title: Quantitative EEG as a predictive biomarker for Parkinson disease dementia.
abstract: OBJECTIVE
  We evaluated quantitative EEG (QEEG) measures as predictive biomarkers for the development of dementia in Parkinson disease (PD). Preliminary work shows that QEEG measures correlate with current PD cognitive state. A reliable predictive QEEG biomarker for PD dementia (PD-D) incidence would be valuable for studying PD-D, including treatment trials aimed at preventing cognitive decline in PD.
  
  
  METHODS
  A cohort of subjects with PD in our brain donation program utilizes annual premortem longitudinal movement and cognitive evaluation. These subjects also undergo biennial EEG recording. EEG from subjects with PD without dementia with follow-up cognitive evaluation was analyzed for QEEG measures of background rhythm frequency and relative power in δ, , α, and β bands. The relationship between the time to onset of dementia and QEEG and other possible predictors was assessed by using Cox regression.
  
  
  RESULTS
  The hazard of developing dementia was 13 times higher for those with low background rhythm frequency (lower than the grand median of 8.5 Hz) than for those with high background rhythm frequency (p < 0.001). Hazard ratios (HRs) were also significant for > median bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for δ, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant.
  
  
  CONCLUSIONS
  The QEEG measures of background rhythm frequency and relative power in the band are potential predictive biomarkers for dementia incidence in PD. These QEEG biomarkers may be useful in complementing neuropsychological testing for studying PD-D incidence.
authors.affiliation: Department of Neurology, Mayo Clinic, Scottsdale, AZ 85259, USA.
authors.forename: B T
authors.initials: BT
authors.lastname: Klassen
authors.forename: J G
authors.initials: JG
authors.lastname: Hentz
authors.forename: H A
authors.initials: HA
authors.lastname: Shill
authors.forename: E
authors.initials: E
authors.lastname: Driver-Dunckley
authors.forename: V G H
authors.initials: VG
authors.lastname: Evidente
authors.forename: M N
authors.initials: MN
authors.lastname: Sabbagh
authors.forename: C H
authors.initials: CH
authors.lastname: Adler
authors.forename: J N
authors.initials: JN
authors.lastname: Caviness
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D058256:Brain Waves / Q000502:physiology*; D003072:Cognition Disorders / Q000209:etiology; D015331:Cohort Studies; D003704:Dementia / Q000150:complications / Q000175:diagnosis*; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D006801:Humans; D008297:Male; D008609:Mental Status Schedule; D008875:Middle Aged; D009483:Neuropsychological Tests; D010300:Parkinson Disease / Q000150:complications / Q000175:diagnosis*; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D012189:Retrospective Studies