TY - JOUR
T1 - Oscillatory connectivity as a diagnostic marker of dementia due to Alzheimer's disease
AU - Musaeus, Christian Sandøe
AU - Engedal, Knut
AU - Høgh, Peter
AU - Jelic, Vesna
AU - Mørup, Morten
AU - Naik, Mala
AU - Oeksengaard, Anne Rita
AU - Snaedal, Jon
AU - Wahlund, Lars Olof
AU - Waldemar, Gunhild
AU - Andersen, Birgitte Bo
PY - 2019
Y1 - 2019
N2 - Objective: Quantitative EEG power has not been as effective in discriminating between healthy aging and Alzheimer's disease as conventional biomarkers. But EEG coherence has shown promising results in small samples. The overall aim was to evaluate if EEG connectivity markers can discriminate between Alzheimer's disease, mild cognitive impairment, and healthy aging and to explore the early underlying changes in coherence. Methods: EEGs were included in the analysis from 135 healthy controls, 117 patients with mild cognitive impairment, and 117 patients with Alzheimer's disease from six Nordic memory clinics. Principal component analysis was performed before multinomial regression. Results: We found classification accuracies of above 95% based on coherence, imaginary part of coherence, and the weighted phase-lag index. The most prominent changes in coherence were decreased alpha coherence in Alzheimer's disease, which was correlated to the scores of the 10-word test in the Consortium to Establish a Registry for Alzheimer's Disease battery. Conclusions: The diagnostic accuracies for EEG connectivity measures are higher than findings from studies investigating EEG power and conventional Alzheimer's disease biomarkers. Furthermore, decreased alpha coherence is one of the earliest changes in Alzheimer's disease and associated with memory function. Significance: EEG connectivity measures may be useful supplementary diagnostic classifiers.
AB - Objective: Quantitative EEG power has not been as effective in discriminating between healthy aging and Alzheimer's disease as conventional biomarkers. But EEG coherence has shown promising results in small samples. The overall aim was to evaluate if EEG connectivity markers can discriminate between Alzheimer's disease, mild cognitive impairment, and healthy aging and to explore the early underlying changes in coherence. Methods: EEGs were included in the analysis from 135 healthy controls, 117 patients with mild cognitive impairment, and 117 patients with Alzheimer's disease from six Nordic memory clinics. Principal component analysis was performed before multinomial regression. Results: We found classification accuracies of above 95% based on coherence, imaginary part of coherence, and the weighted phase-lag index. The most prominent changes in coherence were decreased alpha coherence in Alzheimer's disease, which was correlated to the scores of the 10-word test in the Consortium to Establish a Registry for Alzheimer's Disease battery. Conclusions: The diagnostic accuracies for EEG connectivity measures are higher than findings from studies investigating EEG power and conventional Alzheimer's disease biomarkers. Furthermore, decreased alpha coherence is one of the earliest changes in Alzheimer's disease and associated with memory function. Significance: EEG connectivity measures may be useful supplementary diagnostic classifiers.
KW - Alzheimer's disease
KW - Coherence
KW - Diagnostic
KW - EEG
KW - Imaginary part of coherence
KW - Mild cognitive impairment
KW - Weighted phase-lag index
U2 - 10.1016/j.clinph.2019.07.016
DO - 10.1016/j.clinph.2019.07.016
M3 - Journal article
C2 - 31408790
AN - SCOPUS:85070331768
SN - 1388-2457
VL - 130
SP - 1889
EP - 1899
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
IS - 10
ER -