Quantitative EEG reflects non-dopaminergic disease severity in Parkinson’s disease

Geraedts, Victor J.; Marinus, Johan; Gouw, Alida A.; Mosch, Arne; Stam, Cornelis J.; Hilten, Jacobus J. van; Contarino, Maria Fiorella; Tannemaat, Martijn R.

doi:10.1016/j.clinph.2018.04.752

Cited by 27 publications

(27 citation statements)

References 29 publications

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“…Mean PLI stayed slightly >0.1. Similar PLI values were reported in other studies with epileptic patients (Stam et al, 2007;Wang and Meng, 2016), and also in patients with other neurological diseases such as Alzheimer's dis-ease, brain tumors, and Parkinson's disease (Douw et al, 2010;Geraedts et al, 2018;Stam et al, 2009) and with other acquisition techniques such as MEG or high-density EEG (Douw et al, 2010;Hardmeier et al, 2014;Stam et al, 2009). The amplitude of EEG time-series desynchronization induced by VNS was moderate but consistent with previous studies (Bodin et al, 2015;Douw et al, 2010;Geraedts et al, 2018;Stam et al, 2009;Wang and Meng, 2016).…”

Section: Discussionsupporting

confidence: 90%

“…In Bodin's study, which is closest to ours, ON/OFF differences were *0.01. In other studies, the intraindividual and even interindividual comparisons of functional connectivity assessment with PLI had similar amplitudes (Douw et al, 2010;Geraedts et al, 2018;Stam et al, 2009;Wang and Meng, 2016). A small difference is explained in part by numerous cofactors that influence interictal functional connectivity such as age (Smit et al, 2012(Smit et al, , 2016, cognitive and psychiatric coexisting conditions (Yoo et al, 2018), epilepsy types (Li Hegner et al, 2018) and treatments (Clemens et al, 2014;Haneef et al, 2015).…”

Section: Discussionmentioning

confidence: 76%

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The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

et al. 2020

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Aim: To investigate electroencephalographic (EEG) metrics of functional connectivity in patients with drugrefractory epilepsy treated by vagus nerve stimulation (VNS), between VNS-stimulated ''ON'' and nonstimulated ''OFF'' periods and between responder (R) and nonresponder (NR) patients. Introduction: VNS is an adjunctive neuromodulation therapy for patients with drug-refractory epilepsy. The antiseizure effect of VNS is thought to be related to a diffuse modulation of functional connectivity but remains to be confirmed. Methods: Scalp-EEG was performed for 35 patients treated by VNS, using 21 channels and 2 additional electrodes on the neck to detect the VNS stimulation. Patients were defined as VNS responders if a reduction of seizure frequency of *50% was documented. We analyzed the synchronization in EEG time series during ''ON'' and ''OFF'' periods of stimulation, using average phase lag index (PLI) in signal space and phase-locking value (PLV) between 10 sources. Based on graph theory, we computed brain network models and analyzed minimum spanning tree (MST) for responder and nonresponder patients. Results: Among 35 patients treated by VNS for a median time of 7 years (range 4 months to 22 years), 20 were R and 15 were NR. For responder patients, PLI during ON periods was significantly lower than that during OFF periods in delta (p = 0.009), theta (p = 0.02), and beta (p = 0.04) frequency bands. For nonresponder patients, there were no significant differences between ON and OFF periods. Moreover, variations of seizure frequency with VNS correlated with the PLI OFF/ON ratio in delta (p = 0.02), theta (p = 0.04), and beta (p = 0.03) frequency bands. Our results were confirmed using PLV in theta band (p < 0.05). No significant differences in MST were observed between R and NR patients. Conclusion: The correlation between VNS-induced interictal EEG time-series desynchronization and decrease in seizure frequency suggested that VNS therapeutic impact might be related to changes in interictal functional connectivity.

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Section: Discussionsupporting

confidence: 90%

Section: Discussionmentioning

confidence: 76%

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

et al. 2020

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“…Band ratio measures are commonly used in investigations of attention-deficit hyperactivity disorder (Arns et al, 2013;Loo & Makeig, 2012;Lubar, 1991;Snyder & Hall, 2006). Other investigations into the potential clinical utility of band ratio measures include anesthesia (Long et al, 1989), multiple sclerosis (Keune et al, 2017), cerebral ischemia (Sheorajpanday et al, 2009), and Parkinson's disease (Geraedts et al, 2018). Band ratio measures have also been applied in studies of mild-cognitive impairment, dementia, and Alzheimer's (Bennys et al, 2001;Moretti et al, 2013;Penttilä et al, 1985), recently reviewed in (Cassani et al, 2018), and have also been applied in studies of autism (Wang et al, 2016) and psychotic disorders (Howells et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Donoghue

Dominguez

Voytek

2020

eNeuro

135

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Band ratio measures, computed as the ratio of power between two frequency bands, are a common analysis measure in neuro-electrophysiological recordings. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity. This assumes that the measure reflects relative powers of distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, the latter of which contributes power across all frequencies. Here, we investigate whether band ratio measures truly reflect oscillatory power differences, and/or to what extent ratios may instead reflect other periodic changes-such as in center frequency or bandwidth-and/or aperiodic activity. In simulation, we investigate how band ratio measures relate to changes in multiple spectral features, and show how multiple periodic and aperiodic features influence band ratio measures. We validate these findings in human electroencephalography (EEG) data, comparing band ratio measures to parameterizations of power spectral features, and find that multiple disparate features influence ratio measures. For example, the commonly applied theta / beta ratio is most reflective of differences in aperiodic activity, and not oscillatory theta or beta power. Collectively, we show that periodic and aperiodic features can create the same observed changes in band ratio measures, and that this is inconsistent with their typical interpretations as measures of periodic power. We conclude that band ratio measures are a non-specific measure, conflating multiple possible underlying spectral changes, and recommend explicit parameterization of neural power spectra as a more specific approach. Band Ratios 3 Significance Statement Neural oscillations are a ubiquitous feature of investigation in electrophysiological recordings. Frequency band ratio measures are a common approach to investigate neural oscillations, applied across cognitive and clinical neuroscience, and in recording modalities such as in electroencephalography and local field potentials. In this work we systematically investigate the methodological properties of band ratio measures. We show that band ratio measures are not specific to measuring oscillatory power, as they are intended and interpreted to do. Rather, they often reflect other features of the data, such as aperiodic, or 1/f-like, activity. These findings are significant for interpreting prior empirical and clinical research, guiding future work, and another motivation that aperiodic neural activity should be a key consideration when studying electrophysiological data.

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“…cardiac arrest [40], diagnosis of brain death [41][42] and dementia. In recent times EEGs have also been used to study Alzheimer's disease (AD) [43][44] and other forms of dementia [45][46], multiple sclerosis [47][48][49], pain disorders [50-51], Parkinson's disease [52][53][54][55], migraines [56][57][58], and behavioral disorders such as attention deficit hyperactivity disorder (ADHD) [59][60][61], autism [62][63][64], depression [65][66][67], post-traumatic stress disorder [68][69][70][71], complex developmental trauma disorder [72] and substance abuse [73][74][75]. With the advent of neurofeedback EEG became viable as a treatment option, being used for performance enhancement (academic, athletic or mental), and as treatment for ADHD, autism, Alzheimer's, post-traumatic disorders and substance abuse [76][77][78][79][80].…”

Section: Software Developments In Eeg Technologymentioning

confidence: 99%

Overview of current diagnostic, prognostic, and therapeutic use of EEG and EEG-based markers of cognition, mental, and brain health

Miranda¹,

Cox²,

Alexander³

et al. 2019

Integr Mol Med

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The global burden of cognitive, mental, neurological, and substance-use disorders at 258 million disability adjusted life years calls for immediate "action" in their prevention and management. The electroencephalogram (EEG) is one of the most widely-used instruments for the non-invasive neuro-physiological measure of brain function and health. The EEG was originally used to solely monitor and record electric waves generated by electrical activity in the brain to aid in clinical decision-making and diagnosis. Technological improvements have made it possible for state-of the-art EEG computer-based systems like NeuralScan by Medeia Inc. to evaluate changes in power and in ratios of these brain waves with changes in brain and mental health status. Today's EEG machines can also identifying the precise localization of these changes enabling more accurate diagnosis and treatment. Improvements in EEG technology have made them robust, stationary/portable, high fidelity, versatile with the ability to carry out complex functions and calculations yet still be user/clinician-friendly highlighting their potential for use in clinical, research, epidemiological and public health settings. The present article presents an overview on EEG machines, their use in diagnosis, prognosis and therapy and to generate EEG-based markers in the area of cognition, mental and brain health.

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Quantitative EEG reflects non-dopaminergic disease severity in Parkinson’s disease

Abstract: The described EEG parameters may have complementary utility as determinants of non-dopaminergic involvement in PD.

Cited by 27 publications

References 29 publications

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

The Effectiveness of Vagus Nerve Stimulation in Drug-Resistant Epilepsy Correlates with Vagus Nerve Stimulation-Induced Electroencephalography Desynchronization

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

Overview of current diagnostic, prognostic, and therapeutic use of EEG and EEG-based markers of cognition, mental, and brain health

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