Clinical EEG and neuroscience最新文献

Holoprosencephaly is a congenital malformation of the central nervous system resulting from failure of the rostral neural tube to bifurcate into the two cerebral hemispheres. Deep brain structures including the thalamus, hypothalamus, and basal ganglia can also be affected to varying degrees. Here we present a patient with a rare de novo pathogenic variant in the PPP1R12A gene and the middle interhemispheric (MIH) variant of holoprosencephaly with hypersynchronous patterns on electroencephalography (EEG). The most prevalent abnormal pattern was abundant hypersynchronous rhythmic theta activity most prominent over the bilateral centro-parietal regions. There was also frequent hypersynchronous rhythmic beta activity and rhythmic alpha range activity, which occurred both synchronously and asynchronously. Finally, there were occasional periods of voltage attenuation interrupting hypersynchronous theta activity. While hypersynchronous theta activity and episodic attenuation have been previously described in alobar and semilobar variants of holoprosencephaly, our report is the first to describe these findings in a patient with the MIH variant as well as the first to describe EEG patterns in a patient with a pathogenic variant in the PPP1R12A gene mutations in which are associated with urogenital and/or brain malformation syndrome. Additionally, the hypersynchronous alpha activity is the first report of such an EEG pattern in holoprosencephaly. In order to develop a more complete understanding of EEG patterns in holoprosencephaly further study is needed but this is challenged by the relative rarity of the disease.

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.

Objective: Substance use disorders (SUD) still represent a huge worldwide health problem, as, despite withdrawal, medication, social support and psychotherapy, the relapse rate (around 80% at one year following treatment) remains tremendously high. Therefore, an important challenge consists in finding new complementary add-on tools to enhance quality of care. Methods and Results: In this report we focus on new insights reported through the use of three electrophysiological tools (quantitative electroencephalography (EEG), QEEG; cognitive event-related potentials, ERPs; and neurofeedback) suggesting that their use might be helpful at the clinical level in the management of various forms of SUDs. Empirical evidence were presented. Conclusion: In light of encouraging results obtained highlighting how these electrophysiological tools may be used in the treatment of SUDs, further studies are needed in order to facilitate the implementation of such procedures in clinical care units.

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.

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.

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder affecting cognitive and behavioral functions, resulting in ongoing inattention, hyperactivity, and impulsivity. Early and accurate diagnosis is essential, but traditional methods mainly depend on questionnaire-based assessments, detailed interviews with individuals and their families, and reviews of medical history. These are then scored using standardized scales like the Conners Rating Scale, Vanderbilt ADHD Diagnostic Parent Rating Scale, and Adult ADHD Self-Report Scale. However, these methods are often subjective, time-consuming, and costly, which limits their usefulness for early diagnosis. The proposed approach seeks to improve ADHD diagnosis by using machine learning techniques applied to electroencephalogram (EEG) data. Two classifiers, Random Forest and AdaBoost, are used to identify complex patterns in EEG data. Feature selection is performed with the Reptile Search Algorithm combined with an autoencoder for feature extraction, which improves data representation and model accuracy. The performance of this approach is evaluated based on accuracy, precision, recall, F1-score, AUC, and statistical significance at a 95% confidence level. Random Forest outperformed AdaBoost, achieving 92.36% in precision, recall, accuracy, and F1-score, while AdaBoost reached 89.78% in these metrics. Random Forest showed better effectiveness than AdaBoost in distinguishing ADHD cases, with an ROC AUC score of 0.93 and higher diagnostic accuracy. The study demonstrates that machine learning offers a promising, objective, and reliable tool for diagnosis, providing effective alternatives to traditional ADHD assessments for timely intervention and improved treatment management.

Anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis is a severe autoimmune encephalitis that often demonstrates a favorable response to immunotherapy, including rituximab. While disease outcomes have been widely documented, longitudinal characterization of brain activity changes following treatment remains limited. Electroencephalography (EEG) source localization provides a non-invasive approach for assessing regional brain dynamics. We report a case of a 17-year-old male patient with anti-NMDAR encephalitis who underwent serial EEG recordings before and after rituximab administration, with source power spectral density analysis performed. Symptom improvement following rituximab corresponded with reductions in cortical and subcortical delta power alongside increases in cortical alpha power, while transient symptom exacerbation was associated with elevated delta and diminished alpha activity in the cortex. Cerebellar activity alterations were not observed alongside symptom variations. Moreover, pre-treatment EEG revealed extensive delta band activity in the right hemisphere, with right-sided hypermetabolism observed on ¹⁸F-FDG PET/CT. These findings underscore the potential of source-localized EEG as a promising tool for region-specific monitoring of brain activity in NMDAR encephalitis, warranting rigorous validation in larger patient cohorts.

Facial expressions play a vital role in non-verbal communication, conveying a wide range of emotions and messages. Although prior research achieved notable advances through architecture design or dataset-specific optimization, few studies have integrated multiple advanced techniques into a unified facial expression recognition (FER) pipeline. Addressing this gap, we propose a comprehensive approach that combines (i) multiple pre-trained CNNs, (ii) MTCNN-based face detection for improved facial region localization, and (iii) Grad-CAM-based interpretability. While MTCNN enhances the quality of face localization, it may slightly affect classification accuracy by focusing on cleaner yet more challenging samples. We evaluate four pre-trained models - DenseNet121, ResNet-50, ResNet18, and MobileNetV2 - on two datasets: Raf-DB and Cleaned-FER2013. The proposed pipeline demonstrates consistent improvements in interpretability and overall system robustness. The results emphasize the strength of integrating face detection, transfer learning, and interpretability techniques within a single framework can significantly enhance the transparency and reliability of FER systems. Combining FER with EEG-based systems significantly enhances the emotional intelligence of brain-computer interfaces, enabling more adaptive and personalized user experiences. With this approach the paper bridges the gap between affective computing and cognitive neuroscience, aligning closely EEG-centered interaction methodologies. Besides understanding the relationship between facial expressions of emotions and EEG signals will be an important study for literature.

ObjectiveDepressive symptoms and cognitive impairment are two common complications of cerebral small vascular disease (CSVD). This study aimed to investigate the P300 representation in CSVD patients with depressive symptoms and its relationship with depressive symptoms.MethodsWe selected 242 patients with CSVD (depression: n = 56; non-depression: n = 186) and 30 healthy controls. The Self-Rating Depression Scale and Self-Rating Anxiety Scale scales were used to assess depressive and anxiety symptoms.The latency and amplitude of P300 components were measured using event-related potential (ERP) technique to assess cognitive dysfunction. Cognitive function was evaluated using Mini-mental state examination and Event-Related Potential P300 waves latency & amplitude. Finally, logistic regression model was used to analyze the relationship between P300 representation and depressive symptoms in CSVD patients.ResultsCompared with NPSD group and Control group, the latency of P300 (P3a and P3b wave groups) in PSD group was longer and the amplitude was lower. Multivariate Logistic regression analysis showed that temporal lobe infarction (OR = 10.878, 95% CI = 2.890-40.939), brainstem infarction (OR = 4.185, 95% CI = 1.544-11.341), SAS score (OR = 1.275, 95% CI = 1.174-1.385),and P3b amplitude (OR = 0.779, 95% CI = 0.635-0.957) were independently correlated with depressive symptoms in CSVD patients (P < .05).ConclusionCSVD patients with depressive symptoms had worse cognitive function, and abnormalities in P300 waves amplitude and latency were more pronounced. The amplitude of P3b in patients with CSVD is decreased, which is significantly correlated with the occurrence of depression.

BackgroundSchizophrenia affects millions globally, with up to 30% showing resistance to standard antipsychotics. Clozapine is effective for treatment resistant schizophrenia (TRS), but its use is often delayed. This study explores Quantitative electroencephalogram (QEEG) as a tool to predict clozapine response in Indian TRS patients, aiming to support early, personalized treatment.AimThis study aims to predict treatment response to clozapine in TRS patients using quantitative electroencephalogram (QEEG) by assessing and comparing baseline and 6 weeks QEEG patterns and their changes in responders versus non-responders.Methods39 clozapine-naïve TRS patients were recruited at tertiary care hospital in North India and assessed using BPRS, GASS-C and EEG at baseline, 3 weeks and 6 weeks. EEG data were processed and analyzed for frequency band power to compare responders (≥20% BPRS improvement) and non-responders.ResultsOf the 39 patients included, 36 completed the study, with 67% classified as responders and 33% as non-responders. Responders showed significantly higher right temporal delta power at 3 and 6 weeks, with ROC analysis at 6 weeks yielding an Area under curve of 0.757 (P = .014). Statistically significant increases in delta and theta power were observed in responders.ConclusionsIncreased right temporal delta power was seen in responders, but changes were insufficient to reliably predict outcomes.