDLINE/PubMed и eLIBRARY.RU, а также клинических рекомендаций Международного общества болезни Паркинсона и нарушений движения, Американской и Европейской академий неврологии. Из антипсихотических средств клозапин является препаратом, рекомендованным к применению, кветиапин - препарат, который признается возможным к применению, пимавансерин, вероятно, станет одним из препаратов выбора в терапии ПРБП. Нефармакологические методы лечения оказывают положительный эффект в плане улучшения общего состояния пациентов с ПРБП, однако их эффективность по лечению собственно ПРБП остается неясной.

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.