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 ω, θ, 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 θ bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for ω, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. Conclusion: The QEEG measures of background rhythm frequency and relative power in the θ 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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 118-124 |
| Number of pages | 7 |
| Journal | Neurology |
| Volume | 77 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jul 12 2011 |
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ASJC Scopus subject areas
- Clinical Neurology
Cite this
Quantitative EEG as a predictive biomarker for Parkinson disease dementia. / Klassen, Bryan; Hentz, J. G.; Shill, H. A.; Driver-Dunckley, Erika M; Evidente, V. G H; Sabbagh, M. N.; Adler, Charles Howard; Caviness, John Nathaniel.
In: Neurology, Vol. 77, No. 2, 12.07.2011, p. 118-124.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Quantitative EEG as a predictive biomarker for Parkinson disease dementia
AU - Klassen, Bryan
AU - Hentz, J. G.
AU - Shill, H. A.
AU - Driver-Dunckley, Erika M
AU - Evidente, V. G H
AU - Sabbagh, M. N.
AU - Adler, Charles Howard
AU - Caviness, John Nathaniel
PY - 2011/7/12
Y1 - 2011/7/12
N2 - 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 ω, θ, 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 θ bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for ω, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. Conclusion: The QEEG measures of background rhythm frequency and relative power in the θ 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.
AB - 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 ω, θ, 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 θ bandpower (HR = 3.0; p = 0.004) compared to below, and for certain neuropsychological measures. The HRs for ω, α, and β bandpower as well as baseline demographic and clinical characteristics were not significant. Conclusion: The QEEG measures of background rhythm frequency and relative power in the θ 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.
UR - http://www.scopus.com/inward/record.url?scp=80051547727&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80051547727&partnerID=8YFLogxK
U2 - 10.1212/WNL.0b013e318224af8d
DO - 10.1212/WNL.0b013e318224af8d
M3 - Article
C2 - 21633128
AN - SCOPUS:80051547727
VL - 77
SP - 118
EP - 124
JO - Neurology
JF - Neurology
SN - 0028-3878
IS - 2
ER -