Clinical EEG and neuroscience最新文献

{"title":"Short-Term Changes in Hypsarrhythmia Assessed by Spectral Analysis: Group and Individual Assessments.","authors":"Jessica M Farinha, Peter R Bartel, Piet J Becker, Lynton T Hazelhurst","doi":"10.1177/15500594241258558","DOIUrl":"10.1177/15500594241258558","url":null,"abstract":"<p><p><b>Objectives:</b> To perform spectral analysis on previously recorded electroencephalograms (EEGs) containing hypsarrhythmia in an initial recording and to assess changes in spectral power (µV²) in a follow-up recording after a period of 10-25 days. <b>Methods:</b> Fifty participants, aged 2-39 months, with hypsarrhythmia in an initial recording (R1), were compared with regard to their spectral findings in a later recording (R2). Typically, anticonvulsant therapy was initiated or modified after R1. Average delta, theta, alpha, and beta power was derived from approximately 3 min of artifact-free EEG data recorded from 19 electrode derivations. Group and individual changes in delta power between R1 and R2 formed the main analyses. <b>Results:</b> Delta accounted for 84% of the total power. In group comparisons, median delta power decreased statistically significantly between R1 and R2 in all 19 derivations, for example, from 3940 µV² in R1 to 1722 µV² in R2, Cz derivation. When assessing individual participants, delta power decreases in R2 were >50% in 60% of the participants, but <25% in 24% of the participants. <b>Conclusion:</b> Spectral analysis may be used as an additional tool for providing a potential biomarker in the assessment of short-term changes in hypsarrhythmia, including the effects of treatment.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"159-164"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11800695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Insights in the Treatment of Substance Use Disorders Thanks to Electrophysiological Tools.","authors":"Salvatore Campanella, M Kemal Arikan, Reyhan Ilhan, Bruna Sanader Vukadinivic, Oliver Pogarell","doi":"10.1177/15500594251324506","DOIUrl":"https://doi.org/10.1177/15500594251324506","url":null,"abstract":"<p><p><b>Objective:</b> 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. <b>Methods and Results:</b> 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. <b>Conclusion:</b> 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.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251324506"},"PeriodicalIF":0.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of Quantitative-Electroencephalogram (q-EEG) Measurements Between Patients of Dementia with Lewy Bodies (DLB) and Parkinson Disease Dementia (PDD).","authors":"Mehrnaz Rezvanfard, Ali Khaleghi, Amirhossein Ghaderi, Maryam Noroozian, Vajiheh Aghamollaii, Mehdi Tehranidust","doi":"10.1177/15500594251319863","DOIUrl":"https://doi.org/10.1177/15500594251319863","url":null,"abstract":"<p><p>Dementia with Lewy bodies (DLB) and Parkinson's disease dementia (PDD) are synucleinopathy syndromes with similar symptom profiles that are distinguished clinically based on the arbitrary rule of the time of symptom onset. Identifying reliable electroencephalographic (EEG) biomarkers would provide a precise method for better diagnosis, treatment, and monitoring of treatment response in these two types of dementia. From April 2015 to March 2021, the records of new referrals to a neurology clinic were retrospectively reviewed and 28 DLB(70.3% male) and 20 PDD (80.8% male) patients with appropriate EEG were selected for this study. Artifact-free 60-s EEG signals (21 channels) at rest with eyes closed were analyzed using EEGLAB, and regional spectral power ratios were extracted. Marked diffuse slowing was found in DLB patients compared to PDD patients in all regions in terms of decrease in alpha and increase in theta band. Although, these findings demean between groups after adjusting for MMSE scores, the significant difference still remained in terms of the mean relative alpha powers, particularly in the anterior and central regions. QEEG measures may have the potential to discriminate between these two syndromes. However, further prospective and longitudinal studies are required to improve the early differentiation of these dementia syndromes and to elucidate the underlying causes and pathogenesis and specific treatment.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251319863"},"PeriodicalIF":0.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rhytmic Mid-Temporal Discharges in a Mother and Daughter with Psychogenic Non-Epileptic Seizures.","authors":"Irem Erkent, Candan Gurses","doi":"10.1177/15500594251321213","DOIUrl":"https://doi.org/10.1177/15500594251321213","url":null,"abstract":"<p><p>Psychogenic non-epileptic seizures (PNES) are complex episodes that outwardly resemble epileptic seizures but are not caused by any underlying neurological disease. Unlike true epileptic seizures, PNES are more likely to be linked to psychological factors and do not show any abnormal activity on electroencephalography (EEG) recordings. This differentiation is crucial for accurate diagnosis and treatment, as misdiagnosing can lead to unnecessary treatments.Diagnosis of PNES might become difficult in the presence of particular benign EEG variants such as Rhythmic Midtemporal Discharges (RMTD). RMTD is a rare benign variant of normal EEG, characterized by rhythmic 5-7 Hz discharges in the temporal regions. This pattern could be present in normal individuals, in patients with psychiatric disorders or epilepsy. It could mimic interictal epileptiform discharges. Recognition of this pattern is essential to avoid misinterpretation of EEG findings that might eventuate in inappropriate treatment and adverse effects on a patient's medical condition, especially when there is a recent suspicious event in terms of an epileptic seizure. Among patients with PNES, the occurrence of benign variants might be much harder to interpret and physicians may mistakenly interpret RMTD on the EEG as indicative for epilepsy, especially in the absence of clear clinical criteria for PNES. This report is the first to document RMTD in first-degree relatives with PNES, suggesting a possible genetic predisposition and the need for further research into the interaction between RMTD and PNES.Our aim is to raise awareness that will enable accurate EEG reading and correct diagnosis.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251321213"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143451065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalogram Electrode and Amplifier Temperature Changes During Routine Anatomical and Functional Magnetic Resonance Imaging Sequences at 3 Tesla.","authors":"Adam J Stark, Caleb J Han, Jarrod J Eisma, Alexander K Song, Maria E Garza, Leah G Mann, Daniel O Claassen, Manus J Donahue","doi":"10.1177/15500594251320294","DOIUrl":"https://doi.org/10.1177/15500594251320294","url":null,"abstract":"<p><p>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 <i>b </i>= 1000 s/mm² and <i>b </i>= 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.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251320294"},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frontocentral Delta and Theta Oscillatory Responses are Sensitive to Sleep Deprivation During a Working Memory Task.","authors":"Harun Yırıkoğulları, Esra Dalmızrak, Bahar Güntekin","doi":"10.1177/15500594251316914","DOIUrl":"https://doi.org/10.1177/15500594251316914","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251316914"},"PeriodicalIF":0.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143191472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International QEEG Certification Board Guideline Minimum Technical Requirements for Performing Clinical Quantitative Electroencephalography.","authors":"Tom Collura, David Cantor, Dan Chartier, Robert Crago, Allison Hartzoge, Merlyn Hurd, Cynthia Kerson, Joel Lubar, John Nash, Leslie S Prichep, Tanju Surmeli, Tiff Thompson, Mary Tracy, Robert Turner","doi":"10.1177/15500594241308654","DOIUrl":"https://doi.org/10.1177/15500594241308654","url":null,"abstract":"<p><p>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).</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594241308654"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resting-State Electroencephalogram Microstate and Correlations with Motor Function and Balance in Chronic Stroke.","authors":"Eloise de Oliveira Lima, Letícia Maria Silva, Rebeca Andrade Laurentino, Vitória Ferreira Calado, Eliene Letícia da Silva Bezerra, José Maurício Ramos de Souza Neto, José Jamacy de Almeida Ferreira, Daniel Gomes da Silva Machado, Suellen Marinho Andrade","doi":"10.1177/15500594251317751","DOIUrl":"https://doi.org/10.1177/15500594251317751","url":null,"abstract":"<p><p><b>Objective:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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). <b>Conclusions:</b> Changes in microstates and correlations between topographies and clinical and functional aspects suggest that electroencephalogram could be used as a biomarker in stroke patients.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251317751"},"PeriodicalIF":0.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143124151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STIT-Net- A Wavelet based Convolutional Transformer Model for Motor Imagery EEG Signal Classification in the Sensorimotor Bands.","authors":"Chrisilla S, R Shantha SelvaKumari","doi":"10.1177/15500594241312450","DOIUrl":"https://doi.org/10.1177/15500594241312450","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594241312450"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143061665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"qEEG Neuromarkers of Complex Childhood Trauma in Adolescents.","authors":"Gabriela Mariana Marcu, Raluca D Szekely-Copîndean, Andrei Dumbravă, Ainat Rogel, Ana-Maria Zăgrean","doi":"10.1177/15500594241309456","DOIUrl":"https://doi.org/10.1177/15500594241309456","url":null,"abstract":"<p><p><i>Introduction.</i> 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. <i>Aim</i>. 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. <i>Materials and Methods.</i> 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. <i>Results.</i> 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. <i>Conclusion.</i> 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.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594241309456"},"PeriodicalIF":0.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143018324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}