Menorca Chaturvedi

Quantitative EEG (QEEG) Measures Differentiate Parkinson's Disease (PD) Patients from Healthy Controls (HC)

Objectives: To find out which Quantitative EEG (QEEG) parameters could best distinguish patients with Parkinsons 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 Parkinsons and Alzheimers 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.

Fine Motor Function Skills in Patients with Parkinson Disease with and without Mild Cognitive Impairment.

Aims: The objective of this study was to investigate the relation between impaired fine motor skills in Parkinson disease (PD) patients and their cognitive status, and to determine whether fine motor skills are more impaired in PD patients with mild cognitive impairment (MCI) than in non-MCI patients. Methods: Twenty PD MCI and 31 PD non-MCI patients (mean age 66.7 years, range 50-84, 36 males/15 females), all right-handed, took part in a motor performance test battery. Steadiness, precision, dexterity, velocity of arm-hand movements, and velocity of wrist-finger movements were measured and compared across groups and analyzed for confounders (age, sex, education, severity of motor symptoms, and disease duration). Statistical analysis included t tests corrected for multiple testing, and a linear regression with stepwise elimination procedure was used to select significant predictors for fine motor function. Results: PD MCI patients performed significantly worse in precision (p < 0.05), dexterity (p < 0.05), and velocity (arm-hand movements; p < 0.05) compared to PD non-MCI patients. The fine motor function skills were confounded by age. Conclusions: Fine motor skills in PD MCI patients are impaired compared to PD non-MCI patients. Investigating the relation between the fine motor performance and MCI in PD might be a relevant subject for future research.

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.

P136. Relation of EEG frequency and apathy in patients with Parkinson’s disease (PD)

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

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.

Correlation of Visuospatial Ability and EEG Slowing in Patients with Parkinson’s Disease

Background. Visuospatial dysfunction is among the first cognitive symptoms in Parkinsons 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.