This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschure and Tony Prescott.
Paul Verschure here with the Convergent Science Network podcast.
I'm here with Victor Jesra. Victor, welcome.
You were speaking today at BCPT 2018 about translational neuroscience with a
very strong emphasis on a more that's a whole brain perspective on epilepsy.
There are two issues here. Why epilepsy?
Why do you think epilepsy is a helpful lever to understand how brains work or not work?
Enough. So, first of all, hello. Nice to be here.
I have chosen epilepsy for a variety of reasons.
But epilepsy is a dynamic disorder. It expresses itself through very characteristic
features, spatially and temporally.
Temporally, you have high-frequency oscillations that are, if you have the electrodes,
If you happen to have the electrodes in the brain that are visible with the eye, essentially.
So you have very clear data features that you can recognize that are then also
linked to the semiology, to the science, to the clinical science when the seizure
onsets. That makes it unique.
It's also spatial because it involves a network.
We talk about epilepsy spread. Spread. The seizure spreads through the network.
From a perspective of someone who wants to model the brain, someone who wants
to understand emergent brain function or dysfunction,
here you don't have to look for the features. They're evident.
They have been documented for a long time. They are linked to characteristic
behavioral patterns. When you look at other diseases or disorders linked to
the brain, it's much more difficult.
What you do with schizophrenia, what you do with bipolarity,
what you do even with multiple sclerosis where you have a structural equivalent,
when you want to measure the function or the impaired function,
what you look at in the data, in the measurements, it's extremely difficult.
So even already at the first starting point, you run into difficulties.
And epilepsy promised for me a wonderful entry point, enabling me to apply some
of the tools that were under my control.
So how long ago did you start with epilepsy?
Epilepsy, we started seven years ago.
I followed epilepsy before a little bit, but more as an interested observer,
fascinated by the dynamic features.
But epilepsy per se, the first time I touched epileptic data was seven years
ago. So, epilepsy was among the Greeks known as the holy disease or the sacred
disease, the sacred disease, right?
Because it was believed that this had supernatural features.
So, what are the key features in epilepsy that you think you should try to understand
and control? control there are,
is a number of features that come to the mind that.
Epilepsy is fairly widespread in its expressions.
It depends on the type of epilepsy that you look at.
Today in today's talk, I showed you a patient with a frontal,
prefrontal organization of the network involving the temporal lobe.
And there the
behavioral features are fairly expressive
in the sense of behavior these were normal behavioral features you would not
know yeah if you isolate some of these features you would not think that this
is an epileptic seizure such as rocking off the body or crossing the legs So,
in other features, you just have these muscle spasms in other seizures.
One of the key features we need to get under control is when the seizure propagates
and spread out through the network.
It starts taking away control for the patient.
This is one of the most horrible experiences for the patient,
that the patient starts losing control of his or her behavior,
and that is often not linked to the onset of the seizure, but actually to the
propagation of the seizure.
So, when you talk about some of the features we would have to get under control
is to improve the quality of life for the patient.
If we can limit this type of impairing features on the behavioral level and
just constrain the epilepsy.
The discharge from propagating, from spreading through the network and recruiting other areas,
this would be a wonderful feature to get under control.
Another feature that often is being.
Evoked is a loss of consciousness, which is also often linked to the propagation of the seizure.
This is horrible for a patient when she knows that she can lose consciousness at any moment now.
When the aura appears, then the patient already knows I may be able to lose
consciousness and driving a car.
This is a very debilitating and constraining feature for the patient.
So now you've pointed the way to an important step in your approach, right?
Because then the surprising thing, I guess for the classics.
The symptoms could be so variable.
It might indeed be loss of consciousness, it might be vocalizations,
it might be movements, right? In the end, going back to the same underlying
deficit, if you only look at the surface of expressions of symptoms,
indeed, it looks very mysterious.
So on those grounds, you already said, look, it's a network. It's a network deficit.
So would you really describe it in those terms? You would see epilepsy really as a network pathology?
I would describe it as a network disorder. order
um and technically
speaking uh so in this sense it's not a disease all
of us can have the capacity
to show an epileptic seizure some of my colleagues even even say even claim
that epileptic discharges or the capacity to show epileptic seizures as part
Part of our dynamic repertoire is part of the dynamic repertoire of the brain.
Let's take the short way for ripples and hippocampus, right?
Yeah, but this is more spatially localized.
This is temporal features. The approach that we have taken is a network approach
and reducing it to a statement such as it's the same deficit.
It's simplifying it.
The epilepsies are so different. They are linked to network activations that organize themselves.
The underlying mechanisms may be completely different.
However, the way how the network then expresses itself, this is then linked
to the feature and the subsequent semiology for the patient.
So this is then our entry point. We do not...
My lab is a highly theoretical quantitative lab composed of mathematicians,
physicists, engineers in the institute working very closely together with signal
processing engineers, clinicians, neurologists, neurosurgeons,
all of us together housed in the same institute.
We are...
Not necessarily looking at the genesis of epilepsy and trying to identify the
mechanisms underlying the genesis, at least the quantitative part of the group.
But we are trying to understand once the network is epileptogenic,
the organization of the network that is linked to those features that you were referring to.
And which means you could argue that each epileptic network is different.
However, certain concepts and principles should be obeyed at least as long as
we are looking at it from the network perspective.
I'm always coming back to the network because if you look at an individual area
with the type of approach we have taken, I cannot make a statement about that
beyond some dynamic features, but I cannot make mechanistic statements about that.
I can make mechanistic statements in terms of network language about the epileptic network.
Coming back to your first question is, this is where the power of our approach
lies, where we can, despite the fact that we take a mathematician's approach
to epilepsy, it expresses itself through network features.
This is where we can contribute.
For me, important things now, Because it's a multi-scale phenomenon, right?
Because there's a local circuit that might generate some dynamics that has some
knock-on effect on the surrounding networks. And now I go from micro to macro.
So there are two levels of organization we shouldn't worry about.
But this microscopic, if you want, distortion and perturbation can come in many
forms, that's what you're saying, right?
But that's maybe not the most important part to understand or to control the
perspective of symptomatology.
What you're saying is what you really want to understand is that these knock-on
effects across the network that will be invariant, independent of what this
microscopic deficit exactly is. So this is the consequence.
So you also see a sort of encapsulation of these two levels of operation.
Because it also would mean that if as soon as a network kicks in and starts
to switch itself into a pathological state or dynamic, it doesn't matter anymore
what you do to the local pathological circuit.
The network now is pushed into this part of the state's place where it will
give rise to symptoms that you don't want to have.
This is more or less, actually, this is exactly what I was saying.
So you introduced the language of micro-macro, so the microcircuitry.
What I did not say, though, is that the microcircuitry or the microscopic understanding
is not important by no means.
I'm saying it's not our entry point towards the understanding of epileptic networks.
It's extremely important.
And in fact, if you want to intervene with the epilepsy of a human's brain on
the microscopic level, then pharmaceutics.
This is where the molecular entry points are.
This is where you have signaling pathways where you want to intervene,
that you want to find the right molecule that has the right effect and reorganizes
the microcircuitry and drives the area away from its capacity of discharging. So this is important.
But very often, this magic molecule that does this job, it's simply not to be found.
And if you look at the development of drug history,
so it goes back roughly 80 years, we have essentially four or five families
of anti-epileptic drugs within multiple branching into sub-families, etc., but not more.
Yeah and there.
Since 30% of all epileptic patients are drug resistant there you have to find
well either new drugs or other ways of intervening and this is where we are
then coming in and then it's not the microscopic line yeah well on top of there
are two things here and on top of that,
What are the side effects of these drugs that are being used?
Are they harmless in that sense, or do people pay a price for using them?
I cannot tell you. This is not my expertise.
I do not dare to express myself on the side effects.
But the other thing is that you're saying, I understand you have to say the
microscopic generator that kickstarts this whole process is important.
Because this is, of course, also reflecting how the field itself is organized.
A lot of effort has been put into trying to understand the local genesis of the seizures.
On the other hand, as you also said yourself, over the last 15 years,
we've made actually no progress whatsoever in treating epilepsy.
I did not say this. Over the last 50 years, I was talking about surgery.
Yeah. In treating pharmacoresistant epilepsy.
And if you average across all epilepsies, then you have a fairly flat curve
in the surgery success rate,
which averaged across all epilepsy types is around 50%.
Temporal lobe epilepsy is better. That's 70%. Frontal lobe is lower,
25-30%. So let's say 50% and it has not improved. But that was surgery success.
Okay, I over-generalized. However... It's important. It's important, yeah.
Sure, that's fine. But still, the consequence is still that if you would have
to choose today where you can have the most impact in trying to make progress in treating epilepsy.
The network might be maybe a more opportune target than the local circuit.
If you really want to make progress on intervention planning,
treatment, treatment symptom control and so
on i i if i understand you correctly you're
you would have good reasons to go for the network as
opposed to the microscopic generator of the sea yeah
i would be very comfortable with this
state i would be comfortable with this statement statement uh
from the perspective
of making the biggest progress
um on the microscopic mechanistic level it's important to continue there's no
question about this but there are also many co-dependencies yeah so this magic
molecule it in the testing that is being performed There are co-dependencies on other factors, etc.
So it's kind of one of these blue sky projects that we talked about earlier
that we need a clear plan and agenda organizing our thoughts in order to move
forward more structured.
On the network level, though, there we have at the moment, we may approach a tipping point.
At the moment, we get more and more technology that becomes available to us.
Interact to modulate the network stimulation
is one for instance different types of
stimulation become available non-invasive surgery
non-invasive surgery does not exist less invasive
or minimally invasive surgery such as
thermocoagulation or laser surgery where
you enter into the brain through very little drill
holes and are able to make ablations
at little volumes in
the brain that can help again network concept
to re-equilibrate the network and there so there we have a battery of tools
that allows us to give access what we need is an understanding of how to use
these tools in a well-informed manner.
And there we are at the beginning, really at the beginning.
We have realized that the epilepto focus is actually an epileptogenic network.
It's an epileptogenic zone spread, sometimes very focused, sometimes disparate
with topologically non-connected elements, that the propagation network can
be widespread. So it's clearly a network phenomenon.
How can we make use of that without...
Generating huge cognitive deficits because that
is always when you interfere with the network you generate
cognitive deficits can we ask questions or
build decision-making software guide the
surgeon of making an informed intervention minimally invasive reducing maybe
just the epileptic seizure propagating and minimizing the cognitive cognitive
deficit so So there are loads of possibilities.
And this is where I hope that models can contribute a lot in the future.
And then there will be other ways of interfering. It doesn't have to be surgically,
but there may be some drugs that will be delivered locally, for instance,
that can turn on off populations.
So there will be other possibilities. Drosothicols, right? It might all be non-invasive.
Yeah. You got nox. Yeah. But you're saying two things that really stand out, right?
One, on the one hand, what you are announcing is sort of a revolution of moving
away from the idea of the broken brain.
Like, oh, some molecule is missing.
And if I just now reinsert that molecule, then everything will restore itself to normal.
And from there, you move more to a network medicine perspective on brain pathology.
And I think this, you are, I think, really are one of the big examples of that
movement, I think, right now in neuroscience.
And it's an important one. We have to really, I think, appreciate that also
see it as a very important development in how we think about neuropathology.
Would you agree with that or do you think I'm really exaggerating that? know i would
agree with it that
the network science is definitely entering
into medicine into
computational medicine and the network thinking um is
it a revolution it's changing the way how people
think about brain disorders definitely
in epilepsy but also because
of the successes about the progress we made in epilepsy they start thinking
about how can we think differently about other disorders or diseases also so
i agree with the fact that.
I wouldn't be hesitant to say it is a revolution.
But it's definitely one of the hot topics at the moment.
This computational medicine with a network idea, number one,
and then link it to personalized medicine of being able to render a network patient specific.
Specific, suddenly we talk about virtual brains of my virtual brain and it bears lots of promise.
But there are two consequences now, right? So on the one hand,
it implies that we move to a non-locality principle.
In the past, with the broken brain view, you would also localize the problem somewhere at X, Y, Z.
This is where we have to now fix the thing. while you are also showing in the
results we might touch upon later that these effects can be not co-localized
with the position of your original let's say pathological circuit or lesion.
The real problem might be sitting somewhere else as a dynamic reorganization of a network.
So we have to think about deficits in a non-local global fashion.
But the second thing that you say here, and this is what you show concretely
with your virtual brain project, to make progress in network medicine or network
neuroscience, we must rely on computational methods.
We have to start to build models and we cannot just follow simple lookup tables
and heuristics to try to solve the problem.
So do you really see this as the key strategic step, the strategic trajectory
that we have to explore now of bringing computational models into the clinic to make progress?
I subscribe to that.
You brought up two points. The non-locality has been known actually for a while.
This is not a new feature or lesions or injuries in the brain at a particular
location causes deficits in
functions that are officially localized in completely other brain regions.
So this has been well known.
We can invert that and actually make use of this and propose,
and this is far from standard,
propose interventions at a different brain region that is actually not involved
in the network disorder, but then
has a positive effect upon the network organization and network function.
This is a logical consequence, isn't it? And the,
This to find, and now I'm coming to your second point, how can we empirically find ways of doing this?
For the negative parts, namely injuries in areas causing deficits and other
subnetworks, we run into this through accidents empirically.
But the positive, the interventional therapeutic aspect, we cannot run into by coincidence.
Coincidence so what we cannot
simply operate blindly on human
beings we need a strategy and how
do we do this in silico modeling yeah we understand
the network better at
least on the network level of a human being the brain
network and we try to test out in
silico new interventional strategies that
cannot for ethical reasons performed in
the human being yeah and uh for practical
reasons not in the animal model because
it cannot stimulate all different areas so all
that remains at the moment are in silico approaches
but having understood these concepts
it brings up these in silico approaches
as a vision for the future we have the
computational powers nowadays yeah uh we
have high performance computing and structures we have the
data science that can support it so i
think it's a no-brainer in quotation marks that the trends are going into the
direction of uh brain network in silico modeling and the logic that i just proposed
is completely independent of personal preferences it's uh.
But surprisingly, even though you call it a no-brainer, it's not widely adopted yet.
So there are apparently some obstacles we have to overcome, right? So we're not there yet.
And now, of course, the crux of the matter is, okay, what makes a good model?
And in your case, you made a very strong point for being field models of the
whole brain with emphasis on the neocortex, right?
As a way to start to get a handle on the dynamics of the brain in health and disease.
So why do you believe mean field models are the way to go?
On the level of organization that we look at in my laboratory,
The activity of
individual brain regions when it's being communicated to other brain regions
that expresses itself for the communication is sufficiently described on the mean field level.
We are validating this with high-dimensional microscopic simulations where we
use single-neuron models,
not very detailed single-neuron models, but spiking-neuron models.
And when we perform these very high-dimensional simulations and mimic these
activations in the large brain network, it takes a long time to simulate. But.
We find so far the same consequences for the network and our understanding of
the network's organization.
For this reason, if you ask network questions.
It is sufficient to perform the mean field modeling at least in our hands, in our laboratory.
If you want to pose these network questions and direct them into other directions,
probably linking to microscopic underpinnings, then you have to go beyond that.
We're not doing this at the moment. We seek the proximity to the patient and the clinic.
Yeah the microscopic underpinnings are
in many cases performed in
the experimental laboratory not necessarily
in the clinic unless you extract human tissue etc etc so the b-field model allows
you to collapse the microscopic dynamics across thousands or not millions of
neurons into single state variables right but you say look this whole population
here i can basically capture sure group one state variable that might evolve over time.
Multiple state variables. Yeah, not one but multiple.
Yeah, but not many. A few. A handful. Yeah.
But for the question then comes, what's your benchmark? Right? So...
What are for you the dominant benchmarks to validate that very abstract,
compressed view of brain dynamics?
What is really the benchmark, the gold standard you feel today to validate such a meaningful model?
What is being done in the literature and in the community is you use paradigms such as stimulation.
And stimulate a microcircuit composed of these millions of neurons and then
you get these firings of action potentials of spikes.
There you have certain features with the raster plots with the particular statistics.
That's the way in which you visualize it or assess it, but would it be like
the resting state network?
Is that for you? This is where I'm going to.
So this is what is being done in
the community mean field mean field does not make any
statement about networks yet okay yeah mean
field doesn't make any reference to networks yet it's
a localized population so most
mean field uh modelers mathematicians
that work with that uh look at these features what
we do in our hands the mean
field has to represent uh
for uh the same propagation for
instance through the network when you stimulate and then the same sequence
of brain regions is being activated um we
have not looked at resting state activity yeah
but what we have looked at is we take a very simplified network model with a
reduced architecture and implement it with detailed microscopic neuronal features
and then with mean fields and then we obtain,
under a parameter modulation for instance connectivity or time delays we obtain the same.
Uh behaviors such as a increase
of synchronicity the spatial reorganization at a particular value of the control
parameter that we manipulate etc so we want to manipulate network features and
then we get the same behavior this is what we have done we have not validated this in a.
Full connectome-based brain network models where we have a full implementation
of spiking neuronal networks with a connectome versus a mean field-based model.
The mean field-based brain network model we have done, we have published with
this for over 10 years and it's well established nowadays.
Many labs are working with these type of concepts. There are efforts within
the Human Brain Project trying to generate the neuroinformatics frame in which
we can do these high-dimensional microscopic networks,
but it's not done yet.
There are efforts from the Diesmann Lab in Jülich. There are efforts from my group.
There are mixed efforts in the sense that we built a network partly composed
of mean fields and partly composed of these high-dimensional microscopic networks
to demonstrate this called co-design,
to demonstrate that we get the same dynamics.
But this is all this is still unpublished
so these are efforts for validation and
it's crucial and critical but efforts for validation for
which the first results will come out in
one year two years three years yeah but it's all uh going in place yeah but
this this sounds like validation against more microscopic electrophysiology
right where you can capture that correctly but given given Given the huge amount
of work already done in mean field models,
I assume that within that community, there must be a sense of having gold standards
for the validity of these models.
Because there also have been plenty of publications of mean field models describing
the whole neocortex doing something.
So today, if you today would have to list such a gold standard benchmark to
calibrate a mean field model of the whole brain, what would it be?
Here we are running into a situation where… You have to terminate the interview. No, I'm leaving.
No, it's less a model that is the problem,
but it's more the metrics that you use to describe the resting state activity
and the data feature that you use to calibrate and validate the model. And...
And one of the standards, and since you asked for gold standards,
established today is in connectomics, the notion of functional connectivity,
which even there I said the notion of because there are multiple metrics available
for functional connectivity.
And every single one of them is questionable to some degree,
because these metrics collapse much of this information into a very compressed object.
And we know that mostly this very compressed object of functional connectivity
requires stationarity.
We know it's non-stationary. We cannot measure for longer than typically 20
minutes resting state activity in the human.
So we run into issues of describing correctly or quantifying with a proper metric what we measure.
And then you wanted to calibrate the gold standard in order to compare the mean field models.
And there are you could take
some data fitting approaches that's not good enough
you uh in the naive sense of data
fitting you will always find a minimum yeah and uh or maximum uh depending on
what you're looking for and uh if you become more sophisticated non-linear with
uh uh non-linear Bayesian inference techniques that allow sampling on nonlinear manifolds,
then we are not there yet that these methods can converge and provide us with good outcomes.
The diagnostics is in place, but the methods may never converge in our lifetime.
So at the moment, for this type of validation, it's not the mean field network
model that poses a problem. It's a go-between, the metrics that shall be used for validation.
How do you quantify a spatial temporal trajectory evolving in a high-dimensional
space that is undergoing a random process with some deterministic features?
The situation is worse than initially expected, right? Because even if you had
the gold standard, you wouldn't even know how to sort of quantitatively match to it.
Exactly. Or I could even pervert it even more. I build a virtual brain.
I tell you it's conscious. How would you test it?
You may not even be capable of recognizing. I don't want to go in direction consciousness.
I just said consciousness. But how would you, what type of metrics would you
have to apply in order to go there?
And I think this is something that we are suffering from in system neurosciences.
We cannot simplify in order to be close to the patient or to real world applications.
We cannot simplify everything to a state.
And then we just describe the state or the statistics of the state. It's a dynamic process.
So we need a process-based signal analysis.
Right. I completely get that, and I agree with you.
So I'll confess that, for me, the mean field approach has never been that convincing,
even though it's always presented to me with a lot of confidence, right?
Because in some sense, it has always been anchored to very slow signals.
The majority of mean field models have been calibrated against fMRI data,
which is very coarse, a very, very, very low-pass-filtered representation,
both spatially and temporally, of what goes on in the brain.
So if we move to this low-dimensional state space, in some sense,
many abstract models can do a good job.
Well, so this is arguably a little bit overconfident at this point in time about
the power of these mean-field models.
I mean, you could argue that for the low-dimensional state space,
they're sort of super powerful. They'll always work. They'll always capture these dynamics.
For that, you have enough free parameters. Not a big deal, right? You can always do it.
But, friend, if you go to a more realistic state space, for instance,
we work with stroke patients.
We have shown recently that they have dysrhythmia, as in Parkinson's.
So you get dynamic fluctuations in the cortex that appear transiently.
And if a result was playing out in the thalamus, then itself is going to result
in what goes on in the cortex.
So you have rapid shifts in the dynamics that's playing out in a broad frequency range.
So my mainfield model would not trivially capture that just like that.
I'm playing a different game now. So the concern I'm having,
and so maybe you can resolve this for me, but yes, we have built all these tools and they're fantastic.
If any people got tenure with these kinds of models, it's great,
but it's all anchored to sort of low-dimensional dynamics.
What we really want to be, as you described, is high-dimensional transient dynamical
states to which they might never really generalize.
So would it not be wise at this point in time to all say, well,
we learned a lot using mean field models.
We've learned to appreciate the real problem we're facing, but maybe mean field
models are now essentially not that helpful anymore as a tool.
We should move on and think more about, let's say, multi-scale dynamically configured
networks that operate at micro and macro level simultaneously.
Simultaneously, would you go in that direction or would you still feel that
there's a lot of leverage to be gotten from the mean field approach?
I feel there is still a lot of leverage to be gotten from the mean field for the following reasons.
The way you argue is that the mean field modeling has found mostly application
in the resting state literature of the fMRI.
I would say, no, mean field models have been used in the resting state network literature.
And even very phenomenological models, as you correctly pointed out,
a simple model is fully sufficient.
We don't need four or five dimensional models. It's just a simple phenomenological
oscillator may already capture many of the features that are being observed,
why it's network effects.
And with that, I agree, actually. They are spatially filtered,
they are temporally filtered, and if you just look at temporal or spatial configurations, it's...
Probably not very insightful what you do in the resting state network literature.
You have to look at spatial temporal features. And there it's becoming interesting.
And there, having talked about the non-stationarities, you need nonlinear models
that have certain characteristics that are then being informed by the connectome.
But in fMRI, due to the nature of the signal, looking at non-stationaries and
the fMRI signals, there we are already at the front line of the research.
But having said this, mean field modeling has by far not just been applied to fMRI signals.
This is fMRI modeling neurovascular coupling, yes, uses mean fields.
But you can have multi-level, multi-scale,
mean-field models using the super,
infra and granular layers and assigning a mean-field or neural population,
neural mass to each of these layers.
Interacting with multiple scales interacting with
different types of connections slower and faster
connections in order to introduce a temporal multi-scale
architecture in there which has been very important example is for instance
the work of Fabrice Wendling who's performing following stimulation paradigms
and capturing some of the response features there we're talking of a neuroelectric
signals on time scales that are relevant for the time.
Processing time scales we encounter in the
brain for the for the spectral features
that we are familiar with for the different bands rhythmic bands
that we are familiar with and there these mean
field models are not just simple scalar elements
that are just shifted around on a linear equilibrium point and reorganizing
the networks as in the resting state literature no here here Here we have to
work with detailed organizations that can be stimulated,
that propagate through the network,
that have continuous propagation through the gray matter, that send signals
through the white matter.
This is being done. I agree.
It is the beginning. I fully agree with that.
But your question was can we still gain some leverage out of this and yes yeah
what I see there's a sort of conceptual problem here that,
the definition of mean field modelism is shifting right so he has this famous
quote from Norbert Wiener that the best model of a cat is a cat and preferably the same cat,
what he means with that is to model means to abstract right and in some sense
what I hear you say is that well Well, as long as we abstract, I can call it mean field.
Why do you hear this? Well, because you're saying I have mean field models that
would take into account organization at different layers in the cortex, specific cells.
And so it just means you change the granularity of the averaging that you perform.
Is what we perform, yes. That they have certain properties such as adaptation.
And then in the different layers, you have different populations and this is being reflected.
But that means if you go to the mean field models of some time ago,
that was more a closer link to, let's say, statistical physics. Yes.
They made, I think,
a stronger claim of the boundary because those mean field models also had the
objective or at least a mission to collapse them into some sort of mastery equation
with which you can really describe the macroscopic dynamics of that system.
So we need the search for this abstraction to also, in the end,
have an analytical handle on that system.
Well, what you're saying now, the way I take it, which is not necessarily criticism,
it just means we have to think a little bit about what we mean exactly with the mean field model.
Model is, you know, as long as I'm averaging in some sense, as long as I'm a
collapsing detail into state variables, I'm using a mean field model and therefore
the mean field model can scale and be diversified in all possible directions.
But if something starts to represent everything, it represents nothing,
right? So what then do we really mean with mean field?
And are we maybe we should then
also would it be useful to start
and then also give more specific labels to this granularity
of modeling right like the number of free per map parameters we are going to
allow the commitment to analytic solutions or not right so so don't you feel
that you're sacrificing a
little bit the specificity of the meaning of a mean field approach only to.
No, we are still in my comfort range.
I would not be willing... My comfort range is probably limited up to this point
where we go across the individual layers.
I would not go any further in terms of granularity because at some point it doesn't make any sense.
But in terms of abstraction, it again reduces to what I want to explain,
what type of signals I want to explain, what type of phenomena I want to explain.
And the explanatory power of, I mean, field benefits from the layered organization
and propagation through the gray matter, but also then through the white matter fibers.
But I would not be comfortable of deconstructing it any further for two reasons.
Any further deconstruction or specification may not help in the explanatory
power of the signal that we want to explain, number one.
Number two, at some point, this mechanism of averaging,
because of all the detailed architecture,
physiological architecture that is present, we have glia cells, astrocytes, etc.
It doesn't make sense anymore. Other effects would have to be integrated also.
It has its limits. You have to have a sufficiently coarse,
object in order to average across its inner organization. Otherwise, it doesn't make sense.
But this is interesting, right? Because it doesn't make sense until it does.
So, for instance, if for an epilepsy case, I would find out that some sort of,
infrasupergranular layer distinction would be really critical,
then you would be happily embracing it.
Then you would be happy to exceed that boundary. they should not draw.
Pragmatically thinking I would probably I would then probably consider it I
would have to be convinced that it does matter etc.
Yeah of course yeah yeah yeah exactly yeah exactly and I'm not saying that this
distinction does not matter I have certain objectives with the research I want
to that I want to do I want to reach,
real world questions in the clinic so I'm building the tools that help me to
address this type of questions so I get it but this is important for the field
of mean field approaches,
I think it would be useful to have that discussion to also see okay maybe we
should look at different types of mean field models and also start on their
interrelationship again it is being actually done Paul,
and And there is a community working on this. Imagine heterogeneity.
Neurons in a population are not identical. If you just take the most...
A simple feature of them the threshold distribution yeah how do you take it
into account is actually generates if you have this diversity or no dispersion
within the population which in physical space is zero dimensional but if a dispersion
of thresholds it already.
Generates a huge complexity in the dynamics i'm talking about the model now
just in the model we We know that you can have chimera states, the same population.
Actually, in this case, it's not chimera states, but you can have clustering in the behavior.
And within the population, you get clusters of similarly behaving neurons due
to their similarity in their physiological constants.
But you cannot average over it because some are spiking regularly and others
have the irregular spiking frequency that we know like a Poissonian train.
So, what would I do there?
And again, if I am aware of these things, I split it in multiple mean fields,
which means I complexify.
And this has been done. This is one mean field model that exists and it makes
sense up to a certain degree.
But once it becomes non-handleable anymore, then its pragmatic use,
its pragmatic added value is not there anymore.
And I would not support this approach to go any further.
But one thing you did to give more structure to your mean field model interpretation
was to move towards very distinct models of different kind of oscillators.
And then to use these to sort of, if you want, constrain the function interpretation
and then use that again to go back to your epilepsy physiology to say,
I don't need to model this as some mean field network.
I can actually re-describe the mean field model as an underlying oscillator
with different characteristics, even state variables.
And the question is now becomes, well, this whole family of oscillators,
which one best describes the specific bit of epileptic state that I'm trying to describe, right?
So why did you move in that direction? Why did that become then the next step?
Because in some sense, you then gave up the literal whole brain mean field model,
which is no, no, I'd use that to calibrate my oscillator model.
And the oscillator model becomes now my reference interpreted data,
right? to actually have collapsed again the mean field model of cortex into
these oscillatory models.
So has that given you leverage or is it sort of more like an experiment that's
underway right now and it might not really work out?
It's interesting that you ask this question. It's actually the way how science
sometimes goes, it can be very exciting when you look backwards.
We started off with mean field models, again, driven by a particular question
in the context of epilepsy that is a propagation through the network.
I recognized that an important fundamental characteristic is missing in these mean field models.
It's simply not in there. Through the averaging process, through the methodology
that is being applied, you essentially lose it.
It is an additional dimension,
an additional degree of freedom that we refer to as the slow variable that acts
upon another timescale that is not part of the classic mean field averaging,
But that has certain characteristics that guides the, on a slow timescale,
the mean field dynamics, if you wish, through a sequence of behaviors.
And then also the system from seizure onset through the evolution during the
ictal state all the way to seizure offset.
Said and that was not
something we could overcome we could
look into physiology we knew there are slow processes
classics are extracellular potassium the work of uwe heinemann etc there is
knowledge in there but that would require an additional mechanistic bottom-up
approach developing this And it's multifactorial, as we know.
And we ask the question, can we,
since we are interested in the network question, can we find another...
Another perspective still capturing this additional feature in there justified
by a scientific perspective, but maybe a different perspective.
And there we turned to mathematics.
And since we were looking for a slow variable, we tapped into the theorems of
nonlinear dynamic systems, fast-slow systems.
And we made use
of that to abstract entirely away from the physiological interpretation of these
variables and looked at the dynamic structure of the representative of this
mean field that we needed.
And we did this very, very systematically.
And we learned a lot out of this.
But it gave us also a new perspective about how to look at seizures,
of what features to look at seizures, not physiologically motivated,
not mechanistically motivated anymore, but purely dynamic features.
And that started a trend in the community in terms of slow variable,
onset bifurcation, offset bifurcation, etc.
So that is becoming a language now. This is exciting.
But then, looking back, I still remember how I went to my collaborator, Christoph Bernard,
who did the physiological testing in the hippocampus, and we were discussing
these dynamic features that the meta mean field or this expanded mean field
representation expressed in terms of phenomenological variables should have.
And I said, Christoph, I have a problem with that.
There should be a baseline jump that you see in your data in the time series
because the bifurcations, the dynamics that we find, there is a baseline jump.
And it's not in the data. Can I somehow sweep it under the carpet,
maybe hide it in the signal-to-noise ratio?
Show or what can we do about this
and he said victor what you're ah but look these
are ac data that everyone
is recording an ac but i can make the same recordings in
dc yeah so he went back to the lab yeah did the same recording in terms of dc
and because i told him about the slow variable at the same time he measured
also oxygenation and atp consumption and came back to me and showed me the same recording AC.
There you didn't have a baseline jump. In DC, you had exactly the baseline jump
at seizure onset and offset.
Hippocampus, in TUTO, in the red, exactly as I expected, very beautiful.
And the oxygenation and the ATP consumption traced out very beautifully the
time course of the slow variable that we expected.
There you could say this is not too surprising because it's,
of course, linked to energy consumption and it has to happen.
When the tissue fires very fast.
Well, but it's, and it's, I'm sure it's not the slow variable,
but these are representations or expressions thereof.
But it became consistent and things came together.
And then we went into human tissue. We contacted other institutions,
Ikeda in Japan, Milan Brush Deal in Brno.
They had also DC recordings from the human tissue. And then for the seizure
types that we were looking at, we found the baseline jumps.
And this was coming from the dynamic structure of the system.
And this was really unexpected, very beautiful, and gave us confidence.
Different elements started coming together. And now people, my colleagues,
are looking into possible realizations of what we call the slow variable.
I mentioned extracellular potassium, definitely glial activity are good candidates
that are linked to the neuroelectric discharges, etc. Right.
But do you believe that it's the slow variable that is also of most relevance
with respect to the propagation through the network?
I have to hypothesize. You ask believe.
Yes, I do believe that the slow variable plays a particular role in this.
We know that the glial network is tightly connected to the neural network.
It's evolving on a slow timescale, much slower than the neuroelectric discharges.
We do not know enough about that.
However, I'd like to point out that when seizures propagate and spread,
sometimes everything discharges, the internet network discharges at the same time.
Sometimes it starts discharging at a particular location, one,
two seconds later it recruits another area, then it disengages and recruits another area.
So you have a spatial temporal organization on the timescale of seconds.
What are the mechanisms that are linked directly to this?
You need to have some timescale hierarchies there,
not necessarily separate and expressible in physically separable quantities
such as region 1 and region 2, but more informational.
But there must be a timescale hierarchy in order to describe this multiscale behavior.
But the consequence of that would then be that you also look at a new generation
of network or whole brain or whole cortex models where each node in a network
becomes a distinct oscillator, right?
Coupled through whatever the connectomics has told you.
Plus a slow variable that is locally connected to this oscillator through the
connectomics and then this oscillator is coupled through the connectomics as you point out.
So we look at a co-evolving, a new form of mean field models,
co-evolving on two timescales, fast and slow, localized in tissue, in space,
communicating at least through the connectome, maybe in addition to this,
also at least locally through a glial network.
That would be a new generation of mean field models. Exactly,
yeah. Yeah, absolutely.
And would every node now cycle in a state-dependent fashion through different
models, through some different oscillatory models?
Or would it be just one oscillatory model that is just pushed around in its
state space because of the external perturbations?
I do not know. I cannot tell you. Because the oscillator you take for the node
where you have the seizure origin is in some sense a pathological node, right?
Where you see the DC shift and the low variable and so on.
This might not necessarily be the right oscillatory model for all the other nodes in the network.
Yes. Okay. So there is now, that might be interesting. So it might look like
every node, the dynamics is potentially represented by a certain set of oscillators,
but now depending on the state of that specific node, I'm either in one or the other.
The way we have looked at that so far is we looked at topological equivalence
of the parameter spaces.
I don't want to make it too abstract, but it's like you can identify certain
features in the properties of the oscillators.
And then you know that in the neighborhood of this feature, in this case,
it's a type of bifurcation, a co-dimension 3 bifurcation, Tarkin's Bogdanov.
You know, in this neighborhood, all the behavior and all the classes are more or less the same.
Yeah so it's not multiple oscillators but
the qualitative behavior of the same
oscillator changes as you move
around in this type of neighborhood so it's
a local statement and if you wish to
call it a pathological oscillator i'm comfortable with that
uh uh however i'm
not comfortable with making statements that are not somehow in the local neighborhoods
but that jump into completely different behavioral uh repertoire somewhere else
i simply cannot make any statement about that yeah i do not know,
But you're free to speculate. I would be. I'm free to speculate.
You want me to speculate? Of course.
There is, the way we looked at it is we talk about oscillators and we built
a generic canonical model around a very characteristic bifurcation point,
which is a kind of an anchoring point in a parameter space.
This oscillator has no idea that it's supposed to be pathological or epileptic
at the end of the day it's a mathematical object that has certain properties
that we are manipulating and that are based on certain proximity principles.
There are consequences for its behavior and actually there is just a finite
number of ways how bifurcation lines can collide.
And hence there are not so many types of different behaviors that can occur.
It's almost evident that there is a limited repertoire of behaviors in there.
So having said this, and they are interdependent, so having said this,
why, I'm speculating, this line of reasoning does not apply only to epilepsy.
What's about physiological oscillations? What's about up and down states?
What's about spindles that occur?
Can we apply this to physiological objects or physiological oscillations then
characterize them in a similar way?
Maybe, maybe, but it's a little less controlled.
They appear sometimes, they do not. We know they are linked to sleep and wake
changes, the different sleep stages, there are different behaviors.
A similar way of thinking should apply to that also and may even,
and I'm speculating even further, require the need of a slow variable.
So I would even be comfortable to speculate that we need to generalize this
way of thinking of a co-evolving,
at least two-tiered temporal scale mean field model to physiological applications
and say it should apply actually also to that.
That's very beautiful. But now look, so we made quite a tour here,
trying to understand epilepsy.
The important, the critical step was to move to a network perspective,
right? To a network science perspective, for medicine perspective on epilepsy.
And now, of course, then we're thinking about what, at the heart of that stance,
called computational model grounded approach to get what are these models.
But now it was up to you. I mean, really think about the full-fledged deployment
of this way of thinking to the clinic.
So we have infinite time and infinite resource, now it's there, in the hospital.
What do we see? How will this translate to a technology really at use in the clinic or at home?
How do you see this realized in the real world down the line?
Beyond epilepsy? Well, let's start with epilepsy. Mm-hmm.
The way I would like to see it is as a decision-making system that in the real world,
we're talking about a real-world application now that it's a clinician,
enters in the decision-making process during the patient management conferences.
They develop confidence upon this software.
It will probably express itself as a software and feedback their competences
in order to make it better.
But at the moment, it's extremely primitive where I would like to see it would
be as an in silico platform where we can perform testing,
maybe optimization of procedures.
But that requires validation that requires confidence that the predictive value
is sufficiently strong enough there. there we cannot bypass the animal.
However, we can see it as a means at some point, once we have generated the
confidence, to bypass animal experiments, because they will be substituted by in silico experiments.
And then we will have in silico brains of individual human beings where we can
optimize procedures, maybe rewire, maybe discover new therapeutic interventions, but.
But we have to lean out of the window, beyond what we are doing right now.
Like with a lesion, as we talked about earlier, lesioning in an area that is
not impacted by the impairment.
So we need to go beyond that. And this, that we need to find good strategies how to do this.
Otherwise, we will never have trust and faith in an in silico platform for a particular patient.
So this requires a strategy, confidence, trust building. But especially with the clinicians.
So you're not going in a direction of having devices that will work directly
with the patient to help them to control their epilepsy.
You see it's really more going into the clinic to decision support for the clinicians
to decide on interventions, right, that might mean non-local interventions in
a network affected by epilepsy.
Just as an example, the non-local part, but there may be other ways of dealing with it.
That would be if I generalize on that, which I have no difficulties in doing.
Maybe that also means that at some point we do need to carry a model of our
brain as part of a medical record because we might have a lesion somewhere.
And then to figure out what could go wrong, I need the reference.
And the reference is the model of a cat is a cat, preferably the same cat,
would certainly hold for us.
So this might be then also an automatic consequence of the approach that you're following now.
Which would be wonderful, wouldn't it be?
Then we have a reference brain for you that may have been fingerprinted,
data fit, where the parameters through a battery of cognitive tasks,
maybe stimulation paradigms, your brain has been, your virtual brain of you
yourself has been calibrated and the range of parameters has been identified.
Then the model parameters themselves become a biomarker for your health.
Because maybe we could imagine an ongoing online calibration of the parameters
of your brain model on your favorite app.
And then as you get tired, depressed, I don't know what, the parameters get out of range, etc.
You could imagine, now we are dreaming, now we are speculating.
But yes, a virtual brain model entering in your medical records,
I think this is not necessarily a bad idea.
But the cool thing is that automatically and for free, you achieve the post-humanist
dream of downloading your mind into a computer.
Who has talked about the mind?
Well, you know, we think it's isomorphic. But then that mean field model you
have in my medical record, better be correct.
The mean field model is a model of a regional activity expression,
and you know that very well.
I'm just provoking you. Yeah. But look, so Victor,
this is really very exciting territory, and you made huge tries in really redefining
the challenges and making real concrete progress on answering those challenges
and understanding the brain and also having clinical impact.
So if we would like to follow in that direction, what would be Victor's law
that we have to adhere to?
It would be more specific. If we would follow into this direction,
which direction? Virtual brain. The science.
Virtual brain, yes. What's Victor's law that I should follow?
In order to do? To make progress.
To achieve the dream you just declared, a virtual brain and every medical record. Mm-hmm.
I don't know about Victor's law, but the go beyond states, think processes.
I'm trying to formulate it as a law. But go beyond states, think processes,
let them be spatial temporal or not.
But we need a dynamic way of thinking
or thinking of the dynamic
brain and i would looking
back at the history of science this state's approach has hampered much of the
translational capacity that neuroscience could have had so let's think in terms
of dynamics and not just jargon but make put it to use,
and explore the powers that we have in terms of dynamics to ask different types of questions.
Right. So the last question for me is, so you run a big center there in Marseille.
You have a lot of machinery and a lot of support, so you can really advance
the science at quite a pace.
So four years from now I'm going to go visit you in Marseille to see whether
you falsified or verified,
a key prediction that you're going to share with me now so in your program what's
the most central hypothesis you want to see tested in that four year time frame.
I would be yeah Yeah.
The most central hypothesis in a four-year time frame, in the way how we think
in Marseille about epilepsy,
and here I'm focusing it on epilepsy, is that we can...
Use the network description to a
patient-specific connectomes within the networks that
it has a real explanatory
power and we
can make use of this explanatory power to
help the patient and improve the outcome
of the therapy which means if we use patient-specific and not generic connectomes
to build virtual brains we can make better predictions about what should be
done in order to help the patient.
When you come in four years into my lab in Marseille,
I would like to present you with three to four hundred patient cases where I
have convincing metrics.
Convincing statistical significance that demonstrates unambiguously that using
this patient-specific connectome linked to dynamics,
dynamic mean fields, has actually improved surgery success or interventional success.
But wait, let's be specific now. Today's success is 50%, you're saying, right?
Average across epilepsies, etc. So where's the number going to go, the success rate?
Do you want to pin me down on a number?
60%. Improvement by 10%. I'm really shaking this one out of my sleeve.
You know that very well. Of course, but you know, we need the bet.
Okay, 10% improvement, 400 patients in four years. Fantastic.
Victor Jesra, thank you very much for this conversation. Thank you, Paul.
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