CC
>> Good evening and welcome to
tonight's Wednesday Nite at the
Lab.
We are fortunate to have with us
Professor Beth Meyerand.
She's the principal investigator
at the University of Wisconsin
Madison's Applied Neuro MRI Lab.
This lab is part of the
Department of Medical Physics
and the Department of Radiology
MRI Research.
Meyerand's research is in the
field of magnetic resonance
imaging, or MRI, of the human
brain.
The goal of her research is to
develop new MR methods to
visualize the structure and
function of the brain and to
translate these methods to the
hospital for clinical diagnosis.
Please welcome her.
[APPLAUSE]
>> Thank you very much for the
introduction, and thank you all
very much for being here,
especially on such a
beautiful evening.
When it's lovely outside, it's
hard to come into a windowless
room, so I promise I'll try to
make it worth your while.
So I'll be talking a little bit
this evening about some of our
research to improve balance in
different patient populations
who have been afflicted by
things like traumatic brain
injury or a stroke.
So, there are many problems with
balance dysfunction and balance
disorders.
69 million Americans have some
type of disorder of balance, and
it increases healthcare
expenses, there's increase in
falls, and, especially in
seniors, a fall might be a major
reason for going to a nursing
home and losing independence.
So it's an area that is very
much worthy of research in
trying to improve balance so
that individuals will be able to
retain their independence as
long as possible.
Unfortunately, though, there's,
right now, very few therapeutic
options for permanent
impairment.
So there's not a lot we can do
for people who have balance
disorders beyond just
traditional physical therapy,
which is not effective in all
individuals.
So I have been collaborating
with the Tactile Communications
and Neural Rehabilitation Lab
here at UW Madison, and it's
housed in the biomedical
engineering department.
And the individuals in this lab
have invented a device that uses
electrical stimulation via the
tongue to induce a sustained
behavioral improvement in
balance in patient populations
that have balance dysfunction.
We call this type of improvement
sensory substitution.
And so what I'm going to tell
you about in today's lecture it
how this device works, how we
think it's changing how the
brain works, and then how all of
these changes are sustained.
So how they improve someone's
balance over a long period of
time.
So I'll begin by talking about
the device itself, and it's
called CN-NINM.
And I'll talk about just they
physical therapy measures first,
so how it works, and I'll show
you a video of how it improves
balance.
I'll talk a little bit about the
areas of the brain that are
improved while using the device,
and then I'll talk about some of
our imaging results and show you
the parts of the brain that are
changed after treatment with
this device.
So, in terms of tongue
stimulation, we've demonstrated
that stimulation to the tongue
improves behavioral and
subjective measures of balance.
And you might be thinking why is
she talking about stimulation to
the tongue?
What does this have to do with
balance?
Well, the reason that we ended
up stimulating the tongue is we
chose the tongue because it's
actually the most sensitive
organ in your entire body to
touch or any kind of sensation.
And we're also going to choose
electrical stimulation, and
there's salt in your saliva so
it's like salt water in your
mouth and that makes excellent
conductants between the
electrodes and the tongue.
So then I'm going to spend the
rest of the talk sort of
describing how sensations in the
tongue can alter function in the
brain.
So this is the device, and this
is actually an older version of
the device.
It's about five years old.
We have a smaller version now.
And you can see that it's
basically a helmet that the
person wears on their head and
there's an accelerometer in the
helmet, and then there's wires
coming off the helmet that are
attached to this electrode on
the tongue.
Now, the device that's used now,
you don't need the helmet.
It's just a small accelerometer
that can sort of be positioned
behind the head, and now it's
wireless.
There's so many wireless devices
out there today.
This can be wireless too so you
don't see a wire going into the
mouth, and the strip has been
replaced by a retainer that
looks just like a dental
retainer.
So there's no obvious things
coming out of the person's mouth
at all.
So it's very unobtrusive.
So the idea for this device is
that if the person tips too far
forward, so they're losing their
balance and tipping forward, the
accelerometer measures that and
delivers a particular pattern of
stimulation to the tongue.
If they tip too far backward,
it's a different pattern.
If they tip too far to the left,
it's a different pattern.
Too far to the right, it's a
different pattern.
So the person can be trained to
recognize that these different
patterns of stimulation are
telling them to move in
different directions.
And if you think about it, it's
a little bit like braille where
you're taking sensory
information and you're
converting it to a different
sense.
So in braille you're taking
sensory information, sense of
touch, converting it to visual
substitution.
This is sensory substitution
where we're taking tactile
information and substituting for
the sense of balance.
And what's really interesting
about this device is even after
the person takes it off, after
they've been trained with the
device, their balance is still
improved for a period of about
three to six months.
And from what we know, this is
the only device like this that
has this function.
There are other types of sensory
substitution devices out there,
and there's other types of neuro
stimulation devices, like if
you've ever heard of deep brain
stimulation for Parkinson's
disease.
If you turn that device off, the
tremors come back.
Whereas this, you can turn it
off or remove it entirely and
the person's balance is still
retained.
So my lab is involved in trying
to understand why this device
works so while, why the effects
last over time.
So, we had the subjects
experience different types of
visual sensations in order to
perturb their balance.
We had them watch checkerboard
videos.
So there's a static video which
they're just looking at a
checkerboard.
So you have black and white
boxes, doesn't move, they're
just staring at the
checkerboard.
Then we also have a dynamic
image where the checkerboard is
spinning.
So it's rotating in and out.
And if I were to look at
something like that that was
spinning, even though I have no
balance problems, it would be
very disorienting.
It would give me a sense of
vertigo when your whole field of
view is filled with this
spinning thing.
And for someone who has a
balance disorder, it's very,
very disorienting.
So what we wanted to do by
applying this perturbation to
their balance was to see how
their brain reacts when there's
a static image and then when
there's something that's kind of
making them feel woozy like
they're going to lose their
balance.
And we monitor their head sway
with this accelerometer.
So when they were trying to
stand up and watch this rotating
checkerboard, they're kind of
moving around, and we monitor
this.
And then, again, we want to ask
some questions.
Is this stimulation sufficient?
And here is an example of the
device.
So it's just a strip of
electrodes on the tongue.
And you can see stimulation to
different portions of this
electrode strip, just as I said,
correspond to different head
positions.
So when the person's head is
right straight up and down, they
fell stimulation right in the
center of their tongue, and if
they're tilted too far over this
way, they fell stimulation over
here, etc.
So that's what it looks like.
And then this is the size of the
device.
So here's a quarter so you can
see in relation to a quarter how
large this strip of electrodes
is that's on the tongue.
And, again, what we're trying to
do is understand how the brain
is altered with this device.
So I'm going to show you an
example.
This is someone who has a mild
traumatic brain injury, and this
individual had already undergone
physical therapy for about five
years before we ended up trying
to treat her.
So what we're doing right here
is having her try to walk down a
hallway.
This is before treatment with
the device.
So she's had normal physical
therapy.
We have not treated her with our
device yet, and you can tell
it's very difficult for her to
walk down the hallway.
We're having her to try and walk
and look to the right while
she's walking.
And then look to the left while
she's walking.
And then we're trying to have
her step over a cardboard box.
You can tell this is very
difficult.
Then having her walk around
these traffic cones.
And walk up steps, which needs a
cane and the handrail on the
stairs.
Then we trained her for two
hours a day for five days with
the device, then removed the
device, and you're going to see
another set of videos.
So this is five days later,
after the training.
Now she's walking down the hall.
It's amazing.
Looking to the right.
Looking to the left.
Stepping over the box.
Walking around the traffic
cones.
Walking up the steps.
Yeah.
It's just amazing.
Walking down.
So this was not part of our
test.
[LAUGHTER]
But she was so happy
she wanted to jump rope
afterward which is something,
clearly, there's no way she
could have done before training
with this device.
So this device had actually been
in use for a number of years and
obviously it works, but there's
a lot of questions from the
scientific community.
How does it work?
And some scientific reviewers
and we would try to submit
papers would be saying I think
you're just making this up.
This is a perfectly healthy
person.
We're trying to make it look
like she has balance dysfunction
then she's walking fine.
You have to show me what's going
on in the brain.
So this is where our imaging
studies came into play.
So we were able to actually show
these changes in brain function.
So we looked at 12 subjects with
chronic balance problems.
And, again, as I said, they were
trained twice a day.
They were 20-minute sessions for
five days.
And they were standing with
their eyes closed during the
training period.
And we also used nine healthy
controls.
And we measured behavioral
measures.
So postural sway, how much they
kind of swayed back and forth,
due to this visual motion,
spinning checkerboard, and we
used three standardized measures
to measure their movement.
And then we used functional
magnetic resonance imaging, and
I'll explain that technique in
just a minute, to measure the
brain changes in response to
this visual motion.
So what's really interesting
about this device is I've been
telling you that it's important
to have information on different
parts of the tongue to tell if
the person's tilting too far
forward, too far backward.
So basically there's information
contained in those pulses you
feel on your tongue.
You know if you feel a pulse in
the front, you've got to right
your head in a particular way.
Well something we also tried was
information free pulses.
That just means a pulse of
noise.
So there was no information.
Whether or not they tilted too
far forward, backward, left,
right, they always felt the same
sensation on their tongue.
And I should mention too, the
sensation isn't painful at all.
It feels like a carbonated
beverage.
So like you're drinking a soda
or something like that.
So it's not a painful
stimulation.
So, when the stimulation is
correlated or contains
information about head position,
we call that the HPSS version,
and then CN-NINM is the
information free version.
Just has a noise pulse, no
information contained in that.
So we wanted to see if the
information is important if the
person still had balance
function without any information
contained in that pulse.
So we measured things that a
physical therapist would
normally measure.
They would normally measure
things like dynamic gait index,
activity specific balance
composite scores, dizziness,
handicap index.
And what's interesting is even
with no information contained in
the pulses, the subjects
improved on all three measures.
So, really, all they did was
they stood with their eyes
closed, the device was on their
tongue, and for 20 minutes they
just felt a noise pulse on their
tongue.
And they did this for five days,
twice a day for five days.
That's all we did.
And this is a graph showing that
in all of those standard
physical therapy measures, their
balance improved and you could
tell that their balance was very
much like the lady that you saw
in the video.
So it definitely works even if
there's no information, which
from sort of a brain science
perspective is really
fascinating.
I was thinking how in the world
could this be working?
So, again, no difference between
these two methods of
stimulation.
So here comes the imaging study.
So I said we used something
called functional MRI, and it
uses an MRI scanner.
And probably many of you in this
room have had MRIs.
Whenever I give this talk,
usually about a third of the
people in the room have had MRI
scanners, have had MRI scans.
This is a scanner.
I've had many MRI scans because
every time we test out something
new in my lab, we test it out on
each other.
So I'm often the scan volunteer
for my students.
And, basically, an MRI scanner
is a large superconducting
magnet.
It does not involve radiation
like you would get radiation
with an x-ray or a CAT scan.
That's not true with MRI.
It's just a superconducting
magnet.
And so you'd lie on this table
here, and a normal anatomical
MRI image looks like this.
So if you go in to get a brain
scan, knee scan, wrist scan,
it's a gray-scale image, and it
looks something like this.
And with MRI, you're basically
getting an image of water in
tissue.
So the level of gray on the
image is reflective of the water
concentration in that particular
type of tissue.
So bone shows up black, then fat
and tissue and blood and spinal
fluid, all are different shades
of gray because they have
different water contents,
essentially.
And those anatomical images are
very useful.
They show us anatomy.
If you have a tumor or a torn
ligament, you can see that.
But for brain imagining, we want
to know how the brain is
functioning.
We don't want to know does the
person have a brain or not or is
there a piece missing.
We want to know if it's working,
and that's what functional MRI
lets you do.
So what you're seeing in this
image, the gray-scale image here
is the MRI, normal anatomical
MRI just like the image above,
but what's in color is the
functional MRI part.
So what we have here, the two
colored blobs, are the primary
sensory motor cortices.
So one is the left motor cortex
and the other is the right motor
cortex.
So that gives you functional
information.
If the person's motor cortex,
their motor system, is
functioning correctly, you're
going to see these sort of blobs
of colored activation.
So, how does this work?
What we do when you get an MRI
scan is we put someone in the
scanner and we acquire images
very, very rapidly.
We get an image every 40
milliseconds.
So it's basically a real-time
movie of the brain.
So we have someone in there,
we're getting these real-time
movie images of their brain, and
then we have them do something,
some kind of activity.
So what I've been talking about
with this balance study is
they're watching this
checkerboard.
They're either watching a static
checkerboard or the spinning
checkerboard.
In the example I gave you on the
previous slide, the person was
tapping their fingers like this
while they're in the scanner.
So we were getting images and
they're tapping their fingers.
So in this example, I'm tapping
my fingers, what's happening, I
have brain activation.
The part of my brain that's
controlling the movement of my
hands is active.
And because it's active, we're
going to have an increase in
metabolism in that area of your
brain that's active.
Now, you probably all know that
your body does oxidative
metabolism, meaning that it
needs oxygen.
Whenever you have an increase in
metabolism, you have an
increased need for oxygen.
And you probably also know that
oxygen travels around your body
bound to hemoglobin.
So what you're going to do is
have more oxygenated hemoglobin
traveling to the part of your
body that's active, doing
whatever task it is that we're
asking the person to do in the
scanner.
Well, I told you before that the
MRI scanner is a magnet.
It's a big superconducting
magnet.
So that means if I were to take
a piece of metal, like this
pointer, and I were to stick it
in the MRI scanner, first of all
that would be a very bad thing
to do.
Because it's a magnet, it would
get sucked into the MRI scanner,
but if I were to try to get an
image while that piece of metal
was in there, I'd see a black
spot on the image where the
object was because it's metal
and it's interacting with the
magnet.
Believe it or not, there is
enough iron in the hemoglobin in
your blood to cause a similar
effect.
So the idea is when there's
oxygen bound to the hemoglobin,
it basically shields the iron so
the iron cannot interact with
the main magnetic field, and you
don't get a dark spot.
But when the oxygen is not bound
to the hemoglobin, the iron is
free to interact with the
magnetic field, and you do get a
dark spot.
So for an MRI scan, we always
have the person do an active
state, so this would be tapping
my fingers or looking at a
rotating checkerboard, and we
also have them do a resting
state.
I'm not moving my fingers or I'm
looking at the static
checkerboard.
So I'm always comparing
oxyhemoglobin, active state, to
deoxyhemoglobin, resting state.
And one key here is I told you
there's more oxygenated
hemoglobin traveling to the part
of the brain that's active, your
brain doesn't use all that
oxygenated hemoglobin.
It needs some of the oxygen, but
your brain is so important your
body actually does overkill and
delivers more oxygen than you
need to that area that's active.
So you have an excess of
oxygenated hemoglobin during an
active state.
So you have this active state,
more oxyhemoglobin, no dark
spot; resting state, more
deoxygenated hemoglobin, you
have a dark spot.
So we compare those two sets of
data, and that's where we get an
fMRI signal increase with an
active state.
It's a small change.
It's less than 5%, and that
percentage depends on the area
of the brain we're looking at.
For the visual part of the
brain, the visual cortex, which
is actually quite large, it
changes probably around 5% to
7%.
But if you're looking at tiny
areas of the brain, maybe like
the hippocampus, it's going to
be maybe half a percent.
So that's a real engineering
challenge for my lab.
It's very noisy data.
So any type of engineering
signal processing techniques
that you might use to pull
signals from noisy data, we use
a lot of those in my lab.
So that's basically the idea.
You don't have to inject
anything into the person.
It's a very safe technique.
We're just looking at the
differences between oxy and
deoxyhemoglobin in the brain.
And so here is an example.
So I told you, you have an
active state and a resting
state.
So the curve that you see in
red, this is active, this is
resting.
We always do multiple cycles.
Active, resting.
Active.
And then this is real data
underneath.
This is from a motor study.
And what's interesting is it's
very important that you know the
timing of this pattern of active
and resting because if you were
just to subtract active and
resting, it wouldn't work well
because I'm in the magnet, I'm
tapping my fingers, so you're
going to see motor cortex
active, my whole motor system,
but I might also have my eyes
open, so the visual cortex is
active.
If any of you have ever had an
MRI scan, you know the magnet is
noisy.
It makes like a knocking sound.
My auditory cortices are active.
I may be in the magnet thinking
I don't want to be in here, this
is uncomfortable, I want to
leave, so all those parts of the
brain are active.
So if you just subtracted active
from resting, yeah you get motor
cortex, or whatever area you
wanted, visual cortex, but you
get all this other stuff that
you probably don't want.
So it's just very messy data.
So instead, what we do is we
know that we asked the person,
say, to tap their fingers for 10
seconds, rest for 20 seconds,
tap for 10, rest for 20.
So we are only going to look for
signals that follow this
temporal pattern.
So even though the scanner was
on, probably wasn't on 10
seconds, off 20.
Same with having your eyes open,
thinking about how you want to
get out of the scanner, that
didn't follow this pattern.
So you're going to look for
these signals that look like
this that match the temporal
pattern.
Yeah?
Do you have a question?
>> I don't think your cursor's
working.
>> Oh, it's not.
Okay.
Thank you for telling me.
I've been told I can't use a
laser pointer.
So I won't do that, but thanks
for letting me know.
I'll stop doing it.
So you're looking for a pattern
of activation that follows that
temporal time course.
And what we do is at every
signal, and what I'm going to
talk about is voxel.
A voxel is a volume pixel.
You're all familiar with pixels
on TV screens and things like
that, well an MRI, we have that
third dimension.
We actually have a volume.
It's like a cube of tissue.
So we call it a voxel.
So at every single voxel in our
data set, we compute a statistic
that is telling us how closely
our data matches that on/off
pattern that I showed you.
And then we just attribute a
color intensity to the value of
the statistic.
So what you're seeing here are
three perpendicular slices to
the brain, and this was a motor
study, so what's showing up in
orange is the motor cortex and
what's showing up in blue is
just the rest of the brain.
So the motor cortex was
following that temporal on/off
pattern the way we expected, the
rest of the brain wasn't.
So then all we do is we just
threshold the data.
So we get rid of all those blue
voxels, and then we just keep
the colored ones.
They're hot colors so they have
a high value of that statistic.
Then we overlay them on the
anatomical images.
So everyone gets their own
anatomical image, then they have
a functional image.
So that's the idea.
I also want to make sure that
you understand that this is an
indirect measure of brain
activity.
So it's not like something
called an EEG, if you've ever
had one or seen one.
It's where you have electrodes
that are placed on the surface
of the skull.
They look like wires that are on
the surface of the skull.
They actually measure neuronal
activity.
They measure neurons firing in
the brain pretty much in
real-time.
This fMRI is measuring a blood
flow response that is a result
of the neural activity, so it's
kind of indirect, and the reason
I mention is is because it's
important to realize it's a
blood flow technique.
So anything that affects blood
flow is going to affect our
data.
So even if you drink, say, three
cups of coffee before you get an
fMRI scan, your blood flow will
be different and the results
will be different.
And there's a lot of medications
that you could be on that will
affect your blood flow that will
affect these results.
There's all kinds of brain
pathology that you can have that
would affect this.
So it's just an important thing,
it's kind of a pet peeve of
mine, often when you read about
fMRI in the popular press they
talk about here's a map of brain
activity.
It really isn't.
It's a map of blood flow that's
indirectly related to brain
activity.
So that's how fMRI works.
Now I'll show you examples of
how we used it in our study.
So, as I said, we were having
these people look at static
checkerboards, that was the
resting part, then they were
looking at these spinning
checkerboards, that was the
active part of the scan.
And we had them scanned before
we treated them with the device
and after they were treated with
the device, and then I'm
comparing this to a normal
control.
So these are fMRI maps.
This is pretreatment with the
device, post-treatment, and then
this is a normal control.
So we're going to compare these,
and these blobs of activation
that you see here are three
brain areas.
One, V1, is primary visual
cortex.
Then there's area MT which is
involved in the processing of
visual motion.
So it's the part of your brain
that allows you to see things
that move.
And then PIVC is also part of
the visual system.
Yeah?
>> You still have no cursor.
>> Yep, and I won't have one
either.
Something's wrong with the
computer.
I apologize.
I won't be able to use it.
>> Use the laser pointer anyway.
>> Okay.
All right.
There we go.
At least you guys can see it.
So what's interesting about this
data is you can just look at
this, after treatment with the
device, the patterns of
activation look a lot more
similar to the normal controls.
Notice that these blobs of
activation are a lot bigger
pretreatment than they are in
the normal controls.
So what we're kind of
interpreting for that is that
there's some kind of
hyperactivity.
It's like the brain is
hyperactive in terms of its
processing of visual
information.
And if you've ever had a balance
disorder, like I've had a virus
in my inner ear, and I've notice
I am hypersensitive to visual
motion.
Walking through a grocery store
or seeing moving objects, it
just is very intense, and it's
hard to deal with.
So just having experienced this
myself, I can understand how
that part of the brain would be
affected in this kind of case.
There are other scientific
studies that have investigated
the response of the brain to
visual motion, and that's one of
them here, healthy controls in
patients with, in this case it
was balance dysfunction.
In this other study by other
scientists, again they saw this
hyperactivity of the same brain
regions that we saw.
So that kind of just makes us
more confident in our results.
So, we saw activation of the
structures that are involved in
visually processing, we saw this
larger hyperactive activation in
patients, and this area MT, part
of the brain that's again
involved in processing visual
motion, is stronger in patients,
as I said, before treatment with
the device.
So this was really interesting.
And what, again, was interesting
is, if we're comparing, no
difference between post and the
control.
There was no statistical
difference.
We actually went through and did
statistics on the size of these
regions and the intensity of the
activation, and there was no
difference after treatment.
So our conclusions from just
this imaging part of the study
were that this information free,
just noise pulse on the tongue,
improves balance measures.
So this stimulation alone is
beneficial.
And we also saw that there was
an improvement in their balance
in response to this optic flow
or this rotating checkerboard.
So, again, after treatment with
the device, the visual part of
the brain responded similar to
in healthy people, and we saw
this change in brain function in
the area of the brain called the
dorsal pons, and so we're kind
of curious, okay, this is what
seems to be changing after
treatment, wonder if that's
maybe the target of the therapy
of this device.
Yes?
>> Was the stimulation random?
>> No, the stimulation was not
random.
And what's really interesting,
this is just is pilot study.
Everything I'm showing you today
is hot of the presses.
We have not played with any
attributes of the stimulation.
It would be great to see if it
was random, change the
intensity, change the frequency,
change the area over which the
stimulation occurs.
We would love to do that.
So that's a really good point.
>> So it was driven by the
accelerometer but it was not
specific to an area.
>> Correct.
Yes.
So the next question we wanted
to answer is what areas of the
brain are really important to
balance, and can we investigate
those with MRI.
So, what we used in order to do
this is a technique called ICA,
or independent component
analysis, and it sounds like a
really long word and a fancy
technique but it's really
simple.
And the best way to describe it
is, for example, if you have two
speakers in a room, so these two
people are talking, and then you
have crowd noise in the
background.
And you have two microphones,
one here and one here.
So each of the microphones picks
up the signals from the crowd
and the signal from both
speakers.
So that's true with both of
these mics.
They're picking up a mixture of
signals, and you can run these
audio signals through a computer
program called ICA.
So you can convert the auditory
signals to a digital format, run
it through ICA, and this
computer algorithm, ICA, has the
ability to actually separate out
speaker one and speaker two.
And it's kind of cool.
It's something I know that the
FBI has used.
You can put speakers in a room
and you can use ICA to hear what
individual people in the room
are saying.
So it's an amazing technique.
It has a lot of uses, but what's
cool about using it for MRI is
we can stick all of our data
into it, and what it's going to
give us as an output is all the
different brain regions that are
active for our task.
So different areas of the brain
have different responses to
visual stimuli.
Some of them only become active
at the very beginning of seeing
the visual stimulation.
So rather than having sort of
this sustained, 10 seconds on,
that curve kind of plateaued,
some areas of the brain are like
spikes, like tines in a comb.
They look more like that.
But we don't know if we're going
to get that or not.
This was kind of a fishing
expedition.
We wanted to know what areas of
the brain are going to be active
in response to using this device
under experimental conditions
when we're perturbing the
balance.
So I see as a great method to
use for these kind of fishing
expeditions you don't quite know
what you're going to see, you
put in this mix, it spits out
what's in the mix, and you don't
have to know anything.
You have no prior knowledge of
what you're going to get out.
So what's kind of interesting is
two signals showed task related
time courses.
That means the signals
corresponded to the timing
pattern, roughly, that we had
the static checkerboard and the
rotating checkerboard.
So these were area V1, and this
is area V1 so it's kind of in
the back of your brain.
And like I said, it's pretty
big.
The visual part of your brain is
quite large.
And then also visual motions
area.
So this is area MT, the PIVC,
and the brain stem.
So these are very reasonable
results.
These are areas that we might
expect to be seeing in response
to some kind of task that
involves some kind of thing
that's moving in your visual
field.
So, in terms of the effect of
CN-NINM in our experiment with
these patients, so this is just
a plot of our balance subjects.
So each subject, rather than
having their names here, we've
just got them as letters.
And here we're looking at
percent signal change.
So in pretreatment is in white
and post-treatment is in dark
gray and the controls are in
dashed boxes.
So you can see, pretty much in
every case, like I showed you
with those images before,
pretreatment there was always
greater signals, this
hyperactivity.
So we saw this with the ICA
analysis too.
We're basically trying to repeat
the experiment in as many ways
as we can to be really confident
in our results.
And we saw that these motion
related responses were
up-regulated, or hyperactive.
When I say up-regulated I mean
hyperactive, big signal
intensity, in balance impaired
subjects compared to controls
pretreatment.
And, again, this response was
decreased after this treatment.
No difference between post and
control.
And that's what you see on the
far right-hand side of the
graph.
So we're concluding that there's
a whole network of brain areas.
It's not just one brain area
that responds to visual motion.
So all these areas are showing
up in our ICA analysis.
So that's really important to
know what areas are active.
I also talked about this network
hypersensitivity, this
up-regulation of part of the
brain.
And this CN-NINM affects the
entire network.
So the response of the network
of brain areas to motion
decreases after CN-NINM
stimulation.
We actually saw a decrease in
the area of those blobs of
activation and in intensity of
the activation.
So that's what we see, but we
want to ask more interesting
questions like how does the
information flow through the
network?
I've showed you where all these
areas of the brain are active,
but how do they communicate with
each other?
I've told you they're part of a
network, that's nice, but I want
to know is this part of the
brain active first and does it
drive this part?
Does that part drive this part?
I want to know how the whole
thing works together and
improves your balance.
So that's the next question that
we wanted to ask.
And to do that, we use an
algorithm that my lab uses a
lot, and it's something called
dynamic causal modeling.
It's basically a way of
mathematically modeling brain
function using different types
of data.
So what it lets us do is to
estimate the influence of one
brain region on another, which
is kind of what I just said.
This area is active first, then
that makes the next area become
active, which makes the next
area come active.
You use this algorithm called
DCM, and it lets you do that.
So it allows three types of
interactions.
So it allows you to see is A
influencing B?
It also lets you look at is a
particular task influencing A?
So is this rotating checkerboard
making brain area A light up?
And it even lets you look at
modulatory effects.
So, for example, is the
checkerboard rotating, that's my
task, affecting the influencing
of A on B?
So those are pretty high level
questions.
It's letting you ask a lot of
different questions which lets
you make some very powerful
statements about how all these
brain areas are working
together.
So the one current problem with
this technology that there are
statistics students in my lab
and mathematicians are trying to
improve this, is this dynamic
causal modeling method requires
predefined brain regions.
So in order to run the software,
you have to have a first guess
at what brain regions are going
to be important.
So it could be that there are
other brain regions that are
also really important.
I don't know what they are so I
haven't included them in my
model.
It's not an optimal algorithm.
So in terms of development of
the technology, that's one way
that a lot of this, I think, is
going to move is not needing to
know this predefined model
because if I'm wrong, then you
don't get a lot out of it.
So I'm going to, though, use the
four brain regions that I've
been showing you on those
images.
So area V1, MT, the PIVC, and
the brain stem.
And I'm going to combine them in
as many different ways as I
possibly can.
So in order to select the best
model of how the brain regions
are interacting with each other,
I'm going to look at how well
does the model fit the data, and
I'm also going to penalize my
model for complexity.
I don't want a really complex
model, honestly, because it's
going to take a really long time
for the computer to work if I
let it be too complex.
And I don't know enough about
how the brain works, nobody
does, to make it really, really
complex.
So we're just starting with some
very basic modeling.
And I'm going to look for models
that are strong across all my
groups of data, pretreatment,
post-treatment, and controls.
So here, I've got a plot of
different models.
So the models are the way that
these four brain regions are
interacting with one another.
So you can think, is A driving
B, then C, then D?
Or maybe A is driving C, then B,
then D.
Maybe A is driving D, then C,
then B.
So that's where all these
different models are coming up
with.
You can just imagine those
circles that I showed you moved
in different order and having
feedback loops on each other and
that kind of thing.
So what you're seeing is, in
blue, this is pretreatment, the
dark orange is post-treatment,
and then that kind of yellowish
is the normal controls.
And we saw two models that
really outperformed the others.
It was this one and this one.
And this model right here is one
of the ones, I'm going to show
you a blown up version so you
can see it better, one of the
versions that we looked at.
We didn't have a single model
that was best across all
individuals.
It was kind of down to those
two.
And so we grouped these models
into families by similar
connections.
And here's the way they are.
So this is one model that showed
up as having a very high
probability of matching the
data.
And this is another one that had
a very high probability of
matching the data.
So it's just got a different
order in which these different
brain regions are interacting
with each other.
So the one that finally won out,
that matched the data the best,
we did some more statistical
analyses, and that was this one
right here.
And we found that there was two
important interactions that
showed differences after
treatment with our device.
So what you're looking at here
is area V1, area MT, PIVC, and
the brain stem.
These red arrows imply a
positive interaction, sort of
like a driving force, one area
of the brain pushing the other
one to turn on.
And then you also see a blue
arrow, which is a negative
interaction, which is one area
of the brain telling another one
to turn off, to not be active.
So this is what our model is,
and when we ran it through
dynamic causal modeling, it's
giving us this information.
So the likelihood which areas
are driving the others, this is
the stationary checkerboard
which is driving the primary
visual cortex, and then this is
the rotating checkerboard which
is driving these two areas.
So what we saw is that motion
dependent connection from V1 to
MT, so it's this connection
right here, increased in
balanced subjects.
So after treatment, these same
subjects no longer had an
increase in this connection.
So this connection right here
between these two, that might be
what's making this whole network
hyperactive is this connection
right here.
We saw this was very strong in
patients before treatment, then
this connection was much more
like normal controls after
treatment.
We also saw that the brain stem
that received projections, so it
was receiving information from
area MT, so that's this
connection right here, here's
the brain stem, it responds to
motion in the visual field after
CN-NINM but not before and not
in controls.
So it's kind of this connection
here is acting differently
before and after treatment.
So we know from knowledge of
neuroscience that there's
different areas in the brain
stem that are involved in
balance and processing of motion
information.
And we know from our knowledge
of neuroscience that these are
individual nuclei, or you can
think of those as pieces,
individual pieces of the brain
stem, but all the imaging that
I've showed you so far, the
resolution is not high enough
for us to be able to make out
those individual nuclei.
So the problem is that what we
have, the images that I've been
showing you thus far, are very
blurry.
The voxels are actually quite
large.
They need to be large because I
told you that our signal change
between active and resting is
only a few percent.
So if the voxels are really,
really tiny, we're going to get
even less signal.
So we want to try to have pretty
big voxels so we really have
more stuff that we're getting
signals from in order to get
enough signal so the poor person
isn't in the scanner forever.
If we scan somebody for three
hours, we get lots of signal.
Nobody wants to be in there for
three hours.
So a typical scan time is about
45 minutes.
That's as long as we want to
keep people in there.
We're constrained by that so we
end up having kind of blurry
data like this.
For large areas of the brain,
like that visual cortex, not a
problem.
Big area of the brain, who cares
if we don't have high
resolution.
But for these brain stem nuclei,
it really does matter.
Here I focused in on the brain
stem.
So this is what our functional
resolution data looks like in
the data I've showed you thus
far.
It's just kind of one blocky
blob of activation.
But if we, instead, do high
resolution imaging, you can
actually distinguish two
separate areas.
And this is because of our
knowledge of neuro anatomy, we
know that it's supposed to look
like this, that we would expect
to see two areas.
They should be symmetric, but at
least we see two of them and not
just one big blocky blob on one
side.
So we did a whole other set of
experiments that were much more
high resolution to try to get
information from these brain
stem nuclei because we knew that
they would be important to
understanding this whole mystery
of what was going on in the
brain.
So we did these high resolution
studies, and we performed these
in eight subjects that had this
balance dysfunction.
We used the same stimuli, so the
static checkerboard, the
rotating checkerboard.
And what we found is the visual
motion, so the rotating
checkerboard, activated two
areas of the brain stem, and
again, your brain stem is kind
of down deep.
It's sort of at the level of
your neck.
It's where your brain connects
to your spinal cord.
So that's the part of the brain
we're talking about.
And visual motion activated the
vestibular nuclei and the
inferior olive.
And I've drawn a little cartoon
over here of what it actually
looks like.
So this is the trigeminal
nucleus, the vestibular nucleus,
and this is the inferior olive.
And this is the actual data
superimposed on an MRI scan.
And then this is just another
example of some of our data.
So what we found is after
stimulation with the device,
that increased the response of
the trigeminal nuclei to visual
motion.
And that was actually really
cool.
That was amazing because we know
that the vestibular and olivary
nuclei process visual
information pertinent to
balance.
We knew this based on our
knowledge of how the brain
works.
So we were really happy to see
that data.
It made sense.
We also know that CN-NINM
improves balance measures, so we
have this reduced postural sway
and reduced susceptibility to
optic flow.
That means they were less
bothered by seeing this rotating
checkerboard.
They didn't immediately feel
like they were going to fall
over.
And they had improved scores on
all physical therapy measures.
And we also saw that CN-NINM
normalizes the brain's
processing of optic flow,
meaning that after treatment,
their scans looked a lot more
like those with normal controls.
And we believe that there was a
decreased network response to
optic flow.
You don't have that
hyperactivity anymore that we
saw pretreatment.
So kind of our general
conclusions from all these
studies, and again this is pilot
data.
I've shown you data from eight
subjects and 12 subjects.
There's a lot more we want to do
with this over the next many
years.
So we saw that CN-NINM causes
sustained neuromodulation.
So this is increased task
related brain activity.
All of the trigeminal nuclei and
this is a really exciting
finding because the trigeminal
nuclei receive input, so they
have fibers connecting them,
directly from the tongue.
And that's likely where the
stimulation connects with the
balance processing at work.
The whole time we were thinking
how in the world is the
stimulation of the tongue
somehow connecting to what's
going on in the brain.
And this is kind of the key to
all that is we saw that
activation that I showed you
just in the previous slide from
this area TN, trigeminal nuclei,
we know it's connected via nerve
fibers to the tongue.
So that's probably how the
sensation on the tongue is
traveling up to the brain is
through those fibers that go to
the trigeminal nucleus.
And we also know from other
scientific articles that
previous tracer and behavioral
studies have shown bidirectional
connections between the
trigeminal nucleus and the
vestibular nucleus, and the
increased activity in this area
after CN-NINM may represent the
way in which stimulation induces
this neuro plasticity, the
change in the way the brain
functions, to produce these
sustained beneficial effects.
So we're still trying to
understand how does this work
over time when the device is
turned off.
This may be a secret to that.
So here's kind of what we'd
envision all of circuit to look
like.
This is kind of a little cartoon
that I drew up.
So what you're seeing in the
dark box here, I want this to
represent the brain stem.
What you're seeing in the gray
box is the cortex.
So here's my eyeball, and it
responds to motion, visual
motion goes right to vestibular
nucleus.
And then all visual stimulation
goes right to area V1.
And then there's a bidirectional
connection between area V1 and
area MT.
Area MT is driving the PIVC, and
area MT is driving the
trigeminal nucleus.
So we believe that CN-NINM via
those fibers that I talked about
that run from the tongue up to
the brain to the trigeminal
nucleus, there's that.
So everything I'm showing you
here in solid lines, we
discovered from our experiments.
What I'm showing you in dotted
lines to kind of complete the
picture is data that other
people have published in journal
articles.
So we're trying to combine what
we found with everything that
everybody else has already
discovered to give a complete
picture of what we believe is
going on in the brain, and then
try to see if this makes sense.
So previous studies have showed
a connection between area VN and
TN.
And we also know that this is
the thalamus, and the thalamus
is a structure deep within you
brain that basically straddles
the cortex and the brain stem.
It's kind of like the big
network area where a lot of
different brain connections meet
and go from the brain stem to
the cortex, and we know that
there's a connection from the
thalamus to area MT.
And we also know that there's a
connection from the trigeminal
nucleus to the thalamus.
So this would kind of complete
our whole picture of what's
going on in the brain.
So where we want to go with this
in the future, I told you that
this is very preliminary, we
want to do some dynamic causal
modeling that includes the
thalamus.
The thalamus is a very important
part of your brain.
It's involved in motion and
movement.
And the first time I mentioned
it in my talk is just in this
last slide, where we think it
might be in the model.
So we need to actually run
experiments, use tasks, stimuli,
that include the thalamus.
And we also want to include some
more appropriate vestibular
stimulation.
So we just have people looking
at the spinning checkerboard.
You can also do things like put
water in someone's ear, and that
can really give somebody a sense
of vertigo.
We like to do this actually in
the scanner.
We want to see what's going on
in their brain when they're
feeling that effect because
everything that I've shown you
so far, it was before and after
treatment.
So it's a little bit different.
So we'd also like to look how
neuro modulation of the
trigeminal nucleus, that area
that I talked to you about,
induces it to respond to visual
motion and how this alteration
propagates to the cortex.
We didn't cover that at all.
We know that this area TN is
really important because that's
where those nerve fibers are
going.
From the tongue it goes first to
TN, but we haven't investigated
really anything that goes out of
TN and goes to the rest of the
brain.
So we need to do that.
Also, we need to look at other
cognitive networks.
You know from how you keep your
balance that it's not just
motion or vision.
It also involves memory,
attention, I'm aware that this
podium is right here.
When I touch it, I'm keeping my
balance.
There's a lot of different
factors in your brain that are
involved in keeping your
balance.
So we have just really touched
the tip of the iceberg in terms
of getting to those questions.
And, as I said, we want to look
at using the device actually in
the scanner.
So we'd love to have the device
on their tongue while the person
is in the scanner, not do it
before and after treatment.
And what this is going to mean
is I told you that the MRI
scanner is a magnet, you can't
go putting metal wires in a
magnet.
If any of you have had MRI
scans, you know that there's a
sign that says you can't have a
pacemaker and go in the scanner,
you can't have wires, so we're
going to have to design and MRI
compatible version of the
device, maybe with fiber optic
wires, and we're going to have
to decouple that device from the
scanner.
So sort of make sure they don't
talk to each other or interfere
with each other.
And that's all doable.
That's very feasible and that's
one of the next things that
we're going to try.
Also, we have not dealt with the
whole concept of having a
placebo.
So you know if you've ever been
in a drug trial, there's a
placebo which would be like a
sugar pill.
So you never know, if it's a
blind study, if you're getting
the real drug or a placebo.
And then the person, the
physician, will, say, do brain
scans or whatever tests they're
going to do, and then they can
see if the drug had an effect or
not because there's a placebo
group which they're going to
compare to the treatment group.
The problem with this particular
device, as I said, you always
feel the sensation on your
tongue.
So it's really obvious if you're
in the placebo group, you don't
feel any stimulation on your
tongue.
So you don't want to give away
who's in the placebo, who's not
because then there's also
psychological effects.
You know you're not in the
treatment group, so it's just
very problematic.
So we're struggling a little bit
with how we design and
appropriate control group.
But this is necessary in order
to really interpret our data
correctly.
And this is interesting too.
So I'm talking about using this
device for people who have had a
traumatic brain injury or a
stroke.
But you could imagine even using
this in healthy people who want
to improve their balance, like
athletes for example.
Gymnasts is a perfect example,
but almost any athlete, golfers,
balance is very important if you
play golf.
So you could see this, people
wanting to understand this and
use it in a variety of different
sports.
Even just normal aging, we all
know that our balance
deteriorates as we get older.
Wouldn't it be great to have a
device that you just tune
yourself up on this thing two
hours a day for five days,
you're not going to trip and
fall.
Really, I'm kind of joking about
it, it would be great if it
actually worked and people can
retain their independence,
people aren't falling.
It would be a wonderful thing.
So it's definitely worth further
research, and there's a lot of
diseases that would benefit from
this, possibly.
I haven't talked about multiple
sclerosis, Parkinson's disease,
really any disease where balance
is affected we'd like to explore
this.
It's also quite possible,
though, that very different
brain areas, sort of the network
that's involved, could be very
different depending upon the
disease that we're looking at.
So it'd be a really interesting
study to explore.
So I want to end with some very,
very important thank yous.
The people on this right-hand
part of the slide, these are the
individuals that invented the
device.
Kurt Kaczmarek and Mitch Tyler
have the patent on the device.
Yuri Danilov is a neuroscientist
who's been involved and critical
to this development for many,
many years.
So without them, the device
would not exist.
And so that's extremely
important.
They're the engineers
who created the technology.
I'm the imager who really tested
it.
Then this gentleman here, Joe
Wildenberg, is the graduate
student who did all the studies
that I've just described in my
presentation.
So again, without Joe none of
this would be possible.
He has earned a PhD in
neuroscience, and he will be
graduating with his MD in May.
So he's and MD PhD student and
he's going to be a
neuroradiologist.
So I've had a tremendous amount
of help on this.
It's not like I just did this on
my own.
So I want to thank these people
very much, and I want to thank
you all very much for your
attention.
[APPLAUSE]