Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures

Arıkan, Kemal; Öksüz, Özden; Metin, Barış; Günver, Mehmet Güven; Çetin, Hamide Laçin; Esmeray, Muhammed Taha; Tarhan, Nevzat

doi:10.1177/1550059420918756

Cited by 8 publications

(22 citation statements)

References 39 publications

(41 reference statements)

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“…Patients with PNES-but not those with epilepsy-showed a decrease in beta power (desynchronization) prior to the PNES (minimum beta power at 5-6 s prior to the PNES). Arikan et al (2020) use quantitative EEG (QEEG) and fast Fourier transformation to examine spectral power across all frequency bands in adults with PNES. They found increased power at the following bands and locations: C3-high beta (25-30 Hz), C3-gamma (30-80 Hz), C3-gamma-1 (31-40 Hz), C3gamma-2 (41-50 Hz), P3-gamma (30-80 Hz), P3 gamma-1 (31-40 Hz).…”

Section: Introductionmentioning

confidence: 99%

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Radmanesh

Jalili

Kozlowska

2020

Front. Hum. Neurosci.

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Objectives: Psychogenic non-epileptic seizures (PNES) have been hypothesized to emerge in the context of neural networks instability. To explore this hypothesis in children, we applied a graph theory approach to examine connectivity in neural networks in the resting-state EEG in 35 children with PNES, 31 children with other functional neurological symptoms (but no PNES), and 75 healthy controls. Methods: The networks were extracted from Laplacian-transformed time series by a coherence connectivity estimation method. Results: Children with PNES (vs. controls) showed widespread changes in network metrics: increased global efficiency (gamma and beta bands), increased local efficiency (gamma band), and increased modularity (gamma and alpha bands). Compared to controls, they also had higher levels of autonomic arousal (e.g., lower heart variability); more anxiety, depression, and stress on the Depression Anxiety and Stress Scales; and more adverse childhood experiences on the Early Life Stress Questionnaire. Increases in network metrics correlated with arousal. Children with other functional neurological symptoms (but no PNES) showed scattered and less pronounced changes in network metrics. Conclusion: The results indicate that children with PNES present with increased activation of neural networks coupled with increased physiological arousal. While this shift in functional organization may confer a short-term adaptive advantage-one that facilitates neural communication and the child's capacity to respond self-protectively in the face of stressful life events-it may also have a significant biological cost. It may predispose the child's neural networks to periods of instability-presenting clinically as PNES-when the neural networks are faced with perturbations in energy flow or with additional demands.

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Section: Introductionmentioning

confidence: 99%

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Radmanesh

Jalili

Kozlowska

2020

Front. Hum. Neurosci.

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“…In four studies (based on two fully independent study samples), a comparable number of males and females were examined. In two studies, the patient sample had higher prevalence of females [23,49]. All participants were not taking any medications in three studies [23,48,49], while in the sample shared by the remaining three studies most patients were taking AEDs, benzodiazepines or antidepressant medications [46,47,50].…”

Section: Studies In Pnes Cohortsmentioning

confidence: 97%

“…Effect sizes were calculated by means of standardized mean difference (Cohen's d; [21]). Cohen's d was calculated based on means and standard deviations or standard errors for all studies with available data, except for [22] and [23] for which the F-statistic and t-statistic values, respectively, were used, and [24] for which Mann-Whitney U-values were used. Statistical synthesis was to be performed if 5 or more studies that investigated the same resting-state EEG metric in people with the same diagnosis and a control group were individuated [25].…”

Section: Data Synthesis and Analysismentioning

confidence: 99%

“…All studies used a case-control design. Four studies had group-level descriptors as their only outcome [23,46,47,48], one focused on diagnostic accuracy indices only [49], and one examined both [50].…”

Section: Studies In Pnes Cohortsmentioning

confidence: 99%

“…Resting-state EEG recordings used for the analyses appeared normal on visual inspection in all studies. All examined eyesclosed EEG recordings, except one which examined a mixture of eyes-open and eyes-closed recordings [23]. The number of EEG cap electrodes was 19 or 20 in three studies, and 128 in the remaining three, for which source analysis was used.…”

Section: Studies In Pnes Cohortsmentioning

confidence: 99%

See 2 more Smart Citations

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Faiman

Smith

Hodsoll

et al. 2021

Epilepsy & Behavior

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Quantitative markers extracted from resting-state electroencephalogram (EEG) reveal subtle neurophysiological dynamics which may provide useful information to support the diagnosis of seizure disorders. We performed a systematic review to summarize evidence on markers extracted from interictal, visually normal resting-state EEG in adults with idiopathic epilepsy or psychogenic nonepileptic seizures (PNES). Studies were selected from 5 databases and evaluated using the Quality Assessment of Diagnostic Accuracy Studies-2. 26 studies were identified, 19 focusing on people with epilepsy, 6 on people with PNES, and one comparing epilepsy and PNES directly. Results suggest that oscillations along the theta frequency (4-8 Hz) may have a relevant role in idiopathic epilepsy, whereas in PNES there was no evident trend. However, studies were subject to a number of methodological limitations potentially introducing bias. There was often a lack of appropriate reporting and high heterogeneity. Results were not appropriate for quantitative synthesis. We identify and discuss the challenges that must be addressed for valid resting-state EEG markers of epilepsy and PNES to be developed.

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Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

Faiman,

Sparks,

Winston

et al. 2023

Brain Communications

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Differentiating between epilepsy and psychogenic non-epileptic seizures presents a considerable challenge in clinical practice, resulting in frequent misdiagnosis, unnecessary treatment and long diagnostic delays. Quantitative markers extracted from resting-state EEG may reveal subtle neurophysiological differences that are diagnostically relevant. Two observational, retrospective diagnostic accuracy studies were performed to test the clinical validity of univariate resting-state EEG markers for the differential diagnosis of epilepsy and psychogenic non-epileptic seizures. Clinical EEG data were collected for 179 quasi-consecutive patients (age > 18) with a suspected diagnosis of epilepsy or psychogenic non-epileptic seizures who were medication-naïve at the time of EEG; 148 age- and gender-matched patients subsequently received a diagnosis from specialist clinicians and were included in the analyses. Study 1 is a hypothesis-driven study testing the ability of theta power and peak alpha frequency to classify people with epilepsy and people with psychogenic non-epileptic seizures, with an advanced machine learning pipeline. The next study (Study 2) is data-driven; a high number of quantitative EEG features are extracted and a similar machine learning approach as Study 1 assesses whether previously unexplored univariate EEG measures show promise as diagnostic markers. The results of Study 1 suggest that EEG markers that were previously identified as promising diagnostic indicators (i.e. theta power and peak alpha frequency) have limited clinical validity for the classification of epilepsy and psychogenic non-epileptic seizures (mean accuracy: 48%). The results of Study 2 indicate that identifying univariate markers that show good correlation with a categorical diagnostic label is challenging (mean accuracy: 45–60%). This is due to a considerable overlap in neurophysiological features between the diagnostic classes considered in this study, and to the presence of more dominant EEG dynamics such as alterations due to temporal proximity to epileptiform discharges. Markers that were identified in the context of previous epilepsy research using visually normal resting-state EEG were found to have limited clinical validity for the classification task of distinguishing between people with epilepsy and people with psychogenic non-epileptic seizures. A search for alternative diagnostic markers uncovered the challenges involved and generated recommendations for further research.

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Quantitative EEG Findings in Patients With Psychogenic Nonepileptic Seizures

Cited by 8 publications

References 39 publications

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Activation of Functional Brain Networks in Children With Psychogenic Non-epileptic Seizures

Resting-state EEG for the diagnosis of idiopathic epilepsy and psychogenic nonepileptic seizures: A systematic review

Limited clinical validity of univariate resting-state EEG markers for classifying seizure disorders

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