Time Series Analysis Applied to EEG Shows Increased Global Connectivity during Motor Activation Detected in PD Patients Compared to Controls.
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2020
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Applied Sciences
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Abstract
Background: Brain connectivity has shown to be a key characteristic in the study of both
Parkinson’s Disease (PD) and the response of the patients to the dopaminergic medication. Time series
analysis has been used here for the first time to study brain connectivity changes during motor
activation in PD. Methods: A 64-channel EEG signal was registered during unilateral motor activation
and resting-state in 6 non-demented PD patients before and after the administration of levodopa
and in 6 matched healthy controls. Spectral entropy correlation, coherence, and interhemispheric
divergence differences among PD patients and controls were analyzed under the assumption of
stationarity of the time series. Results: During the motor activation test, PD patients showed
an increased correlation coefficient (both hands p < 0.001) and a remarkable increase in coherence in
all frequency range compared to the generalized reduction observed in controls (both hands p < 0.001).
The Kullback–Leibler Divergence (KLD) of the Spectral Entropy between brain hemispheres was
observed to increase in controls (right hand p = 0.01; left hand p = 0.015) and to decrease in PD
patients (right hand p = 0.02; left hand p = 0.002) with motor activation. Conclusions: Our results
suggest that the oscillatory activity of the different cortex areas within healthy brains is relatively
independent of the rest. PD brains exhibit a stronger connectivity which grows during motor
activation. The levodopa mitigates this anomalous performance.
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Keywords
Parkinson’s Disease, Electroencephalography, Entropy, Connectivity, Levodopa