Antonia Meyer

Apathy in Parkinson's disease is related to executive function, gender and age but not to depression.

Deficits in executive functions occur in up to 93% of patients with Parkinsons 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 Parkinsons 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 Parkinsons 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 Parkinsons 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.

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.

Correlation of EEG Slowing with Cognitive Domains in Nondemented Patients with Parkinson's Disease

Background: Cognitive deficits in Parkinsons 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.

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

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

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.

Apathy in Patients with Parkinson's Disease Correlates with Alteration of Left Fronto-Polar Electroencephalographic Connectivity

Introduction: Quantitative electroencephalography (QEEG) brain frequency and network analyses are known to differentiate between disease stages in Parkinsons 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.

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.