Ok, good afternoon everyone
my name is Victoria Montes-Restrepo
my work, my PhD dissertation entitled
Accurate Skull Modeling for EEG Source Imaging
First I'm going to show you an overview of the presentation
I will present you with an introduction followed by experimental setup, the main results and a conclusion
The human brain
consists of three parts, three main parts
the cerebrum, the cerebellum and the brain stem
the cerebrum is the largest part of the brain, it's in charge of the higher order functioning of the brain
the cerebellum controls the movements and the
brain stem keeps the normal functions of the body like breathing
so
inside the brain
there are approximately 86 billion neurons
connected to each other
forming a complex network.
These neurons
communicate to each other through a synapse. A synapse is a gap between a
presynaptic and a postsynaptic neuron
at the terminal of the presynaptic neuron there are protuberances
containing a substance called a neurotransmitter. This neurotransmitter
binds with the receptors of the
postsynaptic neuron and
through a chemical and electrical
reaction the communication of neurons occurs.
The activity of a single neuron is too small to be recorded on the scalp.
Therefore, it is necessary that
around 10^8 to 10^9
neurons be actively
synchronous, synchronously active, to produce a measurable signal on the scalp. This signal
has a potential of 10 to 100 microvolts.
To record the EEG,
metal plates are used, these are called electrodes,
they are placed on top of the scalp
following a standard called the 10-20 standard, which measures 10 percent or 20 percent following certain
landmarks of the skull
So on top of the patient, this is an example of how it looks and of a recorded EEG signal.
The
EEG can be recorded
with a lot of
with minimum 64 electrodes, sorry not minimum
It can be recorded. In conventional EEG
the number of electrodes utilized is less than 64 electrodes.
Normally in the clinical practice this is what is used.
But there is also high-density EEG that goes up to
256 electrodes. This is more used for research applications.
The applications of EEG are multiple: from brain functioning
the response of the brain is studied through a stimulus
Multiple trials are performed so this response is averaged
to have a good signal-to-noise ratio.
For Brain Computer Interfaces or BCI
the signals of the brain can be translated to commands that a machine can understand.
Therefore,
anything can be controlled with the brain signals. This is useful for example for paralyzed patients.
In sleep medicine
the polysomnogram is the most common, is the most utilised
diagnostic tool. In polysomnogram, the EEG is one of the modalities involved.
The EEG can tell what is the sleep stage of the patient
If he is in a Rapid Eye Movement or non-Rapid Eye Movement
stage and
final application is in epilepsy. In epilepsy, EEG is the most common diagnostic tool.
Because in this dissertation our focus is in epilepsy,
I'll explain further what is epilepsy.
Epilepsy is a neurological
disease
characterized by abnormal synchronous electrical activity in a group of neurons.
It affects about one percent of the world population
The first line of treatment for epilepsy is the anti-epileptic drugs
It goes for around 70 percent of the patients
But there are patients who do not respond to this
pharmacological treatment so they suffer from refractory epilepsy. For patients with refractory epilepsy,
around 60 percent can be treated with vagus nerve stimulation
others
One percent can be treated with deep brain stimulation and around 40 percent with resective surgery.
Resective surgery
consists of the remotion of the region of the brain originating
the epilepsy.
This procedure is nonreversible, unlike the other two
and it is of utmost
importance to localize this region
so the patient after surgery is seizure free and
with his
function, brain functions are not impaired
So to localise this region there's a presurgical evaluation protocol consisting of
multiple examinations.
It consists of
scalp video EEG monitoring, Magnetic Resonance Imaging and other
tests that when they indicate the same
zone,
it means that a surgery can be performed. If all these examinations
Indicate the same zone in the brain, the surgery can be performed. If not
invasive electrodes have to be placed on the brain
to localize
more accurately
the epileptogenic focus.
If with invasive electrodes this can be achieved then a surgery can be performed, otherwise not.
So EEG source imaging, as shown here...
One of the modalities or one of the
examinations involved in the presurgical evaluation of epilepsy is EEG source imaging. EEG source imaging
uses the temporal and spatial characteristics of the EEG
to localize active brain regions.
It consists of three main elements: the source model, the head model and the EEG measurements and
of two subproblems
the forward problem
consists of the computation of the scalp potentials for a set of
neural current sources and the inverse problem is a quantitative estimation of the properties of the sources for
an underlying EEG.
Compared to other neuroimaging techniques,
the EEG has the highest temporal resolution
and when we add
when we perform EEG source imaging,
the spatial resolution of
EEG is increased. So EEG has a very high temporal resolution but a very poor spatial resolution,
but by doing EEG source imaging the spatial resolution of EEG is improved.
So now I'm going to show you each of the elements of the EEG source imaging: The source model
In order to produce a measurable
measurable
scalp potentials, the neurons need to be not only synchronously active
but they also need to be regularly arranged
parallel to each other and
orthogonal to the cortex so that their potentials don't cancel out.
This condition is fulfilled by the pyramidal neurons of the cerebral cortex,
so therefore they are known to be as the generators of the EEG.
In a pyramidal neuron,
there are multiple currents
getting ejected
injected and moving so this
generates a sink and source configuration that can be represented by
a current dipole. A current dipole is thus the source model with
location and orientation.
It's the mathematical model of the source.
The head model, so the other very important element is the head model and it's the main focus of this dissertation.
Head models
range from spherical
to realistic. The simplest model is a spherical head model that is built of concentric
spherical shells, it has an analytical solution
but it has very poor accuracy for ESI
On the other hand, realistic head models are based on the patient's anatomical images
They have a numerical solution,
but they are computationally expensive.
Another important parameter inside the head model is the conductivity.
Conductivity is the property of a tissue to conduct electricity, it is the opposite of resistance and
conductivity
in the head tissues
can be either isotropic or anisotropic.
Isotropic means that it has the same resistance in all the parts of the tissue, and
anisotropic that it depends on the direction of,
on the direction.
So in the case of the skull,
the skull
has
consists of a spongy bone layer surrounded by two compact bone layers, therefore its conductivity is
anisotropic, it's not equal in all directions because it has different thicknesses
throughout its whole structure.
Additionally, at the base of the skull there are multiple holes
connecting the brain to the rest of the body and
its geometry is highly irregular.
Furthermore, the conductivity of the skull is very low compared to the other tissues inside the head.
So it acts as a filter of the potentials generated on the brain, in the brain.
To generate realistic head models
the most commonly
modality, the modality most commonly used is Magnetic Resonance Imaging.
It offers excellent soft tissue contrast of gray matter, white matter, scalp,
but the imaging of the bone is challenging
on MRI.
In contrast, Computed Tomography
gets the correct representation of the skull, of the bone,
but this modality is not commonly performed on epilepsy patients
because it uses ionizing radiation.
So all the parts of EEG source imaging are...
So how to combine all the three elements
that I have shown you.
They are combined in the forward problem that is the computation of the scalp potentials
for a set of neural current sources and the inverse problem the quantitative estimation of the properties of the sources.
So the forward problem solution in a realistic head model
utilizes a segmented head model. For each of the tissues,
a conductivity value is assigned, each
element, each voxel of this model is a computational point where the sources can be placed and
the, and
together with an electrode setup
a numerical method is applied. This numerical method is based on Maxwell's equations.
So in this way,
the potentials for a given head model are computed. This is also called as EEG data simulation.
And to solve the inverse problem,
in a, for a single dipole, that was the case that we used in this dissertation,
we use the forward
solution computed
in a given head model, so we use the solution of the forward problem and
compare these potentials with input potentials. These input potentials can be
EEG, the real signal, or can be
can be simulated data from a reference head model. So the
minimization of this cost function, the comparison of these two values, gives the estimated dipole parameters.
So what are the questions that we wanted to solve in this dissertation?
So how should we model the skull conductivity for ESI?
Can we avoid CT acquisitions and derive the skull geometry solely from MRI or
CT- template images
What results are obtained when ESI is performed with clinical EEG?
So to solve these questions, we performed three studies.
The first study consists of three simulation studies and
it focuses on the skull conductivity values: of the compact bone, spongy bone, air cavities,
tangential or radial conductivity.
The second study
compares CT- and MR-based skull models,  it's a simulation study
so it focuses on the skull geometry, and
on conductivity models. This uses different
geometries and different conductivity models compared against a reference model, and the third study
uses clinical EEG to analyze the role of skull modeling in clinical
ESI and
it uses skull models with different geometries: based on MRI, CT or a CT-template.
For our
simulation studies then we used a reference head model. This model was computed with
a data set of
Magnetic Resonance Images
and
Computed Tomography Images from the Reference Center for Refractory Epilepsy at the Ghent University Hospital.
With this matched data sets, we can build a
realistic head model
in which the soft tissues are
segmented from the MRI and the skull tissues,
the compact bone, spongy bone and air cavities are segmented from the CT.
For a, in total there are seven tissues with conductivity values assigned from the literature.
So the first study of the three simulation studies using,
focusing on the,
on conductivity values of the skull. So the first analyses conductivity perturbations of the three-layered skull,
second determination of the anisotropy ratio of the skull and third skull inhomogeneities.
To analyse the conductivity perturbations of the three-layered skull,
then,
test models were compared against a reference model. The test models
were made by
changing these conductivities of the compact and spongy bone.
Also, two error measures
were taken: the dipole localization error and the relative distance measure.
The results of this study show that the models that
have the same conductivity
value for the compact bone as the reference model presented very
small errors. That indicates that the conductivity of the compact bone has the strongest influence on ESI.
For the second study, the determination of the anisotropy
ratio of the skull, spherical head models were used. The reference head model used a
three-layered skull and
the test models used anisotropic,
different anisotropy ratios.
So the test model with the minimum
dipole localisation error was found as the model with the optimal ratio.
This was found for the values 1:1.82 radial to tangential conductivity.
This value was applied to realistic head models.
By deriving their radial and tangential conductivities
with three different methods: with the volume constraint, the Wang's constraint and simplified three-layered.
From this study
we found that the volume constraint method presented slightly lower results than the other and
from this study
we found that the radial conductivity
had the strongest influence on ESI.
The third study about skull inhomogeneities
simplifies the conductivity of the skull
by as either as isotropic or anisotropic homogeneous and also
simplifications of the air cavities are performed. The air cavities are simplified as compact bone or as spongy bone.
In this study,
so another error measure is introduced, the magnitude error,
so in this
study, we found that the anisotropic model has
slightly lower errors than the isotropic model and the air cavities of the skull,
the models simplifying the air cavities of the skull,
showed
very small errors. It indicates that the air cavities have a
minor influence on ESI.
Our
second study, the simulation study,
compares CT- and MR-based skull models.
We used seven test head models compared against the reference model, EEG simulations for
a configuration of
32 and
128 electrodes
And the
ESI was performed for noiseless and noisy data.
In this study,
the reference model
was compared against seven test head models. The first three models were based on CT
and the other
four models were based on MR. So the geometry was simplified and
the conductivity was also simplified in each of the models,
either as anisotropic, isotropic or
layered or
layered.
As a result of this study, we found that for the CT-based skulls with 128 electrodes
the results were not, were small
and for the anisotropic model slightly larger. In the case of the MR-based skulls,
all the models
have large errors especially at
the base of the skull and
in temporal regions. Because this model did not take into account
here, did not take into account
for example the main opening of the skull, the foramen magnum, and it was very simplified at the base.
So comparing
the models for different signal-to-noise
ratios, for data with different signal-to-noise ratios, we see that for the noiseless case
still the CT-based skull models
perform better than the other, than the MR-based
but when the noise increases
the choice of CT- over MR-based skull modeling
becomes less relevant. There's no distinction and
but
the use of a
high-density EEG of 128 electrodes is important in the case of noisy EEG signals.
In the last study about the role of skull modeling in clinical ESI,
it utilised four test head models for each patient. So we used data from six patients who underwent
epilepsy surgery and
with four test head models, there was an averaged versus single spike analysis and
ESI at two time instants within the spike: half-rising phase and peak.
Here it is a clear overview of the study
so the spikes are used at the half-rising phase and peak of the spike and
they are tested, they are used as input for the test model and
the estimated dipole location,
the distance of the estimated dipole location to the resected zone of the patient as
seen on the
post-operative
MRI is the measure of error.
So the,
the test head models
were built based on
MRI, and
with different skulls. These
models have seven compartments based on MRI, based on CT and
based on a CT-template.
Additionally, another model
simplified the whole skull
where all the three tissues of the skull, the compact bone, spongy bone and air cavities, were set to an optimised
conductivity value,
to analyse also the influence of a more simplified model.
As a result,
I forgot to say something here,
The spikes were,
there were two scenarios: the averaged spike and the single spike analysis.
So the averaged spike averages all the spikes available per patient and single spike
performs
a localisation for a single spike and afterwards selects the ones with the best goodness of fit.
So for the averaged spike,
we found no
distinction
not a significant distinction between the
localisation or the distance to resection at the half-rising phase and peak of the spike.
So here's an overview for all the patients and the mean value for all the models.
It was
approximately one centimeter at both the half-rising phase and peak of the spike with the averaged
spike analysis.
In the case of the single spike analysis,
there is a slight
improvement for the
peak of the spike compared to the half-rising phase of the spike. This is because
the spike, the single spikes are more
affected by the noise in the data.
And back again when we
see the significance, the significant differences, when we test significant difference between the different models
for both time,
time instants of the spike, there is not a significant difference between them
For any of them, also not for the simplified, more simplified model.
So these models can be used in the clinical practice.
So as a conclusion,
we had,
several studies were performed in order to determine optimal guidelines for skull modeling
in ESI.
The skull conductivity is better modeled as a three-layered compartment.
That, as it is in real,
in real life.
The geometry of the skull
can be accurately derived from MR
images or from a CT-template warped to the patient's head, so there's no need for CT acquisitions.
The most relevant conductivity values in ESI according to the skull model
In the case, if the skull is modeled as three-layered,
the most relevant is the compact bone, if it is modeled as anisotropic
the most relevant conductivity is
radial and
if it's modeled as isotropic homogeneous,
a value of around 0.01 Siemens per meter can be utilised as an
optimised value of conductivity.
Air cavities of the skull showed to have a minor influence on ESI and
another important
point is that the base of the skull should be accurately, should be carefully modeled
taking into account the main openings such as the foramen magnum.
As a future work,
the head model can be improved
by
including accurate individual conductivity values of the different head issues.
Other tissues can be added to the model, for example the dura mater or blood vessels.
The geometry of the skull
can be modeled using MRI with UTE sequences that
can
better show the different tissues of the skull on MRI.
The source reconstruction can be performed with combined EEG and MEG,
it has shown to be more reliable than any, than either modality alone in the
presurgical evaluation
of epilepsy.
More complex source models can be used, because we used the simplest source model,
so distributed
dipole solutions for example...
Also the
utilisation of simultaneously recorded EEG and intracranial EEG
as a gold standard
for validation, it's the best option, but
we didn't have this, all the data available,
so we used the
resected zone and
other applications where the,
where
accurate skull modeling is important is
when performing ESI on neonates where the fontanels and sutures have to be accurately modeled and
transcranial direct current stimulation where it is important to have an accurate head model and
of the skull, specifically.
Thank you!
So... What kind of imaging modality we could have to approve your head models?
Which is the future imaging modality that you're looking for so that head models are even better?
I think still MRI can improve the resolution
like where we can model tiny parts of the tissues, go
I think from 5 millimeters, so that we can model better the small parts of the skull
but yeah, of course
other modalities,
l know that
there are more important factors in the head model than the skull, like the gray matter anisotropy,
sorry white matter,
white matter anisotropy and
yeah, I think for example
DTI could be, could help for skull,
to improve the model in that sense. Like for the skull I
think we have seen that the
by using only the MRI, but of course modeled carefully, it can be achieved a
good resolution.
So
specifically which,
maybe by using UTE sequences plus the MRI, so something that can be added to the MRI to make it
Related to that, to construct a head model still needs a lot of manual labour.
Are there possibilities to
to ease that?
So that if you want to apply it in clinical practice,
you just have to press a button,
measure some kind of parameters,
choose some model and so on
and just construct the head model by itself?
Yeah, in that case maybe using more
templates, template warped to the patient space, to the subject space.
That could ease, make it easy to build a model. The thing we really need all the
features of
so much specificity, so
but yeah, it has to resemble the
realistic
geometry of each tissue.
Is it enough with six patients for temporal lobe epilepsy?
Do you know now the answer to the question for what we used them?
Or what do we need more?
Do we need another six temporal lobe epilepsy patients?
What would you ask from us as more data to go further with your work?
I think that to make it more generalized, we need more patients.
A large cohort of patients to make a study that can have more general conclusions.
So to have a more complete data set for patients, for a lot of patients.
That study can be performed with a large number of patients.
Of course, this is a very homogeneous group of patients. They're all of temporal epilepsy.
They were all seizure-free, I think.
For instance, if we think about extra-temporal lobe, occipital lobe is a very challenging group of patients. When there
we get into the region of these skull openings that you mentioned
and where the model is becoming very challenging
and then suggest in your final slides that you want to take more care of modeling there in these regions?
But what is it convenient in clinical practice?
What should we do then on these patients?
More scanners, different scanners or
how would you solve that?
Or do you have any advice?
To model the skull?
Yeah, those models are more (based) manually built.
Ok, that's possible.
But based on the same scanner procedures or would you suggest other approaches in it?
Can you perform CT on neonates?
It could be done
of course if the benefit for the patients is there, you can do it.
In that case,
because for the neonates it is very different than for the adult's
skull
For the other patients and maybe also to use
the template, because the template
also takes into account these holes and air cavities
at the base.
For the other parts of the skull is not that
critical, that difficult to model like from MRI is ok to model the cranial vault, but the
complicated part
is the base and air cavities.
And you also stated in the last sentence of your last slide
that for instance it could also be important for transcanial direct current stimulation
to have accurate modeling.
Why exactly?
Why is that so important in this type of neurostimulation that we have accurate skull modeling?
Because you also need a head model
to compute the potentials
to simulate the potentials that you are going to
induce,
like just to make the simulation how
this stimulation will work on the patient
I have a question related to your source model.
Did you also look at the dynamics that you have
Because here you took like a window
A window? No
I took just a time point.
Time point, and would it be alright for you to use a window or a moving window?
Yeah, because I didn't find any,
like a big difference between performing it at the peak and at the
half-rising phase and it's supposed to
differ because the propagation effects are already.
So when propagated, when it's at the peak, so there maybe trying a moving window
trying to bring other points, maybe.
Yeah, how the source is moving (from)
on time and the problem is
the computational.
A signal-to-noise ratio
It's not that good.
Let's say for the averaged spike was better the signal-to-noise ratio than for the individual spikes.
I think in one study with EEG/MEG they were using more than 3 dB
as a minimum requirement to use that spike, but I couldn't use that because the noise was worse.
so I was almost without any spikes
The problem with the clinical data is that it's too noisy.
You assumed that your conductivity of different tissues is fixed.
Are there any publications where this is also not variant spatially
Measuring the conductivity values in-vivo
That's again another inverse problem
We should search in a specific region
Yeah, it will be more like focus on some certain
part of the brain, because if you know it's temporal lobe epilepsy
search the whole gray matter
you can search only
the side of the brain or the region that is... maybe that could help to have more resolution on an area.
We will turn back to the question of the signal-to-noise ratio of the spikes, so
your signal to noise is very poor
on the other hand you only used 20 spikes.
What would happen if you would mark or detect all the spikes.
Would this be helpful?
The averaged I think could be used, a better averaged spike
See if you have more spikes
But also try if it's possible try to use more electrodes
Yeah, I think if you use all the spikes, more spikes because I didn't have that many spikes also
that's why
that's why I didn't see differences in the model, maybe
because I didn't have enough
Would be better to have more spikes and average them or
would it be better to only have
good signal-to-noise ratio spikes like
only three but with excellent quality
or the EEG with the most typical spikes
so then you can do automatic detection of these extra spikes
or you can focus in clinic
or is it useless and just have a lot of spikes and you can then average them
Yeah, it's also that for the spikes then you have to check the morphology, which ones are
similar and group them by similarity and not just by and not just everything
I did everything for example
because I didn't have the spikes.
But I think there still has to be more
clinical, interaction from the Neurology to classify the spikes
according to the morphology.
Conductivity is very important, as you mentioned,
so there exist a couple of techniques to measure the conductivity.
One supervising technique is to just calibrate it based on your own EEG measurements
so you do a task, a fingering task and then you build several head models
and you try to localise the activity in the right area.
Another technique is to use
electrical impedance tomography
which of these techniques is the most useful for practice?
already
uses
the
somatosensory evoked
potentials or something because the EIT is, I mean,
for everything it is again based on an inverse model, ill-posed
but what if say about fingering task
so you measure the evoked potentials?
But then in that one you can only
optimise one parameter,
one of the,
one parameter I think
I mean like conductivity of only one tissue
not of the whole head model.
You can do different head models and see which head model gives you accurately the
region you expect.
You can do different tasks and different brain regions
and then see which head model or which conductivities
give you enough
very consistent
and I have two remaining questions and
related to EEG, so how is this applied
In MEG?
In MEG, then the skull is not that relevant, the conductivity of the skull is not affected  that much by this.
So, but yeah, they both are complementary techniques
so I think it is better to combine them than to use only
MEG or only EEG.
The combination because then you can take advantage of both, the characteristics of both modalities
These conductivity models will be more necessary in MEG or?
In MEG?
Yeah, it is necessary, but it's less problematic. It affects less
the localisation.
the Air
Yeah, I think
for the skull you can use a template,
for the other tissues there are
software packages or
open source, a lot, that make a good segmentation of the white matter, gray matter,
of the scalp,
so I think you
you could use that combined with a template, for example, to have a good head model, otherwise
a template, just a
template warped to the subject's space.
You mean to use simulated electrode positions?
The positions simulated with the 10-20 standard.
Just a very small question out of curiosity. How long does it take to do such a manual segmentation of your skull
but it's not fully manual is like
first with a CT you can do
thresholding and then
later has some...
many days
It's not that fast.
The computation of such a head model is quite long.
To compute it on arrays because you know more or less where the conductivities are
to just use simplified approaches
to calculate the head model as such
just with a small deviation of a standard value?
On the conductivity values?
Would it work to linearise these conductivity values
with a small perturbation constant?
That could be tested and then there are some great
Depending on the temperature and the frequency of these conductivities also
relationships
yeah, like test different conductivities for
to compare it against
real data
for patients or
other
application?
Yes, also depends on the application.
Yeah
I think that your method is probably far away from being applied in clinics
So imagine I would give you money to do two more years of research
How would you use that? What would you first tackle to get this closer to being applied in clinics?
To being applied
on the head model
I would also focus maybe more on the data
because that's very important.
A cap with more electrodes.
Yeah, to have a better signal-to-noise
ratio
and more electrodes if possible.
And the head model, try to do something realistic but
not really that
sophisticated, it doesn't have to be
perfect
but
It has to take into account certain things for the skull like the openings
Yeah, have a good model, but it doesn't have to be perfect and the data I think it's more important.
No, thank you
