Clinical EEG and neuroscience最新文献

Aims and Objectives: It has been hypothesised that vestibular stimulation may have a modulatory effect on anxiety. The aim of this randomised, double blind, sham-controlled trial was to determine the efficacy and safety of a non-invasive electrical vestibular nerve stimulation (VeNS) device as a treatment for anxiety compared to a sham stimulation device. Materials and methods: A total of 60 participants (mean age [SD]: 35.6 [8.1]) with a generalized anxiety disorder assessment (GAD-7) score of ≥10 were randomised to receive either an active VeNS device (n = 34) or a sham control device (n = 26). Both groups were asked to complete 20 stimulation sessions (30 min duration) at a rate of 3-5 sessions per week at a research clinic. The primary outcome was change in GAD-7 score from baseline to the end of study (when each participant finished their 20 stimulation sessions). Secondary outcomes were change in Insomnia Severity Index (ISI), and the Short Form 36 Health Survey (SF-36) scores (8 domains). Results: One participant allocated to the sham group withdrew from the study. The mean (SD) number of weeks it took to complete the 20 stimulation sessions was 5.8. The active group had a statistically greater reduction in GAD-7 score compared to the sham group (-7.4 versus -2.2, P < .001; respectively). A total of 97% (n = 33) of the active group achieved a clinically meaningful reduction (defined as ≥4-point reduction) in GAD-7 from baseline to the follow up visit compared to 24% (n = 6) of the sham group (P < .001). Additionally, the active group showed a significant improvement in ISI (-4.9 versus 2.2, P < .001) and greater improvements on all eight SF36 domains (P < .001) compared with the sham group. There was no device related reported adverse events. Conclusion: Regular non-invasive electrical vestibular nerve stimulation appears to have a clinically meaningful benefit when used as an intervention for Generalized Anxiety Disorder.

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.

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.

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.

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.

Introduction: The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). Methods: EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. Results: On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. Conclusions: As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.

Background: Deficits in problem-solving may be related to vulnerability to suicidal behavior. We aimed to identify the electroencephalographic (EEG) power spectrum associated with the performance of the Raven as a reasoning/problem-solving task among individuals with recent suicide attempts. Methods: This study with the case-control method, consisted of 61 participants who were assigned to three groups: Suicide attempt + Major Depressive Disorder (SA + MDD), Major Depressive Disorder (MDD), and Healthy Control (HC). All participants underwent clinical evaluations and problem-solving abilities. Subsequently, EEG signals were recorded while performing the Raven task. Results: The SA + MDD and MDD groups were significantly different from the HC group in terms of anxiety, reasons for life, and hopelessness. Regarding brain oscillations in performing the raven task, increased theta, gamma, and betha power extending over the frontal areas, including anterior prefrontal cortex, dlPFC, pre-SMA, inferior frontal cortex, and medial prefrontal cortex, was significant in SA + MDD compared with other groups. The alpha wave was more prominent in the left frontal, particularly in dlPFC in SA + MDD. Compared to the MDD group, the SA + MDD group had a shorter reaction time, while their response accuracy did not differ significantly. Conclusions: Suicidal patients have more frontal activity in planning and executive function than the two other groups. Nevertheless, it seems that reduced activity in the left frontal region, which plays a crucial role in managing emotional distress, can contribute to suicidal tendencies among vulnerable individuals. Limitation The small sample size and chosen difficult trials for the Raven task were the most limitations of the study.

Objectives: Evaluate the diagnostic yield of 24-h video-EEG monitoring in a group of children admitted in our epilepsy monitoring unit (EMU). Methods: 232 children who underwent 24-h video-EEG monitoring was analysed. We divided each patient's monitoring duration into the first 1, 2, 4, 8, 16 h, relative to the whole 24 h monitoring period. The detection of the first interictal epileptiform discharges (IEDs), epileptic seizures (ES), and psychogenic non-epileptic seizures (PNES) were analysed relative to the different monitoring time subdivision. Results: Our findings revealed that: (1) there was no significant difference in the prevalence of detecting initial IEDs between the first 4-h and 24-h monitoring periods (73.7% vs 81%); (2) clinical events detection rate was statistically similar between the first 8-h and 24-h monitoring periods (15.5% vs 19.3%); (4) an 8-h monitoring was sufficient to capture IEDs, ES and PNES in focal epilepsy children; (5) a 1-h monitoring was sufficient to capture IEDs, ES and PNES in generalized epilepsy children; and (6) IEDs were detected within the first 1-h of monitoring in 96.7% self-limited focal epilepsies (SeLFEs) patient. Conclusion: Our study suggests that a 4-h monitoring has more value in increasing the detection rate of IEDs compared to the traditional shorter routine EEG. And in the case of SeLFEs, a 1-h of monitoring might be sufficient in detecting IEDs. A 24-h VEEG monitoring can detect clinical events in 19.3% of patients. Overall, the yield of IEDs and clinical events detection is adequate in children in children undergoing 24-h video-EEG monitoring.

Introduction. Chronic pain is a percept due to an imbalance in the activity between sensory-discriminative, motivational-affective, and descending pain-inhibitory brain regions. Evidence suggests that electroencephalography (EEG) infraslow fluctuation neurofeedback (ISF-NF) training can improve clinical outcomes. It is unknown whether such training can induce EEG activity and functional connectivity (FC) changes. A secondary data analysis of a feasibility clinical trial was conducted to determine whether EEG ISF-NF training can significantly alter EEG activity and FC between the targeted cortical regions in people with chronic painful knee osteoarthritis (OA). Methods. A parallel, two-arm, double-blind, randomized, sham-controlled clinical trial was conducted. People with chronic knee pain associated with OA were randomized to receive sham NF training or source-localized ratio ISF-NF training protocol to down-train ISF bands at the somatosensory (SSC), dorsal anterior cingulate (dACC), and uptrain pregenual anterior cingulate cortices (pgACC). Resting state EEG was recorded at baseline and immediate post-training. Results. The source localization mapping demonstrated a reduction (P = .04) in the ISF band activity at the left dorsolateral prefrontal cortex (LdlPFC) in the active NF group. Region of interest analysis yielded significant differences for ISF (P = .008), slow (P = .007), beta (P = .043), and gamma (P = .012) band activities at LdlPFC, dACC, and bilateral SSC. The FC between pgACC and left SSC in the delta band was negatively correlated with pain bothersomeness in the ISF-NF group. Conclusion. The EEG ISF-NF training can modulate EEG activity and connectivity in individuals with chronic painful knee osteoarthritis, and the observed EEG changes correlate with clinical pain measures.

Background: Holmes tremor (HT) is a rare motor disorder characterized by high-amplitude and low-frequency resting, intentional, and postural tremors. HT typically arises from disruptions in neural pathways, including the dopaminergic system. Its causes include cerebrovascular incidents, neoplasms, demyelination, and infections. Diagnosis involves thorough clinical, neurophysiological, and neuroimaging assessments. Our report details the clinical profile, neuroimaging and EEG results and levodopa treatment response of an HT patient after cerebral arteriovenous malformation (AVM) surgery. Case Report: A female patient who underwent AVM surgery developed head tremor and dystonia. Neuroimaging revealed left thalamus involvement. Video electroencephalography (EEG) revealed high-amplitude, low-frequency tremors. The patient responded well to levodopa treatment. Conclusions: Involuntary rhythmic or non-rhythmic movements are a primary clinical feature of HT. A differential diagnosis of epilepsy and HT can be achieved through neurophysiological monitoring, avoiding the overuse of antiepileptic drugs. Symptoms can be alleviated with levodopa intervention.