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.