Medicine (Austin & Northern Health) - Research Publications

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Learn how to interpret and use intracranial EEG findings

Frauscher, B ; Mansilla, D ; Abdallah, C ; Astner-Rohracher, A ; Beniczky, S ; Brazdil, M ; Gnatkovsky, V ; Jacobs, J ; Kalamangalam, G ; Perucca, P ; Ryvlin, P ; Schuele, S ; Tao, J ; Wang, Y ; Zijlmans, M ; McGonigal, A (WILEY, 2024-02)

Epilepsy surgery is the therapy of choice for many patients with drug-resistant focal epilepsy. Recognizing and describing ictal and interictal patterns with intracranial electroencephalography (EEG) recordings is important in order to most efficiently leverage advantages of this technique to accurately delineate the seizure-onset zone before undergoing surgery. In this seminar in epileptology, we address learning objective "1.4.11 Recognize and describe ictal and interictal patterns with intracranial recordings" of the International League against Epilepsy curriculum for epileptologists. We will review principal considerations of the implantation planning, summarize the literature for the most relevant ictal and interictal EEG patterns within and beyond the Berger frequency spectrum, review invasive stimulation for seizure and functional mapping, discuss caveats in the interpretation of intracranial EEG findings, provide an overview on special considerations in children and in subdural grids/strips, and review available quantitative/signal analysis approaches. To be as practically oriented as possible, we will provide a mini atlas of the most frequent EEG patterns, highlight pearls for its not infrequently challenging interpretation, and conclude with two illustrative case examples. This article shall serve as a useful learning resource for trainees in clinical neurophysiology/epileptology by providing a basic understanding on the concepts of invasive intracranial EEG.
The teratogenesis risk associated with antiseizure medication duotherapy in women with epilepsy

Vajda, FJE ; O'Brien, TJ ; Graham, JE ; Hitchcock, AA ; Perucca, P ; Lander, CM ; Eadie, MJ (ELSEVIER, 2024-02)

PURPOSE: To investigate rates of occurrence of pregnancies associated with a foetal malformation (FM pregnancy rates) following simultaneous intrauterine exposure to two antiseizure medications in 524 pregnancies in women with epilepsy from the Australian Pregnancy Register who were treated simultaneously with various combinations and dosages of two antiseizure medications (duotherapy). RESULTS: FM pregnancy rates tended to be higher in those exposed simultaneously to two antiseizure medications, each of which was a statistically significant teratogen (valproate, topiramate, or carbamazepine), than when there was exposure to only one such teratogen. When there was exposure to only one such teratogen together with clonazepam or levetiracetam, for neither of which there was statistically significant evidence of heightened teratogenicity, the FM pregnancy rates also tended to be higher, but less so. When lamotrigine was the other component of the duotherapy with an established teratogen, FM pregnancy rates tended to be lower than that for the teratogen used as monotherapy. CONCLUSION: Leaving aside issues in relation to seizure control, our data suggest that it would be best to avoid using established teratogenic antiseizure medications (carbamazepine, valproate and topiramate) in combination with each other due to the increased FM risks. When combining an established teratogenic medication with a less teratogenic one, i.e. lamotrigine, levetiracetam or clonazepam, lamotrigine appears to be the safer option.
Predictive models for starting antiseizure medication withdrawal following epilepsy surgery in adults

Ferreira-Atuesta, C ; de Tisi, J ; McEvoy, AW ; Miserocchi, A ; Khoury, J ; Yardi, R ; Vegh, DT ; Butler, J ; Lee, HJ ; Deli-Peri, V ; Yao, Y ; Wang, F-P ; Zhang, X-B ; Shakhatreh, L ; Siriratnam, P ; Neal, A ; Sen, A ; Tristram, M ; Varghese, E ; Biney, W ; Gray, WP ; Peralta, AR ; Rainha-Campos, A ; Goncalves-Ferreira, AJC ; Pimentel, J ; Arias, JF ; Terman, S ; Terziev, R ; Lamberink, HJ ; Braun, KPJ ; Otte, WM ; Rugg-Gunn, FJ ; Gonzalez, W ; Bentes, C ; Hamandi, K ; O'Brien, TJ ; Perucca, P ; Yao, C ; Burman, RJ ; Jehi, L ; Duncan, JS ; Sander, JW ; Koepp, M ; Galovic, M (OXFORD UNIV PRESS, 2023-06-01)

More than half of adults with epilepsy undergoing resective epilepsy surgery achieve long-term seizure freedom and might consider withdrawing antiseizure medications. We aimed to identify predictors of seizure recurrence after starting postoperative antiseizure medication withdrawal and develop and validate predictive models. We performed an international multicentre observational cohort study in nine tertiary epilepsy referral centres. We included 850 adults who started antiseizure medication withdrawal following resective epilepsy surgery and were free of seizures other than focal non-motor aware seizures before starting antiseizure medication withdrawal. We developed a model predicting recurrent seizures, other than focal non-motor aware seizures, using Cox proportional hazards regression in a derivation cohort (n = 231). Independent predictors of seizure recurrence, other than focal non-motor aware seizures, following the start of antiseizure medication withdrawal were focal non-motor aware seizures after surgery and before withdrawal [adjusted hazard ratio (aHR) 5.5, 95% confidence interval (CI) 2.7-11.1], history of focal to bilateral tonic-clonic seizures before surgery (aHR 1.6, 95% CI 0.9-2.8), time from surgery to the start of antiseizure medication withdrawal (aHR 0.9, 95% CI 0.8-0.9) and number of antiseizure medications at time of surgery (aHR 1.2, 95% CI 0.9-1.6). Model discrimination showed a concordance statistic of 0.67 (95% CI 0.63-0.71) in the external validation cohorts (n = 500). A secondary model predicting recurrence of any seizures (including focal non-motor aware seizures) was developed and validated in a subgroup that did not have focal non-motor aware seizures before withdrawal (n = 639), showing a concordance statistic of 0.68 (95% CI 0.64-0.72). Calibration plots indicated high agreement of predicted and observed outcomes for both models. We show that simple algorithms, available as graphical nomograms and online tools (predictepilepsy.github.io), can provide probabilities of seizure outcomes after starting postoperative antiseizure medication withdrawal. These multicentre-validated models may assist clinicians when discussing antiseizure medication withdrawal after surgery with their patients.
Applications for Deep Learning in Epilepsy Genetic Research

Zeibich, R ; Kwan, P ; O'Brien, TJ ; Perucca, P ; Ge, Z ; Anderson, A (MDPI AG, 2023-10)

Epilepsy is a group of brain disorders characterised by an enduring predisposition to generate unprovoked seizures. Fuelled by advances in sequencing technologies and computational approaches, more than 900 genes have now been implicated in epilepsy. The development and optimisation of tools and methods for analysing the vast quantity of genomic data is a rapidly evolving area of research. Deep learning (DL) is a subset of machine learning (ML) that brings opportunity for novel investigative strategies that can be harnessed to gain new insights into the genomic risk of people with epilepsy. DL is being harnessed to address limitations in accuracy of long-read sequencing technologies, which improve on short-read methods. Tools that predict the functional consequence of genetic variation can represent breaking ground in addressing critical knowledge gaps, while methods that integrate independent but complimentary data enhance the predictive power of genetic data. We provide an overview of these DL tools and discuss how they may be applied to the analysis of genetic data for epilepsy research.
Inherent Susceptibility to Acquired Epilepsy in Selectively Bred Rats Influences the Acute Response to Traumatic Brain Injury

Leung, WL ; Dill, LK ; Perucca, P ; O'Brien, TJ ; Casillas-Espinosa, PM ; Semple, BD (MARY ANN LIEBERT, INC, 2023-10-01)

Traumatic brain injury (TBI) often causes seizures associated with a neuroinflammatory response and neurodegeneration. TBI responses may be influenced by differences between individuals at a genetic level, yet this concept remains understudied. Here, we asked whether inherent differences in one's vulnerability to acquired epilepsy would determine acute physiological and neuroinflammatory responses acutely after experimental TBI, by comparing selectively bred "seizure-prone" (FAST) rats with "seizure-resistant" (SLOW) rats, as well as control parental strains (Long Evans and Wistar rats). Eleven-week-old male rats received a moderate-to-severe lateral fluid percussion injury (LFPI) or sham surgery. Rats were assessed for acute injury indicators and neuromotor performance, and blood was serially collected. At 7 days post-injury, brains were collected for quantification of tissue atrophy by cresyl violet (CV) histology, and immunofluorescent staining of activated inflammatory cells. FAST rats showed an exacerbated physiological response acutely post-injury, with a 100% seizure rate and mortality within 24 h. Conversely, SLOW rats showed no acute seizures and a more rapid neuromotor recovery compared with controls. Brains from SLOW rats also showed only modest immunoreactivity for microglia/macrophages and astrocytes in the injured hemisphere compared with controls. Further, group differences were apparent between the control strains, with greater neuromotor deficits observed in Long Evans rats compared with Wistars post-TBI. Brain-injured Long Evans rats also showed the most pronounced inflammatory response to TBI across multiple brain regions, whereas Wistar rats showed the greatest extent of regional brain atrophy. These findings indicate that differential genetic predisposition to develop acquired epilepsy (i.e., FAST vs. SLOW rat strains) determines acute responses after experimental TBI. Differences in the neuropathological response to TBI between commonly used control rat strains is also a novel finding, and an important consideration for future study design. Our results support further investigation into whether genetic predisposition to acute seizures predicts the chronic outcomes after TBI, including the development of post-traumatic epilepsy.
EEG based automated seizure detection - A survey of medical professionals

Wong, S ; Simmons, A ; Rivera-Villicana, J ; Barnett, S ; Sivathamboo, S ; Perucca, P ; Kwan, P ; Kuhlmann, L ; Vasa, R ; O'Brien, TJ (ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023-12)

Diagnosing and managing seizures presents substantial challenges for clinicians caring for patients with epilepsy. Although machine learning (ML) has been proposed for automated seizure detection using EEG data, there is little evidence of these technologies being broadly adopted in clinical practice. Moreover, there is a noticeable lack of surveys investigating this topic from the perspective of medical practitioners, which limits the understanding of the obstacles for the development of effective automated seizure detection. Besides the issue of generalisability and replicability seen in a small amount of studies, obstacles to the adoption of automated seizure detection remain largely unknown. To understand the obstacles preventing the application of seizure detection tools in clinical practice, we conducted a survey targeting medical professionals involved in the management of epilepsy. Our study aimed to gather insights on various factors such as the clinical utility, professional sentiment, benchmark requirements, and perceived barriers associated with the use of automated seizure detection tools. Our key findings are: I) The minimum acceptable sensitivity reported by most of our respondents (80%) seems achievable based on studies reported from most currently available ML-based EEG seizure detection algorithms, but replication studies often fail to meet this minimum. II) Respondents are receptive to the adoption of ML seizure detection tools and willing to spend time in training. III) The top three barriers for usage of such tools in clinical practice are related to availability, lack of training, and the blackbox nature of ML algorithms. Based on our findings, we developed a guide that can serve as a basis for developing ML-based seizure detection tools that meet the requirements of medical professionals, and foster the integration of these tools into clinical practice.
Changes over 24 years in a pregnancy register - Teratogenicity and epileptic seizure control

Vajda, F ; O'Brien, T ; Graham, J ; Hitchcock, A ; Perucca, P ; Lander, C ; Eadie, M (Elsevier, 2023-11)

OBJECTIVES: To trace (i) changes in Australian Pregnancy Register (APR) records concerning antiseizure medications (ASMs) prescribed for women with epilepsy (WWE) over the course of 24 years and correlate the changes with (ii) rates of occurrence of pregnancies involving foetal malformations, (iii) the body organs involved in the malformations, and (iv) freedom from epileptic seizures. RESULTS: Use of valproate and carbamazepine decreased progressively, use of lamotrigine remained relatively static, and the use of levetiracetam increased progressively, whereas the use of topiramate first increased and then fell again, associated with a temporary increase in malformation-associated pregnancy rate. More serious malformations, such as spina bifida, became less frequent, whereas more trivial ones tended to increase, whereas epileptic seizure freedom rates improved. CONCLUSIONS: The increasing use of newer ASMs in pregnant women has been associated with overall advantages in relation to the frequency and severity of foetal malformation and with advantages in relation to freedom from epileptic seizures.
ILAE Genetics Literacy series: Progressive myoclonus epilepsies

Cameron, JM ; Ellis, CA ; Berkovic, SF ; ILAE, GC ; ILAE, GLTF (WILEY, 2023-10)

Progressive Myoclonus Epilepsy (PME) is a rare epilepsy syndrome characterized by the development of progressively worsening myoclonus, ataxia, and seizures. A molecular diagnosis can now be established in approximately 80% of individuals with PME. Almost fifty genetic causes of PME have now been established, although some remain extremely rare. Herein, we provide a review of clinical phenotypes and genotypes of the more commonly encountered PMEs. Using an illustrative case example, we describe appropriate clinical investigation and therapeutic strategies to guide the management of this often relentlessly progressive and devastating epilepsy syndrome. This manuscript in the Genetic Literacy series maps to Learning Objective 1.2 of the ILAE Curriculum for Epileptology (Epileptic Disord. 2019;21:129).
Targeted continuous EEG monitoring in critically ill patients: The long and the short of a scalability problem

Haider, HA ; Perucca, P (WILEY, 2023-09)
Development and validation of a peripheral cell ratio and lactate score for differentiating status epilepticus from prolonged psychogenic nonepileptic seizures

Tan, THL ; Sanfilippo, P ; Colman, B ; Perucca, P ; Kwan, P ; O'Brien, TJ ; Monif, M (WILEY, 2023-12)

OBJECTIVE: Differentiating status epilepticus (SE) from prolonged psychogenic nonepileptic seizures (pPNES) can be difficult clinically. We aimed to define the utility of peripheral cell counts, cell ratios, and lactate levels in distinguishing SE from pPNES. METHODS: Retrospective two-center study investigating the sensitivity and specificity of acute (≤12 h of event offset) peripheral cell counts, cell ratios (neutrophil-lymphocyte ratio, neutrophil-monocyte ratio, monocyte-lymphocyte ratio, platelet-lymphocyte ratio, systemic immune-inflammatory index [SII], systemic inflammatory response index [SIRI]), and lactate levels in differentiating SE from pPNES. Patients were identified from two tertiary hospitals, with one forming the development cohort and the other the validation cohort. Using generalized additive models to generate biomarker vs time curves, optimal blood collection times were defined for set parameters. Three diagnostic scores combining neutrophil count, SII, or SIRI with lactate levels were developed and validated in separate cohorts. RESULTS: For the development cohort, 1262 seizure-like events were reviewed and 79 SE and 44 pPNES events were included. For the validation cohort, 241 events were reviewed and 20 SE and 11 pPNES events were included. Individually, the biomarkers generally had low sensitivity and reasonable specificity for differentiating SE from pPNES, with the neutrophil count, SIRI, and SII performing best with sensitivities of 0.65-0.84, specificities of 0.64-0.89, and ROC AUCs of 0.78-0.79. Lactate levels peaked at 60 min, while cell counts and ratios peaked after 240 min. Combining early peaking lactate levels and later peaking neutrophil count, SIRI or SII resulted in three scores that improved predictive potential with sensitivities of between 0.75 and 0.79, specificities between 0.93 and 1.00, and ROC AUCs of 0.89-0.91. SIGNIFICANCE: Lactate levels peak early post-SE, whereas cell counts and ratios do so later. The differing post-event time profiles of lactate levels vs neutrophil count, SIRI, and SII allow incorporation into three separate scores which can assist in differentiating SE from pPNES.

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