Search Results (444)

Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research. Full article

Epilepsy is a prevalent neurological disorder characterized by recurrent seizures, and electroencephalogram (EEG) signals provide a direct measure of brain activity for detection. Although deep learning achieves high accuracy, it often lacks physiological interpretability. We propose the Excitation/Inhibition Dynamic Polynomial Network (E/I-DynPolyNet), a biologically grounded framework for interpretable seizure detection. Specifically, E/I-DynPolyNet introduces a dual excitatory/inhibitory (E/I) pathway with sign-constrained synaptic weights, encouraging the learned activations to reflect latent E/I representations. Furthermore, a differentiable Wilson-Cowan (WC) module is embedded to govern the temporal evolution of E/I interactions, ensuring consistency with neurophysiological principles. A physics-informed optimization strategy integrates supervised learning with dynamical residual constraints and E/I balance regularization, guiding the model to learn physiologically consistent representations. Experimental results on the CHB-MIT and Bonn datasets demonstrate competitive accuracies of 95.81% and 98.5%, respectively. Crucially, E/I-DynPolyNet enables quantitative estimation of E/I imbalance, revealing that E/I ratios increase from 1.01 in the pre-ictal phase to 1.38 during seizures—a finding consistent with clinical observations of ictogenesis. These results indicate that E/I-DynPolyNet not only improves detection performance but also provides a mechanistic description of seizure dynamics, bridging the gap between data-driven learning and neurophysiological interpretation. Full article

Seizure detection based on intracranial electroencephalography (iEEG) signals is a relevant task in the analysis of epilepsy. In this context, it is not only important to achieve high predictive performance but also to ensure explainability, which allows for the analysis of the model’s behavior. The properties of the problem allow it to be formulated as a spatio-temporal problem due to the multichannel nature of iEEG and the temporal evolution of epileptic activity. Therefore, the data must be modeled jointly due to spatial and temporal dependencies. In this work, we propose the Exact Self Explainable Graph Convolutional Recurrent Network (ESEGCRN) for the detection of ictal and interictal periods in a patient-specific setting. The model represents the iEEG channels as nodes in a graph and the temporal evolution of the signal as a sequence over that structure. To validate the proposal, ESEGCRN is compared with various models that address the same problem. The results show that our model achieves the best overall predictive performance among the compared models. Furthermore, our model incorporates an internal explainability mechanism that generates a mask allowing for the analysis of node relevance. Analysis of the mask shows that, as the use of connections is restricted, incoming edges tend to progressively concentrate on seizure onset zone (SOZ) nodes. This reinforces confidence in the model and suggests that the relevance inferred by ESEGCRN is related to clinically significant nodes. Full article

Background: PPP1CB-related Noonan syndrome-like disorder with loose anagen hair type 2 (NSLH2; OMIM #617506) is a rare RASopathy caused by pathogenic variants in PPP1CB, encoding the catalytic beta subunit of protein phosphatase 1 (PP1C). Since its first description in 2016, only a limited number of patients have been reported, leaving the full phenotypic spectrum and genotype–phenotype correlations largely undefined. Objectives: To systematically review the clinical, molecular, and functional characteristics of NSLH2, we define its phenotypic spectrum, explore genotype–phenotype correlations, and summarize current evidence on therapeutic management. Methods: A systematic literature search was conducted across PubMed/MEDLINE, Embase, Web of Science, and Google Scholar, supplemented by searches of Orphanet, OMIM, and ClinVar, from 2016 to 2026. Studies reporting patients with pathogenic or likely pathogenic variants in PPP1CB were included. Individual patient-level data were extracted and analyzed descriptively. Additionally, we report a novel patient identified at our institution. Results: Thirty patients from 14 publications were included, harboring nine distinct PPP1CB variants. The most frequently identified variant was p.Pro49Arg (n = 17, 56.7%), followed by p.Met182Lys (n = 4, 13.3%) and p.Glu183Ala (n = 3, 10.0%). The majority of variants arose de novo (n = 26, 86.7%). Ectodermal anomalies, predominantly slow-growing and structurally abnormal hair consistent with loose anagen hair, were present in 79.3% of patients. Congenital heart defects were identified in 75.9%, with pulmonary stenosis and atrial septal defect representing the most common lesions. Short stature was documented in 69.2% of cases, and neurodevelopmental delay—encompassing motor and language delay—affected the majority of patients (72.4–84.6%). Brain structural anomalies were detected in 35.7%. Facial dysmorphic features were universal. Macrocephaly was present in 58.6% of cases, intellectual disability was reported in 26.9%, and epilepsy in 6.7%. Three familial cases with inherited p.Met182Lys transmission from an affected mother to three children are described, representing the largest reported familial cluster. Conclusions: NSLH2 is a clinically recognizable RASopathy with a consistent core phenotype comprising loose anagen hair, congenital heart defects, short stature, macrocephaly, and neurodevelopmental delay. The p.Pro49Arg variant accounts for the majority of reported cases and appears associated with a broad phenotypic expression. Larger cohorts and functional studies are needed to fully delineate genotype–phenotype correlations and guide therapeutic strategies. Full article

Traumatic brain injury (TBI) is one of the leading causes of acquired epilepsy, with a significant proportion of patients developing post-traumatic epilepsy (PTE) even months or years after the initial injury. The identification of reliable imaging biomarkers able to predict epileptogenesis remains a major clinical challenge. In recent years, diffusion-weighted imaging (DWI) and structural connectome analysis have emerged as promising tools to investigate brain network alterations associated with late seizure development. Machine learning approaches may further support the detection of predictive patterns in complex neuroimaging data. The goal of this study is to perform a binary classification between seizure-free and late seizure-affected patients following TBI, with a specific focus on the identification of the anatomical regions potentially connected with late seizure development. A dataset of 59 diffusion weighted images (DWI) scans from the EpiBioS4Rx project, including 42 seizure-free and 17 late seizure-affected TBI patients, was analyzed. A Random Forest classification algorithm was applied, incorporating network feature importance based on the Gini index to investigate model’s decisions and allow a clinical interpretation. The model reported a 69% ± 0.03 accuracy for discrimination and a 73% AUC ± 0.05. Despite the limited and imbalanced nature of the dataset, and the fact that the performance does not significantly exceed chance once all data-dependent steps are taken into account, our approach allows us to achieve accurate classification results compared to the literature and to identify brain regions potentially associated with epileptogenesis. Full article

Ring chromosome 14 (RC14) syndrome is an ultra-rare disorder characterized by drug-resistant epilepsy, intellectual disabilities, autism, and recurrent infections, suggesting a possible underlying immune dysregulation. We analyzed immunoglobulin G (IgG) N-glycosylation profiles in six RC14 patients and compared them with age-matched healthy controls using ultra-high-performance liquid chromatography (UHPLC) coupled with fluorescence detection (FLR) and high-resolution electrospray ionization mass spectrometry (ESI-MS). Patients showed decreased galactosylation and sialylation, resembling pro-inflammatory patterns observed in autoimmune diseases. These alterations were not observed in total serum glycoproteins, indicating a selective effect on IgG. One patient treated with intravenous immunoglobulin (IVIG) showed clinical improvement, which led us to investigate causality. Full article

Background/Objectives: Children with single-sided deafness (SSD) have normal hearing in one ear and are deaf in the other. Navigating complex auditory environments with SSD may cause reallocation of cognitive resources necessary for executive functioning (EF), adding potential cognitive burden to listening, though this is not well understood. To characterize EF in children with SSD, we compared their test performance and everyday functioning on performance-based and caregiver-rated EF measures to normative values and to a group of children with temporal lobe epilepsy (TLE). Methods: A retrospective review compared children with unaided SSD (n = 45) to a clinically referred TLE group (n = 39), all aged 6–16 years old, on performance-based measures including verbal fluency (letter, category), digit span, coding, and the BRIEF general executive composite. In the SSD group, those with congenital and acquired onset were compared across the same performance-based measures and BASC-3 executive functioning composite, and BRIEF2 indexes (cognitive, emotional, and behavioral regulation). Within this SSD group, performance-based and caregiver-rated measures were correlated. Results: In the SSD group, caregiver-reported EF and test performance were within age expectations. However, SSD participants with congenital onset had poorer caregiver-reported everyday EF. Children with SSD and elevated caregiver-reported EF had greater challenges on performance measures of auditory working memory. EF profiles were similar in the SSD and TLE groups, except the TLE group showed significantly worse performance on semantic fluency. Conclusions: Caregiver-rated EF measures may serve as an important tool for detecting neuropsychological deficits in children with SSD. SSD children with congenital onset may benefit from closer EF monitoring. There was lower performance on digit span backward tasks that require auditory working memory in children with elevated daily EF. More research is needed to determine what factors, such as hearing technology use, contribute to EF in children with SSD. *The term SSD is used throughout this article as a neutral placeholder with respect to the variation of terms used with this population (e.g., deaf, hard of hearing, hearing loss, hearing differences, etc.). SSD is used to be inclusive of all cultural/medical perspectives and identities. Full article

Epilepsy is mainly characterized by spontaneous seizures caused by hyperactive neural circuits. To delineate the cell-type-specific mechanisms underlying neuronal hyperexcitability, we resolve the hyperexcitability of excitatory neurons across epileptic human brain trans-foci at single-cell resolution to identify the key drivers and potential diagnostic signatures. We constructed a comprehensive atlas encompassing 240,000 cells derived from the temporal cortex and hippocampus, detecting trans-regional cellular and molecular diversity. We further delineated dynamic trajectories, gene expression patterns, and functional reorganization across cell types. Using the LASSO and random forest algorithms, we prioritized the core genes and developed a logistic regression-based diagnostic model. Despite transregional cellular landscape conservation, major cell types varied in abundance. Detailed analysis delineated various excitatory neuron subtypes’ dynamic trajectories, intricate expression, and functional reorganization, with pronounced dysfunction in the posterior hippocampal and temporal cortex networks, indicating hyperactive pro-epileptic effects. Excitatory neurons exhibit an intrinsic ability to autonomously organize themselves into distinct, highly active modules, characterized by a high activation state during epileptogenesis, as illustrated by ten epilepsy-associated functions. Transcription circuits FOSL2/FOS/EGR3/EGR1 promote neuronal hyperexcitability. Integrating epilepsy bulk RNA-seq data, we identified 24 overlapping genes between differential genes and circuit targets. The LASSO and random forest algorithms prioritized three core genes (IL1B, SOCS6, and COL4A1). A logistic regression model based on these three genes showed variable performance, with an apparent AUC of 1.000 in the discovery cohort (GSE256068) and AUCs of 0.974 and 0.722 in and two validation cohorts, indicating the need for further validation. Our study establishes the FOSL2/FOS/EGR3/EGR1 circuit as a master regulator of pathological neuronal hyperactivity across epileptic foci, linking transcriptional activation to network dysfunction. Identifying overactive factors may represent a candidate molecular pathway for future therapeutic exploration against hyperexcitability. Full article

Neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD), are increasingly recognized as conditions with a complex, multisystemic origin. ASD frequently co-occurs with other neurological conditions, such as epilepsy. We report a female patient, born to unrelated healthy parents, presenting with a complex clinical phenotype characterized by ASD level 1 with fluent speech, borderline intellectual functioning (BIF), coordination disorder, and epilepsy. Trio-based whole-exome sequencing (WES) revealed a de novo variant in the USP24 gene (c.3155G>T; p.Ser1052Ile), classified as likely pathogenic according to ACMG criteria (PS2, PM2, PP2, BP4). USP24 has previously been associated with Parkinson’s disease and has recently emerged as a candidate risk gene for ASD. In addition, WES detected two variants of uncertain significance (VUS), both inherited from the clinically unaffected father: c.388G>C (p.Gly130Arg) in NRXN2 and c.6395C>A (p.Ser2132Tyr) in LRP2. Although neither gene shows a fully penetrant causal relationship with the observed phenotype, both have been implicated in neurodevelopmental disorders. Array-CGH analysis did not reveal pathogenic copy number variants; however, the presence of additional genetic contributors not detectable by WES cannot be excluded. Overall, the de novo USP24 variant likely represents the primary genetic driver of the phenotype, while the potential contribution of the inherited NRXN2 and LRP2 variants remains plausible. This case underscores the complexity of the genetic architecture underlying NDDs and supports a model involving cumulative effects of multiple variants rather than a strictly multigenic interaction. Full article

Cataracts represent the leading cause of blindness worldwide, particularly in older adults, and constitute a significant public health challenge. Although cataract surgery is generally associated with a high safety profile, both patients and healthcare providers often face significant challenges due to age-related physiological changes and the high prevalence of comorbidities, which are directly linked to cataractogenesis and other systemic diseases that can complicate both the surgical procedure and postoperative recovery. This narrative review aimed to assess the epidemiological characteristics of age-related physiological and pathological comorbidities in older adults with cataracts, evaluating their impact on preoperative assessment, surgical outcomes, and public health planning. Articles were identified through non-systematic searches of PubMed, EMBASE, and Scopus using a combination of medical subject headings (MeSH) terms and free-text keywords. Among the multiple non-ocular comorbidities, carotid artery disease (CAD) and hypertension (HTN) are among the cardiovascular diseases (CVDs) with the highest correlations with cataract. Diabetes, dyslipidemia, and metabolic syndrome are also highly prevalent and significantly influence surgical outcomes, as poor glycemic control increases intraoperative risks and postoperative complications. Additionally, neurological conditions such as stroke, Parkinson’s disease, and epilepsy often complicate anesthesia administration, contribute to postoperative delirium, and affect adherence to treatment protocols. Given these complexities, a multidisciplinary approach and targeted preoperative screening may offer personalized care to improve safety and outcomes. Despite advances in clinical care, disparities in access to cataract surgery, especially in underserved populations, continue to exist. Thus, a coordinated public health strategy that promotes early detection, equitable access, and the integration of innovations such as teleophthalmology and artificial intelligence is essential to optimize care for older adults with cataracts worldwide. Full article

Neonatal hypoxic ischemic encephalopathy (HIE) is a common birth complication that can cause death or lifelong disabling conditions like cerebral palsy, epilepsy, and autism. It is well established that maternal infection and inflammation are significant risk factors for HIE but reasons for this increase in neurological risk to the offspring remain unknown. Inflammation or infection are associated with epigenetic changes and may contribute to the increased risk of neurodevelopmental disability in exposed offspring. Here, we analyzed and compared DNA methylation patterns in brain monocytes isolated from control, maternal immune activation (MIA), and an inflammation sensitized HIE (IS-HIE) CF-1 mouse model at postnatal day 7. We found that maternal inflammation induced significant methylation differences in neonates relative to control samples in both MIA and IS-HIE samples with no significant differences identified between the MIA and IS-HIE groups. MIA samples showed hypermethylation at loci involving craniofacial development and transcription factors important for regulating neurodevelopment and immune function. MIA samples also demonstrated significant hypermethylation at multiple mitochondrial genome CpGs. These findings suggest that maternal inflammation induces epigenetic alterations in fetal brain immune cells that are detectable in neonates. These changes may contribute to heightened neurodevelopmental risk in offspring following hypoxic injury, highlighting potential molecular pathways for future therapeutic targeting. Full article

This bibliometric study examines the role of ubiquitous computing and intelligent systems in the treatment of mental and neurological disorders. Ubiquitous computing integrates computational intelligence into everyday environments, enabling seamless monitoring and support of patients. Intelligent systems, including wearable devices, environmental sensors, and mobile health applications, collect real-time data on behavior, physiology, and environmental factors. These systems support early detection of symptom changes, adherence to treatment, and crisis prediction through context-aware analysis. Artificial intelligence (AI) processes the collected data to generate personalized therapeutic feedback and notify healthcare providers when intervention is needed. In mental health care, intelligent environments can monitor mood, sleep, and social interaction patterns, providing valuable objective information about mental health status. In the case of neurological conditions such as Parkinson’s disease or epilepsy, intelligent systems facilitate movement tracking, seizure detection, and cognitive assessment outside of the clinical setting. Integration with electronic health records and telemedicine platforms ensures coordinated and responsive care. Ethical design, privacy protection, and patient consent remain key to successful implementation. In this way, ubiquitous computing is transforming care models by increasing autonomy, precision, and continuity in the treatment of complex neurodegenerative diseases, including those related to neurodegeneration in aging. Full article

Experimental evidence indicates that a high seizure burden can induce cerebral overexpression of P-glycoprotein (P-gp) at the blood–brain barrier, a phenomenon associated with drug-resistant epilepsy under the “transporter hypothesis”, but also at the neuronal level, linked to a reduced seizure threshold, increased seizure severity (SS), status epilepticus (SE), and a high spontaneous death (SD) rate. In contrast, we recently described a progressive reduction in SS and the absence of SE and SD in GASH/Sal hamsters subjected to 45 audiogenic seizures. Here, we examined SS, SE, and the SD, and the expression of P-gp, erythropoietin receptor (EPO-R), hypoxia-inducible factor 1 alpha subunit (HIF-1α) and cyclooxygenase 2 (COX-2), in the brains of GASH/Sal hamsters following 20 audiogenic kindling stimulations (AUK-20). SS was evaluated using the midbrain and limbic severity scales; gene expression was assessed by RT-qPCR and P-gp protein levels were measured by immunohistochemistry and Western blot (IHC/WB) analysis. A modest decrease in midbrain SS was observed, without an increase in the already low limbic SS scores, and no SE or SD events occurred. P-gp levels remained low in both IHC and WB analyses. At the mRNA level, we detected increased EPO-R expression, decreased HIF-1α, and increased COX-2 without an accompanying increased in Abcb1b. Unlike findings from other experimental epilepsy models, AUK-20 in GASH/Sal hamsters does not enhance limbic SS, trigger SE or SD, or induce P-gp overexpression in the brain. Independently of the implications for drug resistance, the lack of cerebral P-gp overexpression without increased SS in the AUK-20-GASH/Sal model supports a potential role of P-gp in modulating seizure severity and epilepsy-associated mortality risk. Full article

The accurate detection of epileptic seizures using an electroencephalogram (EEG) is essential for clinical diagnosis and reducing the burden on clinicians but remains challenging due to low detection performance and model interpretability. In this study, we propose a Multiscale Cosine Convolutional Heterogeneous Two-Stream Cosine Convolution Network (MCC-HTSCC) to overcome these limitations. First, the raw EEG signals are input into the Multiscale Cosine Convolution (MCC) module, where multiscale temporal features are extracted by cosine convolutional layers with varying kernel lengths. Subsequently, the extracted temporal features are further processed through spatial convolutional layers to obtain comprehensive spatiotemporal representations. These spatiotemporal features are fused and subsequently fed into the Heterogeneous Two-Stream Cosine Convolution (HTSCC) module, comprising both deep and shallow subnetworks to perform hierarchical feature extraction and classification. Extensive evaluations were conducted on the publicly available CHB-MIT dataset and a clinically collected SH-SDU dataset, achieving accuracies of 98.52% and 94.56%, sensitivities of 97.98% and 88.09%, and specificities of 98.50% and 95.89%, respectively. Furthermore, the cosine convolution operators reduce the learnable parameters of our model by approximately 18.12% compared to the model with traditional convolution operators, making it more suitable for embedded deployment. By employing the Gradient-Weighted Class Activation Mapping (Grad-CAM) technique, we further provide interpretability and transparency in model decision making, highlighting the substantial potential of MCC-HTSCC for effective patient-specific epilepsy monitoring and diagnostics. 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