Quantitative EEG as a predictive biomarker for Parkinson disease dementia

Bryan Klassen, J. G. Hentz, H. A. Shill, Erika M Driver-Dunckley, V. G H Evidente, M. N. Sabbagh, Charles Howard Adler, John Nathaniel Caviness

Research output: Contribution to journalArticle

89 Citations (Scopus)

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 languageEnglish (US)
Pages (from-to)118-124
Number of pages7
JournalNeurology
Volume77
Issue number2
DOIs
StatePublished - Jul 12 2011

Fingerprint

Parkinson Disease
Dementia
Electroencephalography
Biomarkers
Incidence
Demography
Brain

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 journalArticle

@article{250e1c6c94cb42c3a17031f6180a5512,
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 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.",
author = "Bryan Klassen and Hentz, {J. G.} and Shill, {H. A.} and Driver-Dunckley, {Erika M} and Evidente, {V. G H} and Sabbagh, {M. N.} and Adler, {Charles Howard} and Caviness, {John Nathaniel}",
year = "2011",
month = "7",
day = "12",
doi = "10.1212/WNL.0b013e318224af8d",
language = "English (US)",
volume = "77",
pages = "118--124",
journal = "Neurology",
issn = "0028-3878",
publisher = "Lippincott Williams and Wilkins",
number = "2",

}

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 -