Clinical EEG and neuroscience最新文献

ObjectiveThe present study investigated the neural correlates of cognitive control in youth with Attention Deficit Hyperactivity disorder (ADHD) and comorbid learning disability (N = 75, ages 11-17 years) who participated in a 20-week mindfulness martial arts intervention compared to a waitlist control.MethodEEG was recorded pre and post intervention during a blocked Go/NoGo task. Peak amplitude was measured for the inhibitory NoGo N2 and P3 ERP components.ResultsA significant group by time interaction was found for NoGo N2 amplitudes, indicating that waitlist control participants had significantly attenuated N2 amplitudes over time whereas the intervention group maintained similar levels of medial frontal activity during response inhibition. The maintenance of the individual differences in N2 amplitudes were robust in the intervention group.ConclusionsThese findings suggest that participation in mindfulness martial arts may buffer against reductions in N2 activity during adolescence for youth with ADHD.

BackgroundSubacute sclerosing panencephalitis (SSPE) is a rare progressive encephalitis due to persistent measles infection. While classically a childhood disorder, atypical, adult-onset, and subclinical variants are increasingly reported. Ocular findings may precede neurological involvement by years. We aimed to underline EEG's role in tracking disease evolution from isolated ocular signs to neurological progression.CaseA 31-year-old woman presented with isolated ocular complaints and bilateral optic atrophy. Cerebrospinal fluid revealed measles IgG and IgG index positivity, confirming SSPE. For four years, she remained neurologically asymptomatic. EEG initially showed bilateral central theta paroxysms, later progressing to generalized periodic discharges. Serial EEGs demonstrated progressively shortened inter-discharge intervals. Additional features emerged, including frontally predominant generalized rhythmic delta activity and hyperventilation-provoked discharges. Importantly, when EEG abnormalities first appeared, neuropsychometric testing detected deficits in attention and executive function, despite the absence of subjective complaints. With time, cognitive decline became clinically evident, and negative myoclonus appeared.ConclusionThis case illustrates the importance of long-term surveillance in subclinical SSPE. EEG abnormalities preceded overt neurological decline, providing the earliest clues to disease progression. Careful interpretation of evolving EEG patterns may anticipate cognitive impairment and guide timely interventions. Our patient's trajectory underscores that even clinically silent SSPE carries a hidden risk of deterioration, and that vigilant EEG monitoring can act as a window into the disease course.

This manuscript examines the pivotal role of neuroinflammation in the central nervous system (CNS), particularly considering the impact of the COVID-19 pandemic. Neuroinflammation serves as a defense mechanism against various insults, including toxins, infections, and trauma. However, if left untreated, neuroinflammation can become chronic, leading to significant symptomatic and structural brain damage. Notably, neuroinflammation can mimic psychological disorders, complicating diagnosis and treatment. Current diagnostic methods for neuroinflammation-such as lumbar punctures, MRIs, brain biopsies, blood tests, and PET scans-are often hindered by inaccuracy, invasiveness, and cost. This study posits that electroencephalography (EEG), particularly identifying spindling excessive beta (SEB) activity, offers a promising, non-invasive, and cost-effective alternative for detecting neuroinflammation. This study investigates the relationship between SEB activity and neuroinflammation, focusing on traumatic brain injury (TBI). Through statistical analysis of EEG data from 1,233 psychiatric patients, we identified and compared two groups: 75 non-benzodiazepine-using adults without TBI and 79 non-benzodiazepine using adults with TBI exhibiting SEB activity. We identified a significant prevalence of SEB in individuals with refractory psychiatric conditions, underscoring the significance of this biomarker for neuroinflammation. Furthermore, we examine the therapeutic implications of reducing SEB through interventions such as guanfacine combined with N-Acetyl Cysteine (NAC), photobiomodulation, and hyperbaric oxygen therapy, all of which have demonstrated efficacy in mitigating neuroinflammation. These findings suggest that EEG could play a transformative role in the early detection and management of neuroinflammatory conditions, paving the way for more personalized and effective treatments for mental health disorders.

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.

Objective: This study aimed to compare electroencephalogram microstates of patients with chronic stroke to healthy subjects and correlated microstates with clinical and functional characteristics in stroke. Methods: This cross-sectional, exploratory and correlational study was performed with chronic stroke patients (n = 27) and healthy subjects (n = 27) matched for age and gender. We recorded electroencephalography microstates using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classic microstates maps (A, B, C, D). Post-stroke participants were assessed using the modified Rankin Scale and the Fugl-Meyer Scale. All participants were assessed for cognitive function, fear of falling, and static balance. Student's t-test was used to compare groups and Pearson's correlation coefficient was used to assess correlations between microstates parameters and stroke-related clinical outcomes. Results: In the eyes-open condition, moderate correlations were observed between the duration of microstate C and functional disability. In the eyes-closed condition, moderate correlations were observed between the coverage of microstate C, the occurrence of microstate C and D, and the duration of microstate B with functional aspects (eg, lower limb motor function, balance, functional disability, and fear of falling). Conclusions: Changes in microstates and correlations between topographies and clinical and functional aspects suggest that electroencephalogram could be used as a biomarker in stroke patients.

Psychogenic non-epileptic seizures (PNES) are complex episodes that outwardly resemble epileptic seizures but are not caused by any underlying neurological disease. Unlike true epileptic seizures, PNES are more likely to be linked to psychological factors and do not show any abnormal activity on electroencephalography (EEG) recordings. This differentiation is crucial for accurate diagnosis and treatment, as misdiagnosing can lead to unnecessary treatments.Diagnosis of PNES might become difficult in the presence of particular benign EEG variants such as Rhythmic Midtemporal Discharges (RMTD). RMTD is a rare benign variant of normal EEG, characterized by rhythmic 5-7 Hz discharges in the temporal regions. This pattern could be present in normal individuals, in patients with psychiatric disorders or epilepsy. It could mimic interictal epileptiform discharges. Recognition of this pattern is essential to avoid misinterpretation of EEG findings that might eventuate in inappropriate treatment and adverse effects on a patient's medical condition, especially when there is a recent suspicious event in terms of an epileptic seizure. Among patients with PNES, the occurrence of benign variants might be much harder to interpret and physicians may mistakenly interpret RMTD on the EEG as indicative for epilepsy, especially in the absence of clear clinical criteria for PNES. This report is the first to document RMTD in first-degree relatives with PNES, suggesting a possible genetic predisposition and the need for further research into the interaction between RMTD and PNES.Our aim is to raise awareness that will enable accurate EEG reading and correct diagnosis.

IntroductionQuantitative electroencephalography (QEEG) is a neurophysiological tool that analyzes brain oscillations across frequency bands, providing insights into psychiatric conditions like bipolar disorder (BD). This disorder, marked by mood fluctuations, poses diagnostic and treatment challenges, highlighting the need for reliable biomarkers.ObjectiveThis systematic review aims to evaluate QEEG changes in BD patients, investigate its diagnostic and therapeutic potential, and differentiate BD from major depressive disorder (MDD) and schizophrenia.MethodsFollowing PRISMA 2020 guidelines, a comprehensive search of PubMed, Scopus, Web of Science, and Embase was conducted till 30th of October 2024 without timeline restrictions. Studies involving BD patients assessed using QEEG were included. Key outcomes focused on frequency band alterations, treatment responses, and diagnostic differentiation.ResultsThe review included 20 studies with 475 BD patients. Increased gamma and beta activity were consistently observed in BD. However, the directionality of alpha and theta band changes varied, with differences observed depending on brain region and mood state. Delta band alterations were more prominent in BD I. Treatment responses showed reduced power in gamma, theta, and alpha bands. QEEG also distinguished BD from MDD and schizophrenia based on frequency band characteristics.ConclusionQEEG demonstrates significant promise as a diagnostic and therapeutic tool for BD. Despite methodological variability, its integration with machine learning could enhance diagnostic precision and guide personalized treatments. Further research is needed to standardize methodologies and validate findings.

Motor Imagery (MI) electroencephalographic (EEG) signal classification is a pioneer research branch essential for mobility rehabilitation. This paper proposes an end-to-end hybrid deep network "Spatio Temporal Inception Transformer Network (STIT-Net)" model for MI classification. Discrete Wavelet Transform (DWT) is used to derive the alpha (8-13) Hz and beta (13-30) Hz EEG sub bands which are dominant during motor tasks to enhance the performance of the proposed work. STIT-Net employs spatial and temporal convolutions to capture spatial dependencies and temporal information and an inception block with three parallel convolutions extracts multi-level features. Then the transformer encoder with self-attention mechanism highlights the similar task. The proposed model improves the classification of the Physionet EEG motor imagery dataset with an average accuracy of 93.52% and 95.70% for binary class in the alpha and beta bands respectively, and 85.26% and 87.34% for three class, for four class 81.95% and 82.66% were obtained in the alpha and beta band respective EEG based motor signals which is better compared to the results available in the literature. The proposed methodology is further evaluated on other motor imagery datasets, both for subject-independent and cross-subject conditions, to assess the performance of the model.

One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.