Rating scale for psychogenic nonepileptic seizures: scale development and clinimetric testing

A meta-analytic evaluation of the effectiveness and durability of psychotherapy for adults presenting with functional dissociative seizures

BACKGROUND

Psychological interventions are the most recommended treatment for functional/dissociative seizures (FDS); however, there is ongoing uncertainty about their effectiveness on seizure outcomes.

METHODS

This systematic review and meta-analysis synthesises the available data. In February 2023, we completed a systematic search of four electronic databases. We described the range of seizure-related outcomes captured, used meta-analytic methods to analyse data collected during treatment and follow-up; and explored sources of heterogeneity between outcomes.

RESULTS

Overall, 44 relevant studies were identified involving 1,300 patients. Most were categorised as being at high (39.5 %) or medium (41.9 %) risk of bias. Seizure frequency was examined in all but one study; seizure intensity, severity or bothersomeness in ten; and seizure duration and cluster in one study each. Meta-analyses could be performed on seizure freedom and seizure reduction. A pooled estimate for seizure freedom at the end of treatment was 40 %, while for follow-up it was 36 %. Pooled rates for ≥50 % improvement in seizure frequency were 66 % and 75 %. None of the included moderator variables for seizure freedom were significant. At the group level, seizure frequency improved during the treatment phase with a moderate pooled effect size (d = 0.53). FDS frequency reduced by a median of 6.5 seizures per month. There was also evidence of improvement of the other (non-frequency) seizure-related measures with psychological therapy, but data were insufficient for meta-analysis.

CONCLUSIONS

The findings of this study complement a previous meta-analysis describing psychological treatment-associated improvements in non-seizure-related outcomes. Further research on the most appropriate FDS-severity measure is needed.

Physical Rehabilitation of Motor Functional Neurological Disorders: A Narrative Review

Functional Neurological Disorders (FNDs) are one of the most common and disabling neurological disorders, affecting approximately 10-30% of patients in neurology clinics. FNDs manifest as a range of motor, sensory, and cognitive symptoms that are not explained by organic disease. This narrative review aims to assess the current state of knowledge in physical-based rehabilitation for motor/movement FNDs in the adult population, with the goal of improving research and medical care for this patient population. To ensure optimal outcomes for patients, it is critical to consider several domains pertaining to FNDs, including which field of discipline they should belong to, how to investigate and test, methods for rating outcome measures, and optimal courses of treatment. In the past, FNDs were primarily treated with psychiatric and psychological interventions. However, recent literature supports the inclusion of physical rehabilitation in the treatment of FNDs. Specifically, physical-based approaches tailored to FNDs have shown promising results. This review utilized a comprehensive search of multiple databases and inclusion criteria to identify relevant studies.

Differential Semiology Based on Video Electroencephalography Monitoring Between Psychogenic Nonepileptic Seizures and Temporal Lobe Epileptic Seizures

BACKGROUND

Psychogenic nonepileptic seizures (PNESs) are disruptive changes in behavior without ictal correlate of epileptic activity and high prevalence of psychiatric morbidity. Differential diagnosis is difficult particularly with temporal lobe epilepsy (TLE), which is also associated with high prevalence of psychiatric comorbidity. Although video electroencephalography is the gold standard for differential diagnosis, clinical semiology analysis may help the clinician in general medical practice.

OBJECTIVE

In this study, the differential semiology, based on video electroencephalography, between PNESs and TLE seizures was analyzed.

METHODS

The video electroencephalography of patients with diagnosis of PNES and TLE were reviewed and compared between groups. Clinical semiology of all episodes recorded by video electroencephalography in each patient was analyzed and classified in accordance with the presence of behavioral arrest, motor hyperkinetic activity, impaired awareness, aura, and automatisms. Chi square test and binary logistic regression were determined.

RESULTS

Thirty-two patients with PNES (32 ± 11 y) and 34 with TLE (32 ± 12 y) were included. Female patients were predominant in the PNES group (P < 0.05). Mean time duration of episodes was 6.8 ± 10 minutes in PNES and 1.6 ± 0.8 minutes in TLE (P < 0.05). Impaired awareness (odds ratio = 24.4; 95% confidence interval = 3.79 -157.3, P < 0.01), automatisms (odds ratio = 13.9; 95% confidence interval = 2.1- 90.5, P < 0.01), and shorter duration of the events (odds ratio = 2.261, 95% confidence interval = 1.149 - 4.449, P = 0.018) were found as independent factors for detecting TLE seizures comparing PNESs.

CONCLUSION

Clinical semiology analysis may orientate the differential diagnosis in general medical practice, between PNESs and TLE seizures. Further studies comparing PNES semiology with other subtypes of epilepsies may complete these preliminary findings.

Outcome measurement in functional neurological disorder: a systematic review and recommendations

OBJECTIVES

We aimed to identify existing outcome measures for functional neurological disorder (FND), to inform the development of recommendations and to guide future research on FND outcomes.

METHODS

A systematic review was conducted to identify existing FND-specific outcome measures and the most common measurement domains and measures in previous treatment studies. Searches of Embase, MEDLINE and PsycINFO were conducted between January 1965 and June 2019. The findings were discussed during two international meetings of the FND-Core Outcome Measures group.

RESULTS

Five FND-specific measures were identified-three clinician-rated and two patient-rated-but their measurement properties have not been rigorously evaluated. No single measure was identified for use across the range of FND symptoms in adults. Across randomised controlled trials (k=40) and observational treatment studies (k=40), outcome measures most often assessed core FND symptom change. Other domains measured commonly were additional physical and psychological symptoms, life impact (ie, quality of life, disability and general functioning) and health economics/cost-utility (eg, healthcare resource use and quality-adjusted life years).

CONCLUSIONS

There are few well-validated FND-specific outcome measures. Thus, at present, we recommend that existing outcome measures, known to be reliable, valid and responsive in FND or closely related populations, are used to capture key outcome domains. Increased consistency in outcome measurement will facilitate comparison of treatment effects across FND symptom types and treatment modalities. Future work needs to more rigorously validate outcome measures used in this population.

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1-32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

Semiological characteristics of patients with psychogenic nonepileptic seizures: Gender-related differences

Psychogenic nonepileptic seizures (PNES) are more prevalent among women, and diagnosis may sometimes be delayed by as much as seven years. Understanding the effect of gender on the presentation of a PNES may assist with diagnosis based on semiological details in the clinical setting. Although video-EEG monitoring (VEM) is the gold standard for diagnosing PNES, determining gender-related seizure semiology through careful history may prevent diagnostic delay while waiting for VEM. The aim of this study was to investigate gender-related differences in the semiology of PNES. Patients, all aged at least 16 years, diagnosed with PNES following VEM between December 2005 and November 2016 were included in this study. All patients' medical records and video-EEG-documented PNES were reviewed, and the presence or absence of semiological signs was recorded for each documented attack. Demographic features and semiological signs of PNES were compared between female and male patients. Forty-one patients (31 females, 10 males) aged 27.2 ± 12.2 years (range: 16-65) were included in the study. Mean age at onset of PNES was higher for female patients than males, at 24.3 ± 11.5 versus 17.5 ± 3.2 years (p = 0.005). The median duration of PNES was longer for female patients than males, at 10 min (range: 5 s-120 min) versus 2 min (range: 10 s-60 min) (p = 0.016). The most common symptom was forced eye closure in both genders. No significant gender-specific differences were observed in terms of the type or semiology of PNES. Although there are no major gender-related differences in PNES semiology, our findings highlight the importance of greater caution, especially in male patients, when diagnosing PNES, remembering that onset may also occur at young ages and that a short seizure duration does not exclude PNES.

Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach

The aim of this study was to validate a novel classification for the diagnosis of PNESs. Fifty-five PNES video-EEG recordings were retrospectively analyzed by four epileptologists and one psychiatrist in a blind manner and classified into four distinct groups: Hypermotor (H), Akinetic (A), Focal Motor (FM), and with Subjective Symptoms (SS). Eleven signs and symptoms, which are frequently found in PNESs, were chosen for statistical validation of our classification. An artificial neural network (ANN) analyzed PNES video recordings based on the signs and symptoms mentioned above. By comparing results produced by the ANN with classifications given by examiners, we were able to understand whether such classification was objective and generalizable. Through accordance metrics based on signs and symptoms (range: 0-100%), we found that most of the seizures belonging to class A showed a high degree of accordance (mean±SD=73%±5%); a similar pattern was found for class SS (80% slightly lower accordance was reported for class H (58%±18%)), with a minimum of 30% in some cases. Low agreement arose from the FM group. Seizures were univocally assigned to a given class in 83.6% of seizures. The ANN classified PNESs in the same way as visual examination in 86.7%. Agreement between ANN classification and visual classification reached 83.3% (SD=17.8%) accordance for class H, 100% (SD=22%) for class A, 83.3% (SD=21.2%) for class SS, and 50% (SD=19.52%) for class FM. This is the first study in which the validity of a new PNES classification was established and reached in two different ways. Video-EEG evaluation needs to be performed by an experienced clinician, but later on, it may be fed into ANN analysis, whose feedback will provide guidance for differential diagnosis. Our analysis, supported by the ML approach, showed that this model of classification could be objectively performed by video-EEG examination.

Neurologic diagnostic criteria for functional neurologic disorders

The diagnosis of functional neurologic disorders can be challenging. In this chapter we review the diagnostic criteria and rating scales reported for functional/psychogenic sensorimotor disturbances, psychogenic nonepileptic seizures (PNES) and functional movement disorders (FMD). A recently published scale for sensorimotor signs has some limitations, but may help in the diagnosis, and four motor and two sensory signs have been reported as highly reliable. There is good evidence using eight specific signs for the differentiation of PNES from seizures. Recently, diagnostic criteria were developed for PNES; their sensitivity and specificity need to be evaluated. The definitive diagnosis of PNES can be made by recording typical positive features during the spells, and in a low proportion of cases, where the distinction with an organic etiology cannot easily be done, a normal electroencephalogram suggests the diagnosis. FMD diagnosis relies on diagnostic criteria, which have been refined over time and may be supplemented by laboratory tests in some phenotypes. Rating scales for PNES and FMD could be useful for severity measures, but several limitations remain to be addressed.

Clinical events in psychogenic non-epileptic seizures based on semiological seizure classification

OBJECTIVES

None of the classifications of psychogenic non-epileptic seizures (PNES) have been widely accepted and used by physicians so far. In this study we aimed at classifying PNES on the basis of a modified version of semiological seizure classification (SSC). We also sought to assess the interrater reliability (IRR) of the PNES diagnosis based on SSC.

METHODS

We classified PNES into four types on the basis of our modification of SSC: pseudoaura, dialeptic, motor, and special (atonic, astatic, hypotonic) spells. Pseudoauras were not included in the statistical analysis. Ninety-one PNES attacks were observed during the 55 video-EEG sessions recorded for all patients. The interrater agreement was assessed by the kappa coefficient.

RESULTS

Twenty-nine women (78·3%) and eight men (21·6%) were surveyed, with a mean age of 28·4 ± 9·6 (range 16-54). The final diagnosis of PNES was established after a mean of 4·5 ± 2·3 years following the onset of PNES attacks in the patients. The mean seizure duration in the PNES was 241 seconds and 40·5% of our patients had PNES longer than 300 seconds. Motor and special PNES were the most common types observed by all the raters. The kappa values for each pair were as follows: Observers I-II 0·51 (p = 0·000), Observers I-III 0·47 (p = 0·000), and Observers II-III 0·73 (p = 0·000).

CONCLUSIONS

Interobserver agreement was moderate and substantial for three observers who classified PNES according to our modified SSC. The modified version of SSC could be used without difficulty in classifying PNES. Using SSC for PNES both shortens the period before diagnosis and eliminates the need to learn another new and acceptable classification for PNES.

Subgroup classification in patients with psychogenic non-epileptic seizures

INTRODUCTION

In this open non-controlled clinical cohort study, the applicability of a theoretical model for the diagnosis of psychogenic non-epileptic seizures (PNES) was studied in order to define a general psychological profile and to specify possible subgroups.

METHODS

Forty PNES patients were assessed with a PNES "test battery" consisting of eleven psychological instruments, e.g., a trauma checklist, the global cognitive level, mental flexibility, speed of information processing, personality factors, dissociation, daily hassles and stress and coping factors.

RESULTS

The total PNES group was characterized by multiple trauma, personality vulnerability (in a lesser extent, neuropsychological vulnerabilities), no increased dissociation, many complaints about daily hassles that may trigger seizures and negative coping strategies that may contribute to prolongation of the seizures. Using factor analysis, specific subgroups were revealed: a 'psychotrauma subgroup', a 'high vulnerability somatizing subgroup' (with high and low cognitive levels) and a 'high vulnerability sensitive personality problem subgroup'.

CONCLUSION

Using a theoretical model in PNES diagnosis, PNES seem to be a symptom of distinct underlying etiological factors with different accents in the model. Hence, describing a general profile seems to conceal specific subgroups with subsequent treatment implications. This study identified three factors, representing two dimensions of the model, that are essential for subgroup classification: psychological etiology (psychotrauma or not), vulnerability, e.g., the somatization tendency, and sensitive personality problems/characteristics ('novelty seeking'). For treatment, this means that interventions could be tailored to the main underlying etiological problem. Also, further research could focus on differentiating subgroups with subsequent treatment indications and possible different prognoses.

Recent progress in the understanding and treatment of nonepileptic seizures

PURPOSE OF REVIEW

There has been a steady increase in the number of publications about (psychogenic) nonepileptic seizures (NES) over the past two decades. This review focuses on work published in the past 3 years. It summarizes the most important developments in terms of diagnosis, cause, clinical manifestations and treatment of NES.

RECENT FINDINGS

Several recent studies demonstrate the scope and limitation of questionnaire-based and conversation analytic approaches to the differential diagnosis of epilepsy and NES. Experimental work has revealed that patients with NES have increased levels of physiological arousal at rest which are associated with abnormal mental processing. There is a growing understanding of the meaning and clinical significance of the heterogeneous manifestations of NES. Several studies document the therapeutic potential of an early and effective communication of the diagnosis of NES. A number of randomized controlled or uncontrolled long-term follow-up pilot studies suggest that different forms of psychotherapy are effective for NES.

SUMMARY

Recent research has improved our understanding of NES as a biopsychosocial disorder. Clear diagnostic and management pathways for patients with NES are likely to emerge in the near future.