Clinical EEG and neuroscience最新文献

Introduction. Vagal nerve stimulation (VNS) is a therapeutical option for the treatment of drug-resistant epileptic patients. The response to VNS varies from patient to patient and is difficult to predict. The proposed study is based on our previous work, identifying relative mean power in pre-implantation EEG as a reliable marker for VNS efficacy prediction in adult patients. Our study has two main tasks. Firstly, to confirm the utility of relative mean power as a feature correlating with VNS efficacy in children. The second is to validate the applicability of our prediction classifier, Pre-X-Stim, in the pediatric population. Material and Methods. We identified a group of children with drug-resistant epilepsy. We included only children in whom EEG contained photic stimulation (Task 1) or was recorded based on the defined acquisition protocol used for development Pre-X-Stim (Task 2). Relative mean powers were calculated. VNS responders and non-responders were compared based on relative mean powers' values. In the next step, we evaluate the utility of our classifier, Pre-X-Stim, in the children population. Results: We identified 57 children treated with VNS - 17 patients were recruited for the Task 1 and 7 patients for the Task 2. When focusing on relative mean powers in EEG spectra, we observed statistically significant differences in theta range. The Pre-X-Stim algorithm was able to predict VNS efficacy correctly in 6 out of 7 patients (the accuracy 83.3%, the sensitivity 75%, the specificity 100%). Conclusions. Based on our results, it seems that children and adults share a similar pattern of EEG relative mean power changes. These changes can be used for pre-implantation prediction of VNS efficacy.

Objective. Resting-state EEG measures have shown potential in distinguishing individuals with PTSD from healthy controls. ERP components such as N2, P3, and late positive potential have been consistently linked to cognitive abnormalities in PTSD, especially in tasks involving emotional or trauma-related stimuli. However, meta-analyses have reported inconsistent findings. The understanding of biomarkers that can classify the varied symptoms of PTSD remains limited. This study aimed to develop a concise set of electrophysiological biomarkers, using neutral cognitive tasks, that could be applied across psychiatric conditions, and to identify biomarkers associated with the anxiety and depression dimensions of PTSD. Approach. Continuous simultaneous recordings of EEG and electrocardiogram (ECG) were obtained in veterans with PTSD (n = 29) and healthy controls (n = 62) during computerized tasks. EEG, ERP, and heart rate measures were evaluated in terms of their ability to discriminate between the groups or correlate with psychological measures. Results. The PTSD cohort exhibited faster alpha oscillations, reduced alpha power, and a flatter power spectrum. Furthermore, stronger reduction in alpha power was associated with higher trait anxiety, while a flatter slope was related to more severe depression symptoms in individuals with PTSD. In ERP tasks of visual memory and sustained attention, the PTSD cohort demonstrated delayed and exaggerated early components, along with attenuated LPP amplitudes. The three tasks revealed distinct and complementary EEG signatures PTSD. Significance. Multimodal individualized biomarkers based on EEG, cognitive ERPs, and ECG show promise as objective tools for assessing mood and anxiety disturbances within the PTSD spectrum.

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.

Background. Epilepsy is prevalent in the elderly, whose brain morphologies and skull electrical characteristics differ from those of younger adults. Here, using a multivariate definition of signal-to-noise ratio (SNR), we explored the detectability of epileptic spikes in scalp EEG measurements in elderly by forward simulations of hypersynchronous spikes generated at 78 cortical regions of interest (ROIs) in the presence of background noise. Methods. Simulated electric potentials were measured at 18, 35, and 70 standard 10-20 electrode positions using three reference methods: infinity reference (INF), common average reference (CAR), and average mastoid reference (M1M2). MRIs of six elderly subjects were used to construct finite element method (FEM) models with age-adjusted skull conductivities. Results. SNRs of epileptic spikes increased with increasing sizes of the brain electrical source areas, although medial and deep brain regions such as the hippocampus showed lower SNRs, consistent with clinical findings. The SNRs were greater in the 70-channel dataset than in the 18-channel and 35-channel datasets, especially for ROIs located closer to the head surface. In addition, the SNRs were lower for the CAR and M1M2 references than for the ideal INF reference. Moreover, we found comparable results in the standard FEM heads with age-adjusted skull conductivities. Conclusions. The results provide insights for evaluating scalp EEG data in elderly patients with suspected epilepsy, and suggest that age-adjusted skull conductivity is an important factor for forward models in elderly adults, and that the standard FEM head with age-adjusted skull conductivity can be used when MRIs are not available.

Attention-deficit/hyperactivity disorder (ADHD) affects approximately 12% of children worldwide. With a 50% chance of persistence into adulthood and associations with impairments in various domains, including social and emotional ones, early diagnosis is crucial. The exact neural substrates of ADHD are still unclear. This study aimed to reassess the behavioral and neural metrics of executive functions and neural substrates of facial affect recognition. A total of 117 ADHD patients and 183 healthy controls were evaluated by two Go/NoGo tasks: the classic visual continuous performance test and the emotional continuous performance test, which requires facial affect encoding. Group differences between ADHD subjects and healthy controls were assessed using analysis of covariance (ANCOVA), with age and sex included as covariates. Dependent variables comprised behavioral (number of omission and commission errors, reaction time, and reaction time variability) and neurophysiological measures (event-related potentials [ERPs]). As the main result, we identified significant differences between ADHD patients and healthy controls in all behavioral metrics, one neural marker of action inhibition (P3d) and the facial processing marker (N170). The differences were moderate-to-large when expressed as effect size measures in behavioral variables and small-to-moderate for neurophysiological variables. The small-to-moderate effect sizes obtained from the neurophysiological measures suggest that ERPs are insufficient as sole markers for effectively screening emotion and face processing abnormalities in ADHD.

As per United Nations, the youth constitute 16% of total population globally whereas World Health Organization reported that one in every seven young individual suffers from depression. Among various tested therapeutic solutions for depression management, the efficacy of transcranial Direct Current Stimulation (tDCS) is still unexplored specifically in young participants. Therefore, this study aims to investigate the cross hemispheric tDCS intervention with a smaller number of sessions in youth population by means of neurological, neuropsychological, and behavioural measures. A total of 50 young participants were recruited comprising of 25 healthy and 25 depressed individuals. The participants of depressed group were randomly assigned to active tDCS and sham tDCS sub groups and completed 150 min of training over 5 consecutive days. The active tDCS group received stimulation of 2 mA over dorsolateral prefrontal cortex. Unlike healthy individuals, depressed participants demonstrated reduced difference of brain activity between eyes opened and closed resting conditions which gets restored following the intervention in active group. Additionally, the tDCS intervention effectively modified the previously reduced alpha asymmetry observed in depressed participants compared to healthy individuals. These neurological outcomes may also be supported with enhanced neuropsychological score of depression (t = 5.47, P < .01) in active group. The attention score (t = 5.14, P < .01) and reaction time (t = 2.22, P = .02) evaluated through behavioural measure of Stroop task were also significantly improved in active group post tDCS intervention. The reported outcomes of the study highlighted the ability of tDCS for prompt and efficient youth depression management.

Recognized as a transdiagnostic factor, emotion regulation (ER) is increasingly embedded into conceptualizations of psychopathology development and maintenance, emerging as a core component of treatment methodologies. Therefore, the incorporation of ER into various facets of affective sciences, including theoretical frameworks, experimental paradigms, assessment methods, and intervention strategies, raises new challenges, particularly regarding the measurement of ER. In the evaluation and understanding of complex, multifaceted processes like ER, the combination of different assessment methods encompassing diverse units of analysis across multiple domains encompassing cerebral, physiological, and behavioral measures can prove particularly interesting. Among these approaches, the concurrent recording of electroencephalographic (EEG) and electrodermal activity (EDA) emerges as a promising strategy, enabling a more holistic exploration of the ER process at both central and peripheral levels. This brief paper aims to explore current literature concerning the utilization of EEG and EDA in the investigation of ER and to bring arguments supporting their simultaneous recording in order to gain a better understanding of ER processes.

Major depressive disorder (MDD) is a disorder with multiple impairments, among which emotion disorder is the most main one. Nowadays, evoked activity (EA), such as event-related potential (ERP), has mostly been studied for MDD, but induced activity (IA) analysis is still lacking. In this paper, EA, IA and event-related spectral perturbation (ERSP) were studied and compared between MDD patients and healthy controls (HC). Electroencephalogram (EEG) of 26 healthy controls and 21 MDD patients were recorded during three different facial expression (positive, neutral, negative) recognition tasks. Two phases of task execution process were studied, the early stage (0-200 ms after stimuli), and the late stage (200-500 ms after stimuli). ERSP, EA index and IA index of θ (4-7 Hz), α (8-13 Hz) and β (14-30 Hz) frequency bands were calculated and compared between two groups for two phases, respectively. In the early stage, the results indicated a decreased IA in α band in MDD compared to HC in frontal and parieto-occipital areas during neutral and negative face recognition. During the late stage, reduced IA and lower ERSP were also observed in α band in frontal and parieto-occipital areas in MDD during neutral and negative face recognition. Moreover, IA in θ band in MDD was lower than HC during negative face recognition. The findings reflected the abnormality of negative emotion processing in MDD, which could help to interpret the neural mechanism of depression.

Objective. Perinatal hypoxia and/or hypoglycemia (PHH) is a serious condition leading to many neonatal deaths worldwide. It causes motor and cognitive deficits, visual disturbances, and seizures in survivors. There is limited information on the clinical course of seizures, EEG and MRI findings in adults. Methods. Adult patients with epilepsy due to PHH were included. Data on patients' demographic and clinical features, age at seizure onset, type and frequency of seizures, antiseizure medications and EEG features were extracted from electronic health records. Seizure outcome was classified as "seizure-free for at least one year at last follow up" versus "continuing seizures". Clinical and laboratory variables that could be associated with seizure outcome were investigated statistically in a subset of patients. Results. Forty-one patients (median age: 32 years) were included. Bilateral cerebral lesions, predominantly affecting the posterior regions, were present in 88% of the cases. Almost 80% experienced focal to bilateral tonic-clonic seizures. Approximately 60% of patients were on polytherapy. Half of the patients were seizure free at last follow-up. Seizure frequency decreased over time in 75% of the cohort. EEG demonstrated background slowing in 44% of patients, with epileptic discharges detected in 27%. The only variable correlated with seizure freedom was older age at seizure onset (P = .034). Conclusion. Almost half of the patients may reach seizure freedom. Seizure frequency decreases in 75% over time. Cranial MRI or EEG findings are not correlated with seizure outcomes. The only variable associated with seizure freedom at last follow up is older age at seizure onset.

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.