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A New Perspective in Epilepsy Classification: Applying the Taxonomy of Seizure Dynamotypes to Non-Invasive EEG and examining dynamical changes across sleep stages.
eNeuro
January 2025
Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel. Department of Computer Science, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
Epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, significantly impacts patient quality of life. Current classification methods focus primarily on clinical observations and electroencephalography (EEG) analysis, often overlooking the underlying dynamics driving seizures. This study uses surface EEG data to identify seizure transitions using a dynamical systems-based framework-the taxonomy of seizure dynamotypes-previously examined only in invasive data.
View Article and Find Full Text PDFIndividual metabolic brain network abnormalities associated with drug-resistant mTLE vary in surgical outcomes.
Front Neurol
December 2024
Department of Diagnostic Radiology, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, China.
Article Synopsis
- The study investigated how metabolic brain network connectivity changes in patients with drug-resistant mesial temporal lobe epilepsy (DR-MTLE) affect surgical outcomes.
- Patients (87) who underwent surgery were divided into seizure-free (SF) and non-seizure-free (NSF) groups, alongside a control group of healthy individuals (38).
- Results showed that both patient groups had reduced metabolic connectivity compared to healthy controls, with the NSF group experiencing more significant disruptions, particularly in key brain networks, suggesting potential reasons for poor postoperative seizure control and implications for preoperative assessments.
User-defined virtual sensors: A new solution to the problem of temporal plus epilepsy sources.
Epilepsia
December 2024
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA.
Objective: The most common medically resistant epilepsy (MRE) involves the temporal lobe (TLE), and children designated as temporal plus epilepsy (TLE+) have a five-times increased risk of postoperative surgical failure. This retrospective, blinded, cross-sectional study aimed to correlate visual and computational analyses of magnetoencephalography (MEG) virtual sensor waveforms with surgical outcome and epilepsy classification (TLE and TLE+).
Methods: Patients with MRE who underwent MEG and iEEG monitoring and had at least 1 year of postsurgical follow-up were included in this retrospective analysis.
Intrinsic brain activity differences in drug-resistant epilepsy and well-controlled epilepsy patients: an EEG microstate analysis.
Ther Adv Neurol Disord
December 2024
Department of Neurology, Fujian Medical University Union Hospital, Xinquan Road 29#, Fuzhou 350001, China.
Background: Drug-resistant epilepsy (DRE) patients exhibit aberrant large-scale brain networks.
Objective: The purpose of investigation is to explore the differences in resting-state electroencephalogram (EEG) microstates between patients with DRE and well-controlled (W-C) epilepsy.
Design: Retrospective study.
A novel way to use cross-validation to measure connectivity by machine learning allows epilepsy surgery outcome prediction.
Neuroimage
December 2024
Hospital del Mar Research Institute; 08003 Barcelona, Spain; Universitat Pompeu Fabra; 08003 Barcelona, Spain; Epilepsy Unit - Neurology Dept. Hospital del Mar; 08003 Barcelona, Spain.
The rate of success of epilepsy surgery, ensuring seizure-freedom, is limited by the lack of epileptogenicity biomarkers. Previous evidence supports the critical role of functional connectivity during seizure generation to characterize the epileptogenic network (EN). However, EN dynamics is highly variable across patients, hindering the development of diagnostic biomarkers.
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