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UrduListen or read: Circadian rhythms and epilepsy Part I: Dr. Mark Quigg
Reported by Laurent Sheybani, PhD | Edited and produced by Nancy Volkers
Listen below or download the episode.
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Sharp Waves episodes are meant for informational purposes only, and not as clinical or medical advice.
Podcast Transcript
[00:00:00] Dr. Laurent Sheybani: Dear listeners. In today’s podcast, I am discussing with Professor Maxime Baud from Bern University, Switzerland, about circadian and multi-day rhythms in epilepsy. Professor Baud has recently published leading studies in the field, and it’s a great opportunity to discuss in further details these very recent findings.
This episode is part of the two-episode series on multi-day rhythms in epilepsy. The first episode, recorded with Professor Mark Quigg, University of Virginia, USA, was released in early July and is available as any other episode of our podcast, Sharp Waves, on the ILAE website, Spotify, Apple and Google Podcasts, Amazon Music, iHeart Radio, and Stitcher.
Enjoy!
So, hello Professor Baud. It’s a pleasure to have you today for this podcast on multi-day rhythms in epilepsy. Can I maybe ask you to start by presenting yourself?
[00:00:14] Dr. Maxime Baud: Of course. Hi Dr. Sheybani. Of course. It’s my pleasure. I’m Maxime Baud. I’m a professor of neurology at the University of Bern, and I do work only on epilepsy, with a specific focus on the chronobiology of epilepsy. My team carries out work in rodent models of epilepsy, as well as in humans.
[00:00:35] Dr. Laurent Sheybani: And to begin with, I would like to ask you, what is the difference between circadian and multi-day rhythms in epilepsy and why are these rhythms important in epilepsy?
[00:00:49] Dr. Maxime Baud: Sure. So I think the circadian rhythm is the better known of the two. Circadian rhythms have been described for decades now. They were originally found in serial recordings in different biological experiments where the result would change by the time the experiment was made at.
And this is how the whole field started, where it became apparent that it was important to take into account time and to perform the experiments at the same time of the day. But of course then it became its own independent field, chronobiology. And today we are lucky and happy to have a Nobel Prize on circadian rhythms. That was in 2017. And this is an established field. The whole molecular machinery has been discovered. It lives in each and every cell and constitutes the circadian clock of the cell. So this is about circadian. Multidien simply means that we are witnessing rhythms that are longer than 24 hours and can span a period of multiple days.
So when we first observed that in long epilepsy recording, we were discussing, okay, this is a period of multiple days. It must have something to do maybe with hormones, with catamenial epilepsy. And we borrowed the term that was already published in the field of metabolomics, multidien, to describe these cycles over multiple days.
[00:02:24] Dr. Laurent Sheybani: So you think that these two kinds of rhythms, do you think that they depend on the same mechanism, or do you think they have different causes?
[00:02:36] Dr. Maxime Baud: So here we are going straight to the core question, which I will not be able to answer at this stage. I’m open to both possibilities.
These could be completely orthogonal mechanisms that have nothing to do with one another, or it could be the same core mechanism that expresses itself at different periods. To give you an example, if you take two oscillators that are slightly, that have a slight difference in the period length, they will at times seem to be in synchrony and at other times seem to be out of synchrony.
And if you sum up by, for example, by additive interaction these two oscillators, you will create a multidien rhythm. So it is possible to have two circadian rhythms that give rise to a multidien rhythm. On the other hand, as I already said, the only multidien rhythms that we know for sure are the hormonal rhythms of the human being, in particular in women. So for the other explanation that these multidien rhythms could be governed by a different mechanism—it’s possible. Multidien rhythms are known, in the animal kingdom and in humans, of course. The most obvious example is the sexual cycle in women.
[00:03:55] Dr. Laurent Sheybani: So you gave a good example. You said that if we have several oscillators that oscillate with a slight phase shift, it can affect our reading of multi-day rhythms, which means that it’s a very complex thing to assess. How can we identify in a specific patient that that patient suffers from a fluctuation of the risk of epilepsy across multiple days?
[00:04:19] Dr. Maxime Baud: That’s an important question for the practice in epilepsy. And I think the reason why these reasons were not so well described before we finally had access to longitudinal recordings is because when you look at the calendar of a patient, it’s only in a few that you see striking rhythms. But they do exist. There are women with epilepsy that show very clearly in their calendar that the seizures occur during their menstruation. Sometimes also in the middle of the period. But men with epilepsy can have such calendars too. I have several examples in my own patients. So these rhythms are visible in patient’s calendar, but I would say not in all patients.
Because as soon as you have a little bit of variability in the period length or you have the interaction between a period of, let’s say one week and one month, then the picture becomes a little bit more blurry. And it’s only when you have a continuous recording that you can really extract this reason and they can, they become obvious.
[00:05:23] Dr. Laurent Sheybani: Not all patients have multi-day rhythms in epilepsy. What is the proportion of people who have a multidien fluctuation of the risk?
[00:05:31] Dr. Maxime Baud: I say that in calendars you don’t always see them, but once you get to actual objective recordings, I would say it’s a majority. And in a paper by Leguia et al. in 2021 in JAMA Neurology, we estimated, I think rather conservatively, that it comes from 60% of patients with focal epilepsy.
[00:05:53] Dr. Laurent Sheybani: Is there a difference between females and males?
[00:05:55] Dr. Maxime Baud: So there was no difference between men and women; both can have an about monthly rhythm. Both can also have an about weekly rhythm.
That’s another pattern that we often see. Some have a tri-weekly rhythm, others bi-weekly rhythms. We kind of evaluated these rhythms up to a month. It’s clear also that other patients have yet longer periodicity: some two months, three months, but for technical reasons we were not able to evaluate that in our recordings.
But older work from the thirties also shows that some patients even have a circannual rhythm. We also had access to a number of long-term calendars in these patients, up to 10 years. And there we found that we had a third phenomenon of circannual rhythmicity. But it concerns this time a minority of patients, estimated to be around 10%.
Circannual means a periodicity of about one year. And so what we showed is that there were some patients that had an increased risk at certain months of the year, and across patients it could be any month of the year, meaning that Mr. A will have more seizures in January or around January, and Mrs. B will have more seizures around September. I would say the effect size of this circannual modulation is nothing in comparison to multidien and circadian modulation.
[00:07:25] Dr. Laurent Sheybani: So human beings have multidien rhythms in epilepsy. Do animal models of epilepsy also present such fluctuations?
[00:07:33] Dr. Maxime Baud: Yes. They do. There are models, well we can call them models, but they are actually naturally occurring epilepsies in canines, in dogs. And they do too tend to have their seizures at relatively regular intervals, one month, three weeks.
Now the more classical epilepsy models where epilepsy has to be induced, for example, in rodents, in mice, in rats, this is still work in progress. We did find multidien rhythms in epileptic rats together with Christophe Bernard. In mice, it has not been confirmed yet. I have reasons to suspect that in mice it’s not as obvious as in rats, actually.
[00:08:15] Dr. Laurent Sheybani: Can you imagine a reason why in mice it’s less clear than in rats?
[00:08:19] Dr. Maxime Baud: Hard to tell. To go in that direction, we should maybe a little bit speak about another line of work on multidien rhythms. This time across vertebrates, which was done by Professor Tim Bromage at New York University.
This is fundamental biology where Bromage investigated the lengths, the period lengths of multidien rhythm in the teeth of animals because the teeth growth in the infancy of these animals has a deposit of enamel. That’s the outer layer of the teeth that is rhythmic. They remain even into the adult age. So it’s like a, a chronicle of how fast the animal grew. These period lengths of growth inversely correlate with the final weight of the animal. So this means that the heavier animals tend to grow over longer periods of time, whereas the small animals like the mouse have a more rapid period.
And I’m saying that because I think it’s possible that the same rule applies to multidien rhythms in other organs. So I was talking about teeth, which really have nothing to do with the brain, but who knows? But I can imagine that maybe some multidien rhythms are also present in the mouse, but they could be of only two days, let’s say.
[00:09:40] Dr. Laurent Sheybani: And in human beings, can we identify subgroups of patients based on the localization of the focus or the duration of the disease in terms of multidien rhythms?
[00:09:50] Dr. Maxime Baud: We looked at seizure localization. There was no correlation between a given seizure localization or an etiology and a given seizure periodicity. This is why we proposed the term seizure chronotypes. Because we think that this is something that is intrinsic to a given individual with epilepsy and probably independent of the exact nature of the epilepsy, be it from hippocampal sclerosis, be it from posttraumatic epilepsy. There is something about the functioning, the rhythmic functioning of the neurons that is intrinsic to this person.
[00:10:27] Dr. Laurent Sheybani: And do we have an idea of why some patients have a cycle or period of say, 20 days and another one has 27, or where does this heterogeneity come from? Or, or maybe should I start, is there a heterogeneity of rhythms across patients? And if yes, where does that come from?
[00:10:47] Dr. Maxime Baud: So, to the question of heterogeneity, yes. We did find subgroups, which we call chronotypes, that share common periodicities and there is something magic with the number seven. So we essentially found a group of patients that had an about seven-day rhythm. Another group with an about 14-day rhythm. Third group with an about tri-weekly rhythm, a fourth group—a very important one in terms of prevalence—with an about monthly rhythm. And then there is a fifth group that has a little bit of a mix of these different periods, which in the end does not give a clear and obvious apparent rhythm.
[00:11:27] Dr. Laurent Sheybani: Well, I guess an obvious question that comes out when we talk about weekly rhythm is, is there a correlation or is there no correlation with a certain day of the week?
[00:11:37] Dr. Maxime Baud: So that’s another very reasonable question that we asked ourselves. Because for our patients who are working of course the level of stress is different on a weekday or on a weekend, and it is quite well known that in epilepsy, stress can have an influence.
So, obvious question to ask. The answer is no. It’s pretty much no. The about weekly rhythm does not correlate with weekdays and weekends. I think we have long enough recordings with calendars, with the recordings from an intracranial device to be pretty sure about this question. As I mentioned earlier, when two oscillations are close by, period length, so in this case, exactly seven days from the Gregorian calendar, and about seven days from the intrinsic rhythm of these patients, they will be sometimes in sync. And then if you look three months later, they’re out of sync. And it’s really only when you consider very long and longitudinal recordings in the same individual and do the statistics over that duration that you realize there is absolutely no statistical correlation.
[00:12:44] Dr. Laurent Sheybani: So does that mean that your personal feeling is that very long rhythms are actually the expression of shorter rhythms that sometimes are in synchrony?
[00:12:56] Dr. Maxime Baud: So this I cannot answer for sure. And it, I think it’ll require still quite a lot of work, most likely in models with fundamental approaches to answer that question. What I can say, and this time from the clinical side and recordings directly in humans, is that these multidien rhythms really appear to be endogenous, because when you do simple things like looking at different patients that live under the same latitude, the same external rhythm—of course, they may have different habits, but nevertheless—and they live under the same moon and on the same earth. They are not in synchrony, neither with the moon, nor with the rotation of the earth.
If you do a grand average, and we did that across all patients we studied who were living in the United States, you find a flat line. This means that these fluctuations are out of sync. And when you average across oscillations, you find a flat line.
On the experimental side, these rats that Christophe Bernard was studying and were housed together, this is a pilocarpine model of temporal lobe epilepsy. They lived together. They all had an about six-day rhythm, but they were not in synchrony. So some rats would have seizures in a given period, and then the other rats would have seizures in three days later. But each individually expressed an about six-day rhythm.
[00:14:20] Dr. Laurent Sheybani: So far, we haven’t been able to manipulate these rhythms?
[00:14:25] Dr. Maxime Baud: The multidien rhythms? No, not that I’m aware of. But of course there is a lot of interest, since the renaissance of the chronobiology of epilepsy. Because I think in the community, we recognize that this is important, and this is quite fundamental about the disorder. And there is interest to study the rhythms and most likely to manipulate them.
[00:14:46] Dr. Laurent Sheybani: How do you think that this research will affect how we take care of patients with epilepsy?
[00:14:53] Dr. Maxime Baud: Yes. I’m highlighting the fact that I think this is an important phenomenon to recognize and, and it may have a clinical impact in the long run, for a simple reason. Currently we have identified a number of precipitating factors, we sometimes call them, in epilepsy and many others have been investigated and showed no real statistical effects. I’m thinking about the classical ones. So there is still a little bit of a discussion about sleep deprivation—is that affecting seizures only in generalized epilepsy or also in focal epilepsy? I think stress is one of the most well-established precipitating factors, but then at an individual level, there can be many others, like a glass of wine or a particular situation, not to speak then of reflex seizures, which is still a slightly different category.
But all of these factors really, and I’ve mentioned also before the catamenial epilepsy, but all of these factors are when you look at the periods at risk, so in presence of the precipitating factor and the period not at risk, the risk ratio is very small. We are talking about risk ratios around between 1.0 and 1.5 maybe. They rarely go up to 2.
Now we did this same type of calculation of a risk ratio by taking the circadian phase at risk versus the circadian phase not at risk. And we look at how many seizures happen when you are not at risk, and when you are at risk. We found an effect size of 5. And for the multidien rhythms we found effect sizes at 7. And when you combine the two, you get to something like 9. So we are talking about, for the first time, risk stratification that is really impressive in my opinion.
And I think clinicians like to think in terms of relative risk. It’s very intuitive. But this is what we are discussing here. We are talking about risk factors that have an effect size with a relative risk of 1.5, or the ones I cited before, versus close to 10 when you start taking into account chronobiological rhythms.
[00:17:05] Dr. Laurent Sheybani: So do you think that this will change the way that neurologists take care of their patients with epilepsy in a few years?
[00:17:12] Dr. Maxime Baud: Medicine is a pragmatic approach. We have already been doing it. I do prescribe some clobazam to my female patients who have seizures around the time of their menstruation. It works very well for some of them. Epileptologists or neurologists also prescribes drugs, stronger doses of anti-seizure medication in the evening when patients tend to have seizures in the night. So we are practicing chronotherapy and now we are made even more aware of it when we look at these objective recordings that just reveal what we have known intuitively and by experience all along.
[00:17:50] Dr. Laurent Sheybani: I suppose that all this research on multi-day rhythms in human beings was achieved on patients who are taking antiepileptic medications. Do we know how much this treatment affects these rhythms if they do so?
[00:18:05] Dr. Maxime Baud: Yes. So the answer is we do know. I don’t think we know the full extent to which they affect the rhythm.
Maybe let me start with a slightly different question. Is that all the effect of metabolism of medication? So, are those rhythms that we are observing in the brain of epileptic patients actually just a reflection of a systemic metabolism and maybe the circadian rhythms of the kidney that are metabolizing some medication differently or the liver during the night and the day?
The answer is a clear no. The brain obviously has its own circuit and rhythm. And when you, there are some measurements of medication that have been done, of course between day and night, they can differ a little bit, but I think it cannot be the explanation for it all. Especially because okay, maybe you could say fluctuation of medication level could explain something at the circadian level, but then how would that explain multidien cycles?
So these are fluctuations that are truly in the brain, in my opinion. Now the other type of question would be, can the addition of an antiseizure medication change these rhythms? And the answer is yes and no. I think the period remains the same. This means for circadian, the period is always going to be around 24 hours because that’s built in, it’s in our cells.
But the phase might change a little bit. So for example, we were talking about chronotherapy before. I have the impression also from experience that sometimes it can go in the wrong direction. For example, I have patients who have seizures exclusively at night. The typical sleep, hypermotor epilepsy. For example, in the case of a frontal lobe epilepsy, if you cover them with too much anti-seizure drugs in the evening, it’s possible that then these patients will start having seizures in the morning. Which is a disaster of course, because they were used to having seizures in their bed and then all of a sudden, they start having seizures in the morning hours when they are maybe already at work. I have one striking example of that in mind.
So we don’t fully understand what’s at stake. And I’m saying here that at the circadian level you cannot change the periodicity, but you might change the phase of occurrence of the seizures with chronotherapy.
Now at the multidien level we have a study that will come out soon, where we did look at the effect of medication on multidien rhythms. When the medication has some benefit at the level of the seizures that are being reported by the patients, we do see also an attenuation of the amplitude of the multidien rhythms. In some cases it’s striking. And this is an effect on the amplitude and not the periodicity.
So the periodicity remains the same, meaning if someone has a seven-day rhythm and they have a medication that finally helps a little bit, they will continue to have seizures on average every seven days. But maybe instead of having a cluster of three seizures, they will now have only one seizure per cycle.
[00:21:20] Dr. Laurent Sheybani: Which means that it’ll be also more difficult to identify these rhythms in treated patients.
[00:21:26] Dr. Maxime Baud: Yes. This could be, depending on what you base your analysis. And I think this is really we are talking about two different worlds when you try to assess rhythmicity in a patient’s calendar. Which is very difficult because you are just, you have a very sparse observation, right? You are just looking at discrete events, which are the seizures, and as we know are sometimes not very well documented, versus observing objective counts of epileptiform activity in the brain over a continuous scale. So now we are not looking at sparse recording, but continuous recordings, and we have a number to put to every hour or every minute even.
[00:22:06] Dr. Laurent Sheybani: We’ve talked a lot about epileptic seizures so far. Do we know if other epileptic biomarkers present also with multidien rhythms, such as epileptiform discharges, high frequency oscillations, or other kind of biomarkers?
[00:22:22] Dr. Maxime Baud: Yes, we do know that. As a matter of fact, I think it’s only once we started looking at the interictal epileptiform activity and the discharges in between seizures, that the multidien rhythmicity in epilepsy became obvious.
It was known, of course, in certain cases, that some patients can have some regularity. And really there were two ways of interpreting these regular occurrences of seizures. One would be to say seizure number one triggers some sort of memory that would last until seizure number two appears. Seizure number two triggers some sort of memory that will last until se seizure number three appears. And of course there is the very well-established phenomenon of the refractory period. So you could imagine that seizures are temporarily linked between themselves.
But the fundamentally different way of reasoning is that there is an underlying oscillation that changes the risk of seizure. And there is no need to have a memory between seizures because seizures are just the tip of the iceberg. And once you pass a threshold if you want, but under that, the sub-threshold fluctuations are there all the time. And this is exactly what we see in the interictal epileptiform activity.
So now finally, with modern devices in epileptology, we are looking not only at the tip of the iceberg, but we also understand what’s going on beneath the surface. And what’s going on is constant fluctuation of this interictal activity in most of the patients in a rhythmic pattern.
[00:23:57] Dr. Laurent Sheybani: So I guess here you are talking about this interaction between epileptiform discharges and seizures that you discuss in your 2018 paper. Can you say a little bit more about this interaction?
[00:24:10] Dr. Maxime Baud: The mechanisms are not understood. The phenomenon I think is pretty clear and I will insist on a phase relationship between interictal and ictal activity and epilepsy. But where we are coming from is that with shorter EEG recordings, many studies have been contradictively concluding different things: that spikes tend to increase before seizures, or no, they increase after seizures. Or on the contrary, they tend to decrease. And so there was never the possibility to really predict seizures when just looking at an instantaneous spike count over a short window, say one hour.
When we started looking at very long recordings over months and years, what became apparent is that the spiking rate in the hours before the seizure can be very variable. But when you average the spiking rate over days, you kind of get rid of the circadian fluctuation of spiking activity just by running a smoothing and running average and you look at trends on a wider window of time, then you start seeing that the seizures always occur or almost always occur on the rising phase of the spiking activity or when the spiking activity is maximal. That’s, that’s the phase relationship, the interictal and ictal phase relationship.
So we can state that spikes and interictal activity tend to increase in the days preceding seizures and sometimes in the days following seizures. This we can state across patients. Then when you look at the final scale of hours, it can be in any direction, the relationship.
[00:25:57] Dr. Laurent Sheybani: So we said that multidien rhythms can affect the risk of seizures. Do we know if the inverse occurs as well? That is, whether after suffering from a seizure, there is a perturbation of these periodicities of seizures or other biological rhythms.
[00:26:17] Dr. Maxime Baud: Yes, this is also known and has been studied, including by Dr. Krieg. So when the epilepsy is very severe, I’m talking mostly about experimental models, it’ll disrupt the endogenous circadian rhythm. So not only the circadian rhythm in epilepsy, but overall, meaning that the animals will tend to have abnormal behavior. They will not be active during the night, in the case of rodents, and sleeping during the day. They’ll start having fragmented behavior.
[00:26:49] Dr. Laurent Sheybani: Thank you. What do you think is the major finding in the field of circadian and multidien rhythms in epilepsy that has been discovered in these last years?
[00:27:02] Dr. Maxime Baud: Well, I’m slightly biased to that question. Dr. Sheybani, I think multidien rhythms are extremely important mostly because it’s a novel observation, or let’s say it’s a novel quantification of the phenomenon. It’s very prevalent. So again, estimated to be present probably in two-thirds of patients, then it’s really a game changer. Because if someone knew that they had seizure with a relative regularity of about every three weeks, and you could warn them about when they are starting to enter an at-risk period, then we are really talking about seizure forecasting on the scale of days, meaning that you could tell them maybe one, two days in advance, “Be careful. You are starting to be in an at-risk period.”
And this could lead to a strategy like chronotherapy over a few days where you would, for example, give some benzodiazepine to help them get across this at-risk period. This is what we already do with catamenial epilepsy. But you could do it, for example, in a man, even in the absence of this external cue that tells you in which phase of the rhythm you are. It would take a readout of the phase directly at the source, meaning the epileptic activity of the brain.
[00:28:21] Dr. Laurent Sheybani: It’s obviously very difficult to assess these rhythms. What is the mistakes to avoid when we are doing such research?
[00:28:32] Dr. Maxime Baud: There are many statistical subtleties when it comes to circular statistics, and I think my group has tried to really push the rigor with circular statistics. We have a number of methodological papers also on that, but I think, let’s say the two most obvious errors are to assume that different individuals will have the same cycle, either in terms of period or in terms of phase.
We now know that there are different chronotypes, and if you start doing statistics on a cohort where you mix chronotypes, then your effects will just cancel out. Because if you are not in the same phase, some people will have more seizures at 9:00 PM, others at 9:00 AM, and this is exactly an opposite phase, and if you do the mean, you will get nothing. So these cycles need to be studied at an individual level. This is absolutely clear. There are other more subtle issues with circular statistics. The widely used statistical tests are not stringent enough. But I won’t expand on that too much.
There is another very important pitfall in my opinion, which is that with the interest in studying these rhythms, we’ll start finding a lot of correlations, especially at the circadian level. Because as I mentioned previously, it’s in the cells and it’s in each and every cell of the body. So of course you can start seeing correlation between the occurrence of seizures and the sodium concentration in the blood, you can start seeing correlation between any physiological variable that fluctuates with a circadian rhythm, and it’s pretty much the entire physiology.
So having a causal approach will be extremely important and it will also be difficult.
[00:30:28] Dr. Laurent Sheybani: What would be the next important step in this field of epileptology?
[00:30:34] Dr. Maxime Baud: So I think there are really two large avenues that have opened up, now that things have been a bit clarified at the phenomenological level.
Number one is the search for the mechanism and profound understanding of why neurons sick with epilepsy would start expressing these rhythms.
And the other one is much more pragmatic and is taking advantage of these insights and leveraging these rhythms to achieve a long-term dream in epileptology of seizure forecasting. And I truly believe that the discovery of this cyclical modulation of the time of occurrence of seizure is going to be game changing in the field of seizure forecasting.
And one third aspect, one big one, third big avenue of research should be neuro technologies. Because these reasons were finally unraveled and let’s say fully characterized once we started having very long recordings, in this case from intracranial devices that were designed to provide neurostimulation. Or in the case of data coming from Australia that were designed to just record and provide zero predictions. And those were unexpected findings. So, but they really highlight the fact that one has to record over long periods of time to be able to access this longitudinal data.
Now the good news is that there are less invasive devices on the horizon. Starting with smartwatches. A simple smartwatch can inform to some extent probably about multidien rhythms, this time not directly at the source, at the brain activity level, but the heart also has multidien rhythms. Another interesting type of device that is being developed is a sub scalp EEG, which would be less invasive than intracranial EEG, but I believe it’ll allow us to also understand these rhythms and track them, monitor them on broader scales, probably. Because to understand the rhythms, what matters is to just measure the relative fluctuation of interictal activity. You don’t need to detect every single spike to get an idea of how it fluctuates relatively to its baseline.
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