Purpose: The EEG is an essential neurological diagnostic tool. EEG abnormalities can guide diagnosis and management of epilepsy. There are also distinctive EEG waveforms that are seen in healthy individuals. It is critical not to misinterpret these as abnormal. To emphasize the importance of these waveforms, we analyzed different normal variants via the source localization technology.
Methods: This is a retrospective analysis of EEGs performed at the Duke University Hospital between June 2014 and Dec 2019. We selected samples of vertex waves, Mu, lambda, POSTS, wickets, and sleep spindles for analysis. EEG were imported to Curry 8 (Compumedics) to calculate the dipole and current density. The averaged head model from the Montreal Neurological Institute database was used for reconstruction.
Results: Thirty-four patient EEG samples were selected including five vertex, six Mu, four wicket, seven lambda, five POSTS, and seven spindles. Results from source localization showed that vertex waves are localized in the frontocentral area, whereas spindles in the deep midline central region. Mu were identified in the ipsilateral somatosensory cortex. Lambda and POSTS, on the other hand, had maximum results over the bilateral occipital region and wickets in the ipsilateral temporal lobe.
Conclusions: Our results confirm and expand previous hypotheses. This allows us to speculate on the origin of these normal EEG variants. Although this study is limited by small sample size, lack of high-density EEG, and patient-specific MRI, our analysis provides an easily replicable three-dimensional visualization of these waveforms.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1097/WNP.0000000000000948 | DOI Listing |
Mol Clin Oncol
February 2025
Department of Urology Surgery, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi 330006, P.R. China.
Disulfidptosis, which was recently identified, has shown promise as a potential cancer treatment. Nonetheless, the precise role of long non-coding RNAs (lncRNAs) in this phenomenon is currently unclear. To elucidate their significance in bladder cancer (BLCA), a signature of disulfidptosis-related lncRNAs (DRlncRNAs) was developed and their potential prognostic significance was explored.
View Article and Find Full Text PDFMass Spectrom (Tokyo)
December 2024
Graduate School of Engineering, Osaka University, A1/A14, 2-1 Yamadaoka, Suita, Osaka 565-0871, Japan.
Mass spectrometry (MS) is a valuable tool that enables label-free analysis and the ability to measure multiple molecules. The atmospheric pressure MS imaging (MSI) method usually requires tedious sample preparation. A simple ionization method with minimal sample preparation is needed for high-throughput analysis.
View Article and Find Full Text PDFEcol Evol
January 2025
Colección Nacional de Arácnidos, Departamento de Zoologia, Instituto de Biologia Universidad Nacional Autónoma de México Mexico City Mexico.
Extensive grazing carried out freely by exotic goats represents an important source of anthropogenic degradation in seasonally dry tropical forests of Brazil. The presence of these herbivores may negatively impact the local fauna through the reduction of habitat complexity. In this study, we investigate the effect of goat farming in scorpion assemblage from Brazilian seasonally dry tropical forest.
View Article and Find Full Text PDFInterdiscip Sci
January 2025
College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Accurate prediction of drug-drug interaction (DDI) is essential to improve clinical efficacy, avoid adverse effects of drug combination therapy, and enhance drug safety. Recently researchers have developed several computer-aided methods for DDI prediction. However, these methods lack the substructural features that are critical to drug interactions and are not effective in generalizing across domains and different distribution data.
View Article and Find Full Text PDFRev Sci Instrum
January 2025
School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, Shaanxi 710121, China.
Emotion recognition based on electroencephalogram (EEG) has always been a research hotspot. However, due to significant individual variations in EEG signals, cross-subject emotion recognition based on EEG remains a challenging issue to address. In this article, we propose a dynamic domain-adaptive EEG emotion recognition method based on multi-source selection.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!