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Global neuropsychological impairments with intellectual disability (ID) seem to play a major role in the occurrence of psychogenic non‐epileptic seizures (PNES) in epilepsy. Conversely, the pathophysiology underlying PNES combined with epilepsy without ID remains elusive. We investigated the neuropsychiatric profile in 26 average intelligent subjects (15 women, mean age: 40.04 ± 13.53 years) with temporal lobe epilepsy (TLE) plus PNES (TLE + PNES), compared with 28 with TLE and 22 with PNES alone, matched for age and sex. All subjects underwent neuropsychiatric assessment, including Beck Depression Inventory‐2 (BDI‐2), State‐Trait Anxiety Inventory (STAI), Dissociative Experiences Scale (DES), Toronto Alexithymia Scale (TAS‐20), Traumatic Experience Checklist (TEC), and cognitive evaluation. TLE + PNES and PNES groups shared a similar psychiatric profile with higher levels of depression (BDI‐2, P < 0.001), anxiety (STAI‐S, P < 0.001; STAI‐T, P < 0.001), dissociation (DES, P < 0.001), and alexithymia (TAS, P = 0.005) scales than the TLE group. Nonetheless, like individuals with TLE, patients with TLE + PNES had a lower rate of a potentially traumatizing event than PNES. The very low rate of potentially traumatizing event in subjects with TLE + PNES leads us to hypothesize that epilepsy itself may be the psychophysiological distress that contributed to PNES. A psychopathological assessment in subjects with epilepsy is crucial to identify those more likely to develop PNES.

Objective Psychogenic non‐epileptic seizures (PNES) mimic epileptic seizures without electroencephalographic correlation. Although classified as psychiatric disorders, their neurobiological or structural basis remains unclear. This study aimed to assess the prevalence and characteristics of MRI abnormalities in patients with PNES and those with comorbid epilepsy, compared to the general population, to enhance radiological evaluation and management. Method We retrospectively identified patients with a definitive diagnosis of PNES, evaluated in the refractory epilepsy unit of our tertiary epilepsy center. Patients were classified into two groups according to their comorbidity with epilepsy (PNES and PNES+). The MRI findings were evaluated and classified by two radiologists, who reported the category of the findings, laterality, and location. The two groups were compared using the chi‐square test, as well as the frequencies of findings in the general population extracted from the literature. Results Forty‐six patients fulfilled the inclusion criteria. Thirty females and 16 males. MRI findings were present in 25/35 (71.4%) patients in the PNES group and 9/11 (81.8%) In the PNES + group, showing statistically significant differences in the frequency of findings with the general population (8.4–28.1%). Significance MRI anomalies are common in PNES patients and even more prevalent in complex cases referred to epilepsy units, underscoring the necessity of correlating MRI findings with clinical‐electrical patterns. Plain Language Summary In this article, we observed a higher frequency of cerebral magnetic resonance findings in patients with psychogenic non‐epileptic seizures than in the general population. We also observed a higher frequency of this pathology among women, as well as right cerebral hemisphere affections. The exposed findings suggest a potential structural basis of this pathology. This hypothesis requires confirmation with larger studies.

Distinguishing non-epileptic events, especially psychogenic non-epileptic seizures (PNES), from epileptic seizures (ES) constitutes a diagnostic challenge. Misdiagnoses are frequent, especially when video-EEG recording, the gold-standard for PNES confirmation, cannot be completed. The issue is further complicated in cases of combined PNES with ES. In emergency units, a misdiagnosis can lead to extreme antiepileptic drug escalade, unnecessary resuscitation measures (intubation, catheterization, etc.), as well as needless biologic and imaging investigations. Outside of the acute window, an incorrect diagnosis can lead to prolonged hospitalization or increase of unhelpful antiepileptic drug therapy. Early recognition is thus desirable to initiate adequate treatment and improve prognosis. Considering experience-based strategies and a thorough review of the literature, we aimed to present the main clinical clues for physicians facing PNES in non-specialized units, before management is transferred to epileptologists and neuropsychiatrists. In such conditions, patient recall or witness-report provide the first orientation for the diagnosis, recognizing that collected information may be inaccurate. Thorough analysis of an event (live or based on home-video) may lead to a clinical diagnosis of PNES with a high confidence level. Indeed, a fluctuating course, crying with gestures of frustration, pelvic thrusting, eye closure during the episode, and the absence of postictal confusion and/or amnesia are highly suggestive of PNES. Moreover, induction and/or inhibition tests of PNES have a good diagnostic value when positive. Prolactinemia may also be a useful biomarker to distinguish PNES from epileptic seizures, especially following bilateral tonic-clonic seizures. Finally, regardless the level of certainty in the diagnosis of the PNES, it is important to subsequently refer the patient for epileptological and neuropsychiatric follow-up.

Background and Objectives: The purpose of this study was to investigate personality and relationship patterns in patients with psychogenic non-epileptic seizures (PNES) and compare them to patients with epilepsy and healthy controls. Materials and Methods: A total of 68 participants were recruited (mean age = 29.8 ± 9.4 years), including 25 (36.2%) with PNES. The assessment was conducted using the Relationship Questionnaire (RQ), Big Five Inventory (BFI), Relationship Assessment Scale (RAS), Satisfaction with Life Scale (SWLS), and Conflict Behavior Scale (CBS). Results: The IQ of patients with PNES (88.8 ± 13.6) was lower compared to healthy controls (103.5 ± 28.0) but higher than epilepsy patients (84.6 ± 12.9). There were no significant differences between PNES patients and either patients with epilepsy or healthy controls in terms of security, fearfulness, preoccupation, or dismissiveness based on RQ subscale scores. PNES patients tended to be less satisfied (RAS total score, p = 0.10), but did not differ on overall life quality (on SWLS) compared to epilepsy patients and healthy individuals. There were no significant differences in the scores for different attachment styles (secure, fearful, preoccupied, dismissive) among the groups (p > 0.05). Significant differences were found in agreeableness (p = 0.017) and openness (p = 0.009) among the groups. The PNES group exhibits higher scores in Negative—Own (p = 0.009), Positive—Own (p = 0.011), Negative—Partner (p = 0.011), and Positive—Partner (p = 0.028) compared to epilepsy and healthy individuals. No significant differences observed in the Abusive—Own and Abusive—Partner scores (p > 0.05). Conclusions: In conclusion, this study highlights distinct personality traits and relationship patterns in patients with psychogenic non-epileptic seizures (PNES) compared to epilepsy patients and healthy controls, emphasizing the need for targeted interventions to address these psychological nuances effectively.

The objective of the present study was to evaluate the effectiveness of EEG biofeedback for treatment of psychogenic non-epileptic seizures (PNES) in a patient with multiple sclerosis. The patient was a 47-year-old female who has been experiencing several PNES types after being diagnosed with multiple sclerosis. She underwent 16 sessions of the EEG biofeedback over a period of two months. Following EEG biofeedback, the patient reported that her PNES attacks had stopped and the treatment resulted in significant abatement in her clinical seizure symptoms. The analysis of sensorimotor rhythm (SMR) values revealed reduction of psychogenic non-epileptic seizure. The Beck Anxiety Inventory (BAI) and Word Health Organization Quality of Life Questionnaire (WHOQOL) were used before and after treatment. Decreased anxiety as well as increased quality of life was observed after treatment. Generally, the results indicated that EEG biofeedback was a useful procedure in treating PNES, promoting quality of life and reducing anxiety in our patient with multiple sclerosis.

Functional neurological disorders (FNDs) are neurological symptoms that cannot be explained by an underlying neurological lesion or other medical illness and that do not have clear neuropathological correlates. Psychogenic non-epileptic seizures (PNES) are a common and highly disabling form of FND, characterized by paroxysmal episodes of involuntary movements and altered consciousness that can appear clinically similar to epileptic seizures. PNES are unique among FNDs in that they are diagnosed by video electroencephalographic (VEEG), a well-established biomarker for the disorder. The course of illness and response to treatment of PNES remain controversial. This study aims to describe the epidemiology of PNES in the Department of Veterans Affairs Healthcare System (VA), evaluate outcomes of veterans offered different treatments, and compare models of care for PNES. This electronic health record (EHR) cohort study utilizes an informatics search tool and a natural language processing algorithm to identify cases of PNES nationally. We will use VA inpatient, outpatient, pharmacy, and chart abstraction data across all 170 medical centers to identify cases in fiscal years 2002-2018. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and the utilization of psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care. This study will describe the risk factors and course of treatment of a large cohort of people with PNES. Since PNES are cared for by a variety of different modalities, treatment orientations, and models of care, effectiveness outcomes such as seizure outcomes and utilization of emergency visits for seizures will be assessed. Outcome measurements such as seizure frequency, emergency room visits, hospital admissions, suicide-related behavior, and psychotherapy prior to and after PNES diagnosis will be used to assess the effectiveness of models of care.

ObjectivePsychogenic non-epileptic seizures (PNES) are paroxysmal events that, in contrast to epileptic seizures, are related to psychological causes without the presence of epileptiform EEG changes. Recent models suggest a multifactorial basis for PNES. A potentially paramount, but currently poorly understood factor is the interplay between psychiatric features and a specific vulnerability of the brain leading to a clinical picture that resembles epilepsy. Hypothesising that functional cerebral network abnormalities may predispose to the clinical phenotype, the authors undertook a characterisation of the functional connectivity in PNES patients.MethodsThe authors analysed the whole-head surface topography of multivariate phase synchronisation (MPS) in interictal high-density EEG of 13 PNES patients as compared with 13 age- and sex-matched controls. MPS mapping reduces the wealth of dynamic data obtained from high-density EEG to easily readable synchronisation maps, which provide an unbiased overview of any changes in functional connectivity associated with distributed cortical abnormalities. The authors computed MPS maps for both Laplacian and common-average-reference EEGs.ResultsIn a between-group comparison, only patchy, non-uniform changes in MPS survived conservative statistical testing. However, against the background of these unimpressive group results, the authors found widespread inverse correlations between individual PNES frequency and MPS within the prefrontal and parietal cortices.InterpretationPNES appears to be associated with decreased prefrontal and parietal synchronisation, possibly reflecting dysfunction of networks within these regions.

Differential diagnosis in epilepsy is sometimes challenging. Video-electroencephalography (V-EEG) is an essential tool in the diagnosis and management of epilepsy. The prolonged duration of V-EEG recording increases the diagnostic yield of a conventional V-EEG. The right length of monitoring for different indications is still to be established. We present a retrospective descriptive study with a sample of 50 patients with long-term V-EEG monitoring, with a mean age of 36.1 years, monitored from 2013 to 2019 at the Burgos University Hospital. The mean monitoring time was 3.6 days. Events were obtained in 76% of the patients, corresponding to epileptic seizures (ES) in 57.9% of them, with psychogenic non-epileptic seizures (PNES) in 39.5%, and with episodes of both pathologies in 2.6% of the patients. We found that the first event was highly representative, and it correlated with the rest of the events that would be recorded. Moreover, 92% of the first PNES had been captured at the end of the second day, and 89% of the first ES by the end of the third day. V-EEG for differential diagnosis between ES and PNES can be performed in hospitals without specialized epilepsy surgery units. For this indication, the duration of long-term V-EEG can be adjusted individually depending on the nature of the first event.

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.

IntroductionAmongst different subtypes of Conversion Disorder (CD), DSM-V lists the Psychogenic Non-epileptic seizures (PNES). PNES are defined as episodes that visually resemble epileptic seizures but, etiologically, they are not due to electrical discharges in the brain.ObjectivesOur study aims to explore the differences between PNES and other CDs. In particular, we studied the suicidality and its correlations with dissociation and alexithymia.MethodsPatients, recruited from the Psychiatry and Clinical Psychology Unit of the Fondazione Policlinico Tor Vergata, Rome, Italy, were diagnosed with PNES (n=22) and CD (n=16) using the DSM-5 criteria. Patients underwent the following clinical assessments: HAM-D, BDI, DES, BHS, TAS, CTQ.ResultsPNES showed significantly higher scores than CD in all assessments, except for BDI-somatic (p=0.39), BHS-feeling (p=0.86), and the presence of childhood trauma. PNES also showed significantly higher suicidality (p = 0.003). By controlling for the confounding factor “depression”, in PNES suicidality (and in particular the BHS-loss of motivation) appears to be correlated with DES-total score (p = 0.008), DES-amnesia (p = 0.002) and DES -derealization-depersonalization (p = 0.003). On the other hand, in CDs, the BHS-total score shows a correlation with the TAS-total score (p = 0.03) and BHS-Feelings with TAS-Externally-Oriented Thinking (p = 0.035), while only the BHS-Loss of motivation appears correlated with DES-Absorption (p = 0.011).ConclusionsOur study shows significant differences between PNES and CD, in several symptomatologic dimensions, including suicidality. Indeed, in PNES suicidality appears to be related to dissociation, while in CDs it appears mainly to be correlated with alexithymia.