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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.

Epileptic focal seizures can be localized in the brain using tracer injections during or immediately after the incidence of a seizure. A real-time automated seizure detection system with minimal latency can help time the injection properly to find the seizure origin accurately. Reliable real-time seizure detection systems have not been clinically reported yet. We developed an anomaly detection-based automated seizure detection system, using scalp-electroencephalogram (EEG) data, which can be trained using a few seizure sessions, and implemented it on commercially available hardware with parallel, neuromorphic architecture—the NeuroStack. We extracted nonlinear, statistical, and discrete wavelet decomposition features, and we developed a graphical user interface and traditional feature selection methods to select the most discriminative features. We investigated Reduced Coulomb Energy (RCE) networks and K-Nearest Neighbors (k-NN) for its several advantages, such as fast learning no local minima problem. We obtained a maximum sensitivity of 91.14%±1.77% and a specificity of 98.77%±0.57% with 5 s epoch duration. The system’s latency was 12 s, which is within most seizure event windows, which last for an average duration of 60 s. Our results showed that the CD feature consumes large computation resources and excluding it can reduce the latency to 3.6 s but at the cost of lower performance 80% sensitivity and 97% specificity. We demonstrated that the proposed methodology achieves a high specificity and an acceptable sensitivity within a short delay. Our results indicated also that individual-based RCE are superior to population-based RCE. The proposed RCE networks has been compared to SVM and ANN as a baseline for comparison as they are the most common machine learning seizure detection methods. SVM and ANN-based systems were trained on the same data as RCE and K-NN with features optimized specifically for them. RCE nets are superior to SVM and ANN. The proposed model also achieves comparable performance to the state-of-the-art deep learning techniques while not requiring a sizeable database, which is often expensive to build. These numbers indicate that the system is viable as a trigger mechanism for tracer injection.

Abstract Objectives Patients with epilepsy have a high risk of accidents and injuries, resulting in minimized physical activity and social withdrawal. Therefore, we surveyed the prevalence and the types of injuries that patients with epilepsy may endure, and the factors that may increase the risk of injuries. Methods In this cohort study, adult and pediatric patients diagnosed with epilepsy (age ≥ 7 years) and a close family member (parents/guardian) attending the outpatient epilepsy clinics at King Fahd Medical City (Riyadh, Saudi Arabia) were interviewed by neurologists. They reviewed the patients' medical records and administered a structured questionnaire to identify and compare several variables, including injury frequency versus seizure type and seizure frequency, number of antiseizure medications used, medication compliance, and work and social limitations. Results Out of 200 patients, 86 (43%) sustained injuries during an attack of their habitual seizures. Almost half of this group showed a tendency for recurrent injuries. The most common traumas were soft tissue injury (36.5%), head injury (32%), dental injury (8.5%), burns (7%), dislocation (7%), fractures (6.5%), and submersion (2%). Two‐thirds of the patients had their injury at home. 64% of patients who had seizures for more than 10 years sustained multiple injuries (P = .003). Injury frequency was higher among patients with daily or monthly seizures (P = .03). 76% of patients who suffered injuries more than twice had generalised tonic‐clonic seizures, and genetic generalised epilepsy was encountered more in injured patients (P = .02). Also, patients on polytherapy were more likely than those on monotherapy to have an injury (P = .003). Significance Two‐fifths of the patients reported seizure‐related injuries. The most common were soft‐tissue injuries and head traumas, while homes were the most frequent site. In addition, longer epilepsy duration, generalized tonic‐clonic seizures, and polytherapy were associated with a higher prevalence of injuries. Therefore, injury prevention strategies should be developed for PWE, especially for those at higher risk.

Extranodal NK/T-cell lymphoma (ENKTL) is a well-defined cytotoxic lymphoma strongly associated with Epstein–Barr virus (EBV) infection, commonly affecting the nasopharynx and upper aerodigestive tract. Primary central nervous system (CNS) involvement is rare, and only 17 cases were previously reported in the literature. Here, we report the case of a 44-year-old male admitted with a 3-month history of personality changes and progressive right leg weakness. Brain magnetic resonance imaging studies (MRIs) revealed multiple rim-enhancing brain lesions bilaterally. An extensive clinical and laboratory workup was unrevealing, and 2 brain biopsies were initially considered inconclusive. Pertinently, no systemic lymphoproliferative disorder was identified. The patient initially experienced remarkable clinical improvement with dexamethasone, pulse methylprednisolone, and rituximab therapy. However, he eventually had rapid clinical deterioration, was found to have increased brain lesions, and died nearly 6 months after the initial presentation. During this time, the second brain biopsy was found to show involvement by T-cell lymphoma of NK-cell lineage, which was EBV negative. No post-mortem examination was done to identify any systemic lymphoma. This case serves to expand the spectrum of lymphomas involving the CNS.