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Assessment 8 – Nature of Epilepsy

Pathophysiology of epilepsy, ,

Introduction

The electroencephalogram known as the EEG is one of the main clinical tools used by neurologists to capture a seizure so as to make a  diagnosis of epilepsy (Assessment 7).  The EEG is a 20 minute procedure in which electrodes are placed on the scalp and brain wave activity is recorded and analyzed.  It has many flaws.

EEG limitations

The EEG  has serious limitations, chief of which is its short duration. Therefore, the EEG is unable to detect most seizures since most seizures, occur randomly.  In lieu of an EEG seizure, physicians rely on the patient and/or family observations of seizure activity for a diagnosis.  Thus, physicians make a selection of anti-seizure medication without hard evidence of seizure type and brain location.  Although this empirical approach may eventually lead to seizure suppression for the patient, albeit with side effects, it does not advance the field of epilepsy.  Treating the symptoms of epilepsy does not add to understanding the disease in order to find a cure.

Insights from intracranial EEG recordings

Cyclic Nature

Brain wave recordings gathered over a period of years in patients with epilepsy have revealed new and important insights into the nature of epilepsy.  Basically, these findings show that epilepsy creates a brain where abnormal brain waves (interictal epileptiform activity) occur in cycles of 7, 15, 20-30 days or more. 

What this means is that abnormal brain wave activity flows over the brain like ocean waves, rising to a peak and then falling to a baseline.  Cycle length is unique to each patient with epilepsy.  Importantly, these cycles are predictive of a seizure. This is because it is the rising number of abnormal brain waves of a cycle (the upswing of the cycle) that is associated with seizure onset.  Therefore, as the number of abnormal brain waves in each cycle increases to its peak so does the risk of seizures.  The waning number of abnormal brain waves of each cycle is not related to seizure risk. The section below describes the supportive experiments.

These are incredibly significant revelations. Firstly, cyclic brain wave activity exists in epilepsy. Secondly, the upward swing to the peak of each cycle is the time of greatest risk for seizures.  Therefore, cycle intervals are unique for each patient with epilepsy. Each cycle predicts the next round of seizures (since it follows the rise in wave number per cycle).  The knowledge of this relation between cycle and seizure could allow for adequate intervention and prevention.

Source of Insights

These findings appear in a publication by Baud MO et al. Under-sampling in epilepsy: Limitations of conventional EEG. Clinical Neurophysiology Practice 6 : 41–49, 2021.  Since copyright laws prevent reproduction of Baud’s figures, it is recommended that the reader obtain the free publication through PubMed (https://pubmed.ncbi.nlm.nih.gov/).  The representative EEGs with superimposed seizure events provide visual confirmation of these findings.

These findings are important. Two hundred patients with generalized epilepsy of unknown cause (idiopathic) resistant to antiseizure medication participated in Baud’s clinical study.  To suppress their uncontrolled seizures they chose an FDA approved intracranial neurostimulator (RNS_System.NeuroPace, Inc).  The device captures the abnormal brain waves (interictal epileptiform activity) described and can respond with a counter stimulation to neutralize the seizure.  

Sixty percent of the patients in Baud (2021)’s study exhibit cyclic rhythms as described above.  This is certainly a significant number of patients to propose cyclic brain waves rhythms as a basic characteristic of idiopathic generalized epilepsy.  However, in the future, it is important to evaluate patients with different types of epilepsy to determine if these cycles also occur across all epilepsies.

Future Challenges 

The hurdle to more wide spread research is how to obtain years long data without  implantation of electrodes directly into the brain.  Intracranial electrode implantation is major invasive surgery and hence, a choice of last resort.  Since these observations would never have been obtained in a 20 minute diagnostic EEG, there is clearly a need to develop external devices with the same sensitivity and accuracy as intracranial devices that would be suitable for long term use. 

Summary

The exciting findings of Baud et al (2021) uncovered significant novel characteristics in the brains of patients with epilepsy.  Results of years long brain recordings of patients with idiopathic generalized epilepsy show abnormal brain wave activity that continuously cycles over intervals of weeks or months.  As the number of abnormal waves increase so does the risk of seizures.  This is an incredible insight and if confirmed in other types of epilepsy, provides the first step toward understanding the disease of epilepsy.

Myths and Misconceptions

Myths/Misconceptions, , ,

Assessment 2 – Objective seizure detection with biosensors

My first assessment for epilepsy evaluation concluded that persons with epilepsy (PWE) unfortunately have little to no ability to reproducibly predict the onset of their own seizures.  Therefore, subjective impressions are of marginal value.  Given that, there is a vital need for devices or instruments with ability to objectively predict a seizure. 

Many devices called biosensors are already available  (see reviews Nagaraj et al., 2015; Ulate-Campos et al., 2016).  The capability of existing biosensors to predict seizures varies with the specific device.  Unfortunately detection and prevention are generally not part of the same device.  Despite the fact that current biosensors (with possibly one exception) are only able to detect seizures, nevertheless, they represent a significant first step to development of something more sophisticated with therapeutic value.  Furthermore, present day biosensors supply information on seizure frequency, important information that PWE are unable to provide due to an inability to recall the seizure (Hoppe et al., 2015).  As a note of caution, biosensors to date are useful only in certain types of seizures and so to be of benefit, appropriate biosensor selection requires accurate seizure type identification (Ulate-Campos et al., 2016).  

How biosensors work

There are numerous biosensors in use that detect seizures but generally they do not as yet predict seizures within a reasonable time frame for adequate intervention.  Some biosensors record muscle movements e.g. surface electromyography (Conradsen et al., 2012), measure change in muscle acceleration called accelerometry  (Beniczky et al., 2013), or measure movement via mattress sensors (Poppel et al., 2013).  Other biosensors record effects produced by activation of the autonomic nervous system.  This includes measurement of sweating (electrodermal activity, EDA), heart rate (electrocardiogram, EKG), respiration, body temperature and combinations of these with sophisticated computer programs (Ulate-Campos et al., 2016). 

The aforementioned biosensors target seizures in which muscle and autonomic nervous system changes are evident and occur before a seizure.  Other biosensors use near-infrared to detect changes in brain blood flow prior to a seizure (Tewolde et al., 2015).  Ulate-Campos et al., 2016 lists the more than 40 available biosensors and their web sites. 

EEG and intracranial electrodes  – important contributions

However, for all seizures, the gold-standard for seizure detection is the electroencephalogram (EEG).  The EEG records the summation of brain wave activity via topically attached electrodes positioned around the scalp.  Unfortunately, everyday use of the EEG is incompatible with daily life. 

Representative EEG brain wave activity

Another approach, intracranial electrode implantation (iEEG), provides continuous long term measurement of brain activity in the seizure prone region.  Results of small studies and clinical trials with the iEEG (Iasemidis et al., 2003; Cook et al., 2013; Spencer et al., 2016; Karoly et al.,2018; Baud et al., 2018) have confirmed the  presence of

a) unique neurological activity termed epileptiform discharges that occur with a regularity of 24 hours (circadian) or over several days (multi-day), and

b) the relation of aspects of these rhythms (phases) with seizures in PWE with low to moderate seizure frequency rate (Baud et al., 2018).

NeuroVista Advisory System is an example of the iEEG that has undergone clinical evaluation. NeuroVista Advisory System employs an assessment system using sophisticated algorithms.  In clinical trials (Cook et al., 2013; Bergey et al., 2015) it performed with reasonable success.  In several cases where seizure prediction fell below pre-designated criteria, optimization of the computer system i.e. its algorithms, dramatically improved predictability of the seizure.  Thus it appears possible to tailor the algorithm to each patient to achieve the best result (Kuhlmann et al., 2018). 

Although the iEEG successfully analyzes prodrome data (see Assessment 1) to predict a seizure and hence supplies information in a sufficient time frame to avert a seizure, it is not without serious concerns.  An implantation of intracranial electrodes is an invasive technique requiring major surgery and carries the risk of infection and related brain problems.

Biofeedback Biosensors – hope for the future

The most desirable biosensor is one that employs a biofeedback loop with both

1) reliable seizure detection and

2) appropriate and effective therapeutic intervention that prevents the seizure. 

Neuropace Responsive Neurostimulation (RNS) (http://www.neuropace.com)  device is an example of a biosensor with a biofeedback loop.  The device is implanted in the brain and detects epileptiform discharges and delivers neurostimulation to suppress the seizure.  Results of a two year clinical trial with 191 medication- resistant PWE, showed significant decline in seizure frequency (Heck et al., 2014).  The PWE enrolled in this trial had intractable partial-onset epilepsy.  The development of the Neuropace RNS is a significant achievement for select PWE.  However, the biggest limitation is the requirement of major surgery for implantation of this device.

Characteristics of the ideal biosensor

The ideal biosensor would monitor seizure-related external biological phenomena such as EKG, EDA, muscle movement blood flow etc., as discussed above and accurately assess the data to predict a seizure.  Additionally, seizure detection would be linked with effective seizure prevention measures.  This could take the form of a) an alert  to a caretaker to give appropriate therapy, b)  neurostimulation or c) minipump infusion of appropriate mediation (Ulate-Campos et al., 2016).  Additional characteristics of the ideal biosensor include safety and efficacy without false alarms, user friendly instrumentation, ease of care, wireless transmission of data and full compatibility with daily life (Hoppe et al., 2015).

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Questions for readers

Comments on this blog are welcome.  Experience with biosensors would be appreciated.