Predicting Dementia in Parkinson’s Disease by Combining Neurophysiological and Cognitive Markers

Objective: To assess the ability of neurophysiological markers in conjunction with cognitive assessment to improve prediction of progression to dementia in Parkinson’s disease (PD). Methods: Baseline cognitive assessments and magnetoencephalographic recordings from 63 prospectively included non-demented PD patients were analyzed in relation to PDrelated dementia (PDD) conversion over a 7-year period. We computed Cox proportional hazard models to assess the risk of converting to dementia conveyed by cognitive and neurophysiological markers in individual as well as combined risk factor analyses. Results: Nineteen patients (30.2%) developed dementia. Baseline cognitive performance and neurophysiological markers each individually predicted conversion to PDD. Of the cognitive test battery, performance on a posterior [pattern recognition memory score < median; Hazard Ratio (HR) 6.80; p = .001] and a fronto-executive [spatial span score < median; HR 4.41; p = .006] task most strongly predicted dementia conversion. Of the neurophysiological markers, beta power < median was the strongest PDD predictor [HR 5.21; p = .004], followed by peak frequency < median [HR 3.97; p = .016] and theta power > median ]HR 2.82; p = .037]. In combination, baseline cognitive performance and neurophysiological measures had even stronger predictive value, the combination of impaired fronto-executive task performance and low beta power being associated with the highest dementia risk [both risk factors versus none: HR 27.3; p < .001]. Conclusion: Combining neurophysiological markers with cognitive assessment can substantially improve dementia risk profiling in PD, providing potential benefits for clinical care as well as for the future development of therapeutic strategies. Kim T.E. Olde Dubbelink1, Arjan Hillebrand2, Jos W.R. Twisk3, Jan Berend Deijen4, Diederick Stoffers1, Ben A. Schmand5, Cornelis J. Stam2, Henk W. Berendse1 1Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Center 2Department of Clinical Neurophysiology and Magnetoencephalography Center, VU University Medical Center 3Department of Clinical Neuropsychology, VU University 4Department of Clinical Epidemiology and Biostatistics, VU University Medical Center 5Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands Neurology 2014;52:263-270