Search Results (53)

Background: Pathogenic variants in the NEXMIF gene have been linked to a broad neurodevelopmental phenotype, encompassing autism spectrum disorder, intellectual disability, and epilepsy. Among epileptic manifestations, Jeavons Syndrome was observed in 24% of affected females in the largest cohort of NEXMIF-related disorders reported to date, but long-term adult outcomes remain poorly documented. Methods and Results: We report a 25-year-old Italian woman with drug-resistant Jeavons syndrome in which the combined approach of next-generation sequencing and chromosomal microarray analysis allowed us to identify, after a 13-year diagnostic odyssey, a de novo ~350 Kb microdeletion at Xq13.2q13.3 encompassing the entire NEXMIF coding region, with no other OMIM genes involved. To our knowledge, this is the first reported case of a patient harboring a deletion restricted to the entire coding sequence of the NEXMIF gene. The patient presented with moderate intellectual disability and seizure onset at age 10 years. Her epilepsy proved refractory to multiple antiseizure medications. Video-EEG/polygraphic monitoring at age 23 years confirmed epilepsy with eyelid myoclonia, demonstrating characteristic eyelid myoclonia with absences triggered by eye closure. Conclutions: This case provides a detailed clinical description of an adult patient useful for genetic counseling regarding adult outcomes and prognostic expectations. Furthermore, this study underscores the diagnostic value of chromosomal microarray analysis alongside next-generation sequencing in individuals with intellectual disability and drug-resistant epilepsy, in order to expedite the diagnostic pathway and enable timelier and more appropriate patient management. Full article

Background: Gaucher disease (GD) is a rare lysosomal storage disorder caused by mutations in the GBA1 gene. Traditionally, GD is classified into three subtypes based on the severity of neurological involvement; however, overlapping clinical features increasingly suggest a continuum of phenotypes rather than distinct categories. In this prospective observational cohort study, we conducted a multidisciplinary assessment of patients with GD to identify and monitor neurological, cognitive, auditory, and visual impairments. Materials and Methods: A comprehensive clinical and instrumental evaluation was performed at baseline and repeated at follow-up, with a median interval of 37 months (IQR 36–38). Neurological assessments included physical examination, clinical rating scales, video-EEG, and brain MRI. Cognitive status was assessed using a standardized battery of neuropsychological tests. Detailed audiological and ophthalmological evaluations were also conducted. Paired parametric or non-parametric tests were applied as appropriate, with Bonferroni correction for cognitive outcomes (p < 0.05). Results: Of the 22 patients assessed at baseline, 18 completed the follow-up evaluation. Neurological assessments showed a worsening of subtle parkinsonian signs, with significant increases in Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III scores (p = 0.04) and non-motor symptom scores (p = 0.01). Two of the eighteen patients developed epilepsy during follow-up. A high prevalence of sleep disturbances was confirmed, with 27.8% exhibiting excessive daytime sleepiness and 16.7% reporting REM sleep behaviour disorder on standardized questionnaires. Compared with baseline, cognitive assessments revealed a higher proportion of patients with performance below normative population scores in at least one cognitive domain, particularly memory. Sensorineural hearing loss was confirmed in 11 of 15 patients (73.3%) who underwent audiological evaluation, with progressive worsening of audiometric thresholds observed in 7 of 11 (64%). Ophthalmological evaluations showed no changes in visual acuity or OCT findings; however, multifocal electroretinography abnormalities were detected in 12 of 13 patients. Conclusions: Through in-depth phenotyping, this study identifies measurable neurological, cognitive, and sensory progressive changes in patients with GD over time, supporting the value of tailored, multidisciplinary long-term care strategies to monitor and address emerging clinical needs in this rare disease. Full article

Background: The clinical presentation of temporal lobe epilepsy (TLE) and panic disorder can sometimes overlap, particularly when the seizure symptoms include paroxysmal episodes of intense fear and autonomic symptoms. As a result, patients with TLE can be misdiagnosed with a primary psychiatric illness, which leads to inappropriate treatment, worsening of the underlying condition and decreased function and quality of life. Clinical case: We present the case of a 46-year-old woman, known for a 20-year history of generalized epilepsy and major depressive disorder with panic attacks that were refractory and persistent despite trials of SSRIs, benzodiazepines and cognitive behavioral therapy (CBT). While hospitalized for video-EEG monitoring in the context of worsening epilepsy, she was found to have TLE seizures presenting as what the patient had described as panic attacks, and that sometimes progressed to secondarily generalized seizures. Following a transition from a medication regimen targeting generalized epilepsy to one more appropriate for focal seizures, the patient experienced clinical improvement with a decrease in the magnitude and frequency of panic symptoms. Conclusions: This case, in combination with other case reports in the literature, demonstrates the need for clinical suspicion of TLE in patients presenting with atypical panic-like episodes or a refractory panic disorder, especially in cases known for epilepsy or having risk factors for seizure disorder. It also highlights the importance of comprehensive diagnostic evaluation in neuropsychiatric presentations, including EEG and brain imaging, to ensure accurate diagnosis and appropriate management. Full article

Real-time emotion monitoring is increasingly relevant in healthcare, automotive, and workplace applications, where adaptive systems can enhance user experience and well-being. This study investigates the feasibility of classifying emotions along the valence–arousal dimensions of the Circumplex Model of Affect using EEG signals acquired from a single mastoid channel positioned near the ear. Twenty-four participants viewed emotion-eliciting videos and self-reported their affective states using the Self-Assessment Manikin. EEG data were recorded with an OpenBCI Cyton board and both spectral and temporal features (including power in multiple frequency bands and entropy-based complexity measures) were extracted from the single ear-channel. A dual analytical framework was adopted: classical statistical analyses (ANOVA, Mann–Whitney U) and artificial neural networks combined with explainable AI methods (Gradient × Input, Integrated Gradients) were used to identify features associated with valence and arousal. Results confirmed the physiological validity of single-channel ear-EEG, and showed that absolute $\beta$- and $\gamma$-band power, spectral ratios, and entropy-based metrics consistently contributed to emotion classification. Overall, the findings demonstrate that reliable and interpretable affective information can be extracted from minimal EEG configurations, supporting their potential for wearable, real-world emotion monitoring. Nonetheless, practical considerations—such as long-term comfort, stability, and wearability of ear-EEG devices—remain important challenges and motivate future research on sustained use in naturalistic environments. Full article

Background and Objective: Lennox–Gastaut syndrome (LGS) is a severe developmental and epileptic encephalopathy characterized by multiple seizure types, distinctive electroencephalography (EEG) abnormalities, and cognitive impairment. Sleep disturbances are highly prevalent in LGS and contribute substantially to reduced quality of life. However, no comprehensive analysis has yet been conducted to systematically examine key aspects of sleep—including architecture, microstructure, sleep-disordered breathing, and circadian regulation—leaving critical knowledge gaps. To address this, we conducted a scoping review to map the current evidence on sleep abnormalities in LGS and to identify priorities for future research. Method: A scoping review was conducted following PRISMA-ScR guidelines. PubMed, Embase, Ovid, and ClinicalTrials.gov from inception to October 2025 for studies evaluating sleep parameters in individuals with LGS or mixed epilepsy cohorts with ≥50% LGS cases. Eligible designs included observational and interventional studies using polysomnography, video-EEG, actigraphy, or sleep questionnaires. Data were synthesized narratively due to heterogeneity, and methodological quality was assessed using relevant Joanna Briggs Institute (JBI) checklists. Results: After screening 1242 articles, eleven studies met inclusion criteria, spanning 1986–2025 and conducted across four continents. Most were small single-center observational studies (5–16 LGS participants) using polysomnography as the primary assessment, with others employing wearable monitoring, surface and intracranial EEG, or circadian biomarker analyses. Across studies, individuals with LGS demonstrated markedly disrupted sleep architecture—notably reduced or absent rapid eye movement (REM) sleep, fragmented non-rapid eye movement (NREM) sleep, and attenuated spindles. Microstructural analysis showed elevated cyclic alternating pattern (CAP) rates, with epileptiform discharges clustering in CAP phase A. Sleep-disordered breathing (SDB) was common, particularly in adults, and associated with tonic seizures and central apneas. Circadian rhythm dysregulation, including altered melatonin and cortisol profiles, was also reported. A feasibility study demonstrated that home-based wearable devices and sleep apnea monitors were both acceptable and practical for use in children with LGS. No interventional studies have evaluated whether addressing sleep abnormalities modifies seizure or cognitive outcomes. Interpretation: Sleep in LGS is profoundly disrupted at both macrostructural and microstructural levels. These abnormalities may exacerbate seizure burden, cognitive impairment, and SUDEP risk, representing a potentially modifiable contributor to disease severity. Larger, prospective studies integrating polysomnography, wearable monitoring, and interventional approaches are needed to clarify causal mechanisms and therapeutic potential. Full article

Objective: To evaluate the feasibility and effectiveness of wrist-worn wearable devices combined with machine learning (ML) approaches for detecting a diverse array of seizure types beyond generalized tonic–clonic (GTC), including focal, generalized, and subclinical seizures. Materials and Methods: Twenty-eight patients undergoing inpatient video-EEG monitoring at Mayo Clinic were concurrently monitored using Empatica E4 wrist-worn devices. These devices captured accelerometry, blood volume pulse, electrodermal activity, skin temperature, and heart rate. Seizures were annotated by neurologists. The data were preprocessed to experiment with various segment lengths (10 s and 60 s) and multiple feature sets. Three ML strategies, XGBoost, deep learning models (LSTM, CNN, Transformer), and ROCKET, were evaluated using leave-one-patient-out cross-validation. Performance was assessed using area under the receiver operating characteristic curve (AUROC), seizure-wise recall (SW-Recall), and false alarms per hour (FA/h). Results: Detection performance varied by seizure type and model. GTC seizures were detected most reliably (AUROC = 0.86, SW-Recall = 0.81, FA/h = 3.03). Hyperkinetic and tonic seizures showed high SW-Recall but also high FA/h. Subclinical and aware-dyscognitive seizures exhibited the lowest SW-Recall and highest FA/h. MultiROCKET and XGBoost performed best overall, though no single model was optimal for all seizure types. Longer segments (60 s) generally reduced FA/h. Feature set effectiveness varied, with multi-biosignal sets improving performance across seizure types. Conclusions: Wrist-worn wearables combined with ML can extend seizure detection beyond GTC seizures, though performance remains limited for non-motor types. Optimizing model selection, feature sets, and segment lengths, and minimizing false alarms, are key to clinical utility and real-world adoption. Full article

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

Background/Objectives: In a patient suspected of having epilepsy, routine EEG primarily contributes to the recording of interictal epileptiform discharges (IEDs). Similarly, magnetic resonance imaging (MRI) has become the gold standard imaging technique for identifying epileptogenic structural brain abnormalities. Various EEG and MRI tools to improve epilepsy diagnosis will be presented. Methods: When the initial EEG fails to record IEDs, various EEG measures that can improve EEG performance are presented; a comprehensive epilepsy-targeted MRI protocol to identify, localize, and characterize an epileptogenic lesion will also be described. Results: Studies show that the initial routine EEG fails to record IEDs in approximately 47–50% of epileptic patients. To improve the yield of EEG, subsequent EEG recording should include sleep deprivation, sleep recording, prolonged hyperventilation, optimized light stimulation, addition of an inferior temporal electrode chain, extended EEG duration, and continuous video-EEG monitoring, all measures known to activate IEDs. Furthermore, MRI is interpreted as “normal” in many epilepsy patients, even when performed according to an epilepsy-specific protocol and evaluated by a specialized MRI reader. In such case, the use of the Harmonized Epilepsy Structural Sequence Imaging (HARNESS-MRI) protocol and other imaging tools will improve the detection of potential epileptic lesions, as described in this study. Conclusions: In a patient with a clinical diagnosis of epilepsy but a normal EEG and brain MRI, several options can improve the performance of subsequent EEG and MRI examinations, the subjects of this review. Full article

Developing interfaces for seizure diagnosis, often challenging to detect visually, is rising. However, their effectiveness is constrained by the need for diverse and extensive databases. This study aimed to create a seizure detection methodology incorporating detailed information from each EEG channel and accounts for frequency band variations linked to the primary brain pathology leading to ICU admission, enhancing our ability to identify epilepsy onset. This study involved 460 video-electroencephalography recordings from 71 patients under monitoring. We applied signal preprocessing and conducted a numerical quantitative analysis in the frequency domain. Various machine learning algorithms were assessed for their efficacy. The k-nearest neighbours (KNN) model was the most effective in our overall sample, achieving an average F1 score of 0.76. For specific subgroups, different models showed superior performance: Decision Tree for ‘Epilepsy’ (average F1 score of 0.80) and ‘Craniencephalic Trauma’ (average F1 score of 0.84), Random Forest for ‘Cardiorespiratory Arrest’ (average F1 score of 0.89) and ‘Brain Haemorrhage’ (average F1 score of 0.84). In the categorisation of seizure types, Linear Discriminant Analysis was most effective for focal seizures (average F1 score of 0.87), KNN for generalised (average F1 score of 0.84) and convulsive seizures (average F1 score of 0.88), and logistic regression for non-convulsive seizures (average F1 score of 0.83). Our study demonstrates the potential of using classifier models based on quantified EEG data for diagnosing seizures in ICU patients. The performance of these models varies significantly depending on the underlying cause of the seizure, highlighting the importance of tailored approaches. The automation of these diagnostic tools could facilitate early seizure detection. Full article

Eye-tracking technology has emerged as a valuable tool for evaluating cognitive load in online learning environments. This study investigates the potential of AI-driven consumer behaviour prediction eye-tracking technology to improve the learning experience by monitoring students’ attention and delivering real-time feedback. In our study, we analysed two online lecture videos used in higher education from two institutions: Oxford Business College and Utrecht University. We conducted this analysis to assess cognitive demands in PowerPoint presentations, as this directly affects the effectiveness of knowledge dissemination and the learning process. We utilised a neuromarketing-research consumer behaviour eye-tracking AI prediction software called ‘Predict’, which employs an algorithm constructed on the largest neuroscience database (comprising previous studies conducted on live participants n = 180,000 with EEG and eye-tracking data). The analysis for this study was carried out using the programming language R, followed by a series of t-tests for each video and Pearson’s correlation tests to examine the relationship between ocus and cognitive demand. The findings suggest that AI-powered eye-tracking systems have the potential to transform online learning by providing educators with valuable insights into students’ cognitive processes and enabling them to optimise instructional materials for improved learning outcomes. Full article

This research explores the integration of the Dry Sensor Interface-24 (DSI-24) EEG headset with a ChatGPT-enabled Furhat robot to monitor cognitive stress in video gaming environments. The DSI-24, a cutting-edge, wireless EEG device, is adept at rapidly capturing brainwave activity, making it particularly suitable for dynamic settings such as gaming. Our study leverages this technology to detect cognitive stress indicators in players by analyzing EEG data. The collected data are then interfaced with a ChatGPT-powered Furhat robot, which performs dual roles: guiding players through the data collection process and prompting breaks when elevated stress levels are detected. The core of our methodology is the real-time processing of EEG signals to determine players’ focus levels, using a mental focusing feature extracted from the EEG data. The work presented here discusses how technology, data analysis methods and their combined effects can improve player satisfaction and enhance gaming experiences. It also explores the obstacles and future possibilities of using EEG for monitoring video gaming environments. Full article

The hypothalamic–pituitary–adrenal axis is known to be involved in the pathogenesis of epilepsy and psychiatric disorders. Epileptic seizures (ESs) and psychogenic non-epileptic seizures (PNESs) are frequently differentially misdiagnosed. This study aimed to evaluate changes in serum cortisol and prolactin levels after ESs and PNESs as possible differential diagnostic biomarkers. Patients over 18 years with ESs (n = 29) and PNESs with motor manifestations (n = 45), captured on video-EEG monitoring, were included. Serum cortisol and prolactin levels as well as hemograms were assessed in blood samples taken at admission, during the first hour after the seizure, and after 6, 12, and 24 h. Cortisol and prolactine response were evident in the ES group (but not the PNES group) as an acute significant increase within the first hour after seizure. The occurrence of seizures in patients with ESs and PNESs demonstrated different circadian patterns. ROC analysis confirmed the accuracy of discrimination between paroxysmal events based on cortisol response: the AUC equals 0.865, with a prediction accuracy at the cutoff point of 376.5 nmol/L 0.811 (sensitivity 86.7%, specificity 72.4%). Thus, assessments of acute serum cortisol response to a paroxysmal event may be regarded as a simple, fast, and minimally invasive laboratory test contributing to differential diagnosis of ESs and PNESs. Full article

Panic disorder (PD) and focal epilepsy, in particular, temporal lobe epilepsy, often present diagnostic challenges due to overlapping clinical manifestations. This article describes the case of a 25-year-old female, misdiagnosed with PD for 15 years, whose recurring episodes of sudden fear, palpitations, and nausea were later identified as manifestations of focal epilepsy. Initially unresponsive to conventional anti-anxiety medications, the patient’s correct diagnosis was only established through comprehensive electro-clinical, neuropsychological, and neuroimaging evaluations during her admission to our research hospital. Long-term video–EEG monitoring (LTVEM) played a pivotal role in identifying the epileptic nature of her episodes, which were characterized by paroxysmal activity in the right temporal and zygomatic regions, consistent with the location of a dysplastic lesion in the right amygdala, as revealed by high-resolution magnetic resonance imaging. These findings underline the importance of considering focal epilepsy in the differential diagnosis of PD, especially in cases refractory to standard psychiatric treatments. The misdiagnosis of epilepsy as PD can lead to significant delays in appropriate treatment, potentially exacerbating the patient’s condition and affecting their quality of life. This case emphasizes the necessity of a multidisciplinary approach and the utilization of advanced diagnostic tools like LTVEM in elucidating the underlying causes of paroxysmal psychiatric symptoms. Full article

The duration of slow-wave sleep (SWS) is related to the reported sleep quality and to the important variables of mental and physical health. The internal cues to end an episode of SWS are poorly understood. One such internal cue is the initiation of a body movement, which is detectable as electromyographic (EMG) activity in sleep-electroencephalography (EEG). In the present study, we characterized the termination of SWS episodes by movement to explore its potential as a biomarker. To this end, we characterized the relation between the occurrence of SWS termination by movement and individual characteristics (age, sex), SWS duration and spectral content, chronotype, depression, medication, overnight memory performance, and, as a potential neurological application, epilepsy. We analyzed 94 full-night EEG-EMG recordings (75/94 had confirmed epilepsy) in the video-EEG monitoring unit of the EpiCARE Centre Salzburg, Austria. Segments of SWS were counted and rated for their termination by movement or not through the visual inspection of continuous EEG and EMG recordings. Multiple linear regression was used to predict the number of SWS episodes that ended with movement by depression, chronotype, type of epilepsy (focal, generalized, no epilepsy, unclear), medication, gender, total duration of SWS, occurrence of seizures during the night, occurrence of tonic-clonic seizures during the night, and SWS frequency spectra. Furthermore, we assessed whether SWS movement termination was related to overnight memory retention. According to multiple linear regression, patients with overall longer SWS experienced more SWS episodes that ended with movement (t = 5.64; p = 0.001). No other variable was related to the proportion of SWS that ended with movement, including no epilepsy-related variable. A small sample (n = 4) of patients taking Sertraline experienced no SWS that ended with movement, which was significant compared to all other patients (t = 8.00; p < 0.001) and to n = 35 patients who did not take any medication (t = 4.22; p < 0.001). While this result was based on a small subsample and must be interpreted with caution, it warrants replication in a larger sample with and without seizures to further elucidate the role of the movement termination of SWS and its potential to serve as a biomarker for sleep continuity and for medication effects on sleep. Full article

Traumatic brain injury (TBI) can lead to post-traumatic epilepsy (PTE). Blast TBI (bTBI) found in Veterans presents with several complications, including cognitive and behavioral disturbances and PTE; however, the underlying mechanisms that drive the long-term sequelae are not well understood. Using an unbiased proteomics approach in a mouse model of repeated bTBI (rbTBI), this study addresses this gap in the knowledge. After rbTBI, mice were monitored using continuous, uninterrupted video-EEG for up to four months. Following this period, we collected cortex and hippocampus tissues from three groups of mice: those with post-traumatic epilepsy (PTE⁺), those without epilepsy (PTE⁻), and the control group (sham). Hundreds of differentially expressed proteins were identified in the cortex and hippocampus of PTE⁺ and PTE⁻ relative to sham. Focusing on protein pathways unique to PTE⁺, pathways related to mitochondrial function, post-translational modifications, and transport were disrupted. Computational metabolic modeling using dysregulated protein expression predicted mitochondrial proton pump dysregulation, suggesting electron transport chain dysregulation in the epileptic tissue relative to PTE⁻. Finally, data mining enabled the identification of several novel and previously validated TBI and epilepsy biomarkers in our data set, many of which were found to already be targeted by drugs in various phases of clinical testing. These findings highlight novel proteins and protein pathways that may drive the chronic PTE sequelae following rbTBI. Full article