Increasing evidence underscores the influence of social determinants and cultural factors on brain structure and function. Neurophysiological approaches offer unique advantages for assessing brain physiology and cognitive functioning, but challenges remain in building the capacity necessary to support the use of these resources in low- and middle-income countries (LMICs). The establishment of a first-of-its-kind neurophysiology lab in East Africa in January 2022, the Moi University Clinical and Cognitive Neuroscience Center (MU CCNC), promotes dedicated research infrastructure for patients within Moi Teaching and Referral Hospital (MTRH), the second-largest national referral hospital in Kenya, which serves about 24 million individuals. Built within the Academic Model Providing Access to Healthcare (AMPATH), a three-decade long collaboration between MTRH and a consortium of North American universities, the critical lessons learned while building this infrastructure in a low-resource setting included overcoming barriers to using and maintaining highly sensitive equipment in a setting with substantial environmental noise and electrical interference, ensuring secure and accurate data storage for complex files from precisely synchronized programs, and establishing standard operating procedures for laboratory continuity. Within the first two years of the establishment of the lab, capacity building has been prioritized, with a focus on extending neurophysiology training to research staff, students, and faculty throughout Kenya and developing research questions that can be addressed with lab methods and resources. By describing the process of developing a comprehensive human electrophysiology lab in a LMIC, we hope to streamline implementation for researchers to establish neurophysiology infrastructure in similar settings.

BackgroundPsychosocial stress-particularly bullying-has been recognized as a critical determinant of mental and neurophysiological health in children. Chronic stress exposure may disrupt cortical regulatory mechanisms detectable through quantitative electroencephalography (q-EEG).ObjectiveThis study examined the correlation between perceived psychological stress, involvement in bullying, and q-EEG spectral anomalies among Indonesian school-aged children, seeking to determine if bullying-related stress generates unique neurophysiological patterns or indicates a general dysregulation of stress responses.MethodsA two-phase design was implemented. Phase I included 2781 8-13-year-olds who completed the PSS-10 and a validated bullying questionnaire. Using a Mitsar-EEG 201 equipment, Phase II selected 24 students with increased stress levels (PSS ≥ 27) for q-EEG examination. Electroencephalogram signals from 19 scalp sites were converted into z-scores using age-adjusted NeuroGuide database standards. Stress level and brain activity were correlated using Spearman's rho.ResultsThe majority of subjects (23 out of 25; 95.8%) had EEG activity above ±2 standard deviations, especially in the temporal and prefrontal areas. There was a negative correlation between stress and temporal alpha power (r = -0.43, p = 0.028) and a positive correlation between stress with enhanced prefrontal high-beta power (r = 0.47, p = 0.021). Neurophysiological alterations among bullied children closely paralleled those under non-bullying stressors such as academic overload or family conflict.ConclusionElevated stress intensity, regardless of its psychosocial origin, was associated with convergent cortical dysregulation patterns. These results suggest that q-EEG may be a viable non-invasive method for early stress-related neurophysiological dysfunction diagnosis and that pediatric populations require integrated emotional regulation therapies.

To investigate the relationship between age-related brain volume loss and neural functional connectivity (FC), whole brain volume and mean FC were calculated in 75 healthy participants aged 20 to 86 years (39 women, 36 men; mean age: 59.31 years). Resting-state MEG with eyes closed and MRI were conducted. Correlations between age and whole brain volume, mean neural connectivity expressed as amplitude envelope correlation (AEC) in the alpha frequency band across 66 functional parcellations, and the standard deviation (SD) of AEC were analyzed. In seven brain regions showing significant age-related volume loss, mean AEC and SD of AEC with other regions were assessed. Whole brain volume decreased with age (r = -0.322, p = .00480), particularly in participants older than 75 years (p < .05, ANOVA). AEC values also declined with age (r = -0.359, p = .00153), with significant differences observed between generational subgroups under 45 and over 75 years (p < .05). The SD of AEC decreased across the brain with age (r = -0.326, p = .0043). However, seven brain regions with significant age-related volume loss did not consistently show significant differences in AEC or its SD between generational subgroups, in contrast to consistent volume differences observed. Overall, brain volume and neural FC declined with age, accompanied by reduced variability in FC across the brain. Nevertheless, regions exhibiting significant volume loss were not always associated with functional decline in FC or its variability, suggesting the brain may compensate for global decline through localized functional adaptations.

IntroductionTranscranial direct current stimulation (tDCS) is investigated as an adjunct treatment in schizophrenia, but electroencephalographic (EEG) studies have produced inconsistent findings.ObjectiveTo review the literature and elucidate the effects of tDCS on EEG variables in schizophrenia. Method: This is a systematic scoping review according to PRISMA guidelines, consulting four databases: PubMed (MEDLINE), Cochrane Library, Web of Science and ScienceDirect. It was structured following PIO framework (Population, Intervention, Outcome): P: schizophrenia; I: tDCS; O: any EEG variable. For data synthesis, each time a variable was investigated, it was counted as an occurrence.ResultsA total of twenty-five papers were included, totaling forty-two occurrences: twenty-five were event-related potentials and seventeen were based on spectral/power, connectivity or coherence variables. Most papers applied 20 min of 2 mA stimulation (76%), in a bicephalic montage. The most investigated variable was the MMN, followed by N100, P300, EEG coherence, gamma activity, beta and alpha power. N100 was the variable that responded most to tDCS stimulation, with 80% response rate. Gamma activity had 67% response, MMN showed 60%, coherence, alpha and beta power 50%. All papers investigating P300 reported no significant results. Other EEG parameters were investigated only once.ConclusionEEG changes induced by tDCS in schizophrenia predominantly affected the sensory-auditory potential N100, had a lesser impact on pre-attentive potential MMN, and showed no observable effect on higher-order cognitive potentials, such as P300. The modulatory effects of tDCS on cognition are still unclear. This review was registered at the Open Science Framework (osf.io/7yzrj).

ObjectivesChildhood Occipital Visual Epilepsy (COVE) is a self-limited epileptic syndrome that typically begins in late childhood or adolescence characterized by brief visual seizures. The recent 2022 International League Against Epilepsy (ILAE) classification distinguishes COVE from photosensitive occipital lobe epilepsy (POLE), emphasizing the absence of photic-induced seizures in COVE. In this study, we aimed to describe the clinical and electrophysiological features of patients with COVE diagnosed according to the new ILAE criteria.MethodsThis retrospective cohort study analyzed 30 patients diagnosed with COVE at a tertiary epilepsy center between 1988 and 2023. Patients were selected based on ILAE 2022 criteria, and all cases with intermittent photic stimulation (IPS)-induced seizures were excluded.ResultsMost patients (93%) presented with elementary visual hallucinations, such as colorful lights. Orofacial seizures occurred in 7%, and 37% had nocturnal seizures. EEG abnormalities were primarily occipital and resolved in 85% of cases over time. Generalized spike-wave discharges (GSWDs) were rare (5%), and only one patient developed juvenile myoclonic epilepsy during follow-up. At final follow-up, 77% of patients achieved seizure freedom, and 47% discontinued medication.ConclusionCOVE is an epileptic syndrome associated with a favorable prognosis. By excluding photosensitivity in light of the newly proposed diagnostic criteria from the ILAE, future research should focus on a more homogenous group of COVE patients to enhance understanding of this syndrome. Accurate classification using updated ILAE criteria allows for clearer clinical delineation and more reliable outcome predictions.

ObjectiveThis cross-sectional study aimed to identify electrophysiological markers distinguishing stages of development, stability, and early aging in cortical auditory processing to elucidate neurophysiological changes in healthy auditory aging.MethodsWe evaluated 149 healthy participants (both sexes; aged 7-59 years) recruited from the general community via electronic media, posters, radio, and regional television, divided into six age groups (7-11, 12-17, 18-29, 30-39, 40-49, and 50-59 years). Eligibility criteria included normal hearing, no neurological disorders, and normal otoscopy. Cortical auditory evoked potentials (CAEPs) were recorded using the syllable /da/ (binaural stimulation, 70 dB HL) with eye-movement control.ResultsSignificant differences in P1 latency were observed between children (7-11 years) and older participants (12-59 years). N1 latency differed between children and adults (30-59 years), while N1 amplitude varied between adolescents (12-19 years) and adults aged 40-49 years. P1-N1 latency differed between adolescents and adults aged 40-59 years. Age correlated moderately negatively with P1/N1 latencies and weakly positively with N1 amplitude.ConclusionMaturational changes in P1/N1 latencies were evident, but no decline occurred during adulthood or pre-senescence, suggesting stable auditory processing until at least age 59.

Background: Electroencephalography (EEG) is a noninvasive technique for studying brain electrophysiology with high temporal resolution. Microstate analysis examines EEG recordings as a succession of quasi-stable microstates, allowing evaluation of extensive brain network activity linked to neuropsychiatric disorders like tinnitus. Objective: This study distinguishes tinnitus patients from healthy controls by using features acquired by microstate analysis. Methods: This study investigated EEG microstate differences between 16 healthy controls and 10 tinnitus patients. Four microstates were extracted and analyzed using Multivariate Analysis of Variance (MANOVA), revealing significant differences in duration, coverage, and occurrence between groups. Machine learning algorithms, including support vector machine (SVM) and K-Nearest Neighbors (KNN), and others were employed to classify tinnitus patients based on microstate features, achieving high accuracy, precision, specificity, recall, and F1-score. Results: MANOVA analysis revealed a significant difference in the duration of microstate A, which is associated with phonological processing and auditory perception, between the two groups. Additionally, significant differences in the coverage and occurrence of microstate B, related to visual networks, were observed. The SVM classifier achieved the highest accuracy of 96.44% in differentiating tinnitus patients from healthy controls, with impressive precision (97.64%), specificity (95.62%), and F1-score (97.24%). KNN also performed well, achieving a maximum recall of 97.24%. Conclusion: This study reveals the potential of EEG microstate analysis, incorporating time-related features, to improve tinnitus diagnosis and classification. Using SVM and KNN, we achieve high accuracy in identifying tinnitus-associated brain patterns, highlighting the clinical utility of EEG for neurological disease management.

There is limited evidence on the management of patients with glutamic acid decarboxylase (GAD) antibody associated autoimmune epilepsy, or GAD positive (GAD+) epilepsy. We describe six GAD + epilepsy patients presenting with seizure and refractory status epilepticus with special emphasis on the longitudinal electrographic changes in relationship to immunologic and anti-seizure medication therapies. All patients presented with new onset seizure with four patients having refractory status epilepticus. Serial prolonged continuous EEG changes before and after immunotherapy were collected which demonstrated electrographic seizures are often pharmacoresistent and immunotherapy can result in seizure cessation and improvement of interictal discharges prior to clinical improvement. Our findings suggested GAD + epilepsy was controlled more effectively with immunotherapy than ASMs and serial prolonged continuous EEG monitoring can serve as a biomarker for disease outcome.

Objective: Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals' lives. Early detection is crucial for improving treatment outcomes, but the diagnostic process remains complex due to the disorder's multifaceted nature. In recent years, EEG data have been increasingly investigated to detect neural patterns linked to schizophrenia. Methods: This study presents a deep learning framework that integrates both raw multi-channel EEG signals and their spectrograms. Our two-branch model processes these complementary data views to capture both temporal dynamics and frequency-specific features while employing depth-wise convolution to efficiently combine spatial dependencies across EEG channels. Results: The model was evaluated on two datasets, consisting of 84 and 28 subjects, achieving classification accuracies of 0.985 and 0.994, respectively. These results highlight the effectiveness of combining raw EEG signals with their time-frequency representations for precise and automated schizophrenia detection. Additionally, an ablation study assessed the contributions of different architectural components. Conclusions: The approach outperformed existing methods in the literature, underscoring the value of utilizing multi-view EEG data in schizophrenia detection. These promising results suggest that our framework could contribute to more effective diagnostic tools in clinical practice.

Background: As a leading cause of severe morbidity, acute ischemic stroke (AIS) necessitates precise prognostic evaluation to inform critical treatment strategies. Recent advancements have identified quantitative electroencephalography (qEEG) as a pivotal instrument in refining prognostic accuracy for AIS. This investigation aimed to construct a robust prognostic model, anchored in qEEG parameters, to enhance the precision of clinical prognosis 6 months after discharge in AIS patients. Methods: In a retrospective observational study, we analyzed AIS cases from January 2022 to March 2023. Data encompassing demographic profiles, clinical manifestations, qEEG findings, and modified Rankin Scale (mRS) assessments were evaluated for 109 patients with AIS. These metrics were instrumental in developing prognostic models, segregating outcomes into either favorable (mRS: 0-2) or unfavorable categories (mRS: 3-6) at 6 months post-discharge. Prognostic models were developed using clinical and qEEG parameters. Results: The formulation of two distinct prognostic models was predicated on an integration of baseline clinical data (age, unilateral limb weakness, ataxia and red blood cell count) and specific qEEG metrics (T3-P3 (TAR) and T4-P4 (TAR)). The synthesis of these models culminated in the Prognostic Model 3, which exhibited a marked enhancement in prognostic accuracy, as evidenced by an area under the curve (AUC) of 0.8227 (95% CI: 0.7409-0.9045), thereby signifying a superior prediction of AIS prognosis 6 months after discharge relative to the individual models. Conclusion: Quantitative EEG, especially increased theta/alpha power ratio (TAR), might improve the prediction of prognosis 6 months after discharge of acute ischemic stroke in clinical practice.