Distinct psychopathology profiles in patients with epileptic seizures compared to non-epileptic psychogenic seizures

Classification of Epileptic and Psychogenic Nonepileptic Seizures via Time-Frequency Features of EEG Data

The majority of psychogenic nonepileptic seizures (PNESs) are brought on by psychogenic causes, but because their symptoms resemble those of epilepsy, they are frequently misdiagnosed. Although EEG signals are normal in PNES cases, electroencephalography (EEG) recordings alone are not sufficient to identify the illness. Hence, accurate diagnosis and effective treatment depend on long-term video EEG data and a complete patient history. Video EEG setup, however, is more expensive than using standard EEG equipment. To distinguish PNES signals from conventional epileptic seizure (ES) signals, it is crucial to develop methods solely based on EEG recordings. The proposed study presents a technique utilizing short-term EEG data for the classification of inter-PNES, PNES, and ES segments using time-frequency methods such as the Continuous Wavelet transform (CWT), Short-Time Fourier transform (STFT), CWT-based synchrosqueezed transform (WSST), and STFT-based SST (FSST), which provide high-resolution time-frequency representations (TFRs). TFRs of EEG segments are utilized to generate 13 joint TF (J-TF)-based features, four gray-level co-occurrence matrix (GLCM)-based features, and 16 higher-order joint TF moment (HOJ-Mom)-based features. These features are then employed in the classification procedure. Both three-class (inter-PNES versus PNES versus ES: ACC: 80.9%, SEN: 81.8%, and PRE: 84.7%) and two-class (Inter-PNES versus PNES: ACC: 88.2%, SEN: 87.2%, and PRE: 86.1%; PNES versus ES: ACC: 98.5%, SEN: 99.3%, and PRE: 98.9%) classification algorithms performed well, according to the experimental results. The STFT and FSST strategies surpass the CWT and WSST strategies in terms of classification accuracy, sensitivity, and precision. Moreover, the J-TF-based feature sets often perform better than the other two.

Psychiatric and psychological assessment of patients with drug-resistant epilepsy and psychogenic nonepileptic seizures (PNES) with no response to previous treatments

OBJECTIVE

Psychogenic nonepileptic seizures (PNES) are common imitators of epileptic seizures. Refractoriness to antiseizure medication hinders the differential diagnosis between ES and PNES, carrying deleterious consequences in patients with PNES. Psychiatric and psychological characteristics may assist in the differential diagnosis between drug-resistant epilepsy (DRE) and PNES. Nevertheless, current comprehensive psychiatric and psychological descriptive studies on both patient groups are scarce and with several study limitations. This study provides a comprehensive psychiatric and psychological characterization of Spanish patients with DRE and PNES.

METHOD

A cross-sectional and comparative study was completed with 104 patients with DRE and 21 with PNES. Psychiatric and psychological characteristics were assessed with the HADS, SCL-90-R, NEO-FFI-R, PDQ-4+, COPE, and QOLIE-31 tests. Parametric and non-parametric tests were used, and regression models were fit to further explore factors affecting patients' life quality.

RESULTS

Patients with PNES had greater levels of somatization and extraversion and were associated with benzodiazepine intake. Patients with DRE showed greater narcissistic personality disorder symptoms than those with PNES. In patients with DRE, difficulty in performing basic needs-related tasks and greater psychological distress severity and seizure frequency were associated with poorer life quality. In contrast, being a woman, having a psychiatric disorder history, and greater psychiatric symptoms' intensity were associated with poorer life quality in patients with PNES.

CONCLUSION

Patients with DRE and PNES share similar psychiatric and psychological characteristics, with only very few being significantly different.

Dissociation in patients with epilepsy and functional seizures: A narrative review of the literature

Dissociation is a "disruption of the usually integrated functions of consciousness, memory, identity or perception of the environment" according to DSM-5.  It is commonly seen in psychiatric disorders including primary dissociative disorders, post-traumatic stress disorder, depression, and panic disorder. Dissociative phenomena are also described in the context of substance intoxication, sleep deprivation and medical illnesses including traumatic brain injury, migraines, and epilepsy. Patients with epilepsy have higher rates of dissociative experiences as measured on the Dissociative Experiences Scale compared to healthy controls. Ictal symptoms, especially in focal epilepsy of temporal lobe origin, may include dissociative-like experiences such as déjà vu/jamais vu, depersonalization, derealization and what has been described as a "dreamy state".  These descriptions are common in the setting of seizures that originate from mesial temporal lobe epilepsy and may involve the amygdala and hippocampus. Other ictal dissociative phenomena include autoscopy and out of body experiences, which are thought to be due to disruptions in networks responsible for the integration of one's own body and extra-personal space and involve the temporoparietal junction and posterior insula. In this narrative review, we will summarize the updated literature on dissociative experiences in epilepsy, as well as dissociative experiences in functional seizures. Using a case example, we will review the differential diagnosis of dissociative symptoms. We will also review neurobiological underpinnings of dissociative symptoms across different diagnostic entities and discuss how ictal symptoms may shed light on the neurobiology of complex mental processes including the subjective nature of consciousness and self-identity.

Multidimensional psychopathological profile differences between patients with psychogenic nonepileptic seizures and epileptic seizure disorders

OBJECTIVE

Early differential diagnosis of psychogenic nonepileptic seizures (PNES) and epileptic seizures (ES) remains difficult. Self-reported psychopathology is often elevated in patients with PNES, although relatively few studies have examined multiple measures of psychopathology simultaneously. This study aimed to identify differences in multidimensional psychopathology profiles between PNES and ES patient groups.

METHOD

This was a retrospective case-control study involving patients admitted for video-EEG monitoring (VEM) over a two-year period. Clinicodemographic variables and psychometric measures of depression, anxiety, dissociation, childhood trauma, maladaptive personality traits, and cognition were recorded. Diagnosis of PNES or ES was determined by multidisciplinary assessment and consensus opinion. General linear mixed models (GLMMs) were used to investigate profile differences between diagnostic groups across psychometric measures. A general psychopathology factor was then computed using principal components analysis (PCA) and differences between groups in this 'p' factor were investigated.

RESULTS

261 patients (77 % with ES and 23 % with PNES) were included in the study. The PNES group endorsed greater symptomatology with GLMM demonstrating a significant main effect of group (η²_p = 0.05) and group by measure interaction (η²_p = 0.03). Simple effects analysis indicated that the PNES group had particularly elevated scores for childhood trauma (β = 0.78), dissociation (β = 0.70), and depression (β = 0.60). There was a high correlation between psychopathology measures, with a single p factor generated to explain 60 % variance in the psychometric scores. The p factor was elevated in the PNES group (β = 0.61). ROC curve analysis indicated that these psychometric measures had limited usefulness when considered individually (AUC range = 0.63-0.69).

CONCLUSION

Multidimensional psychopathological profile differences exist between patients with PNES and ES. Patients with PNES report more psychopathology overall, with particular elevations in childhood trauma, dissociation, and depression. Although not suitable to be used as a standalone screening tool to differentiate PNES and ES, understanding of these profiles at a construct level might help triage patients and guide further psychiatric examination and enquiry.

Exploring the influence of telehealth on patient engagement with a multidisciplinary Non-Epileptic Seizure (NES) Clinic during the COVID-19 pandemic

The ILAE task force has identified a gap in treatment access for patients with nonepileptic seizures (NES) [1]. Access to multidisciplinary treatment clinics for adults with NES is limited with only 18 institutions delivering care across the United States [2]. Patient engagement has been low in the University of Colorado, NES Clinic treatment program despite our clinic's status as the only clinic of its kind in the mountain west. We analyzed patient factors of those who engaged in treatment before and after COVID-19 regulations were imposed and found a 23.6% increase in treatment engagement using telehealth. Those who engaged using telehealth were more likely to be of white race, of non-Hispanic ethnicity, publicly insured, employed, have a Charlson Comorbidity Index (CCI) of zero, a daily seizure rate of 0-1, did not have suicidal ideation or attempts, and live greater than 25 miles from the NES clinic. Delivering NES treatment via telehealth reduced the logistical and psychological barriers to initiating recovery and with a severe lack of accessible treatments for patients with NES, barrier reduction is necessary. This study describes patient factors that result in higher engagement with NES treatment using telehealth and emphasizes the importance of telehealth utilization to improve access to available treatment.

The Comparison Between Neuropsychological Features of Psychogenic Non-epileptic Seizures and Epileptic Seizures

Background: Both epileptic seizures (ES) and psychogenic non-epileptic seizures (PNES) are often associated with some degree of cognitive impairment. Video electroencephalography (EEG) monitoring is the gold standard for diagnosing PNES. This diagnostic procedure is costly and available in specific tertiary centers. Neuropsychological assessment can provide clues for the differential diagnosis of PNES and ES and help clarify the nature and etiology of these two disorders. Objectives: Therefore, this study aimed to compare the neuropsychological profiles of PNES and ES patients. Methods: In this analytical cross-sectional study, 30 patients with ES and 31 patients with PNES were compared by neuropsychiatric tests, such as the Wechsler Adult Intelligence scale (WAIS), Addenbrooke’s Cognitive examination (ACE), and California Verbal Learning test (CVLT). Results: There was a female predominance in the PNES group (female-to-male ratio = 4.16/1, P = 0.003). In the PNES group, 77.4% of the patients had a psychiatric disorder versus 66.7% of the patients in the ES group; however, the difference was not statistically significant (P = 0.34). The mean score of total intelligence was higher in the PNES group (84.77 ± 16.94), compared to the ES group (83.63 ± 10.04); however, the difference was not significant (P = 0.75). Based on the mean subscale scores, the digit symbol score (WAIS-IV subscale) and memory score (ACE subscale) were significantly higher in the PNES group compared to the ES group (P = 0.037 and 0.032, respectively). Conclusions: This study showed that neuropsychological assessments might not differentiate ES from non-epileptic seizures.

Behavioral phenotypes of temporal lobe epilepsy

Objective

To identity phenotypes of self‐reported symptoms of psychopathology and their correlates in patients with temporal lobe epilepsy (TLE).

Method

96 patients with TLE and 82 controls were administered the Symptom Checklist 90‐Revised (SCL‐90‐R) to characterize emotional‐behavioral status. The nine symptom scales of the SCL‐90‐R were analyzed by unsupervised machine learning techniques to identify latent TLE groups. Identified clusters were contrasted to controls to characterize their association with sociodemographic, clinical epilepsy, neuropsychological, psychiatric, and neuroimaging factors.

Results

TLE patients as a group exhibited significantly higher (abnormal) scores across all SCL‐90‐R scales compared to controls. However, cluster analysis identified three latent groups: (1) unimpaired with no scale elevations compared to controls (Cluster 1, 42% of TLE patients), (2) mild‐to‐moderate symptomatology characterized by significant elevations across several SCL‐90‐R scales compared to controls (Cluster 2, 35% of TLE patients), and (3) marked symptomatology with significant elevations across all scales compared to controls and the other TLE phenotype groups (Cluster 3, 23% of TLE patients). There were significant associations between cluster membership and demographic (education), clinical epilepsy (perceived seizure severity, bitemporal lobe seizure onset), and neuropsychological status (intelligence, memory, executive function), but with minimal structural neuroimaging correlates. Concurrent validity of the behavioral phenotype grouping was demonstrated through association with psychiatric (current and lifetime‐to‐date DSM IV Axis 1 disorders and current treatment) and quality‐of‐life variables.

Significance

Symptoms of psychopathology in patients with TLE are characterized by a series of discrete phenotypes with accompanying sociodemographic, cognitive, and clinical correlates. Similar to cognition in TLE, machine learning approaches suggest a developing taxonomy of the comorbidities of epilepsy.

Psychiatric symptoms are the strongest predictors of quality of life in patients with drug-resistant epilepsy or psychogenic nonepileptic seizures

OBJECTIVE

This cross-sectional study aimed to determine the effect of psychiatric comorbidity and neurocognitive deficits on the quality of life in a cohort of patients admitted for Video-EEG Monitoring (VEM) for investigation into a presumed seizure disorder.

METHODS

Patients were recruited from an inpatient VEM unit between January 2009 and December 2016. All patients had formal neuropsychiatric assessment. All patients completed questionnaires assessing psychiatric symptomatology (SCL-90-R), Anxiety and Depression (HADS), quality of life (QOLIE-89), and cognition (NUCOG).

RESULTS

A total of 451 patients were enrolled. Upon discharge, 204 patients were diagnosed to have epilepsy, 118 psychogenic nonepileptic seizures (PNES), and 29 both epilepsy and PNES, while the diagnosis was uncertain diagnosis in 100. Diagnosis (p = .002), HADS Depression score (p < .001), SCL-90-R positive symptoms total (p < .001), and NUCOG total score (p < .001) were found to be significant predictors of QOLIE-89 total scores, together explaining 65.4% of variance in quality of life. Seizure frequency was not a significant predictor of quality of life (p = .082). Patients with PNES had significantly worse quality of life, and scored higher on measures of psychiatricsymptomatology, compared to patients with epilepsy alone. The prevalence of psychiatric comorbidity was significantly higher in patients with PNES (70.3%) or both PNES and epilepsy (62.1%) compared to patients with epilepsy alone (41.2%) (p < .001).

SIGNIFICANCE

Psychiatric symptomatology, depression, and cognition were stronger determinants of quality of life than seizure frequency in this study population of patients with drug-resistant epilepsy and PNES. Patients with PNES with or without comorbid epilepsy had similar neuropsychiatric profiles.

Clinical predictors of discordance between screening tests and psychiatric assessment for depressive and anxiety disorders among patients being evaluated for seizure disorders

OBJECTIVE

This study was undertaken to identify factors that predict discordance between the screening instruments Neurological Disorders Depression Inventory for Epilepsy (NDDI-E) and Generalized Anxiety Disorder scale (GAD-7), and diagnoses made by qualified psychiatrists among patients with seizure disorders. Importantly, this is not a validation study; rather, it investigates clinicodemographic predictors of discordance between screening tests and psychiatric assessment.

METHODS

Adult patients admitted for inpatient video-electroencephalographic monitoring completed eight psychometric instruments, including the NDDI-E and GAD-7, and psychiatric assessment. Patients were grouped according to agreement between the screening instrument and psychiatrists' diagnoses. Screening was "discordant" if the outcome differed from the psychiatrist's diagnosis, including both false positive and false negative results. Bayesian statistical analyses were used to identify factors associated with discordance.

RESULTS

A total of 411 patients met inclusion criteria; mean age was 39.6 years, and 55.5% (n = 228) were female. Depression screening was discordant in 33% of cases (n = 136/411), driven by false positives (n = 76/136, 56%) rather than false negatives (n = 60/136, 44%). Likewise, anxiety screening was discordant in one third of cases (n = 121/411, 29%) due to false positives (n = 60/121, 50%) and false negatives (n = 61/121, 50%). Seven clinical factors were predictive of discordant screening for both depression and anxiety: greater dissociative symptoms, greater patient-reported adverse events, subjective cognitive impairment, negative affect, detachment, disinhibition, and psychoticism. When the analyses were restricted to only patients with psychogenic nonepileptic seizures (PNES) or epilepsy, the rate of discordant depression screening was higher in the PNES group (n = 29, 47%) compared to the epilepsy group (n = 70, 30%, Bayes factor for the alternative hypothesis = 4.65).

SIGNIFICANCE

Patients with seizure disorders who self-report a variety of psychiatric and other symptoms should be evaluated more thoroughly for depression and anxiety, regardless of screening test results, especially if they have PNES and not epilepsy. Clinical assessment by a qualified psychiatrist remains essential in diagnosing depressive and anxiety disorders among such patients.

Adverse events related to antiepileptic drugs

OBJECTIVE

Adverse events (AEs) related to antiepileptic drugs (AEDs) may interfere with adequate dosing and patient adherence, leading to suboptimal seizure control, and relatedly, increased injuries, hospitalizations, and mortality. This study investigated the clinicodemographic factors associated with AEs related to AEDs as reported by the Liverpool Adverse Events Profile (LAEP), and explored the ability of LAEP to discriminate between epilepsy and psychogenic nonepileptic seizures (PNES). We hypothesized that female sex, mood disorders, AED-polytherapy, duration, and severity of epilepsy are associated with increased endorsement of AEs related to AEDs, and that endorsement of AEs related to AEDs would significantly differ between epilepsy and PNES patients.

METHODS

We prospectively enrolled adult patients admitted to two inpatient video-electroencephalogram monitoring units. Clinicodemographic variables and psychometric measures of depression, anxiety, and cognitive function were recorded. Patient-reported AE endorsement was obtained using the LAEP, which was reduced to four latent domains using exploratory structural equation modeling. General linear models identified variables associated with each domain. Logistic regression determined the ability of LAEP scores to differentiate between epilepsy and PNES.

RESULTS

311 patients met inclusion criteria. Mean age was 38 years and 56% of patients were female. Network analysis demonstrated strong relationships between depression and anxiety with physical, sleep, psychiatric, and dermatological AE endorsement. Depression, female sex, and AED polytherapy were associated with greater AE endorsement. Epilepsy, compared to PNES, was associated with lower AE endorsement. Fewer prescribed AEDs and greater reported physical AE endorsement were associated with PNES diagnosis.

SIGNIFICANCE

There is a strong relationship between patient-reported AEs and psychiatric symptomatology. Those with PNES paradoxically endorse greater physical AEs despite receiving fewer AEDs. Patients who endorse AEs in clinical practice should be screened for comorbid depression or anxiety and treated accordingly.

Development and validation of a screening questionnaire for psychogenic nonepileptic seizures

OBJECTIVE

Epilepsy and psychogenic nonepileptic seizures (PNES) are serious conditions, associated with substantial morbidity and mortality. Although prompt diagnosis is essential, these conditions are frequently misdiagnosed, delaying appropriate treatment. We developed and validated the Anxiety, Abuse, and Somatization Questionnaire (AASQ), a quick and clinically practical tool to differentiate PNES from epilepsy.

METHOD

We retrospectively identified psychological variables that differentiated epilepsy from PNES in a discovery cohort of patients admitted to a video-electroencephalography monitoring (VEM) unit from 2002 to 2017. From these findings, we developed the AASQ and prospectively validated it in an independent cohort of patients with gold-standard VEM diagnosis.

RESULTS

One thousand two hundred ninety-one patients were included in the retrospective study; mean age was 39.5 years (range: 18-99), 58% were female, 67% had epilepsy, and 33% had PNES. Psychometric data for 192 instrument items were reviewed, receiver operating characteristic curves were computed, and a 20-item AASQ was created. Prospective validation in 74 patients showed that a one-point increase in the AASQ score was associated with 11 times increase in the odds of having PNES compared with epilepsy. Low scores on the AASQ were associated with a low probability of PNES with a negative predictive value of 95%.

SIGNIFICANCE

The AASQ is quick, inexpensive, and clinically useful for workup of seizure disorders. The AASQ excludes PNES with a high degree of confidence and can predict PNES with significance when combined with basic clinicodemographic variables. Future research will investigate diagnostic performance of the AASQ in relevant clinical subgroups, such as patients with comorbid epilepsy and PNES.

Psychopathological features in referred adolescents with psychogenic nonepileptic seizures with or without epilepsy

Psychogenic nonepileptic seizures (PNES) are episodic manifestations that mimic epileptic seizures (ES) although not associated with electroencephalogram (EEG) abnormalities. Psychogenic nonepileptic seizures and ES, however, can often cooccur. Emotional distress in adolescents can trigger PNES, but the psychopathological and personality features are still unknown. The aim of this study was to explore psychopathological features in a sample of referred youth with PNES, with or without ES, compared with a control group with ES. Thirty-four patients aged 12 to 21 years, 19 females and 15 males, were included in the study, 15 patients with PNES, 7 with PNES and ES, and 12 with ES. The three groups were compared according to psychiatric categorical diagnoses, psychopathological dimensions, life stressors, and personality traits, including alexithymia, interpersonal reactivity, and resilience, all assessed with structured measures. Patients with PNES, with or without ES, were more severely impaired, had a higher incidence of mood disorders, more frequent lifetime traumatic experiences, and lower resilience. All the three groups presented alexythimic traits and emotional dysregulation. Major limitations are the small sample size and the lack of a control group of healthy subjects. Disentagling psychopathological characteristics in PNES can help clinicians to focus diagnostic approaches and therapeutic interventions.