Diagnosis Of Psychogenic Nonepileptic Seizures Research Articles

Benzodiazepine receipt in adults with psychogenic non-epileptic seizures in the USA

BackgroundCharacterising benzodiazepine (BZD) prescribing to individuals with psychogenic non-epileptic seizures (PNES) is important for optimising PNES outcomes, but existing data is lacking.MethodsUsing a nationwide administrative claims database (2016–2022), incident PNES...

Elevated plasma neurofilament light and glial fibrillary acidic protein in epilepsy versus nonepileptic seizures and nonepileptic disorders.

Research suggests that recurrent seizures may lead to neuronal injury. Neurofilament light chain protein (NfL) and glial fibrillary acidic protein (GFAP) levels increase in cerebrospinal fluid and blood in response to neuroaxonal damage, and they have been hypothesized as potential biomarkers for epilepsy. We examined plasma NfL and GFAP levels and their diagnostic utility in differentiating patients with epilepsy from those with psychogenic nonepileptic seizures (PNES) and other nonepileptic disorders. We recruited consecutive adults admitted for video-electroencephalographic monitoring and formal neuropsychiatric assessment. NfL and GFAP levels were quantified and compared between different patient groups and an age-matched reference cohort (n = 1926) and correlated with clinical variables in patients with epilepsy. A total of 138 patients were included, of whom 104 were diagnosed with epilepsy, 22 with PNES, and 12 with other conditions. Plasma NfL and GFAP levels were elevated in patients with epilepsy compared to PNES, adjusted for age and sex (NfL p = .04, GFAP p = .04). A high proportion of patients with epilepsy (20%) had NfL levels above the 95th age-matched percentile compared to the reference cohort (5%). NfL levels above the 95th percentile of the reference cohort had a 95% positive predictive value for epilepsy. Patients with epilepsy who had NfL levels above the 95th percentile were younger than those with lower levels (37.5 vs. 43.8 years, p = .03). An elevated NfL or GFAP level in an individual patient may support an underlying epilepsy diagnosis, particularly in younger adults, and cautions against a diagnosis of PNES alone. Further examination of the association between NfL and GFAP levels and specific epilepsy subtypes or seizure characteristics may provide valuable insights into disease heterogeneity and contribute to the refinement of diagnosis, understanding pathophysiological mechanisms, and formulating treatment approaches.

A comparison of patients with epileptic seizures (ES) versus those with psychogenic non-epileptic seizures (PNES) on measures of alexithymia, mood, and anxiety

ObjectiveTo compare persons with epilepsy (PWE) to those with psychogenic non-epileptic seizures (PNES) on measures of depression, anxiety, and alexithymia subscales (i.e., difficulty identifying emotions, difficulty describing emotions, and external-oriented thinking). Material and methodsIn this retrospective study, 235 epilepsy patients and 90 patients with PNES were evaluated between 2012 and 2020 at the Northeast Regional Epilepsy Group. These patients had completed the Toronto Alexithymia Scale (TAS-20), The Center for Epidemiologic Studies - Depression Scale (CES-D) and The State-Trait Anxiety Inventory (STAI). Background information was collected regarding work/student/disability status at the time of the evaluation history of psychiatric diagnosis; psychological trauma; and involvement in psychotherapy either at the time of the evaluation or prior. ResultsSignificant differences between PWEs and those with PNES were found not only in historical data (e.g., Psychiatric History, History of Trauma, and History of Therapy) (p < .001) but also on measures of Depression (p = .002) and Anxiety (p < .001). ANOVA analysis also revealed significant differences in the distribution of the TAS-Total score, TAS-Describing emotions, and TAS-Identifying emotions. Using logistic regression (stepwise model) the optimal set of predictors for a differential diagnosis of epilepsy and PNES was combination of TAS-Identifying emotions score, history of psychological trauma, and history of therapy. The accuracy of the prediction was determined to be 80.2 %. ConclusionsAlthough higher alexithymia rates are present in PNES and PWEs, clinicians may find a combination of TAS-Identifying Emotion score, history of trauma, and history of psychotherapy useful in supporting a differential diagnosis. Also, a subgroup may exist among those with PNES with high levels of alexithymia, depression, and anxiety that may require a different treatment approach focused on addressing difficulties in identifying and describing their emotions and their other symptomatology.

New-onset seizures misdiagnosed as psychogenic non-epileptic seizures: a case of paraneoplastic limbic encephalitis with primary testicular cancer

BackgroundBehavioral psychiatric symptoms can be the only warning signs of more serious conditions such as paraneoplastic limbic encephalitis. Differentiating between primary psychiatric disorders and paraneoplastic neurological syndromes is crucial as they require distinct treatment approaches. In this case report, we provide an overview of paraneoplastic encephalitis and introduce a unique case that showcases a misdiagnosis of psychogenic non-epileptic seizures (PNES) in a male as the primary symptom of paraneoplastic encephalitis due to pure seminoma. This case highlights the underlying pathophysiology of antibody-mediated paraneoplastic encephalitis and its significance.Case presentationA 31-year-old male with no known past medical history presented due to recurrent seizures. There was no prior history of epilepsy or exposure to seizure-triggering agents. Imaging and electroencephalogram findings during his initial hospitalizations pointed to a potential diagnosis of PNES. The patient continued to experience seizures following discharge, leading to repeat hospitalizations. During the fourth hospitalization, the patient received mood-stabilizing anti-seizure medications and benzodiazepines, but he deteriorated and required intubation. It was during this time that the patient was transferred to our facility. Magnetic resonance imaging of the brain revealed multifocal areas of hyperintensity and restricted diffusion with avid enhancement. Immunotherapy was initiated with improvement of non-epileptic spells and encephalopathy. Outpatient workup uncovered malignant pure seminoma with metastases to the retroperitoneum. The authors theorize that paraneoplastic neurological disorders stemming from testicular cancer led to the neurological symptoms seen in this case.ConclusionThis report highlights a rare occurrence of paraneoplastic limbic encephalitis associated with pure testicular seminoma, clinically manifested as PNES. The diagnostic challenge posed by variability of presenting symptoms in paraneoplastic encephalitis emphasizes the importance of accurate differentiation from conditions such as autoimmune encephalitis. Current diagnostic approaches for paraneoplastic and autoimmune etiologies involve detection of known antibodies, as well as brain imaging. Notable antibodies associated with psychogenic non-epileptic seizures symptoms include anti-GAD-65, anti-Ma2, KLH11-antibodies, anti-Hu, and NMDA receptor antibodies. Recognizing paraneoplastic limbic encephalitis symptoms is challenging and often leads to misdiagnosis or overlooking of malignancies highlighting the need for awareness, comprehensive evaluation and timely treatment. Through this comprehensive case analysis, we enhance the understanding of underlying mechanisms, associated symptoms, and treatment options.

Outcome of psychogenic non-epileptic seizures following diagnosis in the epilepsy monitoring unit.

To study the outcome of patients with psychogenic non-epileptic seizures (PNES) after their diagnosis in the epilepsy monitoring unit (EMU). Patients diagnosed in our EMU with definite PNES between January 2009 and May 2023 were contacted by phone, and those who agreed to participate were asked a set of predetermined questions. Comparative analyses were carried out on several variables before and after diagnosis: number of participants with daily PNES, number of visits to the emergency department, number of participants who consulted their general practitioner or a neurologist outside of a scheduled follow-up, number of participants who took antiseizure medications (ASMs) or psychotropic drugs, and employment status. Out of the 103 patients with a definite diagnosis of PNES, 61 patients (79% female) accepted to participate in our study. The median age at PNES onset was 35 years, and the median delay to diagnosis was 3 years. Almost two-thirds (62%) were receiving ASMs and 40% psychotropic drugs. The mean stay at the EMU was 5 days. PNES diagnosis was explained to almost all patients (97%) by the end of their EMU stay and was well-accepted by most (89%). When contacted, 46% of participants no longer had PNES; 32% mentioned that their PNES had ceased immediately upon communication of the diagnosis. The median follow-up duration was 51 months. Fewer patients had daily seizures after the diagnosis (18 vs. 38%; p < 0.0455). Similarly, the median number of emergency department visits was significantly lower (0 vs. 2; p < 0.001). Only 17 patients consulted their general practitioner (vs. 40, p < 0.001) and 20 a neurologist (vs. 55, p < 0.001) after a PNES attack outside of a scheduled follow-up. The use of ASMs was also significantly reduced from 70 to 33% (p < 0.01), with only one still taking an ASM for its antiseizure properties. Significantly more participants were working at last follow-up than at PNES diagnosis (49 vs. 25%; p < 0.001). Our study revealed a relatively favorable long-term outcome of definite PNES diagnosed in the EMU that translated in significant reductions in PNES frequency, health care utilization and ASM use, as well as a significant increase in employment rate.

Case study of invalid to valid shift in cognitive performance following successful treatment of psychogenic nonepileptic seizure events

ABSTRACT Patients with psychogenic nonepileptic seizure (PNES) who fail performance validity testing (PVT) may appear to produce non-valid cognitive profiles. Consequently, they may not get referred to treatment and events persist, with worsening disability and high resource utilization. As a result, we report pre- and post-treatment neuropsychological evaluation findings in a 59-year-old woman with a confirmed diagnosis of PNES established using video-EEG monitoring. At pre-treatment baseline neuropsychological evaluation, PNES events occurred weekly to daily. Performance was impaired across PVTs and across multiple cognitive domains. After behavioral intervention specific to PNES, these events substantially reduced in frequency to rare stress-induced flares. Post-treatment neuropsychological evaluation revealed marked improvement of most cognitive and behavioral scores from baseline, and valid PVT scores. We review predisposing, precipitating, and perpetuating factors for PNES and cognitive impairment in this case and discuss the patient’s outcome from treatment. Effectively managing PNES events and dissociative tendencies while reducing unnecessary pharmacological interventions appears to have allowed this patient to function closer to her optimal state. This case illustrates the complexity of Functional Neurologic Disorder (FND) clinical presentation and challenges the assumption that suboptimal neuropsychological performance predicts poor treatment engagement and outcome. We showcase the reversibility of PNES and cognitive manifestations of FND using targeted psychotherapeutic interventions, which resulted in reduced disability and associated healthcare costs, as well as re-engagement in life.

P09.04.B PNES IN BRAIN TUMOR PATIENTS : AN UNEXPECTED CHALLENGE

Abstract BACKGROUND Epileptic seizures (ES) are common in brain tumor patients. The phenomenon of psychogenic non-epileptic seizures (PNES) amongst those patients has not been well described. PNES Phenomenology could be similar to ES, however, epileptogenic activity on electro-encephalogram (EEG) is absent. The diagnosis of PNES can be challenging in brain tumor patients since both types of seizures can present with similar symptoms and might occur together. Risk factors for the development of PNES include a history of negative events in childhood, exposure to trauma, history of psychiatric illness and chronic pain, and substance abuse. Diagnosing PNES is important for delivering the appropriate treatment, and preventing the toxic influence of unneeded treatment and procedures. MATERIAL AND METHODS The Electronic files of patients with ES and PNES were evaluated, in A retrospective analysis from the database of the Neuro-Oncology unit at Sheba Medical-Center. RESULTS 4 patients with brain tumor related epilepsy and PNES were identified: a female 22 years old with right frontal IDH1 mutated non 1p/19q deleted glioma WHO2, a 44 years old male with right frontal IDH1 mutated 1p/19q deleted glioma WHO2, a 48 years old female with left temporo-insular IDH1 mutated non 1p/19q deleted glioma WHO2 and a 65 years old female with multiple radiation induced meningiomas. Seizures semiology were compatible with the lesions. The first two patients had recent stress factors, and the last two suffered severe diagnosis-related anxiety. All patients presented initially with ES and developed PNES subsequently. They were treated with 2-4 Anti-Epileptic drugs(AED’s). They were referred to EEG video monitoring due to worsening seizures frequency despite increasing the AED’s and no evidence of tumor progression at that time. All patients had clinical seizures which did not have any epileptiform activity correlation on Video-EEG. Hence, PNES was diagnosed. Three patients underwent psychological treatment and two patients received medical therapy for underlying psychological factors contributing to PNES. Seizure frequency and number of AED's decreased in all patients. CONCLUSION In patients with brain tumors the diagnosis of PNES can be challenging but should be considered in patients with increased seizures frequency regardless of stable imaging and absence of any other provocative factors. Video EEG is pivotal for PNES diagnosis. Psychological consulting and treatment are recommended in PNES cases. Correct diagnosis and treatment will contribute to the patient's quality of life.

Latency of epileptic and psychogenic nonepileptic seizures.

Due to their semiological similarities, psychogenic nonepileptic seizures (PNESs) can occasionally hardly be differentiated from epileptic seizures (ESs), and long-term video-electroencephalographic monitoring (VEM) is needed for the differential diagnosis. To investigate the time of the first clinical event and its distribution on the days of VEM in ES and PNES patients. In total, a consecutive series of 48 PNES and 51 ES patients matched for gender and age were retrospectively and consecutively evaluated. The time distribution of the seizures during the day was noted. Seizure latency was determined as the time in hours from the start of the video-electroencephalographic recording to the first clinical event. The seizure latency was significantly shorter in PNES patients compared to ES patients (p < 0.001). Seventy-two percent of PNES patients and 49.1% of ES patients had their first seizure in the 24 hours of video-EEG recording (p = 0.023). Recording longer than 48 hours was required for 12.5% of PNES patients and 37.3% of ES patients (p = 0.006). While ESs were almost evenly distributed throughout the day, most PNESs occurred during the evening hours (p = 0.011). We observed that the PNESs appeared earlier than the ESs in the VEM and were concentrated during daylight hours. Although not strictly reliable, seizure latency can contribute to the differential diagnosis of ES and PNES.

Development and Validation of Machine-Learning Models to Support Clinical Diagnosis for Non-Epileptic Psychogenic Seizures

Electroencephalographic (EEG) signal processing and machine learning can support neurologists’ work in discriminating Psychogenic Non-Epileptic Seizure (PNES) from epilepsy. PNES represents a neurological disease often misdiagnosed. Although the symptoms of PNES patients can be similar to those exhibited by epileptic patients, EEG signals during a psychogenic seizure do not show ictal patterns such as in epilepsy. Therefore, PNES diagnosis requires long-term EEG video. Applying signal processing and machine-learning methodologies could help clinicians find helpful information in the clinical diagnosis of PNES by analyzing EEG signals registered in resting conditions and in a short time. These methodologies should prevent long EEG recording sessions and avoid inducing seizures in the subjects. The aim of our study is to develop and validate several machine-learning models on a larger dataset, consisting of 225 EEGs (75 healthy, 75 PNES, and 75 subjects with epilepsy). A deep analysis of our results shows that changes in the evaluation strategy led to changes in accuracy from 45% to 83.98% for a standard Light Gradient Boosting Machine (LGBM) classifier. Our findings suggest that it is necessary to operate a very rigorous control in terms of experimental data collection (patient selection, signal acquisition) and terms of validation strategies to obtain and reproducible results.

Does explaining psychogenic nonepileptic seizures using either a biomedical or biopsychosocial framework affect young people’s illness representations? An experimental vignette study

Lay representations of psychogenic nonepileptic seizures (PNES) are important both for understanding public stigma and anticipating patient responses to PNES diagnosis. The current study presents the first evidence of the general public’s representations of PNES and the malleability of these understandings to different ways of explaining PNES. An online experimental study exposed participants (n = 193, aged 18–25 years) to a vignette describing a case of PNES in biomedical terms, PNES in biopsychosocial terms, or epilepsy. Subsequent questionnaires assessed participants’ illness representations, causal attributions, and stigmatising attitudes regarding the case about which they read. Results suggest that compared with biomedical framings, biopsychosocial explanations increased perceptions of PNES as threatening. While epilepsy was attributed to significantly more biological and less social causes than either of the PNES vignettes, causal attributions did not differ between biomedically- vs. biopsychosocially-framed PNES. Neither were there any differences between the three conditions in stigmatising attitudes towards people who experience seizures. These findings are useful for clinicians delivering a PNES diagnosis and patients disclosing a PNES diagnosis, in helping anticipate responses to these communications. Further research is required to confirm the clinical and societal significance of the study’s first insights into the dynamics of lay responses to PNES.

Clinical manifestations of psychogenic non-epileptic seizures in children: Experiences from a single center.

This study aimed to determine the clinical features of psychogenic non-epileptic seizures (PNES) and to enhance the accuracy of the differential diagnosis of epilepsy. This retrospective case series included patients diagnosed with PNES between December 2003 and February 2019at Taipei Veterans General Hospital. We used International Classification of Diseases (10th revision) codes for screening, and relevant medical records were reviewed. Experienced pediatric neurologists diagnosed PNES based on clinical manifestations, and occasionally on confirmatory video-electroencephalography (EEG) or simultaneous scalp-EEG during the paroxysmal attack. General information, clinical manifestations, psychological conditions, and relevant laboratory or imaging test results were collected and analyzed. Twenty-six patients (mean age, 13 years 8 months) were evaluated, 9 male and 17 female. Ten patients with PNES had a previously established diagnosis of epilepsy. The duration between symptom onset and diagnosis ranged from 1 to 120 (mean, 21; median, 12) days. Sixteen patients showed possible causative psychosocial stressors. Multiple characteristic features or specific clinical manifestations of PNES-that usually differ from epileptic seizures-were observed in all patients with PNES. A detailed evaluation of clinical manifestations and medical history is important for the accurate diagnosis of PNES.

White matter microstructure and serum biomarkers of inflammation in psychogenic non-epileptic seizures

BackgroundNeuroinflammation may contribute to the pathophysiology of psychogenic non-epileptic seizures (PNES). However, it is unclear whether and to what degree comorbid psychiatric symptoms explain this association. In this study, we investigated the neuroinflammatory signature of PNES and how it compares to that of people with psychiatric conditions (PwPCs). MethodsWe prospectively assessed differences in neurite density (NDI), orientation dispersion (ODI), and isotropic diffusion (F-ISO) in 23 participants with PNES and 27 PwPCs, and their relationships to serum levels of tumor necrosis factor (TNF)-α, TNF receptor 1 (TNF-R1), TNF-related apoptosis-inducing ligand (TRAIL), interleukin (IL)-6, intercellular adhesion molecule (ICAM)-1, and monocyte chemoattractant protein (MCP)-1 using voxelwise multiple linear regressions. Pearson correlations between serum biomarkers and clinical symptoms were also obtained. ResultsThere were no white matter (WM) microstructural differences between groups. In PNES, TNF-R1 was negatively associated with NDI in the right uncinate fasciculus (UF) and positively associated with F-ISO in the left UF. IL-6 was positively associated with NDI and negatively with F-ISO in the left UF. ICAM-1 was positively associated with ODI in the left UF. TNF-α was negatively associated with ODI in the left cingulum bundle. The opposite relationships were observed in PwPCs. Higher TNF-R1 was associated with higher depression, anxiety, lower emotional quality of life, and higher levels of disability in PNES. ConclusionsFor the first time, we report relationships between peripheral inflammatory biomarkers and WM integrity in PNES, including abnormalities in the UF and cingulum bundle. Our results suggest that serum biomarkers of inflammation may, with additional studies, become a useful aid to PNES diagnosis, especially in settings where video-EEG is not available. The lack of group differences in WM microstructure suggests that previously identified WM abnormalities in PNES versus healthy controls may be related to psychological comorbidities of PNES.

Development of a core outcome set for quality of life for adults with drug-resistant epilepsy: A multistakeholder Delphi consensus study.

In 2017, the American Academy of Neurology (AAN) convened the AAN Quality Measurement Set working group to define the improvement and maintenance of quality of life (QOL) as a key outcome measure in epilepsy clinical practice. A core outcome set (COS), defined as an accepted, standardized set of outcomes that should be minimally measured and reported in an area of health care research and practice, has not previously been defined for QOL in adult epilepsy. A cross-sectional Delphi consensus study was employed to attain consensus from patients and caregivers on the QOL outcomes that should be minimally measured and reported in epilepsy clinical practice. Candidate items were compiled from QOL scales recommended by the AAN 2017 Quality Measurement Set. Inclusion criteria to participate in the Delphi study were adults with drug-resistant epilepsy diagnosed by a physician, no prior diagnosis of psychogenic nonepileptic seizures or a cognitive and/or developmental disability, or caregivers of patients meeting these criteria. A total of 109 people satisfied inclusion/exclusion criteria and took part in Delphi Round 1 (patients, n= 95, 87.2%; caregivers, n= 14, 12.8%), and 55 people from Round 1 completed Round 2 (patients, n= 43, 78.2%; caregivers, n= 12, 21.8%). One hundred three people took part in the final consensus round. Consensus was attained by patients/caregivers on a set of 36 outcomes that should minimally be included in the QOL COS. Of these, 32 of the 36 outcomes (88.8%) pertained to areas outside of seizure frequency and severity. Using patient-centered Delphi methodology, this study defines the first COS for QOL measurement in clinical practice for adults with drug-resistant epilepsy. This set highlights the diversity of factors beyond seizure frequency and severity that impact QOL in epilepsy.

Entropy Measures of Electroencephalograms towards the Diagnosis of Psychogenic Non-Epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) may resemble epileptic seizures but are not caused by epileptic activity. However, the analysis of electroencephalogram (EEG) signals with entropy algorithms could help identify patterns that differentiate PNES and epilepsy. Furthermore, the use of machine learning could reduce the current diagnosis costs by automating classification. The current study extracted the approximate sample, spectral, singular value decomposition, and Renyi entropies from interictal EEGs and electrocardiograms (ECG)s of 48 PNES and 29 epilepsy subjects in the broad, delta, theta, alpha, beta, and gamma frequency bands. Each feature-band pair was classified by a support vector machine (SVM), k-nearest neighbour (kNN), random forest (RF), and gradient boosting machine (GBM). In most cases, the broad band returned higher accuracy, gamma returned the lowest, and combining the six bands together improved classifier performance. The Renyi entropy was the best feature and returned high accuracy in every band. The highest balanced accuracy, 95.03%, was obtained by the kNN with Renyi entropy and combining all bands except broad. This analysis showed that entropy measures can differentiate between interictal PNES and epilepsy with high accuracy, and improved performances indicate that combining bands is an effective improvement for diagnosing PNES from EEGs and ECGs.

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

ObjectiveEarly 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. MethodThis 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. Results261 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 (η2p = 0.05) and group by measure interaction (η2p = 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). ConclusionMultidimensional 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.

Electroencephalogram Connectivity for the Diagnosis of Psychogenic Non-epileptic Seizures.

Psychogenic non-epileptic seizures (PNES) are attacks that resemble epilepsy but are not associated with epileptic brain activity and are regularly misdiagnosed. The current gold standard method of diagnosis is expensive and complex. Electroencephalogram (EEG) analysis with machine learning could improve this. A k-nearest neighbours (kNN) and support vector machine (SVM) were used to classify EEG connectivity measures from 48 patients with PNES and 29 patients with epilepsy. The synchronisation method - correlation or coherence - and the binarisation threshold were defined through experimentation. Ten network parameters were extracted from the synchronisation matrix. The broad, delta, theta, alpha, beta, gamma, and combined 'all' frequency bands were compared along with three feature selection methods: the full feature set (no selection), light gradient boosting machine (LGBM) and k-Best. Coherence was the highest performing synchronisation method and 0.6 was the best coherence threshold. The highest balanced accuracy was 89.74%, produced by combining all six frequency bands and selecting features with LGBM, classified by the SVM. This method returned a comparatively high accuracy but at a high computation cost. Future research should focus on identifying specific frequency bands and network parameters to reduce this cost. Clinical relevance - This study found that EEG connectivity and machine learning methods can be used to differentiate PNES from epilepsy using interictal recordings to a high accuracy. Thus, this method could be an effective tool in assisting clinicians in PNES diagnosis without a video- EEG recording of a habitual seizure.

Increased suicide attempt risk in people with epilepsy in the presence of concurrent psychogenic nonepileptic seizures

ObjectivesTo test the hypothesis that people with concurrent diagnosis of epilepsy and psychogenic nonepileptic seizures (PNES) are at increased risk of attempting suicide as compared to people with epilepsy or...

Identification of risk factors and distinguishing psychogenic nonepileptic seizures from epilepsy: A retrospective case-control study

IntroductionPatients with psychogenic non-epileptic seizures (PNES) experience significant morbidity and early mortality, secondary to delayed diagnosis. Better characterizing risk factors and exploring how PNES differentially affects sex and racial strata may facilitate earlier diagnosis. MethodsFrom a Hawaiʻi neuroscience institution, 101 PNES patients were investigated in relation to sociodemographic and medical comorbidities. Cases were compared to 202 sex-, age-, and race-matched controls—representing patients with neurological disorders (general controls)—, as well as 404 unmatched epilepsy controls. ResultsRelative to general controls, PNES patients had increased odds (p < 0.05) of being: female, younger age, Native Hawaiian or other Pacific Islander (NHPI), suburban origin, from the lowest income quartile, Medicaid beneficiaries, homeless, current/former smoker, illicit drug users (marijuana, opioids/narcotics, polysubstance abuse), have anxiety, depression, post-traumatic stress disorder, bipolar disorder, traumatic history, World Health Organization obesity class 3, traumatic brain injury, epilepsy, and somatoform disorder. In relation to epilepsy controls, PNES patients exhibited increased odds of being: employed, having attention-deficit/hyperactivity disorder, asthma, migraines, and chronic pain. Relative to females, male PNES patients exhibited increased odds of military insurance, diabetes mellitus type 2, and hypertension. Relative to Whites, the NHPI and Asian PNES patients presented increased odds of asthma, migraines, chronic pain, gastroesophageal reflux disease, and thyroid disease. Per multivariable logistic regression, anxiety was the only consistent predictor of PNES across all sex and race strata. ConclusionPredictors of PNES’s vary amongst the strata of race and sex. Lower socioeconomic status, along with several psychiatric and medical comorbidities, could increase a clinician’s suspicion for earlier medical workup and diagnosis of PNES.

Quality of life for patients with psychogenic nonepilepsy seizures in comparison with age- and gender-matched patients with epilepsy – Cross-sectional study

AimsPatients with psychogenic nonepileptic seizures (PNES), and age- and gender-matched patients with epilepsy (PWE) who utilized an out-patient service were compared regarding quality of life (QoL) and self-reported symptoms of depression. Additionally, the impact of miscellaneous clinical variables including symptoms of depression on QoL in patients with PNES and PWE in real-world settings was assessed. Subjects and methodsAdult patients who had a diagnosis of definite or documented PNES based on LaFrance’s criteria (PNES group, n = 62), or of epilepsy based on results of clinical and EEG procedures (Epilepsy group, n = 61) were enrolled. To assess QoL and evaluate depression, the Quality of Life in Epilepsy Inventory-10 (QOLIE-10) and Neurological Disorders Depression Inventory for Epilepsy (NDDI-E), respectively, were administered. ResultsComparisons between the groups revealed a shorter duration of illness and fewer number of attacks in patients with PNES as compared to the Epilepsy group (p < 0.0001; p = 0.0003, respectively). There was no significant difference between the groups revealed by the QOLIE-10 (p = 0.141), while the patients with PNES tended to have higher NDDI-E scores (p = 0.068). Multiple regression analysis of QOLIE-10 results in the PNES group revealed that NDDI-E score was the sole significant contributor (β = −0.425 p = 0.001). In contrast, NDDI-E score as well as attack frequency had a significant impact on QOLIE-10 results in the Epilepsy group (β = −0.283 p = 0.026; β = −0.272 p = 0.031, respectively). ConclusionIn PWE and patients with PNES who utilized an out-patient service, QoL did not differ significantly between those groups. For treating PNES, psychosocial factors may be a more appropriate indicator of therapeutic goal than attack frequency.

Mortality in patients with psychogenic non-epileptic seizures a population-based cohort study

ObjectivesTo compare mortality, comorbidities and causes of death in people with psychogenic non-epileptic seizures (PNES), epilepsy and the general population.MethodsUsing linkage of multiple Swedish national registers, we identified individuals with...