Category Archives: Current Research Advances

Neuroinflammation of Epilepsy – new diagnostic tools

Current Research Advances, , ,

Key Mediators of Neuroinflammation

Over the past ten years, the number of research papers focused on neuroinflammation of epilepsy has escalated dramatically (Ngadimon et al., 2024).  Thus, neuroinflammation is central to both the initiation (epileptogenesis) of epilepsy and the support for repetitive seizures.  A great deal is known about the inflammatory pathways active in epilepsy (Aguilar-Castillo et al., 2024; Ngadimon et al., 2024).  Furthermore, several key mediators of neuroinflammation are now potential biomarkers of epilepsy (Aguilar-Castillo et al., 2024).  These select biomarkers are possible new diagnostic tools poised to replace the traditional EEG. 

This blog will discuss one of the key mediators of neuroinflammation of epilepsy, termed High-Mobility Group Box 1, also known as HMGB1 (Aguilar-Castillo et al., 2024; Ngadimon et al., 2024; Chen et al., 2023) .  HMGB1 has the potential to be an innovative and relevant diagnostic tool to identify and monitor the disease of epilepsy.

Unmet Medical Needs – Biomarkers and Selective Drugs

Several key neuroinflammatory mediators including HMGB1 and others are present in serum and importantly are associated with the severity and frequency of seizures (see studies below) (Ngadimon et al, 2024).  The ability of the traditional EEG to diagnosis new onset epilepsy is poor and its predictive capability for repeat seizures is limited.  It is, therefore, critical to pursue research to fill this unmet medical need and to identify novel molecules exclusive to epilepsy as valid diagnostic and prognostic tools.

Key Mediators of Neuroinflammation as drug targets

Moreover, select neuroinflammatory mediators and their target receptors have potential as targets for new drug therapy (Maroso et al., 2010).  Current anti-seizure drugs, although there are many, have adverse side effects that cause many patients to discontinue use (Chen et al., 2023).  These drugs suppress seizures but do nothing about the progression of the disease.  Furthermore, 30% of epileptic patients do not benefit from present day drugs. They are considered drug-resistant.  Thus, there is a need to develop more selective drugs.  Therefore, focus on development of drugs that inhibit the epilepsy-promoting effects of key mediators of neuroinflammation should prove of value.

Neuroinflammation of epilepsy – instigator?

Neuroinflammation is an inflammatory disturbance in and among the cells of the brain.  “Increasing evidence indicates that neuroinflammation is a common consequence of epileptic seizure activity, and also contributes to epileptogenesis as well as seizure initiation (ictogenesis) and perpetuation.” (Webster et al., 2017).  Approximately half of all acquired epilepsies occur following a prior neurological injury that produces neuroinflammation.  Neurological injuries are injuries such as a brain tumor/infection, traumatic brain injury, or exposure to destructive nerve agents (Klein et al., 2018; Chen et al., 2023).  Neuroinflammation is also the common denominator between epilepsy and sleep disorders (see blog 11)

Animal Models, Human Epileptic Tissue

Results of studies in animal models of epilepsy and data from human epileptic tissue specimens, define the brain pathways and mechanisms of neuroinflammation.  In general, a latent phase follows a brain injury and progresses to a chronic phase.  These changes eventually heighten the excitability of nerves, giving rise to seizures.  Neuroinflammation is characterized by abnormal alterations in brain support cells e.g. astrocytes and glial cells, harmful modifications in the blood-brain barrier (making it more porous susceptible to entry of unwanted cells and proteins), infiltration of immune cells, nerve cell death and reorganization of remaining neurons (Klein et al., 2018; Chen et al., 2023)(see Figure below). 

Importantly, these revisions produce an abundance of significant pro-inflammatory substances whose effects modify nerve activity favoring excitability.  High-Mobility Group Box 1 (HMGB1) is among one of the significant pro-inflammatory substances. Data show its amplified presence enhances nerve excitability and participates in continued hyper excitability (Maroso et al., 2010; Kamasak et al., 2020).

High-Mobility Group Box 1 (HMGB1) -Major Culprit?

HMGB1 is a nuclear protein with a normal activity of assisting DNA in gene expression (“turning genes off and on”), maintenance and repair.  Therefore, residing in the nucleus it remains beneficial.  However, stress-induced movement to the cytoplasm and the extracellular fluid renders it harmful.  Thus, when released from cells, actively or via cell death, HMGB1 acts as a pro-inflammatory initiator binding to one of two receptors (Toll-like Receptor 4, TLR4 and receptor for advanced glycation endproducts, RAGE) (Paudel et al., 2018; Chen et al., 2023). 

HMGB1 Consequences

This interaction sets off a cascade of changes that continue to generate other proinflammatory factors (cytokines, chemokines, growth factors, lipids) that support continued inflammation, keep the vicious cycle going by enabling more HMGB1 release and induce unwanted nerve hyperactivity. Thus, HMGB1 is one of the key mediators of neuroinflammation.

HMGB1 – Role in humans with epilepsy

As reviewed by Aronica and Crino (2011), there are several studies using resected (surgically removed) human epilepsy foci.  These specimens clearly show the presence of neuroinflammation.  Compared to normal brain tissue, specific inflammation mediators and their receptors are present in large amounts in tissue from epilepsy patients.  It is suggested that these distinct changes account for the neuroinflammation and are responsible for promotion of neuronal hyperactivity.  In particular, elevated levels of HMGB1and its receptor TLR4 were identified in human epilepsy foci, specifically in proactive astrocytes and microglia  (Aronica and Crino, 2011).  This positions HMGB1 as one of the key mediators of neuroinflammation.

Biochemical Analysis

More recently, biochemical analysis of  brain tissue removed from patients with intractable epilepsy compared to normal tissue showed HMGB1 not only in the nuclei of  neurons and glia cells (normal tissue location) but also in the cytoplasm where its release would be expected (Shi et al., 2018).  HMGB1, its two receptors (TLR4, RAGE) were significantly elevated in tissue from epilepsy patients compared to normal controls (Shi et al., 2018).

Activated and possibly damaged brain cells release HMGB1 in epileptic regions and, therefore, it should be detectable in serum.  Indeed, three studies reported elevated HMGB1 levels in children with epilepsy (Zhu et al., 2018), adults with drug resistant epilepsy (Walker et al., 2022) and case-control study of 105 epilepsy patients compared to 100 healthy controls (Kan et al, 2019). 

Serum Levels of HMGB1

The insights gained from these studies are important. The first study measured HMGB1 24 hours prior to a seizure.  Thus, the high levels of HMGB1 (and other inflammatory mediators) was predictive of seizure onset and frequency in children (Zhu et al., 2018), a prognostic benefit.  In the second study, drug-resistant epilepsy patients showed higher HMGB1 levels compared to those controlled with drugs and normal controls.  This suggests a possible screening tool to identify those resistant to standard drugs, all of which dampen ion channels and have no effect on neuroinflammation (Walker et al, 2022). 

Significant Human Study

The third study found that the levels of HMGB1 and TLR4 were higher in patients with more than 3 seizure/month and these levels additionally correlated with duration of the seizure of greater than 5 minutes.  HMGB1 levels were independent of the type of seizure (partial, general) or length of the disease (Kan et al., 2019).  These are important findings but are correlative.  Additional studies such as long term clinical trials are required to establish cause-and-effect. 

Conclusions

Clearly there is a need for validated and precise diagnostic tools to promptly and correctly identify the beginning stage and progression of epilepsy.  Misdiagnosis or delayed diagnosis prove fatal.  The neuroinflammatory mediator, HMGB1, is a reasonable diagnostic and prognostic candidate since it is one of the key mediators of neuroinflammation.  Initial stages of epilepsy exhibit higher levels of HMGB1 and serum levels correlate with onset, frequency and duration of epilepsy in humans.  However, the information at present, although promising, is limited and requires more studies, better assays to detect all forms of HMGB1 and definitely a serious research effort with a large clinical trial.

Figure By: Paudel YN, Shaikh MF, Chakraborti A, Kumari Y, Aledo-Serrano Á, Aleksovska K, Alvim MKM, Othman I.HMGB1: A Common Biomarker and Potential Target for TBI, Neuroinflammation, Epilepsy, and Cognitive Dysfunction. Front Neurosci. 2018 Sep 11;12:628. doi: 10.3389/fnins.2018.00628.

References  (http://Pubmed)

1.  Ngadimon IW, Shaikh MF, Mohan D, Cheong WL, Khoo S. Mapping epilepsy biomarkers: a bibliometric and content analysis.  Drug Discov Today. 2024 Dec;29(12):104247. doi: 10.1016/j.drudis.2024.104247.

2.  Aguilar-Castillo MJ, Cabezudo-García P, García-Martín G, Lopez-Moreno Y, Estivill-Torrús G, Ciano-Petersen NL, Oliver-Martos B, Narváez-Pelaez M, Serrano-Castro PJ.A Systematic Review of the Predictive and Diagnostic Uses of Neuroinflammation Biomarkers for Epileptogenesis. Int J Mol Sci. 2024 Jun 12;25(12):6488. doi: 10.3390/ijms25126488.

3.  Chen Y, Nagib MM, Yasmen N, Sluter MN, Littlejohn TL, Yu Y, Jiang J.  Neuroinflammatory mediators in acquired epilepsy: an update Inflamm Res. 2023 Apr;72(4):683-701. doi: 10.1007/s00011-023-01700-8. 

4.  Maroso M, Balosso S, Ravizza T, Liu J, Aronica E, Iyer AM, et al. Toll-like receptor 4 and high-mobility group box-1 are involved in ictogenesis and can be targeted to reduce seizures. Nat Med. 2010;16:413–419. doi: 10.1038/nm.2127 Webster KM, Sun M, Crack P, O’Brien TJ, Shultz SR, Semple BD Inflammation in epileptogenesis after traumatic brain injury. .J Neuroinflammation. 2017 Jan 13;14(1):10. doi: 10.1186/s12974-016-0786-1.

5.  Klein P, Dingledine R, Aronica E, Bernard C, Blümcke I, Boison D, Brodie MJ, Brooks-Kayal AR, Engel J Jr, Forcelli PA, Hirsch LJ, Kaminski RM, Klitgaard H, Kobow K, Lowenstein DH, Pearl PL, Pitkänen A, Puhakka N, Rogawski MA, Schmidt D, Sillanpää M, Sloviter RS, Steinhäuser C, Vezzani A, Walker MC, Löscher W Commonalities in epileptogenic processes from different acute brain insults: Do they translate?.Epilepsia. 2018 Jan;59(1):37-66. doi: 10.1111/epi.13965.

6.  Kamaşak T, Dilber B, Yaman SÖ, Durgut BD, Kurt T, Çoban E, et al. HMGB-1, TLR4, IL-1R1, TNF-α, and IL-1β: novel epilepsy markers? Epileptic Disord. (2020) 22:183–93. 10.1684/epd.2020.1155.

7. Aronica E, Crino PB. Inflammation in epilepsy: clinical observations. Epilepsia. 2011;52(Suppl 3):26–32. doi: 10.1111/j.1528-1167.2011.03033.x.

8.  Shi Y, Zhang L, Teng J, Miao W. HMGB1 mediates microglia activation via the TLR4/NF-κB pathway in coriaria lactone induced epilepsy. Mol Med Rep. (2018) 17:5125–31. 10.3892/mmr.2018.8485.

9.  Zhu M, Chen J, Guo H, Ding L, Zhang Y, Xu Y. high mobility group protein B1 (HMGB1) and Interleukin-1β as prognostic biomarkers of epilepsy in children. J Child Neurol. (2018) 33:909–17. 10.1177/0883073818801654. 

10.  Walker LE, Sills GJ, Jorgensen A, Alapirtti T, Peltola J, Brodie MJ, Marson AG, Vezzani A, Pirmohamed M. High-mobility group box 1 as a predictive biomarker for drug-resistant epilepsy: a proof-of-concept study. Epilepsia. 2022;63:e1–6. 10.1111/epi.17116.

11.  Kan M, Song L, Zhang X, Zhang J, Fang P. Circulating high mobility group box-1 and toll-like receptor 4 expressions increase the risk and severity of epilepsy. Braz J Med Biol Res. 2019. 10.1590/1414-431X20197374.

12. Paudel YN, Shaikh MF, Chakraborti A, Kumari Y, Aledo-Serrano Á, Aleksovska K, Alvim MKM, Othman I.HMGB1: A Common Biomarker and Potential Target for TBI, Neuroinflammation, Epilepsy, and Cognitive Dysfunction. Front Neurosci. 2018 Sep 11;12:628. doi: 10.3389/fnins.2018.00628.

Zebrafish – a valuable animal model

Current Research Advances, , ,

The zebrafish (Danio rerio), approximately 2-4 cm in length, has become a valuable animal model for investigations of neurological diseases and for use in high intensity screening to discover disease-ameliorating drugs.  In particular, this tiny non-mammalian animal has been of use in research into the genetics and pathophysiology of epilepsy and in the evaluation of anti-seizure drugs.  This blog will discuss this model and some of its contributions to understanding epilepsy.

Animal Models

There are many experimental models of epilepsy.  The greatest research effort has focused on rodent models of epilepsy (chemically or electrically-induced seizure, acute or chronic).  Interest in both experimental and natural models of epilepsy in the non-human primates (monkeys) has risen in recent years as has interest in smaller, non-mammalian models (amoeba, roundworm, fruit fly, zebrafish).  To be of scientific value, scientists must rigorously validate all models.  They must demonstrate neuronal electrical activity, neurotransmitter release, gene expression and response to antiseizure drugs comparable to that observed in patients with epilepsy.  As a result, animal models have contributed significantly to the identification of the multiple causes of epilepsy, genetic and environmental and are absolutely essential to the development of safe and effective antiseizure drugs.

Zebrafish Attributes that Make for a Successful Animal Model

Although rodent models of epilepsy have contributed considerably to our understanding of this disease,  rodents are expensive to breed, house and treat.  In contrast, the zebrafish is inexpensive to breed, maintain and treat.  Thus, large numbers are readily cared for with minimal expense.  Zebrafish mature quickly (~3 months), are highly fertile, giving rise to large numbers of offspring and can live in captivity up to 5 years.  The immature form, the larva, is transparent facilitating visual experimentation especially in observing gene expression and nerve activity.  Larvae and embryos are ideal for screening of large numbers of drugs for antiseizure activity.

Additionally and importantly, the zebrafish model relates well to rodent models and patients with epilepsy.  Firstly, 77% of zebrafish genes are similar to those in humans and approximately 80% of disease-associated genes are also present in zebrafish.  EEG recordings in drug-induced seizures in larvae and adult fish are similar to those observed in patients with epilepsy. 

Zebrafish mutants exist with alterations in genes involved in a number of human epileptic syndromes e.g. Dravet’s syndrome (see blog 17). Zebrafish mutants also show similar electrophysical activity as well as responses to anti-seizure drugs as evident in humans.  Additionally, zebrafish express brain neurotransmitters such as gamma-aminobutyric acid, glycine and acetylcholine. These are the same ones involved in human epilepsy. Neurotransmitters in zebrafish increase or decrease in response to seizure-producing and anti-seizure drugs as reported for rodents.

Contribution from Zebrafish Mutants

Many rare and not so rare genetically driven epilepses in humans have been successfully developed in zebrafish mutants.  One epilepsy of interest is Dravet syndrome, a grave pediatric epilepsy with severe seizures and associated cognitive deficits.  The disease origin is attributed in large part to a mutated neuronal sodium channel.  Zebrafish mutants lacking key elements of the sodium channel as in Dravet syndrome were developed and characterized (Baraban et al., 2013).  Mutant zebrafish exhibit abnormal brain electrical activity, related hyperactive locomotion and convulsions.  Furthermore, anti-seizure drugs that reduce seizures in Dravet syndrome also reduce seizures in zebrafish. Moreover, those that are ineffective in Dravet syndrome fail in mutated zebrafish. 

Zebrafish Mutants and Drug Evaluation

Using this model, over 300 chemicals and known drugs were tested (Baraban et al., 2017).  One drug, chemizole, an FDA approved antihistamine with a good safety profile, was highly effective.  This is a surprise but significant finding.   Since other antihistamines were ineffective, the mechanism of action of chemizole in seizure reduction suggests a unique unknown target. This is an opportunity to develop a new potentially novel antiseizure drug for Dravet syndrome.

Contribution from the proconvulsant-treated Zebrafish

The proconvulsant-treated zebrafish (also shortened to the PTZ-treated zebrafish) is a validated and reliable model of epilepsy.  It is used to identify the initial neuronal changes in epilepsy, to identify new anti-seizure drugs and recently to study the negative role of seizures on cognition.  PTZ (pentylenetetrazole) is a proconvulsant. Researchers treat zebrafish with PTZ to generate seizure activity comparable to generalized absence and myoclonic seizures in humans (Gawel et al., 2020).  Specifically, scientists anesthetize the zebrafish and inject the zebrafish intraperitonally (space between skin and intestines) with PTZ and/or an anti-seizure drug. Scientists then return the zebrafish to the testing tank for observations depending on the experimental protocol.   PTZ-induced seizures in zebrafish are dose and time-dependent and ameliorated by anti-seizure drugs such as valporic acid and diazepam (see blog 5).  Changes in locomotor activity following PTZ administration correlates with EEG activity.  

PTZ (proconvulsant)-treated Zebrafish results

As with Zebrafish mutants, researchers use the PTZ-treated zebrafish to screen for novel anti-seizure drugs.  Another interesting use is the study of the negative role of seizures in memory and learning.  Kundap et al., (2017) approached this by developing a T-maze for fish to evaluate memory and behavior.  In this study, researchers collected data on seizure activity, memory, neurotransmitter release and gene expression.  Interestingly, not only did seizure activity affect performance in the T-maze but the anti-seizure drugs that reduced seizure activity also reduced memory.  Tested drugs were phenytoin (Dilantin) oxcarbazepin (Trileptal), gabapentin (Gralise), diazepam (Diastat), rivastigmine (used in dementia ).  The authors conclude that this study provides “proof-of-concept” that the PTZ-treated zebrafish is a valuable model to study the negative effects of repetitive seizures on cognition.  These results emphasize the need to use cautiously those anti-seizure drugs that are additive to the harmful effects of seizure on memory.

Zebrafish Rendition

Conclusions

Investigations with validated animal models are an essential strategy to reveal the genetics and pathophysiology of epilepsy. Also, validated animal models are necessary to develop candidate drugs to eliminate seizures and cure epilepsy.  Although the most frequently used models for epilepsy are rodent models, the smaller, less expensive and equally useful model is the zebrafish.  Its many attributes and its validated relevance to genetics and etiology of human epilepsies may make it the model of choice for anti-seizure drug screening.

References

Baraban SC, Dinday MT, Hortopan GA. Drug screening in Scn1a mutant zebra-fish identifies clemizole as a potential Dravet syndrome treatment. Nat Com-mun 2013;4:2410.

Cunliffe VT et al., Epilepsy research methods update. Understanding the causes of epileptic seizures and identifying new treatments using non-mammalian model organisms Seizure 24 (2015) 44–51.

Gawel K et al., Seizing the moment: Zebrafish epilepsy models. Neuroscience and Biobehavioral Reviews 116 (2020) 1–20.

Kandratavicius L et al.,  Animal models of epilepsy: use and limitations.   Neuropsychiatric Disease and Treatment (2014):10 1693–1705.

Kundap UP et al., Zebrafish as a Model for Epilepsy-Induced Cognitive Dysfunction: A Pharmacological, Biochemical and Behavioral Approach Front. Pharmacol. 8:515, 2017.

Niemyer JE et al., Seizures initiate in zones of relative hyperexcitation in a zebrafish epilepsy model Brain (2022): 145; 2347–2360.

Precision Medicine – Promising Future

Current Research Advances, , ,

Introduction

There is a rising number of epilepsies that have been associated with genetic mutations called genetic variants (see Blog 14) .  For example, some of these epilepsies result from gene variants in ion channels that produce a loss of function or a gain of function.  Such changes promote seizures and other neurological deficits.  Specifically, the common gene variants account for 30% of adult general epilepsies. As more genetic expertise is developed, this number is expected to significantly increase.  Ongoing research from Ingo Helbig and collaborators at the Children’s Hospital of Philadelphia, Perelman School of Medicine and other University neurological departments) suggests that precision medicine is the promising future therapy of genetic-based epilepsies.

Precision Medicine

As reviewed by Knowles et al., (2022), precision medicine is an encompassing approach to optimal treatment.  In particular, it is based on classifying patients according to measurable biology, susceptibility to disease, and response to treatment to assure an efficacious intervention for those who would benefit and avoid those who would not.  For genetic epilepsies, “ideal precision treatment would correct a well-defined genetic mechanism in the context of individualized factors, to impart freedom from seizures and comorbidities” (Knowles et al., 2022).  However, this calls for integration of genetic analysis, natural history and clinical data into searchable databases.  Already, genetic testing alone yields better treatment and less hospitalizations.  Adding natural history and clinical data to genetic analysis would achieve considerably more treatment success.

Achievements Toward Precision Medicine

Identification of genetic variants in certain epilepsies allows for the application of molecular biology techniques to interfere with unwanted mRNA produced by genetic variants.  Therefore, appropriately designed oligonucleotides as well as vector-promoter-gene complexes successfully treat animal models of epilepsy. Currently, evaluation of the latter is ongoing in a clinical trial (NCT05419492) (https://clinicaltrials.gov/ct2/show/) for a genetic epilepsy. 

The discovery of the gene termed SCN8A exemplifies the feasibility of  rapid trajectory from gene identification to precision medicine.  Specifically, SCN8A (voltage-gated sodium channel gene) was discovered in 1995 in mice. Later, its pathological variants were associated with epileptic encephalopathies (seizures with neurological pathology).  By 2019, following intense research, the testing of a specific sodium channel inhibitor (NBI-921352) for the genetic variant was initiated.  Presently, “NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures” (Johnson et al., 2022).  Although this is impressive, “coordinated and systematic streamlining of the epilepsy precision medicine pipeline” from gene discovery to effective therapy is the essential goal (Knowles et al., 2022).

Challenges for Precision Medicine

Technological improvements in genome-wide association studies (GWAS) have facilitated the ability to screen large groups of patients to detect genetic variants associated with disease.  The result is the creation of large available, searchable databases of genetic variants.  However, not only must the gene variant associated with the disease be considered but more importantly, the unique expression of the gene variant in a particular patient must be defined. 

The expression of a gene variant is termed the phenotype of the disease. The phenotype includes all of the clinical observations and measurements in the patient relevant to the epilepsy. Therefore, gene expression may take many forms creating multiplicities and complexities of expression (heterogeneity) from patient to patient. Hence, the correct association of a gene variant with a defined phenotype poses a serious challenge.  According to Helbig and Tayoun, (2016) accurately defining the phenotype of a rare genetic variant, e.g. STXBP1 producing an epileptic encephalopathy represents a “hurdle” due to the phenotypic spectrum that it produces. 

To address this challenge, Helbig et al., (2019) characterized the phenotype of patients with “missense variant in AP2M1” (coding a protein needed for uptake mechanisms on the cell membrane).  The use of the searchable database, Human Phenotype Ontology, set up in 2008 to formalize the phenotypes of all diseases, enhanced this work.  More recently, the International League Against Epilepsy added neurological data and guidelines to it (Kohler et al., 2021).  This database presents “a standardized format to provide both terminology and semantics to a broad range of phenotypic features, including neurological features” (Helbig and Tayoun, 2016).  Consequently, with this type of analysis, researchers (Helbig and Tayou) were able to show “significant phenotypic overlap in individuals with the recurrent AP2M1”. 

Conclusions

To successfully treat epilepsy and its comorbidities both the genotype and phenotype of the epilepsy requires accurate assessment.  Researchers at Children’s Hospital of Pennsylvania and the Perelman School of Medicine and their collaborators are focused on this assessment.  The challenge lies in the interpretation of the epilepsy phenotype since both severe and less severe epilepsies are driven by the same genetic variants.  Sorting out this phenotypic heterogeneity will be worthwhile and lead to benefits of precision medicine for all patients.

References (pubmed)

Clatot J et al., SCN1A gain-of-function mutation causing an early onset epileptic encephalopathy. Epilepsia.64(5):  1318-1330, 2023.

Ganesan S, et al., A longitudinal footprint of genetic epilepsies using automated electronic medical record interpretation. Genet Med. 22(12):  2060-2070, 2020.

Helbig I et al., A Recurrent Missense Variant in AP2M1 Impairs Clathrin-Mediated Endocytosis and Causes Developmental and Epileptic Encephalopathy.  Am J Hum Genet .104(6):  1060-1072, 2019.

Helbig I, Tayoun ANA. Understanding Genotypes and Phenotypes in Epileptic Encephalopathies. Mol Syndromol  7:  172–181, 2016.

Kohler S et al.,  Human Phenotype Ontology in 2021, Nucleic Acids Research  49, Database issue D1207–D1217, 2021.

Knowles JK et al., Precision medicine for genetic epilepsy on the horizon: Recent advances, present challenges, and suggestions for continued progress. Epilepsia. 63(10):   2461–2475, 2022.

Lewis-Smith D et al., Phenotypic homogeneity in childhood epilepsies evolves in gene-specific patterns across 3251 patient-years of clinical data. Eur J Hum Genet. 29(11):  1690-1700, 2021.

Seiffert S et al., KCNC2 variants of uncertain significance are also associated to various forms of epilepsy. Front Neurol. 14: 1212079, 2023.

Xian J et al., Delineating clinical and developmental outcomes in STXBP1-related disorders. medRxiv. 2023 May 11;2023.05.10.23289776. doi: 10.1101/2023.05.10.23289776. Preprint

Early Detection of Seizures

Current Research Advances, , , ,

For decades,  the early detection of a seizure was a serious research effort.  It was obvious that if a seizure could be detected before its initiation, the patient would be better prepared to seek help to suppress the seizure.  However, early detection of a seizure with a lead time of hours proved to be unattainable.  Over the past decade, early detection of a seizure within minutes not hours of its initiation seemed a more reasonable objective.  This blog will focus on the state of research on early detection of seizures with advances developed by the epilepsy laboratory at Johns Hopkins University.

Introduction

As reviewed by Jouny et al., (2011), “The window of opportunity is the time of the earliest detectable changes in the EEG and the onset of disabling clinical symptoms”.  Thus, this provides a definition of early detection.  It may sound straightforward but there are numerous challenges to early detection of seizures. Jouny et al., reviews some of them.

1. Early detection of seizures –  Sensitivity and specificity

Early detection is just that.  Firstly, it must detect the earliest change in the electrical (neuronal) activity that leads to a seizure (called a true positive) and not an electrical event that would not lead to a seizure (called false positive).  Stated another way, the seizure is termed the ictal event and non seizure activity is the interictal activity.  Secondly, early detection must distinguish between lots of interictal activity and a smaller amount of ictal activity.  Many times initially there is little neuronal difference in the two.  Therefore, both sensitivity and specificity are required elements of any measurement targeting early detection of a seizure.

2.  Early detection of seizures – Capacity of microelectrodes.  

Microelectrodes in high density arrays are useful in obtaining detailed data of abnormal neuronal activity (termed pathological high frequency oscillations) indicative of  localized seizures. Specifically, researchers attach microelectrodes to the EEG or intracellular electrodes to capture as much information as possible.  Seizures that spread over extensive areas, unfortunately, surpass the present day recording capabilities of the microelectrode arrays.

3.  Early detection of seizures – Sampling rate.

Higher sampling rates are desirable but this produces more data for analysis.  This requires more powerful computers and better electronics.  Improvements here are needed to detect unique potential changes that distinguish the initiation of the seizure and the seizure itself from all the electrical activity in between.

Current Advances in Early Detection of Seizures.

Early detection devices depend on what is called support vector machines (SVM) which are a type of machine learning algorithms.  “Several algorithms have been developed for early-seizure-onset detection with real-time capabilities for scalp-EEG” (Ehrens et al., 2022).  In particular, they include early detection algorithms worldwide from labs in the Netherlands (Bogaarts et al., 2016); in India (Sridevi et al., 2019); in Italy (Chisci et al., 2010); in Austria/Netherlands (Fürbass et al., 2015); in Germany (Meier et al., 2008; Manzouri et al., 2021); in Canada (Saab and Gotman, 2005) and in the US, (Minasyan et al., 2010, Yidiz et al., 2022, Ehrens et al., 2022). 

Application of early detection algorithms

Early detection devices are appropriately evaluated in an epilepsy monitoring unit (EMU).  The EMU needs to gather reams of data for precise surgical intervention in patients with epilepsy that are resistant to drugs.  The Johns Hopkins Epilepsy Laboratory developed an “dynamical and self-adapting algorithm” (Ehrenss et al., 2022) which detects in real time the seizure phase of early onset as well as the seizure itself.  It is patient specific and does not require prior EEG training.  Basically, the ” algorithm uses a dynamic training set, based on a 20-min dataset that is updating every second” and ” requires no prior training because the algorithm compares the past 20 minutes of activity with the current activity and looks for novelty” (Ehrens et al., 2022)  A processing time of 0.5 seconds allows  algorithm processing, removal of artifacts, and extraction of essential features in the 20 minute learning session.

One-class SVM in early detection of seizures.

This algorithm, referred to as a one-class SVM, was tested in 35 patients with epilepsy (male: female, 62%:38%, 34 years average age) in the Johns Hopkins Hospital EMU.  “Patients were implanted intracranially with a combination of subdural grids, strips and/or depth electrodes to record cortical EEG and stereo EEG signals respectively ” (Ehrens et la., 2022).   For other information on intracranial recordings, check out Blog 10.

As presented above, the algorithm collected EEG data, eliminated non-neural artifacts and identified features essential to seizure identification  This preprocessing trains the one-class SVM and determines novelty.  The output is “post processed” such that it will then identify and signal the detection of an epileptiform event in real time. 

Although multiple SVM configurations were evaluated, the single SVM configuration yielded the best results.  The John Hopkins Laboratory SVM achieved a rate of 87% for true positives and a rate of 1.25% for false positive/hour.  That is, the SVM algorithm detected a seizure correctly 87% of the time and was wrong 1.25% of the time.  The true positive rate increased another 6% points and the false positives declined by half with removal of artifacts post analysis.  The mean detection latency (early detection) was 10.4 seconds. 

Conclusions

There is definitely a need for devices capable of early detection of seizures.  In the EMU, early detection alerts the staff and enables immediate treatment of the patient with epilepsy.  Outside the EMU, an early detection device would dramatically enhance the quality of life of patients with epilepsy.  The algorithm (one class SVM) is moving closer to achieving these goals.  Presently, 100% sensitivity of early detection is achievable in 74% of patients in the EMU setting with the unique algorithm developed at Johns Hopkins Laboratory (Ehrens et al., 2022). 

References (pubmed)

Bogaarts JG et al.,. Improved epileptic seizure detection combining dynamic feature normalization with EEG novelty detection. Med Biol Eng Comput  54:1883–92, 2016.

Chisci L. et al., Real-time epileptic seizure prediction using AR models and support vector machines. IEEE Trans Biomed Eng. 57(5):1124-32, 2010.

Ehrens et al., Dynamic training of a novelty classifier algorithm for real-time detection of early seizure onset.  Clin Neurophysiol.  135: 85–95, 2022.

Furbass F. et al., Prospective multi-center study of an automatic online seizure detection system for epilepsy monitoring units Clin Neurophysiol. 126(6):1124-1131, 2015.

Jouny CC. et al., Improving Early Seizure Detection Epilepsy Behav.  22(Suppl 1): S44–S48, 2011.

Manzouri F et al., A Comparison of Energy-Efficient Seizure Detectors for Implantable Neurostimulation Devices Front Neurol 12:703797, 2022.

Meier R et al., Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns J Clin Neurophysiol. 25(3):119-31, 2008.

Minasyan GR et al., -specific early seizure detection from scalp electroencephalogram J Clin Neurophysiol. 27(3):163-78, 2010.

Saab ME, Gotman J. A system to detect the onset of epileptic seizures in scalp EEG Clin Neurophysiol. 116(2):427-42, 2005.

Sridevi V, et al.,. Improved Patient-Independent System for Detection of Electrical Onset of Seizures. J Clin Neurophysiol 36:14–24, 2019.

Yidiz I et al., Unsupervised seizure identification on EEG Comput Methods Programs Biomed. 215:106604, 2022.