Emily L. Johnson

Population of the ictal-interictal zone: The significance of periodic and rhythmic activity

Highlights • Many patterns – the “ictal-interictal continuum” – are associated with seizures to varying degrees.• The degree to which these patterns should be treated is not known.• We review significance of these patterns, and propose an approach to treatment.

Caring for transgender patients with epilepsy

Approximately 25 million individuals older than age 15 identify as transgender, representing about 0.3–0.9% of the worlds population. The aim of this paper is to identify and describe important medical and social considerations facing transgender persons with epilepsy.

Using network analysis to localize the epileptogenic zone from invasive EEG recordings in intractable focal epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60–65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ. Author Summary Epilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often require observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patient’s brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during, and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

Evaluating and Treating Epilepsy Based on Clinical Subgroups: Elderly Onset Seizure and Medically Resistant Partial-Onset Epilepsy.

For optimal treatment, clinicians must recognize key subgroups of patients with epilepsy who have distinctive patterns of seizures, causes, and treatment needs. Particularly important subgroups are patients who develop seizures in their late adult or elderly years and patients with medically resistant epilepsy. Currently the largest age group of patients diagnosed with new-onset epilepsy is older adult and elderly patients; these patients have a rapidly increasing incidence of seizures beginning in the late 50s and represent the graying of America and the influence of vascular risk factors on producing seizures.

A Novel Method for Epileptic Seizure Detection Using Coupled Hidden Markov Models.

We propose a novel Coupled Hidden Markov Model to detect epileptic seizures in multichannel electroencephalography (EEG) data. Our model defines a network of seizure propagation paths to capture both the temporal and spatial evolution of epileptic activity. To address the intractability introduced by the coupled interactions, we derive a variational inference procedure to efficiently infer the seizure evolution from spectral patterns in the EEG data. We validate our model on EEG aquired under clinical conditions in the Epilepsy Monitoring Unit of the Johns Hopkins Hospital. Using 5-fold cross validation, we demonstrate that our model outperforms three baseline approaches which rely on a classical detection framework. Our model also demonstrates the potential to localize seizure onset zones in focal epilepsy.

Cefepime-Associated SIRPIDs in a Patient With Normal Renal Function

Cefepime has been associated with encephalopathy and with nonconvulsive seizure activity, primarily in patients with renal impairment. Here, we report a case of cefepime-associated encephalopathy in a patient with normal renal function with stimulus-induced rhythmic activity seen on electroencephalogram, which resolved on discontinuation of cefepime. We bring this to the attention of the neurohospitalist community, as cefepime is widely used in the hospital setting, and cefepime-related neurotoxicity may go overlooked, especially in patients with normal renal function. Neurologists must recognize drug-related patterns, as the treatment is removing a medication rather than adding an antiepileptic medication.