Publications by authors named "M Z Koubeissi"
Introduction: Medication-resistant epilepsy (MRE) is characterized by the failure of adequate trials of two antiseizure medications (ASMs). Numerous studies have shown that once two ASMs fail to control seizures, the likelihood of subsequent ASM regimens providing seizure control diminishes significantly. Recent clinical data on cenobamate (CNB) suggest it may offer higher rates of seizure freedom in MRE patients.
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- Current epilepsy treatment often relies on trial-and-error with anti-seizure medications (ASMs), which can delay finding the best treatment for patients.
- Machine learning (ML) is emerging as a helpful tool to predict how well patients will respond to ASMs based on various data inputs like clinical history and genetic information.
- Although 37 studies show mixed results with some ML models performing excellently, more research is needed to enhance these models and make them practical for use in clinical settings.
Continuous EEG (cEEG) is indicated for the workup of paroxysmal events. We aimed to assess whether primary admission diagnoses predict the yield of cEEG when ordered for evaluating paroxysmal events. We identified patients in the ICU who underwent at least 6 hours of cEEG monitoring to evaluate paroxysmal events.
View Article and Find Full Text PDFBackground: While single pulse electrical stimulation (SPES) is increasingly used to study effective connectivity, the effects of varying stimulation parameters on the resulting cortico-cortical evoked potentials (CCEPs) have not been systematically explored.
Objective: We sought to understand the interacting effects of stimulation pulse width, current intensity, and charge on CCEPs through an extensive testing of this parameter space and analysis of several response metrics.
Methods: We conducted SPES in 11 patients undergoing intracranial EEG monitoring using five combinations of current intensity (1.
Mesial temporal lobe epilepsy (mTLE) is associated with variable dysfunction beyond the temporal lobe. We used functional anomaly mapping (FAM), a multivariate machine learning approach to resting state fMRI analysis to measure subcortical and cortical functional aberrations in patients with mTLE. We also examined the value of individual FAM in lateralizing the hemisphere of seizure onset in mTLE patients.
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