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Background: Recently, common genetic risk factors for intracranial aneurysm (IA) and aneurysmal subarachnoid hemorrhage (ASAH) were found to explain a large amount of disease heritability and therefore have potential to be used for genetic risk prediction. We constructed a genetic risk score to (1) predict ASAH incidence and IA presence (combined set of unruptured IA and ASAH) and (2) assess its association with patient characteristics. Methods: A genetic risk score incorporating genetic association data for IA and 17 traits related to IA (so-called metaGRS) was created using 1161 IA cases and 407 392 controls from the UK Biobank population study. The metaGRS was validated in combination with risk factors blood pressure, sex, and smoking in 828 IA cases and 68 568 controls from the Nordic HUNT population study. Furthermore, we assessed association between the metaGRS and patient characteristics in a cohort of 5560 IA patients. Results: Per SD increase of metaGRS, the hazard ratio for ASAH incidence was 1.34 (95% CI, 1.20–1.51) and the odds ratio for IA presence 1.09 (95% CI, 1.01–1.18). Upon including the metaGRS on top of clinical risk factors, the concordance index to predict ASAH hazard increased from 0.63 (95% CI, 0.59–0.67) to 0.65 (95% CI, 0.62–0.69), while prediction of IA presence did not improve. The metaGRS was statistically significantly associated with age at ASAH (β=−4.82×10 −3 per year [95% CI, −6.49×10 −3 to −3.14×10 −3 ]; P =1.82×10 −8 ), and location of IA at the internal carotid artery (odds ratio=0.92 [95% CI, 0.86–0.98]; P =0.0041). Conclusions: The metaGRS was predictive of ASAH incidence, although with limited added value over clinical risk factors. The metaGRS was not predictive of IA presence. Therefore, we do not recommend using this metaGRS in daily clinical care. Genetic risk does partly explain the clinical heterogeneity of IA warranting prioritization of clinical heterogeneity in future genetic prediction studies of IA and ASAH.

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.

In multiple sclerosis (MS), the combination of visual, somatosensory and motor evoked potentials (EP) has been shown to be highly correlated with the Expanded Disability Severity Scale (EDSS) and to predict the disease course. In the present study, we explored whether the significance of the visual EP (VEP) can be improved with multichannel recordings (204 electrodes) and topographic analysis (tVEP). VEPs were analyzed in 83 MS patients (median EDSS 2.0; 52 % with history of optic neuritis; hON) and 47 healthy controls (HC). TVEP components were automatically defined on the basis of spatial similarity between the scalp potential fields (topographic maps) of single subjects’ VEPs and reference maps generated from HC. Non-ambiguous measures of latency, amplitude and configuration were derived from the maps reflecting the P100 component. TVEP was compared to conventional analysis (cVEP) with respect to reliability in HC, validity using descriptors of logistic regression models, and sensitivity derived from receiver operating characteristics curves. In tVEP, reliability tended to be higher for measurement of amplitude (p = 0.06). Regression models on diagnosis (MS vs. HC) and hON were more favorable using tVEP- versus cVEP-predictors. Sensitivity was increased in tVEP versus cVEP: 72 % versus 60 % for diagnosis, and 88 % versus 77 % for hON. The advantage of tVEP was most pronounced in pathological VEPs, in which cVEPs were often ambiguous. TVEP is a reliable, valid, and sensitive method of objectively quantifying pathological VEP in particular. In combination with other EP modalities, tVEP may improve the monitoring of disease course in MS. Electronic supplementary material The online version of this article (doi:10.1007/s10548-013-0318-6) contains supplementary material, which is available to authorized users.

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.

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.

Ziel der Studie: Validierung der deutschsprachigen Version der Checkliste zur Erfassung neuropsychiatrischer Symptome bei der Parkinsonerkrankung (CENS-PE) von Martinez-Martin et al. (2012). Methodik: Die CENS-PE wurde an insgesamt 96 Patienten mit Parkinsonerkrankung (PE) (Alter: 65,3 Jahre ± 9,6, 29 Frauen) evaluiert. Die Checkliste umfasst 12 Items, die in die drei Domanen psychotische Symptome, Stimmung/Apathie und Storung der Impulskontrolle unterteilt sind. In mehreren statistischen Analysen wurde die Item- und Reliabilitatsanalyse durchgefuhrt und die Validitat uberpruft. Ergebnisse: Die untersuchten Patienten mit PE wiesen leichte neuropsychiatrische Symptome auf. Die drei Domanen konnten mit der Hauptkomponentenanalyse identifiziert werden. Die Items der Stimmung/Apathie-Domane erwiesen sich als homogen und trennscharf, die interne Konsistenz war akzeptabel. Fur die Domanen psychotische Symptome und Storung der Impulskontrolle waren die Kennwerte nur teilweise ausreichend. Die Checkliste erwies sich als konvergenz-, diskriminanz- und konstruktvalide. Schlussfolgerung: Die deutsche Ubersetzung der CENS-PE ist gesamthaft hinreichend reliabel und valide, um im deutschen Sprachraum eingesetzt zu werden. Die Domanen psychotische Symptome und Storung der Impulskontrolle konnen aufgrund der geringen Anzahl gemessener Symptome in diesem Bereich nur limitiert beurteilt werden.

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.

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.

Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinson's disease (PD) with and without Mild Cognitive Impairment from healthy individuals and to find an optimal method for feature selection. Background: Certain QEEG parameters have been seen to be associated with dementia in Parkinson's and Alzheimer's disease. Studies have also shown some parameters to be dependent on the stage of the disease. We wanted to investigate the differences in high-resolution QEEG measures between groups of PD patients and healthy individuals, and come up with a small subset of features that could accurately distinguish between the two groups. Methods: High-resolution 256-channel EEG were recorded in 50 PD patients (age 68.8 ± 7.0 year; female/male 17/33) and 41 healthy controls (age 71.1 ± 7.7 year; female/male 20/22). Data was processed to calculate the relative power in alpha, theta, delta, beta frequency bands across the different regions of the brain. Median, peak frequencies were also obtained and alpha1/theta ratios were calculated. Machine learning methods were applied to the data and compared. Additionally, penalized Logistic regression using LASSO was applied to the data in R and a subset of best-performing features was obtained. Results: Random Forest and LASSO were found to be optimal methods for feature selection. A group of six measures selected by LASSO was seen to have the most effect in differentiating healthy individuals from PD patients. The most important variables were the theta power in temporal left region and the alpha1/theta ratio in the central left region. Conclusion: The penalized regression method applied was helpful in selecting a small group of features from a dataset that had high multicollinearity. Keywords: Parkinson's disease, QEEG, cognitive decline, Parkinson's disease dementia, neurodegenerative disorders, machine learning

Objective: In this study, we compared a cognition-specific computer-based cognitive training program with a motion-controlled computer sports game that is not cognition-specific for their ability to enhance cognitive performance in various cognitive domains in patients with Parkinson disease (PD). Methods: Patients with PD were trained with either a computer program designed to enhance cognition (CogniPlus, 19 patients) or a computer sports game with motion-capturing controllers (Nintendo Wii, 20 patients). The effect of training in 5 cognitive domains was measured by neuropsychological testing at baseline and after training. Group differences over all variables were assessed with multivariate analysis of variance, and group differences in single variables were assessed with 95% confidence intervals of mean difference. The groups were similar regarding age, sex, and educational level. Results: Patients with PD who were trained with Wii for 4 weeks performed better in attention (95% confidence interval: −1.49 to −0.11) than patients trained with CogniPlus. Conclusions: In our study, patients with PD derived at least the same degree of cognitive benefit from non–cognition-specific training involving movement as from cognition-specific computerized training. For patients with PD, game consoles may be a less expensive and more entertaining alternative to computer programs specifically designed for cognitive training. Classification of evidence: This study provides Class III evidence that, in patients with PD, cognition-specific computer-based training is not superior to a motion-controlled computer game in improving cognitive performance.

OBJECTIVE: Mild cognitive impairment in Parkinson's disease (PD-MCI) is diagnosed based on the results of a standardized set of cognitive tests. We investigate whether quantitative EEG (qEEG) measures could identify differences between cognitively normal PD (PD-CogNL) and PD-MCI patients. METHODS: High-resolution EEG was recorded in 53 patients with Parkinson's disease (PD). Relative power in five frequency bands was calculated globally and for ten regions. Peak and median frequencies were determined. qEEG results were compared between groups. Effect sizes of all variables were calculated. The best separating variable was used to demonstrate subject-wise classification. RESULTS: Lower mean values were observed in global alpha1 power and alpha1 power in five brain regions (left hemisphere: frontal, central, temporal, occipital; right hemisphere: temporal, P > 0.05), differentiating between PD-CogNL and PD-MCI groups. Effect sizes were high, ranging from 0.79 to 0.87. Median frequency was 8.56 ± 0.74 Hz and was not different between the groups. The variable with the best subject-wise classification was the power in the alpha1 band in the right temporal region. The area under the corresponding receiver operating characteristic (ROC) curve was 0.72. The optimal classification threshold yielded a sensitivity of 65.9% and a specificity of 66.7%. The positive and negative predictive values were 87.1% and 36.4%, respectively. INTERPRETATION: Reduction in alpha1 band power in nondemented PD patients, particularly in the right temporal region, is highly indicative of MCI in PD patients. The results might be used to assist in time-efficient diagnosis of PD-MCI and avoid the drawbacks of test-retest effect in repeated neuropsychological testing.

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

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. 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.

Objective: The aim of this study is to compare a Cognitive Behavioral Group Therapy (CBT) with a health enhancement program (HEP) for stress reduction and the impact on quality of life (QoL) in patients with Parkinson`s disease (PD). Method: Thirty patients with PD participated in the study: 16 received CBT including stress-reducing elements and 14 took part in a HEP. The two groups did not differ significantly in their baseline demographic characteristics. The patients in both groups underwent weekly sessions of 2 hours’ duration for 9 weeks. The Parkinson`s Disease Questionnaire with 39 items (PDQ-39), the Burden Questionnaire for Parkinson`s Disease (translated from the original German: Belastungsfragebogen fur Parkinsonpatienten (BELA)) and the Disease-Related Questionnaire (Fragebogen zur krankheitsbezogenen Kommunikation (FKK)) were primary outcomes. Ratings were completed at baseline and after 9 weeks (immediately after the last treatment session). Results: The patients in the CBT group achieved significantly better BELA, FKK and PDQ-39 scores (p

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.

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 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.

Background and purpose: The risk of stroke after a transient ischaemic attack (TIA) can be predicted by scores incorporating age, blood pressure, clinical features, duration (ABCD-score), and diabetes (ABCD2-score). However, some patients have strokes despite a low predicted risk according to these scores. We designed the ABCDE+ score by adding the variables etiology and ischaemic lesion visible on diffusion-weighted imaging (DWI) – DWI-positivity – to the ABCD-score. We hypothesized that this refinement increases the predictability of recurrent ischaemic events. Methods: We performed a prospective cohort study amongst all consecutive TIA patients in a university hospital emergency department. Area under the computed receiver-operating curves (AUCs) were used to compare the predictive values of the scores with regard to the outcome stroke or recurrent TIA within 90 days. Results: Amongst 248 patients, 33 (13.3%, 95%-CI 9.3–18.2%) had a stroke (n = 13) or a recurrent TIA (n = 20). Patients with recurrent ischaemic events more often had large-artery atherosclerosis as the cause for TIA (46% vs. 14%, P < 0.001) and positive DWI (61% vs. 35%; P = 0.01) compared with patients without recurrent events. Patients with and those without events did not differ with regard to age, clinical symptoms, duration, blood pressure, risk factors, and stroke preventive treatment. The comparison of AUCs [95%CI] showed superiority of the ABCDE+ score (0.67[0.55–0.75]) compared to the ABCD 2 -score (0.48[0.37–0.58]; P = 0.04) and a trend toward superiority compared to the ABCD-score (0.50[0.40–0.61]; P = 0.07). Conclusion: In TIA patients, the addition of the variables etiology and DWI-positivity to the ABCD-score seems to enhance the predictability of subsequent cerebral ischaemic events.

Progressive carotid artery disease has been shown to cause cerebrovascular events years after a patient's carotid thromboendarterectomy (CEA). Yet, some late cerebrovascular events in CEA patients are attributable to other etiologies.We sought to determine frequency and characteristics of late cerebrovascular events in post-CEA patients attributable to etiologies other than progressive carotid disease.In a post hoc analysis of data from a CEA-registry with long-term follow-up, all patients with transient ischaemic attack (TIA) or stroke occurring1 month post-CEA were identified. The etiologies of these events were dichotomized into the groups large-artery atherosclerosis (LAA) and that non-large-artery atherosclerosis (non-LAA), i.e. all other etiologies (Trial of Org 10172 in Acute Stroke Trial-criteria). Frequency and characteristics of both groups were compared.Sixty of 361 post-CEA patients (16.6%; 95%CI 12.9-20.9%) had late cerebrovascular events after 7 years (median). Thirty patients had ischaemic strokes and 30 had TIAs. These events were attributable to LAA in 48% (29/60) and to non-LAA in 52% (31/60). In the LAA group, contralateral carotid stenosis (62%; 18/29) was more frequent than recurrent ipsilateral stenosis (38%; 11/29). Amongst non-LAA patients, cardioembolism (29%; 9/31) and small-artery-occlusion (23%; 7/31) were the most frequent causes. LAA and non-LAA patients did not differ in age, time since CEA, risk factor profile, type of event, and baseline medication.In post-CEA-patients, half of the late cerebrovascular events were attributable to etiologies other than LAA. Clinical features did not distinguish LAA-events from non-LAA events. Thus, stroke prevention in post-CEA patients should not be confined to screening for progressive carotid disease but includes efforts to optimize the management of risk factor and cardiac diseases.

Parkinson's Disease Dementia (PD-D) is one of the most important non-motor signs in advanced PD and is the most influencing factor predicting nursing home placement. PD-related Mild Cognitive Impairment (PD-MCI) is a potential prodromal stage of PD-D. The Grand Total EEG (GTE) score is a rating scale for clinical EEG (Electroencephalography) analyses which is useful in the evaluation of different types of dementia. The purpose of the present study was to investigate the relationship between a short version of the GTE score and severity of cognitive deficits in PD. Nineteen patients with PD underwent neuropsychological testing and resting state EEG. Significant correlations with deteriorating cognition (combined Mini Mental Status Examination/Clock Drawing Test) were found for the overall short GTE score (Spearman Rank correlation, ρ=-.6; p.05) and for the subscore "Frequency of Rhythmic Background Activity" (ρ=-.6; p.05), indicating that these EEG measures increase with deteriorating cognition.

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.

In patients who had carotid endarterectomy (CEA), the significance of newly acquired cerebrovascular risk factors (CRFs) is unknown. Newly acquired CRFs are defined as CRFs not present prior to CEA (baseline CRFs) but acquired during long-term follow-up.

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

Introduction Electroencephalography (EEG) microstates and brain network are altered in patients with Alzheimer’s disease (AD) and discussed as potential biomarkers for AD. Microstates correspond to defined states of brain activity, and their connectivity patterns may change accordingly. Little is known about alteration of connectivity in microstates, especially in patients with amnestic mild cognitive impairment with stable or improving cognition within 30 months (aMCI). Methods Thirty-five outpatients with aMCI or mild dementia (mean age 77 ± 7 years, 47 % male, Mini Mental State Examination score ≥24) had comprehensive neuropsychological and clinical examinations. Subjects with cognitive decline over 30 months were allocated to the AD group, subjects with stable or improving cognition to the MCI-stable group. Results of neuropsychological testing at baseline were summarized in six domain scores. Resting state EEG was recorded with 256 electrodes and analyzed using TAPEEG. Five microstates were defined and individual data fitted. After phase transformation, the phase lag index (PLI) was calculated for the five microstates in every subject. Networks were reduced to 22 nodes for statistical analysis. Results The domain score for verbal learning and memory and the microstate segmented PLI between the left centro-lateral and parieto-occipital regions in the theta band at baseline differentiated significantly between the groups. In the present sample, they separated in a logistic regression model with a 100 % positive predictive value, 60 % negative predictive value, 100 % specificity and 77 % sensitivity between AD and MCI-stable. Conclusions Combining neuropsychological and quantitative EEG test results allows differentiation between subjects with aMCI remaining stable and subjects with aMCI deteriorating over 30 months.

Cerebral inflammation plays a crucial role in the pathophysiology of ischemic stroke and is involved in all stages of the ischemic cascade. Fullerene derivatives, such as fullerenol (OH-F) are radical scavengers acting as neuroprotective agents while glucosamine (GlcN) attenuates cerebral inflammation after stroke. We created novel glucosamine-fullerene conjugates (GlcN-F) to combine their protective effects and compared them to OH-F regarding stroke-induced cerebral inflammation and cellular damage. Fullerene derivatives or vehicle was administered intravenously in normotensive Wistar-Kyoto (WKY) rats and spontaneously hypertensive rats (SHR) immediately after transient middle cerebral artery occlusion (tMCAO). Infarct size was determined at day 5 and neurological outcome at days 1 and 5 after tMCAO. CD68- and NeuN-staining were performed to determine immunoreactivity and neuronal survival respectively. Cytokine and toll like receptor 4 (TLR-4) expression was assessed using quantitative real-time PCR. Magnetic resonance imaging revealed a significant reduction of infarct volume in both, WKY and SHR that were treated with fullerene derivatives. Treated rats showed an amelioration of neurological symptoms as both OH-F and GlcN-F prevented neuronal loss in the perilesional area. Cerebral immunoreactivity was reduced in treated WKY and SHR. Expression of IL-1β and TLR-4 was attenuated in OH-F-treated WKY rats. In conclusion, OH-F and GlcN-F lead to a reduction of cellular damage and inflammation after stroke, rendering these compounds attractive therapeutics for stroke.

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.

Background and purpose: The aim of this study was to determine the prognostic significance of microbleeds in TIA-patients. In patients with a transient ischaemic attack (TIA), the prognostic value of microbleeds is unknown. Methods: In 176 consecutive TIA patients, the number, size, and location of microbleeds with or without acute ischaemic lesions were assessed. We compared microbleed-positive and microbleed-negative patients with regard to the end-point stroke within 3 months. Results: Four of the seven patients with subsequent stroke had microbleeds. Microbleed-positive patients had a higher risk for stroke [odds ratios (OR) 8.91, 95% CI 1.87–42.51, P

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

Renal function has been shown to be a prognostic marker for cardiovascular events.1 We performed a databank-based analysis to investigate the prognostic value of renal function regarding functional outcome, recurrent stroke, and symptomatic intracranial hemorrhage (SICH) in stroke patients treated with IV recombinant tissue plasminogen activator (rtPA). ### Methods. All consecutive stroke patients (n = 196) treated with IV-rtPA (1998–2006) were included. IV-rtPA was used according to current guidelines.2 Baseline variables were extracted from our prospectively ascertained thrombolysis database.3 The ethics committee approved of our approach to ascertain and analyze data of all rtPA-treated stroke patients. Estimates of renal function included serum-creatinine levels and glomerular filtration rates (GFR). Creatinine was measured at admission. GFR was calculated applying the simplified Modification of Diet in Renal Disease (MDRD) formula.4 Endpoints were good (modified Rankin scale [mRS] ≤2) vs poor outcome (mRS >2, including death [cause of immediate death based on information of treating physician]), recurrent ischemic stroke at 3 months (WHO criteria), and SICH (National Institute of Neurological Disorders and Stroke trial definition). All patients had CT or MR scan (72 hours) and additional scans in case of clinical deterioration. Univariate analyses regarding good vs poor outcome were performed for creatinine, GFR, and baseline variables using Fisher exact tests or t tests. Secondly, multiple logistic regression was performed with the mRS >2 as dependent …

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.