Clinical EEG and neuroscience最新文献

Objective: To investigate the neurophysiological and cognitive impairments in patients with obstructive sleep apnea (OSA) among the acute stroke population. Methods: A total of 268 acute ischemic stroke patients with OSA underwent sleep monitoring within 24 h of admission and event-related potential tests within three days. They were categorized into groups based on their AHI: stroke only, and stroke with mild, moderate, or severe OSA. This classification served to analyze the electrophysiological profiles associated with stroke and OSA severity. Results: Compared with the control group, in the P3b series, the P3b-FZ amplitude was significantly reduced in the stroke with mild, moderate, and severe OSA group; the N2-PZ latency was significantly prolonged in the stroke with severe OSA group; and the P3b-FZ, P3b-CZ, and P3b-FZ latencies were significantly prolonged in the stroke with mild, moderate, and severe OSA group; in the P3a series, the N2-CZ amplitude was decreased in the stroke with severe OSA group, P2-FZ latency was significantly prolonged in the stroke with mild and moderate OSA group, P3a-FZ latency was significantly prolonged in the stroke with mild OSA group, P3a-CZ latency was significantly prolonged in the stroke with severe OSA group, and P3a-PZ latency was significantly prolonged in the stroke with mild and severe OSA group. Conclusions: The electrophysiologic changes compared with the stroke-only group were mainly characterized by prolonged latencies of the endogenous components P3a and P3b, suggesting that they are related to attention allocation and cognitive control.

Magnetic resonance imaging (MRI) sequences commonly used in simultaneous electroencephalogram (EEG)-MRI studies include blood oxygenation level-dependent (BOLD) and anatomical T₁-weighted MRI. Safety and electrode heating profiles for these sequences have been well-characterized. However, recent improvements in EEG design may allow for additional sequences to be performed with similar expectations of heating safety, which would expand the EEG-MRI infrastructure for quantitative physiological studies. We evaluated temperature changes ex vivo and in vivo over a wider range of preparation and readout modules with differing specific absorption rate (SAR). A 32-channel EEG cap was used at 3 T and ex vivo heating was assessed for 2D- and 3D-pseudo-continuous-arterial-spin-labeling, 2D-cine, 2D-phase-contrast, 2D T₂-Relaxation-Under-Spin-Tagging, 32-direction b = 1000 s/mm² and b = 2000 s/mm² 2D-diffusion tensor imaging, multiband-BOLD, 3D-T1 MPRAGE, 3D-FLAIR, and 3D-T2. Temperature was monitored with a fiberoptic probe system and plotted over six different electrodes, the amplifier, and battery pack. In vivo assessments were conducted in three participants with the same system. A further in vivo supplemental cohort (n = 10) was used to further evaluate qualitative self-reported heating. Device integrity was evaluated by the manufacturer following experiments. Peak temperature and maximum temperature increases were 23.0°C and 0.4°C respectively ex vivo, and 37.6°C and 0.7°C respectively in vivo. Temperatures did not approach the safety heating threshold of 40°C (defined as a conservative threshold based on manufacturer recommendations and burn injury data). Participants completed in vivo scans without adverse events. No manufacturer-reported device damage was identified. Overall, the tested scans induced heating below critical limits at the clinical field strength of 3 T.

Sleep deprivation has become a severe public health problem in modern societies. Negative consequences of prolonged wakefulness on cognitive abilities have been demonstrated and working memory is one of the main cognitive functions that can be affected by sleep deprivation. This study aims to investigate the effects of sleep deprivation on working memory through EEG event-related oscillations. Thirty healthy young adult university students and graduates were included in this study (15 rested control - 15 sleep-deprived). A 2-back task was used to evaluate working memory, and both groups performed the task during EEG recording. The sleep-deprived (SD) group was required to stay awake for 24 h, and then the EEG session was conducted. The rested control (RC) subjects participated in the morning after a regular night's sleep. Event-related power and phase-locking analyses were applied, and delta (1-3.5 Hz), theta (4-6.5 Hz) and alpha (8-13 Hz) frequencies were investigated in the time-frequency domain. In the 2-back task, significantly prolonged reaction times were observed in the SD group. However, the decrease in accuracy rate was not significant. The EEG analyses revealed that the SD group had decreased frontocentral event-related delta and theta power responses after the presentation of stimuli. Moreover, task accuracy was positively correlated with the left frontocentral delta power in the SD group, and theta power in the RCs. Thus, we propose that the adverse effects of sleep deprivation on working memory can be observed through low-frequency oscillatory responses in the brain.

Quantitative electroencephalogram (QEEG) is a technology which has grown exponentially since the foundational publication by in Science in 1997, introducing the use of age-regressed metrics to quantify characteristics of the EEG signal, enhancing the clinical utility of EEG in neuropsychiatry. Essential to the validity and reliability of QEEG metrics is standardization of multi-channel EEG data acquisition which follows the standards set forth by the American Clinical Neurophysiology Society including accurate management of artifact and facilitation of proper visual inspection of EEG paroxysmal events both of which are expanded in this guideline. Additional requirements on the selection of EEG, quality reporting, and submission of the EEG to spectral, statistical, and topographic analysis are proposed. While there are thousands of features that can be mathematically derived using QEEG, there are common features that have been most recognized and most validated in clinical use and these along with other mathematical tools, such as low resolution electromagnetic tomographic analyses (LORETA) and classifier functions, are reviewed and cautions are noted. The efficacy of QEEG in these applications depends strongly on the quality of the acquired EEG, and the correctness of subsequent inspection, selection, and processing. These recommendations which are described in the following sections as minimum standards for the use of QEEG are supported by the International QEEG Certification Board (IQCB).

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.

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.

Introduction. Complex childhood trauma (CCT) involves prolonged exposure to severe interpersonal stressors, leading to deficits in executive functioning and self-regulation during adolescence, a critical period for neurodevelopment. While qEEG parameters, particularly alpha oscillations, have been proposed as potential biomarkers for trauma, empirical documentation in developmental samples is limited. Aim. This preregistered study investigated whether adolescents with CCT exhibit qEEG patterns similar to those reported for PTSD, such as reduced posterior alpha power, increased individual alpha peak frequency (iAPF), right-lateralized alpha frequencies, and lower total EEG power (RMS) compared to controls. Materials and Methods. EEG data from 26 trauma-exposed adolescents and 28 controls, sourced from an open database, underwent similar preprocessing. qEEG features, including alpha power, iAPF, alpha asymmetry, and RMS, were extracted from eyes-open and eyes-closed conditions and analyzed using mixed ANOVAs. Results. Significant group differences were found in total EEG power, with trauma-exposed adolescents showing lower RMS than controls. No significant differences were found in posterior absolute alpha power, iAPF, or alpha asymmetry. However, we observed that posterior relative alpha power was higher in the trauma group, though the difference was not statistically significant but showing a small to medium effect size. Additionally, a negative correlation between CPTSD severity and EEG power in the EO condition was observed, suggesting trauma-related cortical hypoactivation. Conclusion. Reduced total EEG power and modified alpha dynamics may serve as candidate neuromarkers of CCT. These findings underscore the need for further research to validate qEEG biomarkers for understanding and diagnosing trauma-related disorders in developmental populations.

Evoked potential metrics extracted from an EEG exam can provide novel sources of information regarding brain function. While the P300 occurring around 300 ms post-stimulus has been extensively investigated in relation to mild cognitive impairment (MCI), with decreased amplitude and increased latency, the P200 response has not, particularly in an oddball-stimulus paradigm. This study compares the auditory P200 amplitudes between MCI (28 patients aged 74(8)) and non-MCI, (35 aged 72(4)). Data were collected in routine clinical evaluations where EEG with audio oddball ERPs were measured as part of a health screening exam from 2 clinics serving MCI patients and one clinic serving a non-MCI population as part of a wellness/preventative care program. We also investigated the disease course for 3 patients as case studies. The results revealed the P200 amplitudes to be significantly increased in the MCI compared to the non-MCI groups, alongside the expected reduction in P300, Trail Making, and reaction time. Moreover, the ratio of P200-to-P300 was also increased in the MCI groups even in cases where the P300 was strong. This trend continued for patients who were tracked from early-to-later stages in the case studies. While the pathophysiology of the P200 response in a 2-tone auditory oddball protocol is not well understood, this measure may help indicate signs of early MCI, particularly in cases where the P300 is still strong.

Background: Quantitative electroencephalography (qEEG) data can facilitate the monitoring of treatment progress and the evaluation of therapeutic responses in patients with Major Depressive Disorder (MDD). This study aims to compare the qEEG data of MDD patients and healthy controls, both before and after treatment, to assess the effect of treatment response on neural activity. Methods: A total of 72 patients, aged 18-60, who had not used any psychopharmacological medication for at least two weeks, were included in the study. Based on a minimum 50% reduction in scores on the Hamilton Depression Rating Scale (HDRS-17) and Hamilton Anxiety Rating Scale (HARS), the patients were divided into two groups: responders (n = 51) and non-responders (n = 21). qEEG data were recorded before and after treatment. Results: Responders exhibited a significant shift in cortical activity-particularly in theta, alpha, and high-beta power-toward patterns resembling those observed in the healthy control group (improvement range: 15% to 67%). In contrast, non-responders showed minimal changes in cortical activity (improvement range: 38% to 46%). These findings suggest that while qEEG spectral data reflect marked neural changes in responders, no significant alterations occur in non-responders. Conclusion: The use of qEEG spectral analysis to monitor MDD patients provides valuable insights into treatment efficacy. The distinct patterns of cortical activity observed across most brain regions before treatment, compared to healthy individuals, highlight the potential of qEEG to predict treatment outcomes.

Objectives:Neurotoxicity, encephalopathy, and seizures can occur following chimeric antigen receptor (CAR)-T cell therapy. Our aim was to assess what value electroencephalography (EEG) offers for people undergoing CAR-T treatment in clinical practice, including possible diagnostic, management, and prognostic roles. Methods: All patients developing CAR-T related neurotoxicity referred for EEG were eligible for inclusion. Reasons for EEG referral and qualitative EEG findings were analysed and reported. The relationship between objective quantitative EEG (QEEG) encephalopathy grade and clinical neurotoxicity (immune effector cell-associated neurotoxicity syndrome; ICANS) grade was determined. The prognostic ability of QEEG grade was assessed for survival and functional status. Results: Twenty-eight patients with 53 EEG recordings were included. Common reasons given on EEG referrals were possible seizure diagnosis (n = 38), reduced consciousness (n = 8), and superimposed cerebral infection (n = 4). Four focal seizures were detected on three (3/53; 5.7%) EEGs. There was a moderately positive correlation between QEEG grade and ICANS grade (r = + 0.41, p = .030). QEEG grade could not predict survival at 3 months (Area Under Curve; AUC = 0.673) or 6 months (AUC = 0.578), nor could it predict functional status at 1 month (r = + 0.40; p = .080), 3 months (r = + 0.19; p = .439), or time to return to baseline (r = + 0.32; p = .156). Conclusions: EEG was useful in seizure diagnosis. QEEG has a possible role as a specific biomarker of encephalopathy/neurotoxicity. EEG generated no tangible changes in patient management. QEEG was unable to prognosticate survival or functional status.