Neurological update: structural and functional imaging in epilepsy surgery

Utility of genetic testing in the pre-surgical evaluation of children with drug-resistant epilepsy

We evaluated the utility of genetic testing in the pre-surgical evaluation of pediatric patients with drug-resistant focal epilepsy. This single-center retrospective study reviewed the charts of all pediatric patients referred for epilepsy surgery evaluation over a 5-year period. We extracted and analyzed results of genetic testing as well as clinical, EEG, and neuroimaging data. Of 125 patients referred for epilepsy surgical evaluation, 86 (69%) had some form of genetic testing. Of these, 18 (21%) had a pathogenic or likely pathogenic variant identified. Genes affected included NPRL3 (3 patients, all related), TSC2 (3 patients), KCNH1, CHRNA4, SPTAN1, DEPDC5, SCN2A, ARX, SCN1A, DLG4, and ST5. One patient had ring chromosome 20, one a 7.17p12 duplication, and one a 15q13 deletion. In six patients, suspected epileptogenic lesions were identified on brain MRI that were thought to be unrelated to the genetic finding. A specific medical therapy choice was allowed due to genetic diagnosis in three patients who did not undergo surgery. Obtaining a molecular diagnosis may dramatically alter management in pediatric patients with drug-resistant focal epilepsy. Genetic testing should be incorporated as part of standard investigations in the pre-surgical work-up of pediatric patients with drug-resistant focal epilepsy.

Navigating Neural Landscapes: A Comprehensive Review of Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) Applications in Epilepsy

This review comprehensively explores the evolving role of neuroimaging, specifically magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS), in epilepsy research and clinical practice. Beginning with a concise overview of epilepsy, the discussion emphasizes the crucial importance of neuroimaging in diagnosing and managing this complex neurological disorder. The review delves into the applications of advanced MRI techniques, including high-field MRI, resting-state fMRI, and connectomics, highlighting their impact on refining our understanding of epilepsy's structural and functional dimensions. Additionally, it examines the integration of machine learning in the analysis of intricate neuroimaging data. Moving to the clinical domain, the review outlines the utility of neuroimaging in pre-surgical evaluations and the monitoring of treatment responses and disease progression. Despite significant strides, challenges and limitations are discussed in the routine clinical incorporation of neuroimaging. The review explores promising developments in MRI and MRS technology, potential advancements in imaging biomarkers, and the implications for personalized medicine in epilepsy management. The conclusion underscores the transformative potential of neuroimaging and advocates for continued exploration, collaboration, and technological innovation to propel the field toward a future where tailored, effective interventions improve outcomes for individuals with epilepsy.

Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques

The electroencephalography (EEG) signal is a noninvasive and complex signal that has numerous applications in biomedical fields, including sleep and the brain-computer interface. Given its complexity, researchers have proposed several advanced preprocessing and feature extraction methods to analyze EEG signals. In this study, we analyze a comprehensive review of numerous articles related to EEG signal processing. We searched the major scientific and engineering databases and summarized the results of our findings. Our survey encompassed the entire process of EEG signal processing, from acquisition and pretreatment (denoising) to feature extraction, classification, and application. We present a detailed discussion and comparison of various methods and techniques used for EEG signal processing. Additionally, we identify the current limitations of these techniques and analyze their future development trends. We conclude by offering some suggestions for future research in the field of EEG signal processing.