Focality in childhood absence epilepsy

BACKGROUND AND PURPOSE

Childhood absence epilepsy (CAE) has a typical electroencephalography (EEG) pattern of generalized 3 Hz spike and wave discharges (SWD). Focal interictal discharges were also documented in a small number of documents. The aim was to investigate the amplitudes of interictal 3 Hz SWD within the 1st second in drug-naïve CAE patients. In this way, areas with maximal electronegativity at the beginning of clinically generalized discharges will be documented.

METHODS

The EEG records of children with drug-naïve CAE were evaluated retrospectively by two child neurologists first for 3 Hz SWD. Then, a machine-learning model evaluated the amplitudes of 3 Hz in the 1st second of SWD. Maximum electronegativity areas were documented and classified as focal, bilateral, and generalized.

RESULTS

One hundred and twelve 3 Hz SWD were evaluated in 11 patients. Among discharges within the 1st second, maximum electronegativity areas were documented as focal for 44 (39.2%), bilateral for 8 (7.1%), generalized for 60 (53.5%). Among focal electronegativity areas, mostly right central, left occipital and midline parietal areas were documented in 12 (10.7%), 7 (6.2%), and 6 (5.3%), respectively. Eight (7.1%) of the maximum electronegativity areas were detected bilaterally, of which 7 (6.2%) originated from the frontopolar areas.

CONCLUSIONS

Focal maximal electronegativity areas were frequently observed in drug-naïve CAE patients, comprising approximately half of non-generalized discharges. Focal discharges might be misleading in diagnosis. Focal areas within the brain may be responsible for and contribute to absence seizures that appear bilaterally symmetrical and generalized clinically. Although its clinical implication is unknown, this warrants further study.

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models

PURPOSE

Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities. The primary purpose of this research was to develop unimodal and multimodal deep-learning-based diagnostic models to predict bone mineral loss of the lumbar vertebrae using magnetic resonance (MR) and computed tomography (CT) imaging.

METHODS

Patients who received both lumbar dual-energy X-ray absorptiometry (DEXA) and MRI (n = 120) or CT (n = 100) examinations were included in this study. Unimodal and multimodal convolutional neural networks (CNNs) with dual blocks were proposed to predict osteoporosis using lumbar vertebrae MR and CT examinations in separate and combined datasets. Bone mineral density values obtained by DEXA were used as reference data. The proposed models were compared with a CNN model and six benchmark pre-trained deep-learning models.

RESULTS

The proposed unimodal model obtained 96.54%, 98.84%, and 96.76% balanced accuracy for MRI, CT, and combined datasets, respectively, while the multimodal model achieved 98.90% balanced accuracy in 5-fold cross-validation experiments. Furthermore, the models obtained 95.68%-97.91% accuracy with a hold-out validation dataset. In addition, comparative experiments demonstrated that the proposed models yielded superior results by providing more effective feature extraction in dual blocks to predict osteoporosis.

CONCLUSION

This study demonstrated that osteoporosis was accurately predicted by the proposed models using both MR and CT images, and a multimodal approach improved the prediction of osteoporosis. With further research involving prospective studies with a larger number of patients, there may be an opportunity to implement these technologies into clinical practice.

A Near-Global Slowing of Background Activity and Epileptic Discharges in Children With Mild to Moderately Symptomatic COVID-19 Infection: An Electro-Neurophysiological Study

Background. To assess the functional involvement of the central nervous system (CNS) via quantitative electroencephalography (EEG) analysis in children with mild to moderate COVID-19 infection who were otherwise previously healthy children. Methods. This prospective, case-control study was conducted between June and September 2020. Sleep EEG records of at least 40 min were planned for children who tested positive for COVID-19 using real-time PCR analysis and within 4-6 months post-recovery. All of the EEG analyses in this study were performed on an Ubuntu 20.04.2 LTS Operating System with the developed software using Python 3.7.6. The quantitative analysis of the epileptic discharges within the EEG records was performed using random forest after elimination of the artifacts with a model training accuracy of 98% for each sample data point. The frequency analysis was performed using the Welch method. Results. Among the age and sex-matched groups, the global mean frequency was significantly lower among the COVID-19 patients, with a P-value of 0.004. The spike slow-wave and sharp slow-wave indices were significantly higher in the patients when compared to the controls. The mean frequency values were significantly lower in almost all of the electrodes recording the frontal, central, and occipital areas. For the temporal and parietal areas, those significantly low mean frequencies were limited to the right hemisphere. Conclusion. A near-global involvement of background activity with decreased frequency, in addition to epileptic discharges, was recorded in mild to moderately COVID-19 infected children post-infection.