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Updated: Apr 19, 2026

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
Published on: July 24, 2019
Power spectra for screening parkinsonian patients for mild cognitive impairment
Habib Bousleiman1, Ronan Zimmermann2, Shaheen Ahmed2
1Department of Neurology, Hospital of the University of Basel Petersgraben 4, 4031, Basel, Switzerland ; Swiss Tropical and Public Health Institute, University of Basel Socinstrasse 57, 4051, Basel, Switzerland.
Objective:
Mild cognitive impairment in Parkinson's disease (PD-MCI) is diagnosed based on the results of a standardized set of cognitive tests. We investigate whether quantitative EEG (qEEG) measures could identify differences between cognitively normal PD (PD-CogNL) and PD-MCI patients.
Methods:
High-resolution EEG was recorded in 53 patients with Parkinson's disease (PD). Relative power in five frequency bands was calculated globally and for ten regions. Peak and median frequencies were determined. qEEG results were compared between groups. Effect sizes of all variables were calculated. The best separating variable was used to demonstrate subject-wise classification.
Results:
Lower mean values were observed in global alpha1 power and alpha1 power in five brain regions (left hemisphere: frontal, central, temporal, occipital; right hemisphere: temporal, P < 0.05), differentiating between PD-CogNL and PD-MCI groups. Effect sizes were high, ranging from 0.79 to 0.87. Median frequency was 8.56 ± 0.74 Hz and was not different between the groups. The variable with the best subject-wise classification was the power in the alpha1 band in the right temporal region. The area under the corresponding receiver operating characteristic (ROC) curve was 0.72. The optimal classification threshold yielded a sensitivity of 65.9% and a specificity of 66.7%. The positive and negative predictive values were 87.1% and 36.4%, respectively.
Interpretation:
Reduction in alpha1 band power in nondemented PD patients, particularly in the right temporal region, is highly indicative of MCI in PD patients. The results might be used to assist in time-efficient diagnosis of PD-MCI and avoid the drawbacks of test-retest effect in repeated neuropsychological testing.
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