Clinical EEG and neuroscience最新文献

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.

Neurofeedback, a form of biofeedback using electroencephalography, enables individuals to self-regulate brain activity through operant conditioning. This technique shows promise as a non-invasive intervention for neuropsychiatric disorders like post-traumatic stress disorder (PTSD) and may improve cognitive functions such as attention and working memory. However, limited research, particularly using Z-Score neurofeedback, exists on its effects on PTSD-related symptoms, cognitive function, and identifying treatment-specific EEG markers. In this study, twenty-one individuals diagnosed with PTSD (17 females; mean age = 26.02 [SD = 9.51]) received a diagnostic interview using the MINI Neuropsychiatric Interview and completed self-report measures on PTSD, depression, and insomnia symptoms. Participants completed 5-min eyes-open and eyes-closed EEG recordings and received 10 20-min Z-scoring neurofeedback sessions. Results indicated significant reductions in PTSD and insomnia symptoms, with the most pronounced effects observed in intrusion, negative alterations in cognition and mood, and arousal/reactivity symptoms. Additionally, executive attention improved post-treatment. Alterations in cognition and mood were negatively correlated with alpha power globally and positively correlated with beta power in the parietal region. Beta power at T3 significantly decreased following neurofeedback training. These findings provide further support for neurofeedback as a viable intervention for PTSD, with implications for both symptom reduction and cognitive enhancement. Future studies are needed to investigate individual differences in treatment response and assess long-term outcomes to improve the clinical applicability of this approach.

ObjectiveMost existing studies on cognitive rehabilitation in epilepsy focus on patients undergoing epilepsy surgery or classify interventions based on epilepsy type. This study aimed to determine whether antiseizure medications (ASMs) cause cognitive dysfunction in epilepsy patients by using neuropsychological assessments and auditory event-related potentials (ERPs), and whether cognitive rehabilitation can reduce this potential impact.Materials and MethodsThe study included patients scheduled to begin ASM monotherapy. All participants first underwent a face-to-face Montreal Cognitive Assessment (MoCA). Auditory ERPs including P300 and N200 latencies, and N2 to P3 peak-to-peak amplitudes were recorded in the electrophysiology laboratory. Patients were randomly divided into two groups: Group A (no cognitive rehabilitation) and Group B (received cognitive rehabilitation). After two months, both MoCA and auditory ERP measurements were repeated, and the results were statistically analyzed.ResultsIn Group A, patients using carbamazepine (CBZ), zonisamide (ZNS), or valproic acid (VPA) showed a statistically significant decline in MoCA scores and auditory ERP results (P < .05), suggesting a protective role of rehabilitation. For topiramate (TPM), cognitive decline was weakly significant even with rehabilitation (P = .031).

BackgroundAlzheimer's disease is a neurodegenerative condition characterized by the accumulation of misfolded proteins disrupting connectivity between brain regions. Electroencephalography provides optimal temporal resolution for assessing neuronal communication.ObjectiveTo detect and compare the localization of brain rhythms and the directional flow of oscillatory activity among default mode network nodes during the resting state in patients with amnestic mild cognitive impairment (aMCI) and healthy older adults (HOA).MethodsWe recruited 94 aMCI patients and 66 HOA. We conducted functional localization and connectivity analyses using scalp recordings of neuronal activity, estimated by eLORETA approach. We calculated the effective connectivity by applying the isolated effective coherence method, allowing the frequency decomposition of the directional flow of oscillatory activity between pairs of brain regions. Eight brain regions from the default mode network were selected.ResultsAlthough trends in spectral power were noted, no statistically significant differences were found between groups. Concerning iCOH analysis, both groups showed increased information flow from the posterior to the anterior nodes. Specifically, the precuneus was dominant in transmitting information to the anterior nodes of the DMN. Furthermore, aMCI patients had lower effective connectivity values than HOA.ConclusionsiCOH analysis effectively profiles default mode nodes during the resting state, adding information on both localization and directionality of information flow, as well as the involved EEG oscillations. Furthermore, it is well-suited to detect between-group connectivity differences, suggesting its usefulness as a biomarker in the prodromal clinical stage of AD.

IntroductionTranscranial direct current stimulation (tDCS) is investigated as an adjunct treatment in schizophrenia, but electroencephalographic (EEG) studies have produced inconsistent findings.ObjectiveTo review the literature and elucidate the effects of tDCS on EEG variables in schizophrenia. Method: This is a systematic scoping review according to PRISMA guidelines, consulting four databases: PubMed (MEDLINE), Cochrane Library, Web of Science and ScienceDirect. It was structured following PIO framework (Population, Intervention, Outcome): P: schizophrenia; I: tDCS; O: any EEG variable. For data synthesis, each time a variable was investigated, it was counted as an occurrence.ResultsA total of twenty-five papers were included, totaling forty-two occurrences: twenty-five were event-related potentials and seventeen were based on spectral/power, connectivity or coherence variables. Most papers applied 20 min of 2 mA stimulation (76%), in a bicephalic montage. The most investigated variable was the MMN, followed by N100, P300, EEG coherence, gamma activity, beta and alpha power. N100 was the variable that responded most to tDCS stimulation, with 80% response rate. Gamma activity had 67% response, MMN showed 60%, coherence, alpha and beta power 50%. All papers investigating P300 reported no significant results. Other EEG parameters were investigated only once.ConclusionEEG changes induced by tDCS in schizophrenia predominantly affected the sensory-auditory potential N100, had a lesser impact on pre-attentive potential MMN, and showed no observable effect on higher-order cognitive potentials, such as P300. The modulatory effects of tDCS on cognition are still unclear. This review was registered at the Open Science Framework (osf.io/7yzrj).

ObjectivesChildhood Occipital Visual Epilepsy (COVE) is a self-limited epileptic syndrome that typically begins in late childhood or adolescence characterized by brief visual seizures. The recent 2022 International League Against Epilepsy (ILAE) classification distinguishes COVE from photosensitive occipital lobe epilepsy (POLE), emphasizing the absence of photic-induced seizures in COVE. In this study, we aimed to describe the clinical and electrophysiological features of patients with COVE diagnosed according to the new ILAE criteria.MethodsThis retrospective cohort study analyzed 30 patients diagnosed with COVE at a tertiary epilepsy center between 1988 and 2023. Patients were selected based on ILAE 2022 criteria, and all cases with intermittent photic stimulation (IPS)-induced seizures were excluded.ResultsMost patients (93%) presented with elementary visual hallucinations, such as colorful lights. Orofacial seizures occurred in 7%, and 37% had nocturnal seizures. EEG abnormalities were primarily occipital and resolved in 85% of cases over time. Generalized spike-wave discharges (GSWDs) were rare (5%), and only one patient developed juvenile myoclonic epilepsy during follow-up. At final follow-up, 77% of patients achieved seizure freedom, and 47% discontinued medication.ConclusionCOVE is an epileptic syndrome associated with a favorable prognosis. By excluding photosensitivity in light of the newly proposed diagnostic criteria from the ILAE, future research should focus on a more homogenous group of COVE patients to enhance understanding of this syndrome. Accurate classification using updated ILAE criteria allows for clearer clinical delineation and more reliable outcome predictions.