Clinical EEG and neuroscience最新文献

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.

Autism Spectrum Disorder (ASD) is a severe neurodevelopmental disorder characterized primarily by social impairments and repetitive behaviors. Imbalance in excitatory-inhibitory (E/I) activity within the central nervous system may be a key mechanism underlying ASD. Electroencephalography (EEG) is a useful tool for recording brain electrical signals, reflecting the activity of cortical neuron populations, and estimating both global and regional E/I balance. Various EEG methods can estimate E/I balance, including non-periodic exponent, corrected alpha power, sample entropy, average spatial phase synchronization (ASPS), and detrended fluctuation analysis (DFA) based on E/I indices. However, research on using EEG proxy markers to assess E/I imbalance in autism is limited, and there is no study indicating which method is most sensitive. Therefore, this study employed a high-density EEG acquisition system to collect data from a relatively large sample of autistic and typically developing (TD) children. We computed EEG proxy markers and used the Coefficient of Variation (CV) to compare the sensitivity of five EEG markers between the two groups. The results indicated that non-periodic exponent based on power spectra and corrected alpha power from non-periodic neural activity were more advantageous. The findings may provide theoretical support for the exploration of EEG biomarkers based on E/I balance theory.

BackgroundsIdentifying state biomarkers in major depressive disorder (MDD) is critical for understanding neurobiological underpinnings of disorder. Quantitative electroencephalography (qEEG) has emerged as a promising tool for distinguishing stable versus dynamic neural alterations associated with MDD.MethodsThis study included 70 patients diagnosed with MDD and 98 healthy controls (HC). Resting-state qEEG recordings were obtained at three time points: baseline(T0), early treatment(T1), and late treatment(T2). Patients were categorized as responders(≥50%HDRS-21) or non-responders. Changes in absolute band power across delta, theta, alpha, beta, and gamma frequencies were compared with HCs. Associations between qEEG activity with HDRS and HARS scores at each time point were calculated.ResultsResponders showed longitudinal reductions in delta power with normalization toward HCs. Gamma activity increased marginally over time. Non-responders exhibited stable and elevated delta and alpha power that persisted across sessions. Decreased fronto-central delta and increased left fronto-central gamma power were also associated with improvement in depression and anxiety.ConclusionMDD Responders demonstrated state-dependent electrophysiological normalization, while non-responders show stable pattern with unchanged depressive state. These findings highlight the utility of qEEG state-markers in monitoring clinical improvement in depression.

Ictal swearing primarily manifests as involuntary cursing detached from social and emotional contexts, representing an exceptionally rare form of speech automatism during epileptic seizures. In this case report, we describe a patient with refractory focal temporal lobe epilepsy associated with bilateral hippocampal sclerosis, where epileptic activity originated from the right hippocampus and led to swearing when the left hippocampus and amygdala were secondarily activated. We aim to analyze the role of the bilateral paralimbic temporal network in ictal swearing and propose potential pathways for the expression of ictal swearing.

Emotion regulation is essential for maintaining daily functioning. Previous studies indicate that individuals with migraine have difficulty identifying emotions and exhibit higher alexithymia scores. The P300 and N400 components, associated with attention and semantic processes, provide insights into neural changes during emotion regulation. The aim of this study is to investigate the relationship between migraine and emotion regulation by evaluating the P300 and N400 responses obtained during emotion regulation tasks. Participants included individuals with and without a migraine diagnosis. They were shown negative and neutral photographs, followed by instructions to enhance, suppress, or maintain their emotional responses. Afterward, they were asked to evaluate negative or neutral words. During EEG recording, event-related potentials were analyzed, focusing on the P300 and N400 components. P300 responses were recorded from the P3, P4, and Pz electrodes, while N400 responses were collected from FC1, FC2, and Cz electrodes. The migraine group generated a higher P300 response to neutral words compared to the control group. However, for negative words, the control group exhibited a higher P300 response than the migraineurs. According to the data from the FC1 channel, the migraine group produced a higher N400 response to negative words compared to the control group. In the FC2 channel, the migraine group showed a higher N400 response to neutral words than the control group. Although the effect of command did not differ between groups, the migraineurs showed inefficient attention allocation to negative stimuli and alterations in semantic processing of the emotional words depending on the electrode location.

Dacrystic seizure is a rare phenomenon of crying during an epileptic episode. It has an established connection to hypothalamic hamartoma, but was also reported to be associated with frontal and temporal epileptic foci. We present two cases of dacrystic epilepsy. Patient 1 had suffered from magnetic resonance imaging-negative epilepsy that was characterized by both gelastic and dacrystic seizures; stereo-encephalography showed onset in the anterior cingulate/Brodmann area 8 with rapid prefrontal and orbitofrontal propagation, leading to crying onset. Patient 2 had dacrystic seizures arising from a temporal lobe lesion with spreading to the orbitofrontal cortex. Both patients became seizure-free following resection targeting these networks. These cases represent intracranial correlates of dacrystic seizures occurring outside the context of hypothalamic hamartoma and suggest a central contribution of the anterior cingulate and/or orbitofrontal cortices in their generation.

The purpose of this study was to assess the change in frequency and distribution of focal interictal epileptiform discharges (IEDs) as measured on scalp EEG after anti-seizure medications (ASMs) were weaned in the epilepsy monitoring unit. We retrospectively reviewed the EEG of patients with focal epilepsy on a single ASM. A two-hour EEG epoch was selected at sleep onset during the first day of admission and defined as the high-ASM epoch. This was compared to a two-hour low-ASM epoch at sleep onset after the ASM was weaned, at least 6 h before or after a seizure. IEDs were manually counted and characterized. A total of 115 patients were included. For those on levetiracetam, there was a significant increase in IED quantity when comparing the high-ASM to the low-ASM epoch (mean 40.6 to 71.4, p < 0.001). For those on sodium channel blockers, there was a non-significant trend towards a decrease in IED quantity as the ASM dose was decreased (p = 0.065). There was no statistically significant change found for other individual ASMs. For the cohort, 12 patients had IED observed only on the low-ASM epoch (which were not present on the high-ASM epoch), 6 of which were treated with levetiracetam. In summary, our findings showed weaning of levetiracetam was associated with a significant increase in IEDs whereas other ASMs were not. Some populations of IEDs were only seen after ASMs were weaned. These findings suggest that different ASMs may have unique effects on IEDs when weaned.

Acceptance, nonjudgmental awareness of the present-moment experiences, is a central component of mindfulness. This study used a pretest-posttest design to examine whether a brief mindfulness intervention (MI) could increase self-reported acceptance and reduce affective reactivity to errors, as indexed by error-related negativity (ERN), error positivity (Pe), and post-error slowing (PES). Meditation-naïve participants (n = 121, ages 18-31 years, 69% female) were randomly assigned to either a mindfulness group, which engaged in 10 min of guided mindful breathing, or a control group, which listened to a Ted talk on green living. Both groups completed a Flanker task before and after the intervention to elicit errors under time pressure. Results showed that participants in the mindfulness group reported greater acceptance following the intervention; however, no corresponding changes were observed in ERN or PES. Instead, both groups showed practice effects, with faster reaction times and larger Pe amplitudes reflecting increased response certainty. These findings suggest that while a brief MI may enhance subjective acceptance, it may not be sufficient to alter neural or behavioral markers of affective error reactivity. Longer or more intensive mindfulness training may be required to influence these deeper cognitive and emotional processes.