Differential spectral quantitative electroencephalography patterns between control and Parkinson's disease cohorts

Caviness, John N.; Utianski, Rene L.; Hentz, Joseph G.; Beach, Thomas G.; Dugger, Brittany N.; Shill, Holly A.; Driver‐Dunckley, Erika; Sabbagh, Marwan N.; Mehta, Shyamal H.; Adler, Charles H.

doi:10.1111/ene.12878

Cited by 43 publications

(49 citation statements)

References 18 publications

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“… 16 22–24 Similarly, patients with Parkinson's disease and cognitive decline have increased θ and decreased α power and significantly lower median frequency compared to healthy controls. 25 26 When subdividing the patient group, we found lower background frequency, α power and α/θ power ratio in patients with sCJD when compared to the IPD group, in parallel to differences in the MRC Scale scores. Asymptomatic carriers of prion protein gene mutation did not show any EEG abnormalities.…”

Section: Discussionmentioning

confidence: 73%

Quantitative EEG parameters correlate with the progression of human prion diseases

Frankó

Wehner

Joly

et al. 2016

J Neurol Neurosurg Psychiatry

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BackgroundPrion diseases are universally fatal and often rapidly progressive neurodegenerative diseases. EEG has long been used in the diagnosis of sporadic Creutzfeldt-Jakob disease; however, the characteristic waveforms do not occur in all types of prion diseases. Here, we re-evaluate the utility of EEG by focusing on the development of biomarkers. We test whether abnormal quantitative EEG parameters can be used to measure disease progression in prion diseases or predict disease onset in healthy individuals at risk of disease.MethodsIn the National Prion Monitoring Cohort study, we did quantitative encephalography on 301 occasions in 29 healthy controls and 67 patients with prion disease. The patients had either inherited prion disease or sporadic Creutzfeldt-Jakob disease. We computed the main background frequency, the α and θ power and the α/θ power ratio, then averaged these within 5 electrode groups. These measurements were then compared among participant groups and correlated with functional and cognitive scores cross-sectionally and longitudinally.ResultsWe found lower main background frequency, α power and α/θ power ratio and higher θ power in patients compared to control participants. The main background frequency, the power in the α band and the α/θ power ratio also differed in a consistent way among the patient groups. Moreover, the main background frequency and the α/θ power ratio correlated significantly with functional and cognitive scores. Longitudinally, change in these parameters also showed significant correlation with the change in clinical and cognitive scores.ConclusionsOur findings support the use of quantitative EEG to follow the progression of prion disease, with potential to help evaluate the treatment effects in future clinical-trials.

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Section: Discussionmentioning

confidence: 73%

Quantitative EEG parameters correlate with the progression of human prion diseases

Frankó

Wehner

Joly

et al. 2016

J Neurol Neurosurg Psychiatry

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“…Though remarkably similar to those measured during speech perception ( Saltuklaroglu et al, 2017 ) and resting state ( Joos et al, 2014 ), reduced μ-β spectral power in the right hemisphere in PWS in the current study did not reach statistical significance. Group spectral differences may be important as they distinguish healthy controls from clinical populations such as individuals with dyslexia ( Galin et al, 1992 ; Papagiannopoulou and Lagopoulos, 2016 ), insomnia ( Buysse et al, 2008 ), epilepsy ( Adebimpe et al, 2015 ), and Parkinson's ( Caviness et al, 2016 ). Thus, further examination is warranted in an attempt to identify a neural biomarker for stuttering.…”

Section: Discussionmentioning

confidence: 99%

Trait related sensorimotor deficits in people who stutter: An EEG investigation of μ rhythm dynamics during spontaneous fluency

Jenson

Reilly

Harkrider

et al. 2018

NeuroImage: Clinical

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Stuttering is associated with compromised sensorimotor control (i.e., internal modeling) across the dorsal stream and oscillations of EEG mu (μ) rhythms have been proposed as reliable indices of anterior dorsal stream processing. The purpose of this study was to compare μ rhythm oscillatory activity between (PWS) and matched typically fluent speakers (TFS) during spontaneously fluent overt and covert speech production tasks. Independent component analysis identified bilateral μ components from 24/27 PWS and matched TFS that localized over premotor cortex. Time-frequency analysis of the left hemisphere μ clusters demonstrated significantly reduced μ-α and μ-β ERD (pCLUSTER < 0.05) in PWS across the time course of overt and covert speech production, while no group differences were found in the right hemisphere in any condition. Results were interpreted through the framework of State Feedback Control. They suggest that weak forward modeling and evaluation of sensory feedback across the time course of speech production characterizes the trait related sensorimotor impairment in PWS. This weakness is proposed to represent an underlying sensorimotor instability that may predispose the speech of PWS to breakdown.

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“…EEG spectral differences have been used to differentiate matched controls from those with pathological conditions like dyslexia (Galin et al, 1992;Papagiannopoulou and Lagopoulos, 2016), insomnia (Buysse et al, 2008), fibromyalgia (Gonzalez-Roldan et al, 2016), epilepsy (Adebimpe et al, 2015), adolescents with sports related concussions (Balkan et al, 2015), and Parkinson’s disease (Caviness et al, 2016). In the current study, μ-β amplitudes did not correlate with a behavioral measure of stuttering severity making it unclear how well μ-β amplitude defines the disorder.…”

Section: Discussionmentioning

confidence: 99%

EEG Mu ( µ ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults

et al. 2017

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Stuttering is linked to sensorimotor deficits related to internal modeling mechanisms. This study compared spectral power and oscillatory activity of EEG mu (μ) rhythms between persons who stutter (PWS) and controls in listening and auditory discrimination tasks. EEG data were analyzed from passive listening in noise and accurate (same/different) discrimination of tones or syllables in quiet and noisy backgrounds. Independent component analysis identified left and/or right μ rhythms with characteristic alpha (α) and beta (β) peaks localized to premotor/motor regions in 23 of 27 people who stutter (PWS) and 24 of 27 controls. PWS produced μ spectra with reduced β amplitudes across conditions, suggesting reduced forward modeling capacity. Group time-frequency differences were associated with noisy conditions only. PWS showed increased μ-β desynchronization when listening to noise and early in discrimination events, suggesting evidence of heightened motor activity that might be related to forward modeling deficits. PWS also showed reduced μ-α synchronization in discrimination conditions, indicating reduced sensory gating. Together these findings indicate spectral and oscillatory analyses of μ rhythms are sensitive to stuttering. More specifically, these analyses can reveal stuttering-related sensorimotor processing differences in passive listening and auditory discrimination tasks which may be influenced by basal ganglia deficits.

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Differential spectral quantitative electroencephalography patterns between control and Parkinson's disease cohorts

Cited by 43 publications

References 18 publications

Quantitative EEG parameters correlate with the progression of human prion diseases

Quantitative EEG parameters correlate with the progression of human prion diseases

Trait related sensorimotor deficits in people who stutter: An EEG investigation of μ rhythm dynamics during spontaneous fluency

EEG Mu ( µ ) rhythm spectra and oscillatory activity differentiate stuttering from non-stuttering adults

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