Menorca Chaturvedi1, Jan Guy Bogaarts1, Vitalii V Kozak Cozac1, Florian Hatz2, Ute Gschwandtner2, Antonia Meyer2, Peter Fuhr2, Volker Roth3. 1. Department of Neurology, University Hospital Basel, Basel, Switzerland; Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland. 2. Department of Neurology, University Hospital Basel, Basel, Switzerland. 3. Department of Mathematics and Computer Science, University of Basel, Basel, Switzerland. Electronic address: Volker.Roth@unibas.ch.
Abstract
OBJECTIVES: To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients. METHODS: We recorded EEG data for a group of PD patients with MCI (n = 27) and PD patients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains). RESULTS: PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI. CONCLUSION: PLI is an effective quantitative EEG measure to identify PD patients with MCI. SIGNIFICANCE: We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.
OBJECTIVES: To identify quantitative EEG frequency and connectivity features (Phase Lag Index) characteristic of mild cognitive impairment (MCI) in Parkinson's disease (PD) patients and to investigate if these features correlate with cognitive measures of the patients. METHODS: We recorded EEG data for a group of PDpatients with MCI (n = 27) and PDpatients without cognitive impairment (n = 43) using a high-resolution recording system. The EEG files were processed and 66 frequency along with 330 connectivity (phase lag index, PLI) measures were calculated. These measures were used to classify MCI vs. MCI-free patients. We also assessed correlations of these features with cognitive tests based on comprehensive scores (domains). RESULTS:PLI measures classified PD-MCI from non-MCI patients better than frequency measures. PLI in delta, theta band had highest importance for identifying patients with MCI. Amongst cognitive domains, we identified the most significant correlations between Memory and Theta PLI, Attention and Beta PLI. CONCLUSION:PLI is an effective quantitative EEG measure to identify PDpatients with MCI. SIGNIFICANCE: We identified quantitative EEG measures which are important for early identification of cognitive decline in PD.
Authors: Monique S Boord; Bahar Moezzi; Daniel Davis; Tyler J Ross; Scott Coussens; Peter J Psaltis; Alice Bourke; Hannah A D Keage Journal: Clin Neurophysiol Date: 2020-10-01 Impact factor: 3.708