Increase of EEG Spectral Theta Power Indicates Higher Risk of the Development of Severe Cognitive Decline in Parkinson’s Disease after 3 Years

Abstract

Objective: We investigated quantitative electroencephalography (qEEG) and clinical parameters as potential risk factors of severe cognitive decline in Parkinson’s disease.Methods: We prospectively investigated 37 patients with Parkinson’s disease at baseline and follow-up (after 3 years). Patients had no severe cognitive impairment at baseline. We used a summary score of cognitive tests as the outcome at follow-up. At baseline we assessed motor, cognitive, and psychiatric factors; qEEG variables [global relative median power (GRMP) spectra] were obtained by a fully automated processing of high-resolution EEG (256-channels). We used linear regression models with calculation of the explained variance to evaluate the relation of baseline parameters with cognitive deterioration.Results: The following baseline parameters significantly predicted severe cognitive decline: GRMP theta (4–8 Hz), cognitive task performance in executive functions and working memory.Conclusions: Combination of neurocognitive tests and qEEG improves identification of patients with higher risk of cognitive decline in PD.

Characterization of EEG Data Revealing Relationships With Cognitive and Motor Symptoms in Parkinson's Disease: A Systematic Review

“…The characterizations of EEG signals include: EEG frequency spectrum characteristics, evoked potentials and cortical-muscle coherence (CMC). The remaining 11 papers (37%) used resting state EEG to investigate possible biomarkers for cognitive decline in PD patients (Schlede et al, 2011 ; Caviness et al, 2015 , 2016 ; Latreille et al, 2015 ; Cozac et al, 2016 ; Utianski et al, 2016 ; Arnaldi et al, 2017 ), evaluate therapeutic effects (Galvez et al, 2018 ; Babiloni et al, 2019 ), and distinguish PDD patients from other dementia patients, such as AD and DLB (Babiloni et al, 2011 ; Bliwise et al, 2014 ). The EEG frequency spectrum characteristics (the spectral power and/or power density), the event-related desynchronization/synchronization (ERD/ERS), and the EEG connectivity were studied in rest state EEG while sleep EEG, microstate analysis and the grand total EEG (GTE) score were also investigated in PD patients.…”

“…More studies revealed the increased activities in θ and δ bands of EEG in PD patients (Babiloni et al, 2011 ; Caviness et al, 2016 ). Cozac et al ( 2016 ) analyzed low frequency bands and demonstrated that global relative median power (GRMP) spectra of θ band can be used to predict cognitive decline. Despite the attenuated mid-frontal θ activity associated with disease duration, PD patients exhibited increased central θ power and decreased occipital θ power (Palmer et al, 2010 ; Singh et al, 2018 ).…”

A systematic review of MEG‐based studies in Parkinson's disease: The motor system and beyond

“…When comparing the MEG findings discussed in this review with the EEG studies recently reviewed by Geraedts and colleagues (Geraedts et al, ), there is a prominent agreement on the link between spectral slowing and cognitive decline. Lower peak frequency and higher delta/theta power were the best predictors for future conversion to PDD in longitudinal EEG studies (Caviness et al, ; Cozac et al, ; Klassen et al, ; Latreille et al, ) and in an MEG study a lower beta band power was the best predictor (Olde Dubbelink, Hillebrand, Twisk, et al, ). The effect of DRT on whole‐brain power was inconclusive for both EEG (e.g., (Mostile et al, ) and MEG studies (Stoffers, Bosboom, Wolters, et al, )), as well as the relationship between EEG/MEG‐findings and UPDRS‐III scores.…”

The association between high levels of cumulative life stress and aberrant resting state EEG dynamics in old age

“…Abnormalities in the power spectra of different frequency ranges have been linked to altered cerebral blood flow, impaired cognitive functioning as well as reduced structural integrity of associated brain regions (Rodriguez et al, 1999a(Rodriguez et al, , 1999bSloan et al, 1995;Babiloni et al, 2012). In recent years, they have gained widespread credibility as an indicator of age-related cognitive change and have been used extensively to study both healthy and pathological aging (Bruce et al, 2009;Knyazeva et al, 2010;Dauwels et al, 2011;Scheltens et al, 2012;Morabito et al, 2012;Babiloni et al, 2016;Cozac et al, 2016;Neto et al, 2016).…”