Wavelet olayı ile ilişkili EEG faz uyumu Parkinson ve Lewy vücut hastalığında bilişsel durumun ayırıcı bir biyobelirteç olarak.

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BackgroundHuman cognition is derived from functional cortical long-range connectivity, as reflected by phase synchronization between electrode pairs of low-frequency electroencephalographic (EEG) activity MethodsWe tested the hypothesis that such an EEG marker, combined with machine learning, can discriminate between Parkinson's disease (PD) with mild cognitive impairment (MCI) and dementia (D) and those with dementia with Lewy bodies (DLB). Event-related EEG delta (1-3.5 Hz) and theta (4-7 Hz) phase coherence were computed from EEG activity recorded during a visual oddball task in healthy controls (HC, N = 24) and PD-MCI (N = 20), PDD (N = 18), and DLB (N = 11) patients.ResultsUsing delta-band coherence as input, the comparison between HC and PD-MCI yielded an AUC of approximately 0.79 and an accuracy of 86.4%. Higher discriminative performance was observed for HC versus PDD, reaching an AUC near 0.92 with an overall accuracy of 94.6%. In the classification of HC versus DLB participants, the model achieved 83.3% sensitivity and 88.9% specificity, with an AUC around 0.77. Theta-band models showed comparable results, with average AUC values of about 0.75 for HC vs. DLB and slightly above 0.80 for HC vs. PDD, while classification of HC vs. PD-MCI remained in a moderate range.ConclusionThese findings suggest that event-related EEG phase coherence at <8 Hz is a promising EEG correlate of cognitive deficits in patients with PDD and DLB, offering insights into disrupted network dynamics of cortical activity related to cognitive processes and potential biomarkers for testing new drugs for cognitive enhancement and disease monitoring.

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