- Hello, my name is Peter Molfese.
I'm the science lead here
at the Center for Multimodal Neuroimaging
at the National Institutes of Mental Health.
Welcome to our workshop on PET-MRI.
This is an entirely online workshop.
You can access more information on our website,
cmn.nimh.nih.gov/cmnworkshop2022.
You can also rewatch this workshop anytime
at the videocast.nih.gov link below.
We've collected seven leading experts on PET-MRI
to talk today,
and their presentations range from 30 minutes to 60 minutes.
Following today's workshop,
we will have a round-table discussion this fall,
yet to be scheduled, in either August or September.
It will also be online streaming only.
And you can register for information
about this on Eventbrite or using this link,
tinyurl.com/3tzfuuv5.
You can also find out more information
on how to submit questions to this workshop
using the link cmn-meetings.nimh.nih.gov.
The agenda for the workshop today
is this talk, "You Are Here,"
followed by a kickoff by Dr. Bruce Rosen,
covering basics of PET-MRI and different classes
of experiments that can be conceptualized within.
We then proceed alphabetically by last name of our speakers.
So next is Dr. Richard Carson, then Dr. Audrey Fan,
then Dr. Andreas Hahn, then Dr. Sharna Jamadar,
followed by Dr. Christine Sanders
and, finally, Dr. Dardo Tomasi.
If at any time you have questions,
please refer to our websites or email me
at Peter.Molfese@nih.gov.
Thank you.
- Hi. My name is Bruce Rosen.
It's my pleasure to be here today to talk to you
about MR/PET.
Give a bit of an introduction,
both with a backward and mostly a forward look.
When we think about the history of the field of MR/PET,
it's worth remembering that both of these modalities
have been around for more than 50 years now.
So when I think about the early history of MR/PET,
this is what comes to mind.
The notion of parallel play.
Here are two toddlers.
They're playing next to each other,
but not really interacting directly.
And this was largely the case
for the first 30, even the first 40 years of MR And PET.
They were both around doing great work,
but not really interacting very much.
And in fact, this was equally true,
not just of individuals within the field,
but really of our societies.
Large groups of investigators working in each domain
with not very much interaction between them.
Yet this is surprising in some ways,
given that we're really not so different
in terms of our technologies.
First of course, we both have nuclear in our name.
And it's worth their memory, of course,
that magnetic resonance imaging
really started as nuclear magnetic resonance.
So, we both can image carbon-11 or 13, as in this case.
Though, in case the PET folks are complaining
about the challenges of the short half-lives of carbon-11,
carbon-13's half-life is less than 60 seconds.
So, you better get your procedures for working quickly
in the pharmacy in order.
In addition to carbon, oxygen.
We can image oxygen-15 with PET.
We can image oxygen-17 with MR
And just like the expense
of generating radioisotope tracers,
oxygen 17-is about $500 in ml.
And because it's not a tracer, we need lots and lots of mls
if we're gonna image a human.
So, we both have the challenges of paying for our agents.
Fluorine, of course, is another common tracer.
Probably the most common in PET.
And fluorine-19
is a viable MR of visible nucleus.
However, in this case,
perhaps the vaunted image quality of MR
really may be on PET side.
So perhaps, we should be thinking about using the PET image
as the priors for our reconstruction of the MR data,
rather than the other way around.
Finally, even imaging perfusion with water.
We can do it with PET.
We can do it with MR.
And it's rather difficult to choose
which is your preferred image,
as you can see in these pictures here.
In addition, of course, we both have our little issues.
Spatial fidelity is an issue.
We have distortions on the MR side.
We have geometric distortions on the PET side
that we need to deal with.
We also have lively discussions,
sometimes heated discussions,
over optimal modeling of our data.
And I especially like the title of the presentation
that Rich Carson recently gave
talking about the challenges on the PET side of modeling.
Plenty of madness in that field, perhaps,
but the same is equally true on the MR side.
Finally, of course, safety is an issue
for all of our imaging modalities.
On the MR side, RF heating, metal projectiles,
contrast agent reactions.
On the PET side, we have the QA associated
with the radioisotope production,
radiation safety.
Combined device, of course,
has all of these safety issues that need to be dealt with.
But if you're having trouble sorting them out at this point,
just remember we do MRI mostly with stable nuclei
in reference to our world leaders here.
PET, unstable nuclei.
Now given their similarities,
why are we interested in combining PET and MR?
Of course, it's because
that they largely have complementary features
as well as these areas of overlap.
MR, of course, generally does
have very high spatial resolution.
And thus, is the generic anatomical framework
for most of our multimodal studies.
It's quite a good tool for measuring different facets
of physiology and biophysics.
It's not invasive.
On the other hand, it generally has quite poor sensitivity
and it's quite challenging.
They're not impossible to quantify parameters from MR.
PET has its limitations.
Lesser spatial resolution perhaps.
And with that, limited anatomical detail,
especially with tracers that have focal uptake.
It also uses ionizing radiation,
which is a limitation in our ability in particular
to study brain development in children.
On the other hand, it is exquisitely sensitive.
Readily quantifiable,
albeit with all the caveats that Rich talked about.
And most important, it allows us to assay
a wide range of biological processes.
The reason we're interested in combining the two modalities
of course, is to get the strengths of both.
High resolution physiology with MR
and the exquisite sensitivity
and wide range of biological processes accessible with PET.
And if we look at the list of features
that we can get from PET and MR,
this group, of course, is well familiar
with this list and could certainly add to it.
On the PET side,
great measures of metabolism.
Hemodynamics perhaps will increasingly fall to MR
Although, we certainly can measure it with PET.
Protein synthesis, amino acid transport, DNA synthesis,
and especially in the brain neurotransmitter
and neuromodulator action, all can be studied with PET
really uniquely.
MR, of course,
is our anatomical modality of choice
to for look for subtle changes over time in brain anatomy.
Hemodynamics with MR is good
in getting better biophysical properties like diffusion,
functional properties like with the BOLD
or hemodynamic measures with CBF and CBV
to allow us to infer neural activity
through its correlate to hemodynamics.
MR spectroscopy is a way
to look at endogenous neurochemistry.
MR angiography, et cetera.
Combine all of these capabilities in one device
and you have a pretty powerful tool
to begin to study the brain.
So, given that these technologies
have been around for so long,
why is it that we're having this
a first of its kind conference now almost 50 years on?
Well, there were two fundamental technical advances
that were required
in order to bring these two modalities together.
The first on the PET side
was the development of solid state detectors
that were insensitive to magnetic fields.
The conventional photo multiplier tube,
unfortunately, was exquisitely sensitive to magnetic fields
and would not operate within the high fields required
for MRI.
Of course, on the PET side,
these new detectors, like avalanche photodiodes
and more recently silicon photomultipliers,
have made all the difference
because of their insensitivity to magnetic field.
And this is just an example from the wonderful work
of Craig Levin looking at silicon photomultipliers
as a key element in moving PET technology
into high magnetic fields.
In addition to the developments on the PET side,
there were important advances on the MR side.
The most important of which
was the design of new gradients
which allowed a high quality performance
with larger inner diameters.
Now, Siemens was really the first
to be able to move MR systems
from the conventional 55 or 60 centimeter inner diameters
out to 70 centimeters.
And that gave the bore space.
Of course, they developed these wide bore systems
mostly for wide bore Americans.
When we say that Americans are as fat as a horse,
indeed, they sometimes can be.
And that was the clinical driver
for wide bore systems.
But of course, that extra bore size
could be used to incorporate the technology required
for PET detection.
And indeed, the ability
to create high performance gradient systems
with that extra bore real estate was key
to being able to develop
human scale simultaneous MR/PET devices.
And since that time,
many of the commercial MR manufacturers
have subsequently gone to these wide bore systems
for their conventional MR
And thus, have the advantage
to be able to integrate PET scanners into their MR devices.
And at least three of the large commercial vendors,
General Electric, Siemens, United,
all now have fully integrated PET scanners
into their MR scanners.
And of course, in addition to the human scale scanners
shown here, several pre-clinical companies
now have pre-clinical scanners.
So from everything from mice all the way up to humans,
we can now do fully integrated PET and MR scanning.
Well, how are we going to use this combined tool?
I'd like to kind of break it
into several different classes of experiments
that we can consider now that we have a combined device.
And what I'll call Class 1 experiments,
we're really thinking about ways to use the data
from one machine to infer properties about the other
or perhaps use the data from each of these machines
independently.
Here, the focus is really on unique information
and properties of each modality,
but simultaneous acquisition is not essential.
However, I'll just say parenthetically
that we shouldn't discount the value
of simultaneous acquisition,
especially in studies in sensitive patient populations,
where moving from one scanner to another scanner
actually is a significant prohibition.
So this in typically includes this class of experiments,
typically includes many of the clinical studies
that we routinely use MR And PET for,
but as we'll see
and there are a number of other examples of this class.
So, this single image
from my colleague Onofrio Catalano
just highlights the utility of the MR and PET together
in clinical use.
Most clinical use is focused on oncology
and much of it in the body.
In this case, the combined MR
relative to say the traditional PET CT
was able to rule out the presence of a metastasis
in a patient that was suspicious for that.
And thus, made this patient potentially resectable.
The combined MR/PET study made an important difference
in the treatment path of this patient.
In the brain side,
we are increasingly using the anatomical frameworks
that we can get from MR to better inform our PET data.
And this very nice paper by Vincent Beliveau.
He worked with Doug Greve, who's one of the pioneers
of the so-called cortical flattening technique.
The FreeSurfer tool to basically take receptor maps
across five different
5-HT receptor classes.
And not only image them in the tomographic space,
but bring them into the natural framework of the cortex
to begin to more readily identify
the different cortical distributions of these patients,
of these receptors, I should say.
So, this is FreeSurfer meets PET or PETSurfer.
And it's certainly worth highlighting
that the NIH's own colleague Robert Innis
is leading a large scale effort from multiple groups
to develop a new database of PET data
that directly incorporates the MR data.
Notice that Doug Greve
is an important part of this consortium.
So that the anatomical framework
that the PET data is analyzed from
can be visualized in all of the different anatomical formats
that we can use routinely with MR.
Of course, there are other studies that people are doing
that are thinking about how we can begin
to compare the individual measurements
made from the different modalities.
And this very nice study of neuroinflammation
using the NIH's PBR28
and studying a patient group in this case,
ALS and Nicole.
Zurcher and her colleagues were looking at the relationship
between TSPO uptake and expression
and the changes in the associated white matter bundles
within the ALS patients,
within their cortico spinal tracts.
And another nice study from Nicole here,
she was looking at C-11 Martinostat
and an HDAC targeted agent
looking at correlates with age
and the relationship between changes
in HDAC uptake
and changes in white matter microstructure.
Again, independent measurements
but where we can begin to think about the relationship
between these two quantities.
My colleague Greg Zaharchuk,
when I asked him to share his thoughts
on the combined modality, mentioned to me his own interest
and his thought that PET and MR really the ideal modality
for deep learning applications,
where we can combine PET and MR physiological imaging
to co-inform each other.
So for example,
he's been using AI-based image transformations,
taking multiple different input data,
both the PET and multimodal MR data, and contrasts
to try to infer novel features
on the output side.
So his post-doctoral fellow, Kevin Chen,
a wonderful young scientist,
has been developing AI algorithms
focused, for example, on radiation reduction,
looking at simultaneous amyloid imaging
with MR/PET.
And hereby, including the MR data in the analysis,
he's was able to show significant reductions
in dose required when MR data
is included in priors for the network.
And here's just another example of a more recent paper
from Kevin showing the utility
and potential for the CNN enhanced PET images
to significantly reduce radiation dose.
And as noted here, this may enable the opportunity
to do low and high dose dual tracer protocols.
Taken to its extreme, Ouyang et al,
again working on Gregs Zaharchuk lab,
has actually begun using the MR data essentially
as a prediction for the PET data,
so-called Zero-dose FDG Imaging.
Maybe this is an example of a Class 0 experiment.
We'll have to see how this particular field evolves.
But of course, this is not the only way
that we can think about using MR and PET together.
In what I'll call Class 2 experiments,
here we'd like to use the ability
to acquire these two data sets simultaneously
to make measures with each modality.
And in this case,
the real goal is to understand the relationship
between the measures we make in each of the modality
in the face of dynamic state changes.
This then are at least strongly benefits
from simultaneous acquisitions,
because we can be assured that the brain
is in the same state during each of the MR and PET measures.
And a great example of this, for example,
is fPET studies combined with fMRI studies
in a variety of different dynamic brain states.
I think the fPET is a really nice example
of how we can begin to take some of the concepts
that we developed in doing fMRI and apply them to PET
to look at dynamic imaging.
In this case, the dynamics is a simple behavioral state
of the brain responding to a visual checkerboard.
Marjorie Villien et al published several years ago
that using a constant infusion protocol,
it is possible to be able to look for changes
in CMRglucose
in dynamics of just a few minutes.
And thus, use the same kind of block paradigm acquisitions
that we had routinely used for fMRI acquisitions
for the previous 20 years.
And perhaps not surprisingly
in such a simple stimulus,
we can now directly relate the FDG changes,
the changes in CMRglucose associated
with the flashing checkerboards
with perfusion changes measured with arterial spin labeling,
that's the ASL here, as well as with BOLD contrast.
A number of investigators have since replicated
and extended these interesting initial findings.
Here's two papers from Hahn and from Rischka,
which are looking at primary sensory tasks.
And again, showing the ability
to measure CMRglucose changes
and functional changes with fMRI,
in this case BOLD contrast,
in the same acquisition during the same behavioral paradigm.
Now, of course,
just seeing the same results
perhaps is it's good to see,
but not the most interesting finding.
What I think is more interesting
is that people are now beginning to look at these tools
and the relationship between the changes in metabolism
and the changes in hemodynamics.
I begin to try to understand
when we not only see similarities,
but when we see differences.
For example, in the setting of neuromodulation,
either by tDCS
or with optogenetics,
we're beginning to get an understanding of the fact
that there are sometimes spatial mismatches
between the fPET signals
and the fMRI activations and deactivations.
And that's highlighted in these two very nice papers
or abstracts highlighted here.
Another example, and I apologize
that I'm now covering up the covering up the,
covering up the authors of this paper.
Let's see.
Here we go. This is how it was supposed to look.
Here in this paper, by the same group,
they've now moved from simple sensory tasks
up to more sophisticated studies
looking at brain network function
and the metabolic cost associated with task performance.
And their conclusion
was that in addition to the regional changes
that they had previously documented,
that in this cognitively challenging task in this paper,
they showed the association between glucose metabolism
and functional network dynamics.
And interestingly, that these changes
were specific to feed forward connections,
linking visual and dorsal attention systems.
And the conclusion was that these roles
highlight the key role of metabolic factors
in supporting brain network reconfigurations
as a function of cognitive engagement.
Very interesting set of studies.
Similarly, Jingyuan Chen
within our own Martino Center
has been studying sleep states again
and arousal-induced dynamics
where we can measure electrophysiology,
hemodynamics, and metabolic signals
all within a single experimental paradigm.
And again, another example
of what I would call this Class 2 experiment,
where you really want to be imaging simultaneously
because of the idiosyncratic nature
of the dynamics of the brain state.
She was able to show that
while there's a, of course, a general,
a positive correlation between the changes in hemodynamics
measured in BOLD and the changes in metabolism,
there were, this was not uniformly true
across all cortical patches.
And now, we're left to try to understand those areas
where there're positively correlated in other areas,
where in fact, we actually see negative correlations
during sleep states.
Finally, another study.
This was shared with me from the Copenhagen group,
Joergenson and Gitte Knudsen.
Here, they're looking at a DBS and a model,
and they observed BOLD responses in the limbic system
related to changes in C-11,
5-HT1B tracer.
They measured interstitial 5-HT levels.
And again, using the DBS, they were looking at,
able to look at network dynamics in these animals,
with the goal, ultimately,
towards understanding psychiatric disorders,
which may involve dysfunctions of these systems.
DBS is actually a wonderful testbed
for activation-based and displacement studies in general.
It can be repeatable.
It can be titratable.
It can be lateralized.
So, for example, in this study performed by Joe Mandeville,
Wim Vanduffel, Christin Sanders, and others.
There was a unilateral stimulation of the VTA,
which gave us the opportunity to look
at the evoked dopamine release in the nucleus accumbens.
Now, the challenge in this of course,
is that the changes that we saw are really quite subtle.
And I think highlight the challenges we're going to have
in doing dynamic displacement studies.
Kind of analogous to fMRI studies, but doing so
looking at neurotransmitter specific modulations.
And this study, for example, the conventional SRTM model
showed a significant biases in its patterns.
However, Joe has developed
a new forward model fRTM,
which produces results
that can minimize those biases
and interestingly seem to now show responses
within the nucleus accumbens quite similar
to that seen in fMRI.
Now, perhaps the ultimate use
of the combined modalities
is to make simultaneous measurements
or to use simultaneous data, I should say,
to make completely novel measurements.
And here the key is to use,
to make new kinds of measurements.
Measurements that can't be made
with either modality by itself
and which require input from both modalities.
And I think studies, for example, of receptor trafficking,
receptor network interactions, et cetera,
are good examples of that.
One of the nicest examples, I think,
comes from Christin Sanders and her work
on the dopamine system and her modeling,
which now has allowed her using both the data streams
from the PET and the MR to measure things
like baseline occupancy or internalization rates.
These are measurements that couldn't be made
with either modality on its own.
But when the modalities are combined
and the pharmacologic response can be combined
with the functional response,
we now have the ability to make these unique measurements.
And I won't say more about this,
but you should definitely listen to her talk.
She, of course, though is not the only one
using this tool in this way.
Hsiao-Ying Wey or Monica Wey, you may know her as,
has been studying the mu opioid system.
Now this is, of course, a complex receptor
that has both G protein, coupling,
and a beta-arrestin pathways.
And so in such systems,
understanding receptor trafficking
requires both understanding desensitization
as well as internalization.
And so, her work is really targeted
to try to teasing those apart and teasing out the influences
of the G protein and beta-arrestin pathways.
In this case, she's using TRV130,
which is a G protein, a biased mu opioid receptor agonist,
which does not induce the same kind of receptor trafficking.
And has now begun to perform experiments,
comparing morphine, which involves both the G coupled
and beta-arrestin pathways and then TRV130 which doesn't,
showing in fact that there significant discrepancies
between the PET responses,
even though there are similar functional responses.
Here, again, using the combined fMRI and PET data
to try to give us greater insights
in terms of the underlying molecular processes
in these complex receptor systems.
Hanne Hansen from the Copenhagen group
has been taking a similar approach studies
in non-human primates looking at serotonergic system
and has drawn a number of interesting conclusions
based on non-penetrable agonists
and their comparison against agonists and antagonists.
Again, using the dynamics of MR to try to tease out
some of the underlying functional properties
of the receptor system.
And here for those of you interested,
there was a very nice review paper
written by these three wonderful colleague,
Christin Sanders, Hanne Hansen, Hsiao-Ying Wey,
talking about the use of MR/PET
in looking at dopamine, opioid, and serotonergic system.
And I definitely would encourage you
to read that review paper.
So, what's next?
And what might a Class 4 experiment be?
So, what does the future hold for the technology?
And then, how might we be able to use it?
Well, certainly the technology of MR/PET
will continue to expand
and that will give us some new opportunities.
One example that I'm, I think, may be quite important
is the work that Ciprian Catana is doing
based on a brain initiative grant
to develop a 70 compatible device,
with at least an order of magnitude improvement
in sensitivity.
As you can see, it uses this novel spherical PET geometry
with a greater than 70% solid angle coverage.
So, the equivalent of systems
like these very long bore scanners
that United and now as Siemens are developing,
but in a, excuse me, in a compact design.
Design to image the human brain
and developed in such a way
as to be fully compatible
with ultra high field MR Scanner.
Of course, showing that the technology
can be made compatible with MR
even at very high magnetic fields,
where very subtle changes diamagnetic
and subtle power magnetic effects
can significantly distort the MR field of you
was very important.
Fortunately, work from colleagues Bastien Guerin
and Larry Wald has shown that it's certainly possible
to build such systems that do not distort the field
and don't distort the PET detectors.
And the bottom line
is that when we compare what this scanner is,
we hope likely to deliver to us relative
to the current class of human PET/MR scanners.
Notice that the sensitivity
is almost in order of magnitude more
than what is currently measured
from the existing human MR/PET scanners.
Of course now, if we combine this say
with the AI-based reconstructions and denoising
a la Zaharchuk's work,
maybe there's yet another order of magnitude
improvement to be gained.
So, that would be almost a hundred fold improvement
over today's machines.
And that's not a completely fanciful target.
And so, the question then is, well, what would we do
if we had a hundred fold sensitive improvement?
Another point, of course,
is that MR/PET technology continues to move
to higher and higher spatial resolution.
This is another brain initiative funded
technical improvement grant.
In this case, led by Georgia Pakref
from the Gordon Center at the Mass General Hospital.
Here pushing a spatial resolution
to a millimeter or sub-millimeter for the human brain.
Again, this particular design
not designed to work in an MR scanner,
but it certainly points to the possibility
to develop very high spatial resolution,
in addition to the very high sensitivity scanners
that Ciprian Catana was working on.
And of course, MR technology is also advancing in ways.
For example, this again,
brain initiative supported project
on an MR corticography is how David Feinberg,
the PI of this grant, has labeled it.
Very high resolution imaging of the brain's cortex,
where it should be possible to get
less than half millimeter isotropic voxels.
Certainly, well within the size that should allow us
to be able to measure function
at the level of cortico laminar and cortical columns.
Another brain initiative funded technical advance
on the MR side.
This is the "Connectome 2.0," led by Suzy Huang
and her colleagues.
Here, we're pushing that initial generation of machines
designed to measure microstructure and connectivity
using diffusion techniques to more than quadruple
the fundamental capabilities of Gmax and slew rates.
And then,
looking at how we can validate microstructure measures
using these advanced tools.
Again, both of these tools,
the last two I talked about,
require high performance gradient systems
that frankly would be challenging to put an MR in,
but never say never.
I think it's certainly...
Excuse me, to put a PET in.
But I think it's certainly possible and in fact likely
that we're gonna see more and more devices
that take advantage of the technical improvements
in both domains.
So, what are we gonna do with these kind of machines
as they come around?
Just some things to think about.
Certainly, we're in a position I think now,
but certainly in the future
with a great improvement in sensitivity,
to think about and study the role of nerve modulators
on hemodynamics, on network function,
and on behavior.
And to link these regional neuromodulatory dynamics
to the cytoarchitecture within the brain.
The work in the brain initiative on the cell census
certainly is going to give us exquisite information
about the nature of brain cells throughout the brain.
Now, let's try to relate that to receptor
and neuronal function.
We can certainly think about mapping neuroreceptor dynamics
with network function in diverse cognitive paradigms.
I'm really hopeful that with increase in sensitivity,
we can begin to do more and more experiments
like the fPET, but now with neuroreceptor
and neuromodulatory specificity.
And with cognitive paradigms that are much more analogous
to those that we routinely use with fMRI.
In order to do this, it's possible that we may need
to rethink the tracers that we use
to take better advantage of this increased sensitivity,
the temporal resolution of our cameras
and of course, the ability
to measure physiology simultaneously.
So for example, tracers,
which may have a significant flow-based modulation,
which have been discounted in the past because of that,
may not be discountable in the future.
Because, of course, we can simultaneously quantify flow
using MR and build that into our modeling.
As Greg Zaharchuk mentioned
in the slides that he graciously shared with me,
we can do multiple tracer injections
with function and microstructure
to better understand neurotransmitter
and neuromodulator interactions.
And if we really can gain
a factor of a hundred in sensitivity,
perhaps two is not the limit to our ability
to look for these kind of interactions.
This will allow us to begin to understand paradoxes in drugs
that seem to be of the same class,
but which we know to work differently.
Or in some cases,
different drugs seemingly in the same class
don't work at all.
Finally, we can combine our efforts
with the brain initiative, Armamentarium,
an effort that I think will be extremely relevant
to the PET community especially,
to study the effects of molecular targeted treatments,
chemogenetics, for example, like DREADDS.
And MR/PET can be combined to manipulate receptors
and measure it and understand the changes
in occupancy and how that then relates to function
and ultimately behavior.
And finally and in a very, to my mind, exciting development,
will we be able to reduce dose enough
to allow studies of normotypic
and abnormal human brain development?
I.e. can we ultimately study children?
I think all of these give us hope
that there are going to be things that we can do
with the combined MR/PET devices
that really will extend the tool.
Not only to do the kinds of experiments
we've been doing in the parallel play,
but to really do completely novel experiments.
You're gonna hear about many of them
in the rest of the, of this conference.
So again, I want to thank all of my colleagues
who shared slides with me.
And I thank you for listening
and I look forward to the opportunity to discuss this
in the other talks as the conference goes on.
Thanks very much.
- I'd like to thank the organizers
for inviting me to present at this symposium.
And the topic I'd like to share is
our work in recent years on imaging synaptic density
in the human brain here using PET,
and in the spirit of the event,
to really look about how we were using this
in a single modality, multi-modality, multi-tracer,
how we integrate all of these together.
So here's the outline.
We'll talk about the development of our tracer,
initial human PET studies,
how we're validating the use of the tracer
as a measure of synaptic markers,
spend some time on our Alzheimer's results,
which really gives a good understanding
of both primary results looking at SV2A differences
as well as multi-modality and multi-tracer.
In understanding it, we'd like to understand a little bit
about its sensitivity to neuronal activation.
That is, is this a static measure of synapsis?
We'll look into two relevant disorders for NIMH,
looking in the schizophrenia
and then major depressive disorder.
And then I'll end with some non-analytic strategies
how we'd like to be able to look at network-based.
And a lot of these derive from fMRI,
looking at independent component analysis
or how we can relate resting state fMRI measures to SV2A
and how we can apply those into various disease states.
Well, in studying the brain,
it's pretty obvious that it would be useful
to have an in vivo marker of synapses,
a common factor in so many disorders,
whether we're looking at neurodegeneration,
the loss of synapses, in focal areas like epilepsy,
or in interesting areas in the neuropsychiatric realm,
of looking at perhaps diseases of pruning of synapses
in autism and schizophrenia
or other areas like depression where we see dynamic changes
in the amount of spines that we see
under chronic uncontrolled stress
or how that can be modulated with drugs like ketamine.
The target we're gonna use is
the synaptic vesicle glycoprotein 2A.
It is a protein that sits in the synaptic vesicles,
just like synaptophysin and other proteins
that have been used for a long time.
And this molecule, the target was found based on the use
of study of the epileptic anticonvulsant drug Keppra,
which binds to SV2A.
Now here's an example of that,
that this has been used as a surrogate marker
for synaptic density for years.
Certainly synaptophysin was doing that.
That goes back a long time,
both in development or Alzheimer's disease.
And more recently, this is being used
in terms of looking for example, in animal models
where we can show reductions in a transgenic animal,
which actually can be recovered after therapy.
So here's how we started.
This is the molecule.
It's called UCB-J.
It's labeled with carbon 11,
and we developed that first in animal models.
And then we took that into human beings.
So here's our initial human data.
We did this with healthy controls,
doing test-retest studies.
And we're doing that the hard way with time
with the bolus injections of our tracer,
measurements of the input function in arterial blood
and studies on our high resolution HRT.
And we measure the activity in the blood.
We show how that drops over time,
that we produce radiolabeled metabolites
that are more polar than our parent compound.
We also found that we have a good free fraction,
which means it's gonna be readily entering the brain.
There's an example of what the images look like.
They're not surprising.
We have high activity of our tracer
throughout the gray matter
and lower in the white matter.
There's a very high uptake in the brain, partially due
to that nice lipophilic characteristic of the tracer,
but also due to the high amount of binding sites
compared to the white matter here
in the centrum semiovale.
We did quantification of that with compartment modeling
and fortunately found that the simplest model,
the one tissue model gives us very good agreement.
And we can therefore measure two parameters,
the volume of distribution,
that's the equilibrium ratio of tissue to plasma,
as well as the net uptake rate, K1.
And with values of 0.3 and 0.4,
that says this is gonna be very sensitive to blood flow.
We also found that our test-retest reliability
was very good in these initial data.
So we have a highly reliable measure
and therefore we can calculate this
on a voxel-by-voxel level.
These are parametric images that we can see
to be able to look at a very high quality.
And so finally, since we know that the binding site
is displayable by levetiracetam,
we could do dynamic displacement from the scan
showing that we are specifically binding to this target
on the presynaptic terminals.
Now we went on to do some validation.
This was done in the baboon
where we did an in vivo scan and the uptake of that,
and then sacrificed the animal,
did the Western blots and ligand binding assays
and a little bit of confocal microscopy.
Here's when we look at the Westerns,
we can see, for example, that we have high binding
in all cortical regions
and very little in the centrum semiovale, the white matter,
which can be used as a reference region.
We found that we also have good agreement
between SV2A binding and synaptophysin binding there.
We did the binding assays there
to be able to look at what the affinity would be.
We basically were able to find that we have an affinity
of about four nanomolar.
For PET traces, that's not that great,
except that we have a huge Vmax.
The amount of this target of SV2A is very large
and our centrum semiovale region was extremely low.
And we could show that the in vivo measures
could be nicely correlated with the in vitro measures,
that basically all the measures from the binding assays
and the Westerns and the in vivo were looking the same.
So this gave us some confidence
that we were on the right track
of being able to measure synapses.
We are also collecting other data sets.
And here's an example with resected human tissue
from epilepsy patients following surgery.
We were able to show a relationship to the SV2A
and its optical density and the binding site.
And we have a correlation.
It's a sloppy one with a small N,
but it's certainly showing we're still on the right track,
even in resected human tissue.
And we are doing more work there looking at how our measure,
in this case, a binding potential measure,
correlates well with SV2A.
When we look at other markers
for excitatory or inhibitory markers,
basically a little, the correlation breaks down.
We need more data to be able to understand
to what extent is our SV2A reflecting
both GABAergic and glutamatergic synapses.
So let me take it on to our first population
going on in the AD world.
This is an obvious one to do,
and we've done quite a number of studies in there.
So in looking at a population of both cognitively normal
and relatively mild AD or MCI,
all of these subjects have have PIP scans to validate
that they're am appropriately amyloid,
amyloid positive for the AD,
amyloid negative in the controls.
Neuropsych tests are being measured in all of these things.
And here our measure is normalized to the cerebellum.
We found that provides a more reliable measure.
We're also correcting for atrophy
with partial volume corrections.
So here's the cohort that we published recently there,
19 cognitive with controls,
34, both MCI and AD broken down,
and pretty much no differences there in education and age,
and of course, great differences in the cognitive measures,
including the clinical dementia ratios
and the other logical memory
and auditory, verbal learning test scores.
This is example what the average images look like.
And we are seeing reductions across the cortex.
In our initial study,
we had our primary results were in the hippocampus,
but now with a bigger N,
we're seeing that more across the brain.
So if we look at that in a regional fashion,
and we're looking at the cognitively normals in blue
and the AD subjects in red,
we're seeing significant reductions in many regions
with larger effect sizes
in entorhinal cortex and hippocampus,
smaller ones in other cortical regions.
If we correct for atrophy,
we lose some of these in the cortex.
We still maintain our primary effects,
especially in the hippocampus.
And so here's if we map the magnitude of changes.
And we're looking at here
at either with or without partial volume correction
or with partial volume, the magnitude of the effect sizes.
And we compare that to just the gray matter volumes
that we see, and we have larger effect sizes
that we're seeing with the synaptic markers
than we have with just the volume markers.
And the most interesting one that we've published recently
is the correlations that we're getting
of our synaptic markers here using a global measure of that
to a variety of the cognitive scores.
And we're seeing that across a myriad of different ranges
of different cognitive measures there.
And these are very strong correlations.
Again, in this case,
we're only looking here at the MCI and AD subjects.
We're not including the healthy controls,
which would give us, you know,
help along inappropriately
to be able to look for those correlations.
So we had in our original 28 pilot data,
we saw those reductions.
We've now improved this with cerebellum,
and now we're seeing with a larger cohort
these reductions are very widespread,
and with a very importantly significant correlation,
so that the SV2A is giving us
some measure of cognition here,
at least cross-sectionally in this population.
Let's take it a little further
and start to begin looking at one aspect
of network effects in the brain.
And a natural thing to do is
to compare the synaptic binding that we see
to the deposition of tau.
And here we're gonna be using AV-1451 Flortaucipir
as a measure of tau.
And we're particularly looking at the perforant path
of projections from entorhinal cortex
up to hippocampus, able to see.
So this was a small study
with 10 subject cognitively normal, 10 Alzheimer's disease,
basically related group to what you've seen before.
Here, we allowed a couple of our cognitively normals
who are amyloid positive to remain in the group.
If we look at the tau measures,
obviously we have nice strong effects throughout that
with our 10 and 10.
With the 10 and 10 SV2A,
this is on the margin of being able to be significant.
We have changes in hippocampus,
but the Ns are small to be able to do that.
If we look at it visually, the tau load,
again, this is those averages,
the SV2A that you've basically seen already,
large increases in tau, no surprises here.
But now is there a relationship between these two measures?
And this is where it gets interesting.
So on the x-axis here,
We're plotting the amount of entorhinal cortex tau,
here it's measured by an SUV ratio,
to the distribution volume measured
upstream in the hippocampus.
We get a very nice relationship, nice separation, of course,
between the cognitively normals and the ADs.
Interestingly, the two PIP positive cognitively normals
tend to fall in between.
If you look at that against hippocampal volume,
we still have that relationship as well,
but it is not as statistically significant.
So it's interesting that we're finding
this relationship to entorhinal tau,
how it projects and affects
synaptic density in the hippocampus,
which we see that in AD compared to controls.
And so this is a interesting relationship,
just beginning to talk about a very specific network
that we can look at within the disease.
And this is an interesting one to follow longitudinally
as our ongoing studies will do.
But in looking at our measures and thinking about this,
what is it that we're measuring?
You know, we're measuring these synaptic vesicles.
We're looking at these proteins on the synaptic vesicles
but of course the purpose of the vesicles
is to release neurotransmitters into the synaptic cleft.
And how those are released,
they recycle and they come around.
And that became the question
of, well, are we sensitive to this?
If we have an activated state,
is this going to be something where we're gonna have
a change in our signal or not?
And so how do we go about that?
Well, we kind of went
back to old-fashioned looking at activation studies.
So we took some healthy subjects and they did two scans,
one that was done at baseline, and one was
with classic eight hertz radial checkerboard activations,
being able to look and see what's going on in there.
And at the same time,
these people were also independently did fMRI studies.
Because of the way PET is taking longer period of time,
we used the PET-optimized where we were on and off
for longer periods of time compared to fMRI.
And we did it both ways to be able to compare.
And so here are our summary images.
What we're gonna get is we're gonna get our binding image
of the volume of distribution.
We're also gonna get that delivery image,
that K1 image there, which we know is related to blood flow.
And here's an example of what the average of these images
would look like at baseline or activation.
If we look at the flow image,
we get a lovely activation in the primary visual cortex,
just as we'd expect.
And in that V1 area, we're getting a very large change,
about 35% increase in K1.
If we look at other control regions,
temporal cortex, the white matters, no changes.
If we look at our binding measure,
our reflection of the synaptic density,
we've got no change at all.
So the fact that we're having this dynamic change going on
with huge flow increases there,
it's not changing the signal that we see
in the primary visual cortex.
Active binding potentials again,
that measure again, normalized to white matter,
no changes at all.
So that suggests that based on this activation,
that something that produces a very large flow change,
that our measure is gonna be stable.
There's a representation of what's going on
in terms of the number of this binding site of SV2A
in the synaptic vesicles.
And of course we also, as I mentioned, did it with fMRI
and of course we get the very clear obvious activation here
back in the primary visual cortex.
And one thing that was very satisfying is
that we looked at the percent change in the BOLD signal
correlated at the percent change
in K1 that I just showed you.
And across our seven subjects,
we got a very good correlation.
Of course, the PET signals are producing very large changes,
but the signal to noises are gonna be pretty comparable.
So this was saying that one thing we wanna think about is
that our measures, and I'll go back one slide,
since we can get a measure of flow
and a measure of synaptic binding,
we have two pieces of information
that we can get at the same time.
And that's gonna affect our interpretation
in terms of how we use that to understand
both the structure and the function of the brain.
And we had seen that before.
This was the result from our early cohort
in Alzheimer's disease, where we saw in the VT measures,
our primary effects were in hippocampus
and up in medial temporal lobe.
And again, this was the small N study there,
but we had noticed in our K1 changes, our flow changes,
that along with synaptic changes,
we were seeing changes in temporal parietal regions.
We were seeing changes in precuneus.
So again, with the one image, with the one injection,
we can get a binding measure
reflective of the synaptic vesicles
and a flow measure, two for the price of one.
So we'll come back to that as we go forward.
So we wanted to look at that in Alzheimer's disease.
And so in Alzheimer's what's been used before
as a functional measure has been FDG,
and being able to do that.
That's been classically used for years.
Well, now we have three measures.
We have a synaptic measure.
That'd be that distribution volume ratio
we get with our UCB-J PET,
a flow measure, and this is with our delivery constant.
Here we're using a relative one R1
that would be normalized to cerebellum,
as well as FDG, where we can get a glucose analog.
So the question is, how do these relate?
When we have multiple measures in the same patients,
how can we merge and interact those and put those together?
So one approach we use
is called canonical correlation analysis.
And basically it looks at pairs of datasets,
two datasets at a time.
So we have a value of a regional value
across different subjects.
So for example, this might be our SV2A data,
our synaptic data.
This might be our FDG data or our functional data.
And what we will then do is say,
what kind of weighted sum of the regions can we do
to produce a canonical variant for data,
for example, datas U and V.
And then we find those that maximize the correlation.
We can find, where are the patterns similar?
How do they support each other
in terms of their regional pattern?
So we could do, in this case, we did three pairs.
We could look at the DVR of UCB-J against glucose, the FDG,
or we could look at that against the flow measure.
This would be a within scan effect.
Or just look at how blood flow measures correlate with FDG.
But let's concentrate first on comparing
these two different scans in the same subjects,
FDG looking at glucose metabolism
and UCB-J looking at the synaptic density.
So firstly, we look at the regional loading.
So this is the values that are applied for each region.
This is what we got for the pattern
for the synaptic measure, for the glucose measure.
There's a pretty good correlation between them, okay?
And that's saying that both measures
are containing some related information.
And remember here, when we've done this analysis,
we've thrown in the healthy controls and the Alzheimer's.
In that part, we're not telling them
about which patients are which.
And here's where those weights look like for synapses.
So what are we seeing?
Not surprisingly, we're getting weights on the hippocampus.
We're getting weights on precuneus,
we're getting weights on temporal parietal,
so regions that are not surprising
and come on that do correlate with the FDG measures.
So now what do we get
if I plot the values in this relationship?
So each of these is the loading for the synaptic measure
and the glucose, coming from UCB-J, coming from FDG.
We see a nice distribution of our dementia subjects,
our MCI subjects, and our cognitively normals control.
So we can do a principle component.
Let's turn that into one variable along this dimension.
And now we get really elegant separation
between all three groups,
so the cognitively normals which separate from the MCI,
separate from the ones with classic Alzheimer's dementia.
And here by combining two pieces of information,
here, both the FDG, the more functional, the synaptic one,
probably more stationary
in terms of synapses that are there.
Now, how did that compare with other measures?
I mean, before, if we just looked at the hippocampus,
we have no problem separating
our cognitively normal from our MCIs and ADs,
but pretty much we can't separate those.
FDG pretty much the same story.
So we're doing a little bit better
in this ability to separate MCI and AD
by combining the two measures together,
and to see how that would work.
And how does that work for our other measures there again?
So here are the regional patterns that we see again.
We saw some relationship, but some variability around that.
If I look at the blood flow measure,
again, that's the early part
of our SV2A scan versus glucose,
they're very strongly correlated.
They're saying pretty much it's the same measure.
So that opens up the question
about, could I use these two measures?
Now these are two values that come from one scan,
a synaptic marker, the equilibrium measure, the flow marker.
Can we use that with a single UCB-J scan to do as well?
And the answer turns out to be yes,
that just with data where I'm combining there,
the synaptic measure and the flow measure
both coming from one scan,
we could separate out MCI and AD and get nice correlations
here with the Mini-Mental score.
I didn't do the other cognitive measures here,
just within the AD and the MCI groups.
So that suggested that with these three measures here
doing pairwise comparison or synaptic measure,
our flow measure, the delivery and glucose measure,
they all contain related information,
but the flow and glucose are very, very similar.
And so we think that when we just use
both pieces of information from one scan,
we can provide additional information
in terms of either diagnostic accuracy
and/or propagation of measurements there.
So let's move on into a different disorder here.
So let's look into depression.
And I mean, synaptic loss has been demonstrated
to be a long hold in postmortem data,
about those loss of synapses there going back quite a while
and be being very significant measures there
if you're measuring
other synaptic vesicle protein measurements there.
And as well, as we mentioned that we know this,
but in the animal models,
that the chronic stresses I mentioned before,
that can produce the alterations in synaptic spines
and the synaptic density would be there,
coming out of Ron Duman's seminal work.
So naturally it made sense
to be able to do a study in depression.
And so this was a study led by Irina Esterlis
looking at 21 healthy controls,
and then 26 with depression here
grouped into a low and high severity group.
And so they're well matched for age and other markers.
And we certainly have
no substantive changes
in terms of the imaging markers that we used here.
So here's what those values look like.
And here we've focused on a couple key regions here,
dorsolateral prefrontal cortex,
interior cingulate and the hippocampus.
And we're not seeing a change
if we compare the low severity, that HAM-D scores are higher
compared to that of the healthy controls.
But in the high severity group,
we are seeing reductions across the board in these measures,
here using a volume of distribution measure.
And when we correlate that volume of distribution measure,
again, in all three of these regions
against a HAM-D score as one example,
we get a negative correlation.
That is, higher depressive symptoms are gonna be correlated
with lower synaptic density in all of these three regions.
And we'd like to take that a little further
and see what we can relate that into fMRI,
and so the measure that we use a lot at Yale,
something called the intrinsic connectivity distribution.
And so doing a resting state fMRI, you get the time courses,
you generate your correlation map,
and then looking at a given voxel
or given region in the brain,
we can look at the distribution of those correlations,
and be able to have a measure
of where we're showing excessive correlations,
so this measure of this, in the connectivity,
the distribution of the connectivities.
And by that measure, we were able to show certainly
a significant difference between a healthy control group
and a clinical depression group
that was shown some time ago.
But how can we tie that together
when we think about networks?
So we've long established the kind of anti-correlations
one sees between a default-mode network
and central executive-mode networks.
And we can do that within this data as well.
For example, if we look in DLPFC
in the central executive network,
and to see how does that correlate with posterior cingulate
as part of the default-mode network,
and just the connectivity that across subjects
is also a measure that correlates
with the HAM-D that we see,
that the anti-correlation is reduced
as you get to higher depression scores.
So does that all tie together with our synapses?
And it does; I can use this connectivity measure.
That means that the more positive that connectivity is,
the less negative that that would be.
We are reflecting that with a higher symptom score
and as well as lower synaptic density in there.
So again, looking and trying to build
multiple measures together to see how we can inform
our understanding of the brain networks.
Now let's take this one further,
look at a second tracer, a second target.
And that would be looking at mGluR5 in MDD.
And we've done extensive studies there
using the tracer FPEB,
and we weren't seeing baseline differences there.
Intrinsically the FPEB is a little bit more variable there,
but we don't see that.
Even with higher symptom severity we didn't see that.
But we're really interested in this target,
obviously because of the effects of ketamine.
Now, ketamine either in healthy controls or depressed
can show dynamic changes in the measures of mGluR5,
being able to show that in short-term acting.
So it's an exciting target.
It's relevant to think
about how the excitatory marker for mGluR5
is gonna relate to our total synaptic marker.
So here's the population that we have.
We have 12 controls and 20 MDD.
We've had both tracers, and we've been careful
to be able to look, to try to match that.
Even though these were done on different days,
the HAM-D scores don't change too much
from one day to the next.
So how does that relation work?
So this is just some pilot data I wanna share
'cause it's just interesting
about directions that we may wanna go.
So what we're looking at here is we have values,
and this is of looking at their mGluR5,
a measure with the tracer FPEB,
the SV2A marker, the VT coming out of UCB-J.
But just look at our healthy control populations.
We have very nice positive correlations,
0.5, 0.4, et cetera, between the different tracers
within the cross-subjects in these regions.
But when we go to the MDDs,
all those correlations fall apart.
We could see that in one region.
We're just looking at the DLPFC with itself as one measure.
And in the healthy controls in green,
we have a nice positive correlation.
In the MDD, the correlation is breaking down.
So we're suggesting some interesting pattern here
where we're getting differential effects
on our synaptic marker and our glutamate markers here
that are relevant in depression,
but not present in the healthy controls.
Much more work need to be done,
but an example of bringing two tracers together
in the same patients in a different area.
Let's move to schizophrenia,
obviously another area where there's been long field data
to suggest that there's gonna be losses of synapses.
Measurements of spine density there
has been shown in postmortem data.
And also the interesting part about how C4 overexpression
affects dendritic spine density
and how that is an effect that we can see
in schizophrenic patients,
increased expression of C4 again in postmortem.
So naturally that led us to doing a schizophrenia study.
Right now, this is a study with 13 schizophrenia
and 15 healthy controls,
and some mixture about what they're on, antipsychotics,
various cognitive measures, structural clinical scores,
cog state, et cetera,
and following our standard approaches here,
and also applying that with partial volume correction
because of possible loss of tissue measurements.
Here's the balance of our patient populations,
and no differences there in any of the measures
or the structural measures there.
And we'll also notice we're gonna use
a binding potential measure normalized to the white matter
and no significant differences
in the white matter binding values.
Clinical characteristics of the patients
in terms of the typical scores,
typical exposure of antipsychotics,
and we can be using these as covariates, as you'll see.
So if we look in the regions that we were targeting there,
we are seeing mean reductions and across multiple regions,
seeing that in the anterior cingulate,
in frontal cortex, hippocampus,
in fact, in multiple cortical regions, so really feeling
like a widespread difference throughout the brain.
And then that when we look at those numbers,
we're seeing that these
were those values without corrections.
If we also look at volume,
we're also seeing volume losses in some of these,
not all of them significant.
So that was why it was important
to do the partial volume corrections.
But we still have significance in those primary regions
after correcting for the volume losses
that would be the same.
But the interesting part often becomes
when we begin to correlate our measures,
the in vivo binding measures here using a BPND
against in this case, the positive symptom score.
And that basically that the higher
of these positive symptom scores that we get,
we're seeing lower in this case, in the frontal cortex,
and the reverse effect when we look at a different measure
and we look at the social cognition measure.
So overall the study, this was not the first,
the first one came from the UK from Onwordi et al,
we replicated those reductions in schizophrenia,
and we're seeing them in multiple regions,
so fairly widespread.
We also saw that we had correlations in frontal
with varied symptom severity cognitive scores,
but we didn't see associations with duration of illness
or with the total amount of antipsychotic exposure.
So more needs to be done here,
just beginning that relationship.
All right, so in the last section of the talk,
I'd like to kind of go through some newer methodology
that we're working with in terms of trying to be able
to look at these in multiple variates and multiple ways,
and especially with something as interesting as synapses,
how that might correlate
with other measures that we can get with fMRI.
So I don't need to tell this audience probably
about resting state networks
that have been around for a while.
That has been this really interesting
and really reproducible in general measures
that we can be getting from resting state fMRI,
but not just from fMRI.
I mean, this has been around from FDG as well.
And if we look at the FDG patterns,
we find that those patterns,
the spatial patterns that we get
have a lot of visual similarities
that we might get to the resting state values,
although there's some components that are unique.
And that makes sense if we're talking
about what's going on in the kind of the functional states
that are being there that are also being driven
by the metabolic signals.
So those things might be very, very similar.
So that's the goal is let's go
and look in our synaptic density data and say,
well, what can we do?
How can we use the patterns and look in resting state
using independent component analysis
to be able to extract them
from our synaptic density markers?
All right, so independent component analysis,
and many of you probably know this better than me,
but the idea is to separate the signal.
So for example, if I looked at this colorful square,
it really could be recognized
that it's broken down into different intensities
of red, green, and blue, and how they add up.
So we could break this down into its sources there
with a different loading for each voxel.
And of course, when we do imaging data,
the same logic can be here.
There can be different patterns of there
that are merged in different ways.
Now in fMRI, you've got that nice dimension of time
in the spatial loadings with the resting state.
In PET we don't have it organized that way.
So here, what we're gonna do
is doing that across different subjects.
So each subject we're gonna have its PET data,
and for each voxel or each region going across.
So this is our input matrix
to be able to do independent component analysis.
And we'll then be able to use that
to break that down into images that'll be source maps.
And for each subject, we're gonna have a loading score
to be able to map towards those source maps.
You may have to decide how many of these to do
and how reproducible that they might be.
And that's kind of the interesting thing
is really understanding those loading weights.
What can they tell us about different patients
or how they correlate with other individual measures?
Now we started doing this back a while ago.
Pat Worhunsky pioneered this in our hands,
looking at in PHNO.
PHNO binds to dopamine D2 and D3s.
It is a sum of two patterns.
And it worked out really, really well
that we could look at this both in a healthy control
and cocaine use disorder population,
where we found multiple measures
automatically pulling that out of the data.
A striatopallidal portion,
which is really lined up with the dopamine D2 values,
which showed differences, which showed lower,
consistent with separate D2 studies
in relations to cocaine use.
But then when we focused on a separate one
that was leaning towards the D3 areas,
being able to pull out the nigra
and other areas in the globus pallidus
showing increases in CUD, which we saw
if we just looked individually at those regions.
It was a nice thing because this is a tracer
that is a combination of multiple sources.
ICA was able to find them.
So let's see how we're doing this with SV2A.
We have a large cohort of healthy controls, 80 subjects,
all of them that are rest-scanned,
60 minutes with arterial blood samples.
We're using that total binding measure VT,
trying to start with our most conservative measure,
registering everybody in the MNI template
and smoothing the images there by by about 12 millimeters.
And we used the source-based morphom SBM toolbox,
the ICA toolbox there.
And part of what we wanted to know
is just, well, how reliable might these be?
I mean, we looked at that FDG study
that had 18 sources there, and we weren't really sure.
So we looked at multiple source levels
to be able to see what would be most reliable.
We checked how stable they were using ICASSO
to be able to iterate, to be able to find that.
And then we also split the sample to be able to see
which of those were reliably restored.
We wanted to know basically how far to take
our PET data in this way.
So here are the images, and I'm sorry that they're small.
But basically be taking the same data set
where we extracted 8 or 12 or 18 or 24 components there.
And what I'm showing you are those
that were reliably characterized
as we moved into multiple components there.
So for example, out of the eight,
four of them were replicated very nicely.
As we went to 12, we added new ones,
which were replicated nicely,
when we went to 18 here at that quality coefficient.
And when we got up to 24,
we were starting to see many of the components
that just were not there to be able to be measured reliably.
So we focus that down back on 18.
Of those 18, looking a little bit more carefully,
we've really focused that basically 13
we would consider to be reasonable networks,
able to say in terms of that.
And then we looked again.
This is just a healthy control.
We just said, are there any relationships here
with sex or age?
We saw nothing with sex,
but we saw these relationship
in these four networks with age,
suggesting that we might have a pattern
that would be valuable there.
And here we're bothering to reconvert the data
back into VT units so we know the magnitude of the effects
in terms of what the synapses that might be there.
So overall, we identified a total of 13
that were reliably obtained from our UCB-J data.
There was some spatial consistency, more with FDG perhaps
than what was seen with the resting states.
And again, we noticed this effect on age.
So now we wanted to take this to the next step.
And kind of that next step is to combine,
to look in the same subject where we have
SV2A PET and resting state fMRI.
When we combine those together, will that work?
As I mentioned, there was some similarity in sources,
but how do these tie together in some way?
Well, we realized we didn't wanna open this
up to a full fishing expedition.
So we were really focusing in one area,
looking at the relationships
between the striatum and the cortex
based on years of preclinical studies there
and tractography studies there,
being able to show that those relationships should be there.
So that's what we wanted to focus on.
We wanted to look at resting state networks
in the same subject
compared to the synaptic density measures that we could get.
How do those relate to each other
and focusing on corticostriatal regions?
So we have 35 data sets,
all in cognitively normal, healthy controls,
again, with the VT images there.
We had a short fMRI sequence, 10 minutes there.
Everything was motion-corrected
and brought into standard template space.
So for the fMRI, we were able to estimate 30 components.
And the measure that we're taking it out is
the fractional amplitude
of low frequency fluctuations or fALFF.
Again, that's looking at,
when we look at the Fourier transform of the data,
what is the low frequency components
as normalized to the high frequency,
trying to look at just a reflection
that's been used commonly as a measure of neuronal activity.
In UCB-J we, as again, went with our 18 components
as a starting point to be able to do that,
but we're gonna focus down on a few networks.
And remember, keeping something in mind
that the SV2A measures, we said that the fMRI
is gonna be more functional, more flow related going on.
UCB-J with the VT is gonna be
more of that static synaptic measure.
So of the fMRI, we really felt like 17
were visually consistent with resting state networks.
We're gonna focus on these.
We have two of the dorsal,
the default-mode networks and the anterior and posterior,
a sensory motor network, and two others,
the executive control and a salience network
that were identified that came out.
In the UCB-J of those, we're gonna focus on the ones
that are relevant for corticostriatal,
looking at one that is primarily striatal.
So we display that.
And these two are medial prefrontal
and medial parietal region there, these two networks,
and much more needing to be done with all of this stuff.
So now what I wanna show you is when we relate
the loading of a subject
from their PET SV2A to, in this case,
to their measure, to their fALFF measure
coming out associated with that region.
So if I look here first, we have a striatal network.
We're seeing some correlations there
when we look at the default-mode networks across that.
The more interesting one is in our medial prefrontal region.
And that we see in ProTec
we're seeing large correlations
across the loadings of multiple networks.
So here's an example of the fALFF measurement
coming out from these subjects in that,
according to that network correlating that
within the medial frontal loading that we see,
and we get this very significant correlation here.
Interestingly, when we looked at the medial parietal source,
we really didn't show any of that relationship at all,
which was a little bit surprising
given the relationship we have
with the posterior dorsal default-mode network.
So a lot of really early kind of discussions
or thoughts going on.
We certainly saw that spatially,
we saw a couple of the sources that looked similar.
And in fact, we saw that the medial prefrontal
and the UCB-J source correlated nicely
with the resting state default-mode network,
the anterior one,
but we did not see that with the posterior.
In the cortical world,
we really did not see associations
with the striatal and the sensory default network,
although we did see that with the default-mode
and the salience network.
And really this is interesting
that this medial prefrontal is the strongest one,
correlating really across all the functional domains.
So we're just getting started with this.
We recognize that maybe there should be differences here.
If our UCB-J measure is a more static measure of synapses,
is that gonna be reflecting
the current state of those patients?
And so we're looking more at that flow map,
that K1 map or VT map as well that's going on
and really thinking about this
in terms of some network activities.
So finally, we really wanted to be able to apply this.
So if we can take this into the Alzheimer's world
or into some patient world,
if we can extract another better measure,
especially in a data-driven fashion
that we've got right here.
We've now done in a small cohort,
we take a subset of our AD subjects
where we're trying to now go back to their SV2A data.
We haven't done this with the fMRI yet.
And say, okay, let's apply ICA.
Let's see what it extracts.
And again, remember, this is a data-driven.
We're throwing in our cognitively normals, our MCI and AD,
and let them find the patterns and those loadings. Okay.
Again, we did that with SV2A coming out with 18 measures.
And now the question was,
do the loadings tell us something about the disease?
'Cause of course the ICA is not told
about what population is which.
And so we found that five patterns
if we looked at the loading,
so here we're plotting for five of these components,
we looked at the loading in the cognitively normal,
in the MCI and in the AD.
And these were the five components
that showed statistically significant group differences,
at least between two of the groups.
And we have the regions that we're seeing
are perhaps not surprising.
We're seeing a lot of temporal lobe,
a lot of frontal lobe regions
and a little bit in occipital as well,
which we think actually may be reflecting
more of a normalization pattern there.
So how does that relate
when we look at that compared
to some of the cognitive scores?
And now of course, we're just looking
within the MCI and AD populations.
And now we're seeing some really interesting results.
The first pattern in particular is very strong,
and being able to separate out and get a nice correlation
with a number, with the various sums of boxes,
Mini-Mental scores, et cetera.
So we're seeing that we can extract that
in cognitively normal ways across that.
So this one is there.
And again, we're not using
including the cognitively normals.
We're just doing this within the patient populations,
similar to what we saw
with when we used the full large components there,
the large average,
but here doing this in a data-independent way.
If we look at component two,
a little less convincing here,
but on the border of being able to see correlations
of some of these measures there,
and just trying to begin to understand to what extent
the regional pattern were related
to the specific cognitive domain that is in deficit.
So we've been excited with this ICA.
It's interesting how it will separate
out these source networks, again, with no a priori knowledge
of the subject characteristics.
And we certainly found again without telling it,
that we had certain components
that significantly differentiate the AD,
and that we found particular sums that separate MCI and AD
with their cognitive measures.
And so we're still left with a lot of questions here.
And just one of them is just, will this method
give us more sensitivity
to be able to both follow patients over time,
differentiate patients or look at drug effects?
So let me give an overall summary
of the issues I've talked about here today
with our SV2A synaptic marker.
We believe and although there's obviously more
that needs to be done, that this provides
a really good general imaging biomarker
for synaptic density.
We are looking at a vesicular protein,
and that's been used traditionally
to be able to assess that in postmortem data.
And so far the data are consistent and supportive of that.
The tracer that I've shown you here is an excellent one.
It has very high test-retest,
very high-quality measurements.
It really lets us look in very small regions.
Of course, carbon 11 has a 20 minute half-life.
That's not ideal.
We now have versions of that, F18-SynVesT-1
that is basically identical in terms of its characteristics,
very similar structure,
and it's something that is gonna be very useful
in multicenter trials.
When we've applied that in our patient populations,
I've shown you Alzheimer's,
depression and schizophrenia today,
we are seeing significant results clinically
and basically biologically relevant results.
I have not shown you our other populations
in Parkinson's, HIV, epilepsy that we've already published,
ongoing work, even work in development using a monkey model,
and then all sorts of small animal models
of these different disorders, which are ongoing.
Overall, in the neurological disorders
we see the more focal losses.
We see it in the hippocampus in AD.
In the Parkinson's, we see it most clearly
in the substantia nigra,
and epilepsy in the site of the focus.
In the psychiatric diseases, in depression
in MDD and other areas like that,
we're seeing that as more widespread of disorders.
And then I think what's really interesting is
if we combine these measures together,
what's that gonna open in terms of unique analytic tools,
combining what we can do in MR.
Looking just within the SV2A networks,
we saw the age effects.
We saw the ability to separate that in Alzheimer's disease,
or even really multi-parametric where we can be saying,
okay, I wanna look within the two measures
of volume of distribution,
a measure of the synaptic concentration
compared to the blood flow measure,
that that is very useful.
It's also exciting to do these multi-tracers.
We looked at tau and FDG in AD.
We looked at our mGluR5 measure in depression,
and being able to learn more about how we can combine that.
Obviously, all the more and more combinations with fMRI,
how we do it's important
because, with this much data, we're gonna find things.
How to do that in a reproducible way will be fascinating.
And for me, a lot of anything
where we're showing correlations with cognitive measures
suggests that we're on the right track,
being able to do that.
So finally, a lot of people to thank.
In the picture in the back is the people in our PET center
back in pre-COVID times.
So many people contributing to the work here.
I've highlighted in yellow
the people who have contributed slides
that I've shown today.
Many, many others involved in different aspects of our SV2A.
Lots of wonderful support from the NIH.
Thank you, and from other foundations.
And I thank you for your attention.
- Hi, I'm Audrey Fan from
the University of California Davis,
and I'm in the Departments of Biomedical Engineering
and Neurology here.
I'm delighted to participate in the NIMH workshop
on PET fMRI and I'm hoping this talk
will provide a complimentary perspective
to some of the other talks you've seen
with the focus here specifically
on using PET MRI to study brain physiology,
biomarkers and brain vascular health.
I'm really interested in this topic because
the brain is so uniquely metabolically demanding.
It's only two to 5% of our body mass,
but the brain takes up about 20% of our oxygen supply.
And because our brain tissues
cannot store oxygen as a reserve,
it relies on constant circulation delivery of oxygen
and essential nutrients through
the cerebral blood flow, CBF,
on the flip side of the coin, oxygen extraction fraction.
So the ability of tissues to extract oxygen
to meet its metabolic needs,
and these vascular or physiological parameters
may be familiar to you as parts of the FMRI BOLD signal,
but are critical for normal brain function.
And unfortunately,
when this circulation and supply is disrupted,
it can lead to devastating consequences.
Most dramatically,
as many of us are aware in acute ischemic stroke,
in acute stroke for many decades,
the standard of care has been relatively crude.
And what I mean by that is it's been primarily
based on the time window.
So if you have arrived to the hospital
with symptoms of acute stroke, within 4.5 hours,
then you are eligible for standard treatment
called TPA or clot buster drugs.
And beyond that, the risk value benefit changes
so that you might have,
overall more risk of hemorrhage
and you're ineligible, right?
But what this means is you actually miss a lot of patients
who perhaps don't know when their symptoms started say,
you woke up with a stroke.
This would miss the opportunity to help many patients.
Instead there's a complimentary framework,
which actually focuses more on exactly the physiological
mechanisms that are happening during this pathophysiology.
So in stroke,
it's well known for a long time
that there is an area of infarct,
this ischemic core of tissue in the brain
that's destined to die because a lack of blood flow,
but that there is also this area of tissue
in some patients around the core called penumbra.
And this penumbra is vulnerable.
at risk, but potentially saveable tissue.
And so one of the keys is could we identify patients
who have penumbra and could benefit
from stroke treatments like TBA,
even beyond the 4.5 hours.
And I think from basic science histology
and preclinical studies,
there is a physiological basis for this.
So what I'm showing here is a representation
of physiological changes as we reduce the blood flow,
emulating what happens in the stroke.
And certainly there are neuronal features
that could be detected,
but physiologically some of the hallmarks of this vulnerable
or at risk tissue in the penumbra is characterized
by reduced cerebro blood flow, not surprisingly,
but a compensatory OEF oxygen extraction that's elevated.
The tissue is trying to extract more oxygen
to compensate for the lack that it's receiving.
And so I think this is really a shift in how we're
identifying patients that could benefit
from treatment and stroke.
It was the basis of multiple studies in
the last five years.
One of them led by Stanford called the DEFUSE-3 trial.
And what you're seeing here are example MRI images,
where the purple represents diffusion
MR segmentations of this ischemic core.
Again, tissue destined to die because of the stroke,
but also a complimentary hypoperfusion.
So a contrast based dynamic susceptibility MR scan,
where the green indicates area that's been affected
with reduced perfusion, relatively speaking,
and in this trial imaging played a huge role.
It was this mismatch involved metric delineation
between these two regions that suspected
there likely is an area of tissue adjacent
to the core potentially saveable.
And in these trials that used imaging to identify patients,
the patients who actually
had potential penumbra benefited from TPA,
or even newer thrombectomy treatments,
way beyond the 4.5 hours up to 16 to 24 hours
in other trials where we're reaching patients
seen based on their physiological state
and not just a standard time window that we can help them.
And so I think this is a very exciting time.
where imaging specifically
physiological biomarkers plays a big role,
and we can do even better than this
because when you look at this,
actually the hypoperfusion is based on contrast injection
and also requires difficulty in quantification.
And so usually this is a relative marker.
I think where PET and MR both could actually
be pushed is to provide quantitative
imaging of these biomarkers across the whole span
of oxygenation, blood flow and metabolism.
This has traditionally been in the purview of PET
because it has established kinetic modeling models.
It has been around for a long time, but as we all know,
it has some complications with radioactive tracers,
the need for arterial blood sampling for quantification,
and specifically for some of the biomarkers that
I'm laying out here requires O-15 label tracers,
which only have a half-life of two minutes
and is quite prohibitive in terms of requiring a cyclotron.
And so I think there's complementary benefits
of combining PET quantification with MR,
especially some new sequences
that can directly assess cerebro
blood flow oxygenation without ionizing radiation
and reduce some of these barriers of set up
and cyclotron needs,
at the same time providing accurate quantification.
And so this is the synergy that we're hoping
to leverage with PET and MR going forward
for not only stroke,
but a broad set of neurological and psychiatric
conditions that potentially have systemic
and brain focused physiological disruptions.
So with that overview,
I hope these biomarkers can be quite broadly used.
And I wanna with the rest of the talk,
indicate how with these examples
fully simultaneous PET and MR have actually
added to our knowledge
of brain physiology and how to measure it.
So specifically I wanna cover how we've used
(indistinct) been labeling.
So an MRI-based measure of brain perfusion
and used PET MR to validate it,
including during a stress test.
I wanna then highlight how further quantification
can be achieved in a way that's less invasive,
but still accurate using PET MR.
And beyond that, what are other applications
that are not specifically cerebrovascular,
but target some of the physiological contributions
to neurological conditions and other biomarkers
that would benefit in the future
from this PET MR technology.
Let's move to this first section
where we will leverage PET MR simultaneously
to validate brain profusion biomarkers.
And the case study I wanted
to show here is of intracranial stenosis.
And specifically of patients with Moyamoya disease,
Moyamoya disease is a relatively rare,
but progressive disorder.
There are some genetic components to it,
but we don't really understand why it happens.
Arteries of the base of the brain begin
to over time become stenosed or occluded.
And these patients tend
to have sevenfold increased risk of stroke.
And it's really important to capture these brain
physiological markers in patients to decide
who might benefit from surgical treatments.
An example of these moyamoya cases as shown here is
highlighted in these catheter based angiograms,
where compared to the healthy volunteers,
the moyamoya circulation has blocked occlusions,
but also these wispy collateral patterns of flow
that potentially is trying to compensate.
But it's unclear whether all
of these collaterals are well formed in patients.
And so we need to actually measure what's happening
in the tissue physiology to decide
which patients actually need surgery.
And one of the things about moyamoya
that is a great test case for our situation,
it actually poses challenges.
It takes a very long time,
the longest transit delays among
many cerebrovascular disorders
to get through some of these collateral patterns.
So we have this severe pathology in relatively young
patients that we can test in the vasculature
and really make sure that our measurements
are comparable to some of our PET established standards.
One of the methods I'm going to focus
on here is arterial spin labeling,
and specifically a type of ASL called pseudo continuous ASL.
This method with MRI does not require
any injection of a contrast agent.
Instead it uses radiofrequency pulses
in the MR scanner at the approximate neck level.
And it labels spins into arterial blood that are eventually
going to flow into the brain
where this blue imaging slot is.
And one of the controls that the user
as a imager has is this post label delay.
So how long between these RF pulses
do we wait before we capture the image in the brain?
If we are able to take this labeled acquisition
and repeat it with a control image,
the difference between
the two will take out all static spins
in the brain and in the end,
what you're left with is this exactly the labeled flow
that has arrived to the imaging slab.
And what you see here are example profusion maps,
where the scale, the brightness of each
voxel is indicative of the microvascular
perfusion that has arrived to the tissue.
So this is a general framework for how ASL works,
but you can imagine, especially in the moyamoya cases,
I was pointing out on the previous slide that this
is challenging to do in patients with abnormal vasculature.
How do we know what the post label delay is?
And how can we validate some of these ASL MRI maps
in such challenging patient cases?
So embarking on this study,
one of the first things I did was I looked in the literature
and I compared previous studies
that performed both PET and ASL, MRI profusion assessment.
And one thing that's very clear is
that the self-reported R squared correlations
from the study itself.
So between PET and MR is higher
if the two scans are spaced closer together,
that's not surprising.
With our daily diurnal variations, with your diet,
if you've taken a couple coffee now to stay awake during
this talk, your blood flow could have reduced by 30%.
And so with these natural fluctuations,
it's really important to capture the PET
and MR together to quantify brain physiology,
which varies so much across day to day.
So in order to do a more careful
physiologic controlled comparison,
we leveraged simultaneous PET MR, specifically,
experiments were done on the GE Sigma system
where we're truly capturing
with this whole body system.
Perfusion in the same state by PET and by MRI.
So let me walk you through one example
in a moyamoya patient of what these experiments look like.
Here's a standard ASL profusion map
in a 26 year old female with bilateral moyamoya disease.
Moyamoya disease tends to affect both hemispheres.
Sometimes it's unilateral, many times it's bilateral.
And it typically is in the anterior circulation.
So we see some heterogeneous signal here.
The standard ASL was acquired with a post label delay
wait time of two seconds,
but it's really hard to tell from this one perfusion map,
what exactly the interpretation should be.
In the simultaneous acquisition? However,
we see a reference from O-15 water PET,
which has been established
to be a gold standard for human imaging.
So this is with radio labeled water.
What we see is actually much more uniform,
less heterogeneous signal in the PET image,
although with less perfusion anteriorly, like we expect,
but certainly these two images do not look
in terms of the heterogeneity quite similar.
And it actually gives us some insights as to what's
happening underlying in the brain, so for instance,
a hypothesis that we had is on the left hemisphere,
there is these hotspots of higher signal,
and this may reflect labeled blood
that has arrived through the ASL labeling.
It has arrived into the brain,
but it's still stuck in low,
slow flowing, larger macro vessels.
It hasn't yet perfused into the capillaries.
Whereas on the other hemisphere,
in the right hemisphere of the patient,
there's low signal compared to the PET.
And this may reflect an even longer transit time.
These patients have even more severe pathology.
It takes longer on this hemisphere for the blood to arrive.
It's possible that the labeled blood from the RF ASL tagging
has not even yet arrived in the imaging voxel.
And so these are two hypotheses
that we can test and a couple of ways that we can
add confirmatory information to this is
taking a contrast based MR Measurement
that does not give us a quantitative
MR Profusion map like ASL,
but it gives us a sense of how long it takes
to arrive for the blood.
And this time to maximum, as you can see,
is elevated anteriorly,
especially on the right hemisphere here.
And another way to address this and test
and also improve the maps is to use multi delay ASL.
So instead of only one post label delay,
their strategy's now to do sequential,
but also tying interleaved, multi delay acquisitions.
Here I'm showing a simple example
with five labeling delay times.
And what that means is you can actually capture different
times of arrival inflow into the capillary bed,
as well as washout of this RF labeled ASL signal.
And ultimately this gives us a sense of how long it will
take for the blood to arrive and also correct for it.
There are some limitations in terms of time efficiency,
as well as signal to noise ratio.
If you're trying to acquire many of these post label delays,
however, in the end, when you compare to the PET signal,
this multi delay map looks
much more homogenous over the cortex,
and actually could also give you
at the same scan in arterial transit map,
which mimics the same hypothesis that we have.
So we have longer delays due to moyamoya vascular pathology
on both hemispheres, especially in the right hemisphere.
If we were to wait even longer
and just capture one long delay ASL signal,
you would actually see the blood flow arrive
to the cortex with the ASL, if you waited long enough,
but some of the signal in the basal ganglia,
which has different relaxation properties has decayed away.
So this is an overview in a patient case of how it actually
teaches us something about the transit time and also how
to best measure it in comparison to a reference PET.
Really quickly,
here's a second example in a moyamoya case where this
participant actually had a previous stroke.
And in this case,
actually both with standard ASL and multi delay acquisitions
up to three seconds waiting time at post label delay,
both of these are not able to capture the true remaining
unilateral signal of perfusion
on the right hemisphere that we see on the 15 PET.
And it's really the longer delay
that allows us to capture this.
So I think it's a combination of both capturing multi delay,
getting a sense of the transit times,
but potentially for these cases where you suspect due
to pathology, or in other cases, age,
other conditions related to vascular pathology,
you may have to wait longer with the ASL compared
to what's standard for healthy volunteers.
If we look across regions with very severe delays,
so time to maximum, greater than five seconds,
we do see a high correlation for any
of these ASL signals with PET,
even for regions with severe vascular delays
and still with the Bland Altman plot.
So I'm showing now the same data except
on the y-axis there's MRI and PET difference.
And the x-axis is the mean between the two modalities.
You'll see that actually
there's high correlation,
but there's bias until we have longer delays.
So it's really the long delay as well as multi delay
that kind of gets us consistency between MRI and PET.
And what that allows us to do on a group level
with these recommendations is actually compare okay.
If we take PET and compare patients
to controls and each modality separately,
it's not surprising that the standard ASL
will overestimate the hypoperfusion.
So it's saying a lot of these patients
have under perfused regions,
but we know actually that some
of the signal has just not arrived.
And so we think that this is actually artifactual.
And when we switch to a recommended multi delay strategy,
especially with longer post label delays,
this allows us to better discriminate patients
who are actually suffering
from low perfusion in a way that's more
consistent with PET and more focused a lot,
especially in the anterior circulation.
So in summary,
these types of careful validations with different ASL
parameters perform at the same time with O-15 water PET,
gives us a better sense of a recommended ASL strategy,
getting at quantitative profusion
in these challenging patients,
and also able to discriminate patients
from healthy controls.
Now you may ask,
is it really necessary to do this at the same time?
And some of these pathologies,
these changes are different enough across from a patient,
especially if it's like a unilateral case
and there's asymmetry,
it would be different enough that I think
the same profusion abnormalities
would show up on PET and ASL.
However, I think it's even more important
to capture the same physiological states
if you're looking at more than one physiological state.
And this leads me to describe some of the experiments
we've done to assess cerebrovascular reactivity.
This is the ability of the brain to augment perfusion
in response to an external challenge.
And it could test the ability
of your brain to auto-regulate if there is a stenosis,
the downstream vessels or capillaries
may already be maximally dilated,
and unable to respond to an exogenous challenge
such as hypercalcemia or a drug
that actually performs fatal dilation like acetazolamide.
So in giving a stress test to the brain,
we're doing something quite like a cardiologist
would have you run on a treadmill.
And we're trying to see if there's pathology
that shows up not only at baseline,
but after stressing these vessels in the brain.
This is important because you not only is some pathology
only identified after the stress test,
but it could identify a very poor prognosis.
In this case,
here's a PET image at baseline and a PET image
after vasodilatation with a drug called acetazolamide.
And even though we're expecting increase
in profusion with vasodilation,
there's actually a paradoxical decrease
in this right hemisphere.
And that's indicative of cerebral vascular steal,
and it's a poor prognosis for this particular patient.
We're starting to see that with ASL
we're able to do some of the same assessment
in multiple physiological states.
And this is challenging because
it's hard to deliver the same exact gas
or ensure that the same dose
on separate sessions to create
vasodilation is exactly consistent.
And so I would argue that
specifically in this case of a stress test,
we're looking at more than one physiological condition.
This is even more reason that the validation
for profusion has to happen
simultaneously between PET and MR.
There's been several studies where we've looked at this,
not only in patients, but in healthy controls as well.
And it's actually quite interesting
because areas that have short transit times.
So we talked about the deep gray matter,
the basal ganglia previously and intrinsically these deep
gray matter regions tend to have short transit time.
And if the transit time is too short,
the ASL signal might actually decay quicker.
And for the same post they will delay.
We have also underestimation.
So in blue, the ASL underestimating,
just in the same way that long delay times led
to underestimation that ASL.
We're also seeing that with short transit times,
if you give a vasal dilation challenge,
some of the blood arrives even faster,
you get these larger areas after vasal dilation
of green indicating short transit times,
and thus much more underestimation coincident
with these areas of short transit time.
So that was kind of a complicated logic,
but the main takeaway that we have
to be cautious when there's fast blood flow,
as well after vasodilation and the stress test
and in areas of short ATT, short transit times,
as well as long transit times that we discussed,
this could be a problem
when we compare to PET as in this case.
And my colleague Mo Dr. Moss Sal at Stanford
is continuing to evaluate this with CBR
and has done a really beautiful job
of comparing PET super vascular reactivity.
So this is the percent response and profusion due
to acetazolamide and showed nice correspondence
with multi post able delay ASL MRI
in comparing across these modalities.
So in summary,
I think it's really critical to look at the same
physiological state for accurate quantification.
And this is particularly so when you want
to track multiple physiological states
that you've induced in order to see in a stressed condition
is the brain showing abnormalities
that we didn't detect before at baseline.
Now that we've motivated the need for physiological
simultaneity in PET and MRI validation studies,
I wanna share some technical considerations
as to how PET MR can reduce the invasiveness
while keeping the quantitative accuracy
of these physiological biomarkers.
So one established need for quantification in standard PET
acquisitions is the need to sample arterial blood
as an input function and understanding
how much tracer is delivered at which times,
in order to perform a quantitative kinetic model.
We are able to see some of the signal
in the head PET field of view,
but the arteries are quite small to compare
with this typical PET spatial resolution.
And so with PET MR studies,
what we have is a really well aligned MR
high spatial angiogram, for instance,
shown here with the time of flight image,
we could compare this to the early timeframes
where the tracer's being developed.
So if you look at the total PET counts
in an initial injection,
there's a window of time where the counts are likely
in the vasculature before it's actually stabilized
and arrived into the brain tissue.
So in taking a short timeframe
with high enough sensitivity, you're able to see,
a similar angiogram from the PET,
albeit with lower spatial resolution.
And there are ways to model these spill in
and spill out effects that are due
to this limitation in spatial resolution,
and kind of look at these different arterial mass from MR
and PET aligned from the simultaneous acquisition
to then extract and remove spill
and swell effects in the image derived in function.
So the blue curve here is a representation
of an injection of O-15 water.
This is the initial bolus and input function delivery,
and using PET MR information,
we're able to correct for some
of the spill and spill out artifacts.
And this has a lot of advantages first and foremost,
being noninvasive.
So not needing to sample arterial blood,
but it's also closer to the brain area of interest compared
to sampling from the peripheral arteries in the arm.
And it's also automatically calibrated
to the rest of the brain time activity
curves or brain tissue signals
for the later kinetic modeling part.
Other studies have taken this image
direct input function approach,
not only to take into account point spread functions
and spill in and spill out,
but also to use in the early timeframes
where the bolus is being injected,
MR and navigator.
So very quick navigator acquisitions
that show you the position
with a MR acquisition and allow you
to then adjust the mask over the time
of the dynamic acquisition, say,
in this case up to 60 minutes and adjust the mask
of the vasculature so that you're
innately correcting for motion.
So not only are we able to address spacial resolution
limitations but we're able to use MR navigators
to address patient motion and get again in this study,
very high fidelity image derived input functions,
low difference from the actual arterial blood
that was sampled as the ground truth,
and ultimately quantitative profusion,
or in this case, glucose maps,
after you use an image derived input function.
So these strategies which do involve extraction
of the vessels are broadly applicable.
And I think play a role both on in physiological biomarkers,
but other tracers that you may want to use in order
to account for these motion artifacts
or spatial resolution artifacts on the input function,
beyond that, for physiological biomarkers,
there's actually an opportunity that even overcomes
the need for input functions to begin with.
And this concept kind of is based on the fact
that there are acquisitions that globally
with MRI can be sensitive to some
of these parameters that we care about,
like brain perfusion.
So here's a example paradigm
that acquires both MR and O-15 water PET.
And the main MR acquisition is called phase contrast,
phase contrast uses the phase MRI signal
to look at major arteries and give you
a global profusion representation in the brain.
And what previous studies have shown
is that phase contrast and O-15 water PET,
the reference standard are highly correlated across
different conditions on the global scale.
So this is again a whole brain profusion measurement.
This has been tested in different gas breathing conditions,
which actually changed the profusion
so much like the stress test
we were talking about before.
But phase contrast is actually going to be a nice global
representation that has been validated
with O-15 water PET,
and what that allowed other investigators,
including our group to do is to think about, well,
maybe we don't even need to capture
the input function itself if we trust this global scaling.
So if a static O-15 water PET scan,
such as this, say over two minutes is a good representation
of the distribution of the profusion tracer.
If our goal is to get to a quantitative map of profusion,
we could calculate a scaling from this PET relative static
scan to a quantitative scan using a main whole brain CPF.
And that main whole brain CPF
could exactly be from phase contrast,
'cause it's been validated as based on my previous slide.
And this paradigm, as you can see,
would require the phase contrast MR
to be acquired at the same time as the PET,
but it would simplify the need for
extraction vessel segmentation
of image derived in per function,
and still avoid our arterial blood sampling.
So specifically for perfusion and using phase contrast
these large vessels to get a global scaling factor,
this is another clear way in which physiology from both
PET and MR can be leveraged together and get at something
quantitative like this absolute map of CPF.
This PC PET approach again requires phase contrast
to be acquired with the PET in different conditions,
but it is sensitive.
We've shown that it shows
clear reactivity in healthy controls.
If you look at baseline and postal dilation CPF,
as well as limitations and reactivity,
both reductions and paradoxical negative reactivity
in the presence of a vasal dilator
for patients with moyamoya disease.
So again, these images are profusion maps
that have been scaled with phase contrast MR
and it's really leveraging both physiological measurements
in different conditions in order
to show this and actually in this graph here,
show that we can stratify cerebrovascular reactivity
with this hybrid approach and really
understand differences in CBR that stratify across normal,
mild to moderate and severe occluded stenosis.
Finally, beyond this hybrid approach
there's another way to synergize PET and MR
which there's a lot of excitement in the field,
of course around AI and the ability
to leverage more complicated models
with deep learning networks.
In this case,
we go a step further and actually ask, okay,
we have initial ASL and anatomical MRI as inputs.
Can we actually synthesize a quantitative CBF map based
on these sort of ASL and anatomical scans.
And this is work for my colleague,
Professor Jia Guo at UC Riverside.
The way that this architecture was developed is to try
to avoid actually injecting PET tracers
and asking whether the ASL maps are sufficient
to make a prediction.
And the training itself in this algorithm
was based on simultaneous O-15 PET.
So if you have these combined
simultaneous PET MR acquisitions,
you have the ASLs at multiple post label delays,
you can train the network to synthesize
a PET like gold standard image of perfusion.
And this was achieved with an architecture
that is familiar to many in the medical imaging field.
It's a unit with both an encoder and decoder arm
in order to learn hidden
or deep features from the images
and then actually recreate
a synthesized quantitative profusion map.
What's really interesting in this case is that, of course,
this comparison between standard ASL
and O-15 water PET as the gold standard,
we know from our previous discussion
in the first part of this talk,
that there are limitations in artifacts
with standard delay ASL,
these errors are slightly reduced with multi delay ASL,
but in particular, the neural network output outperforms
both of the ASL acquisitions,
the neural network is only derived from MRI inputs,
but it's able to reproduce
after training a CBF map that is even more consistent,
even more quantitatively accurate
with the PET reference standard
than the other ASL scans.
And when we're training across these different cohorts,
it's not surprising that it does depend on the training set.
So one of the metrics you could consider
in evaluating the deep learning network
is structural similarity.
And it's not surprising that if you only train on controls
in the initial establishment of weights
in the architecture, your structure similarity's
not going to be as good as if you trained
on patients or on a mixed group with more data sets.
And that's because the controls wouldn't have some
of the problems of long transit times
that the network could potentially learn
when it sees patient cases and learn to correct for.
So it does matter in terms of generalizability,
what patient cohorts and what's
the balance that you include in the training.
We see a similar trend where
you get a benefit in root mean squared error reduction
if you train on a mixed cohort of healthy patient cases,
but overall, this is a very promising approach that
could avoid the need for tracer injection.
And in our recent work,
actually even avoid the need for acetazolamide
or vasodilation injection to predict reactivity.
And it's a approach that is sophisticated
in terms of the deep learning network,
addressing some of the artifacts
that we talked about before,
but the deep learning network needs
very good quality training pairs,
and we believe the best quality training pairs
are simultaneous ASL or other physiological maps
at the same time in the same physiological condition
as the PET scans.
And the last part of this talk,
I just wanna project forward a little bit
and broaden some of the topics
that we've talked about beyond just
specifically to cerebrovascular disease
to other neurological conditions and other biomarkers
that you know you may not have been familiar with.
So one example that has gained a lot of traction
is not only the shift from understanding Alzheimer's disease
as a set of clinical symptoms,
but actually looking at the underlying
biological construct, and this A/T/N framework,
amyloid, tau and neuro degeneration framework
is highly critical in terms of both CSF and flood,
but also imaging biomarkers to detect amyloid
and tau as Alzheimer's specific pathologies,
but also non-Alzheimer's pathological changes.
And I think one thing that belongs
within neurodegenerative like cortical or tissue atrophy,
it could be part of the neurodegenerative process that there
are also vascular risk factors and vascular conditions
such as hypertension and diabetes,
which lead to cerebrovascular impairment that is more subtle
than a stroke or a steno occlusion
like we've been talking about,
but still either independently or interacts
with amyloid and tau to create cognitive impairment.
So I think vascular conditions and imaging approaches
to understand cerebrovascular changes falls
within this category of neurodegeneration
within this biological construct
of cognitive impairment in ED.
And what this means is,
so we can't just understand one facet of the disease,
but because there are multi
pronged sources for this pathology,
we need to be able to measure all of them.
And I think a clever way in which we can actually leverage
PET MR is to get more out of some of these biomarkers.
So here's an example that I find very inspiring,
which is to use the amyloid PET signal
and actually in a PET MR framework,
reduce the time and potentially get multiple biomarkers.
So a typical PET acquisition for amyloid is to acquire,
maybe a static scan only at the later part.
It depends on the exact amyloid tracer,
but let's say Florbetaben,
sometimes even ninety to a 110 minutes,
what you could do to get quantitative information
is perhaps not have the subject have
to stay in this entire dynamic period
where you would see the initial bolus
and first pass of the tracer,
but have this data acquisition only in the later parts,
but really use simultaneous MR measurements,
say of perfusion to estimate actually
what happened in this earlier part.
So you're cutting down the scan time
that the patient has to tolerate in order
to get quantitative amyloid binding maps,
and actually with simultaneous MR using the ASL,
not only as a separate marker of perfusion,
which is important in and of itself, but also to capture,
recreate the early part of this amyloid time signal
and what these initial studies
have shown is that the binding potential
not only can capture regional differences quantitatively,
it's quite consistent with the gold standard,
gold standard meaning you have actually acquired
the whole time duration in the patient,
and it's more consistent than something
that's just static like an SUVR at the late time points.
So I think this is very promising
because you could get multiple information
about quantitative amyloid
and profusion from a shorter scan.
And I would encourage you to think
for your own tracers, whether that's a neuroreceptor system,
could you also leverage one scan with PET MR
to get multiple biomarkers or cut down on the time
that the patient has to tolerate.
In other settings,
there are also clinical studies
where FDG PET as a marker of glucose metabolism
is critical for patients with cognitive impairment.
For instance, in delineating Alzheimer's disease
from frontal parietal dementia.
And one question is instead of sending
a patient to get a separate FDG scan in these situations,
would an MR, which typically is required for structural
atrophy assessments or longitudinal assessments,
would an MRI suffice.
In this case, we're showing ASL images
in healthy controls and AD patients.
And you can see not surprisingly that the ASL
has a lot of spatial features that are similar to FDG PET.
This is not surprising because glucose metabolism
is so tightly coupled to profusion.
And I also thought it was interesting in this study
where these arrows have pointed out
the deep gray matter as problematic areas
with ASL that would take advanced strategies,
including deep learning to address,
but this special feature of deep gray matter decaying
quicker was something that we saw earlier in the talk too.
But on general,
if you look at this holistically
in both healthy controls and AD patients,
you see ASL could potentially recapitulate the FDG signal
and help distinguish between different patient types.
In this case, the statistical parametric
mapping results in this study actually showed quite
a good overlap between FTG
and ASL based discrimination.
So what the clusters, the colored clusters represent
here are areas of the brain where there's
less perfusion in AD patients compared to controls.
And again, on visual average,
you can see quite a bit of consistency between FDG
only based parametric statistics
and ASL only based,
there are some discrepancies
as shown in this right column here,
but I think on the balance, it's also very promising.
And in this case,
could consider using ASL as a surrogate
to assess certain parts of the vascular or metabolic health,
if FDT is typically used.
And that way you would avoid using an additional tracer,
especially in neurodegenerative cases.
And finally, I wanna give an example
of beyond profusion studies now are taking
the next step and more challenging step
of validating other biomarkers,
and it's more challenging on both fronts.
So with PET, it actually requires three separate tracers.
The established or reference way to measure oxygen
metabolism is requiring 3 O-15 label tracers
to create CMR O2 maps or oxygen extraction,
fraction maps that are shown here.
And with MR there's also more
and more new methods to be able to map OEF,
but all of them are quite complicated in terms
of modeling and require testing,
which is what this group from Cornell
is pursuing at this point.
So the MR maps here are showing quantitative
BOLD plus a combination of susceptibility
mapping reconstructions based on base signal,
again, a complicated MR metric.
But I think the same ideas that we talked about earlier
in terms of using both PET and MR simultaneously
will be a good framework in terms
of moving forward for even more complicated biomarkers.
And I think this is very promising.
The study was nicely designed because of repeat scans.
There is room for improvement.
So in this Bland Altman plot,
the OEF difference between PET and MR
had a limited range of difference in terms of percentage.
So it was promising, but you definitely see
a linear correlation where higher
OEF values tend to have more positive difference.
And so there's this bias that,
I think with further PET MR studies, with challenge studies,
like the cerebrovascular reactivity we discuss
can actually change the oxygen extraction fraction as well,
and use PET MR to continue
to improve the parameters on the MR side,
get better quantitative comparisons with PET and validation.
And if you want more framework for this,
there's a review paper that I wrote with some
of my Stanford colleagues that kind of has a perspective
on the next step of O-15 gas PET
and how we could use PET MR to actually validate
even more difficult biomarkers like blood volume,
oxygen extraction fraction beyond the perfusion
that we focused on in this talk.
So in summary,
I hope I've given you a nice perspective today,
with example cases in stroke and cerebrovascular disorders
of how we could dial and tailor
ASL MRI to match a simultaneous
physiologically matched PET reference scan,
both at baseline and during a stress test,
that could be helpful
in teasing out additional auto regulatory pathology.
I hope there's some new insight also
in terms of input functions scaling,
and deep learning methods
that really use simultaneous PET MR information
to get better perfusion maps quantitatively.
And that this framework with PET MRI focused
on physiology is not just applicable to stroke,
but I encourage you to think beyond
the tracers that we've talked about,
but also to the patients that you're most interested in
and see whether some of these concepts
can also help design studies that are less invasive,
that are more accurate for your scientific
and clinical needs.
With that,
I wanna thank you for your attention and acknowledge
many excellent collaborators,
I think, and my group,
as well as my funding sources,
none of this would've been possible without your support.
And I look forward to chatting
with you more at the discussion session.
Thank you very much.
- Hello everybody and welcome to my presentation
where I would like to highlight some of the advantages
to combine functional PET and functional MRI
in the assessment of task specific brain responses.
My name is Andreas Hahn.
I'm located at the Medical University of Vienna in Austria,
at the Department of Psychiatry and Psychotherapy.
And there, I am part of the neuroimaging labs,
whose head is Rupert Lanzenberger.
I would like to thank the NIMH for organizing this workshop
and for inviting me and giving me the
opportunity to talk here.
It's a particular pleasure.
I would like to start off with some background
about FDG imaging.
It's an irreversible radioligand
for imaging glucose metabolism,
which means that after a bolus application,
you have this irreversible uptake into the cell,
which results in the steady increase
of the time activity curve.
One can also image task specific effects with this approach.
However, only if the task
is carried out in the beginning of the scan.
And this applies that repeated measurements
would be necessary, which in turn increases
the test-retest reliability.
Another option would be to use
repeated applications within the same scanning session
as shown here.
But this still would require about 30-70 minutes
per state for a reliable quantification.
And also, it's kind of a complex setting
and complex modeling so it's not a really popular approach.
A neat solution to this problem is the constant infusion
of FDG because the metabolism is proportional to the slope
of the time activity curve, and this can be easily
quantified with graphical solutions such as
the pattern plot.
So when using constant infusion, there will be free
radioligand available throughout the scan
and also for the actual metabolism and the metabolic demands
of a certain task.
And this means that any change in the metabolism
will also result in a proportional change
of the slope of the TAC.
And task specific assessment has been successfully
demonstrated by pioneering work of (indistinct) et al,
in 2014.
However, this was only done in three subjects so far.
So the first aim we had was to replicate this and
validate this.
So we had a task, very simple, eyes open, eyes closed,
and right finger tapping two times 10 minutes each.
And with this example I will show you the general workflow
of the fPET analysis with general linear model.
So basically we extract time activity curve here,
for instance, for the precentral gyrus
and we have our regressors.
One which describes the baseline metabolism,
and one, the task for eyes open here,
and the other one for finger tapping.
And we feed all of this into a general linear model
and that's, I think also where the name comes from
because it's so similar to fMRI,
naming this technique functional PET.
And with the TLM, we're able to distinguish or resolve
baseline activity, separate this from
task specific activity.
So basically, the curve here and here
summing together will again, result in this curve here.
And after separation, we can do quantification
for each of these conditions with, for instance,
a pattern flow.
So let's have a more detailed look at the quantification.
Here's the equation for a bonus application.
And it's known as a graphical solution.
So basically plotting this term on the Y-axis
and this term on the X-axis
yields a curve that will look like this,
and importantly, it gets linear after a certain time.
And the slope of this
linear curve describes our glucose metabolism,
or here in this case, the net influx constant, Ki.
For the constant infusion, as I said,
we use the general linear model.
Here's the basic equation.
And our data, Y, is given by the time activity curve,
then we have our beta estimates, which we want to
get out of this, and the design matrix, X,
which is given by the regressors as I showed before.
One our baseline, one for finger tapping,
for eyes open and an additional one to correct for
head motion.
For the pattern plot, we could see
that the glucose metabolism is directly proportional
to the concentration in tissue.
Which again, is the baseline regressor
times the beta estimate.
So if we then have a task, then
the total glucose metabolism will
be the sum of the baseline metabolism and as well,
instead of the task given by the regressor of the task,
again, multiplied by the beta estimate.
So this means that the task specific metabolism is given
by our task regressor times the beta estimate.
Because, as we said, any change in the metabolism
will result in a proportional change of the slope
of the time activity curve.
The baseline regressors as we defined them,
where the baseline,
was given as the average of the whole gray matter,
all gray matter voxels,
modeled as a third order polynomial,
and also correcting for task effects.
Then we had the task regressors.
They were defined as a ramp function with the slope
of 1 kBq/frame.
So basically always when this ramps up, this indicates
active task block.
And then as a movement regressor we used the first principle
component of six realignment parameters.
For the first results, in the first study, we compared the
baseline metabolism that was obtained from this
infusion protocol to a common baseline protocol,
just to validate if this is actually very similar,
and indeed it is, we saw very good agreement
despite with some variants, but this is
expected because the scans took place on a separate day.
And as I said before, this will be subject to some
test-retest variability.
More importantly, for the task specific metabolism,
we could absolutely quantify these changes
in micro mole per hundred gram per minute.
And it was about 20% change from baseline.
So the approach was sensitive to the task.
It was originally specific because for instance,
for the eyes open condition, we didn't see anything
in the motor cortex and for finger tapping,
we didn't see anything in the visual cortex.
And the most important point is that we could image
all of these effects in a single PET scan.
So this is the perfect basis for combining this with fMRI
with hybrid PET-MR scanners.
Of course the approach is subject to several assumptions
and limitations.
For instance, the Patlak plot assumes that
the rate consent, k4, is zero,
which is not exactly the case.
So we have an underestimation of our glucose metabolism,
which is basically known.
Another assumption is that the coupling between the
neuronal response and the glucose metabolism
is linear and time-invariant.
Linear means that any change in the neural response
will translate into a proportional change
of the glucose metabolism, and time-invariant means that
these affects
are also temporarily coupled.
These assumptions basically come from fMRI imaging,
but I think they're still very useful here
and also apply here.
We also assume that the glucose metabolism
is at steady state also during the task.
That's not really the case for all of the functional
imaging approaches, but still it's a required approximation.
And furthermore, we assume that the glucose metabolism's
constant throughout the task.
It has been proposed that different regressors
that are a bit different from a simple ramp function
might be more accurate,
but this is subject to further research.
We also want to improve the fPET methodology a little bit.
So there are several approaches to obtain a task-free
baseline regressor.
I would just like to stress that it's very important
that your baseline regressor is free of any task effects,
because otherwise it will bias your task estimate.
So you could choose, for instance, a third order polynomial
as we have done before.
It worked pretty good.
I would just like to mention that
it looks very much as like a linear function,
the time activity occurs, but clearly it's not.
And again, an ill defined baseline will
mess up your task effects.
One could also choose an atlas-based approach
basically by excluding all the
brain regions that are assumed to have some task activation
and take just the time activity curve from the remaining
gray matter voxels or since we are talking about PET-MRI,
one could use the individual fMRI estimates,
the individual estimates
which voxels are active in a certain task.
We have basically shown that the last approach
is the most robust one.
But if one doesn't want to rely on the fMRI,
the other two approaches are still quite valid.
Another thing is that we
also included a bolus plus an initial bolus,
and then started with the infusion in a ratio of 20% to 80%.
This is basically done to increase the
signal to noise ratio.
And I think the images here after 10 minutes
speak for themselves.
You can clearly see much better signal in the
bolus plus infusion protocol on the right.
So with this increased SNR,
this might enable us to decrease the task duration,
because in the beginning, we had a task duration
of 10 minutes and show very stable task effects.
Was still very nice for five minutes here.
It's also possible to go lower with the timing.
But of course, then the frames will also get shorter
and
this means less counts
and this then means more noise.
But still doing so and increasing SNR,
decreasing task duration, we could show
a very nice spatial overlap between glucose metabolism
shown here on the right and the bolus signal,
sorry, glucose metabolism on the left and the bolus signal
on the right, and also some nice specificity
in this example during finger tapping especially
in the motor cortex.
Next step, we wanted to go a little bit more complex
and getting away from these simple visual and motor
stimulation tasks.
So we had a task
with a task design
that included PET imaging for 52 minutes
with simultaneous MRI imaging.
For instance, old imaging for connectivity
as well as arterial spin labeling
for estimation of cerebral blood flow.
And in the end, a conventional block design
for fMRI BOLD based neural activation.
And that as a task, we use the video game, "Tetris,"
which is very well known, that requires rapid visual
spatial motor coordination and we had two task levels,
and the hard one basically the bricks were falling down with
more speed and there were more incomplete lines
filled at the bottom.
So I would just like to say that this was a
impolitic continuous task design here,
so people really played six minutes continuously,
and also the acquisition of bolus was continuously.
So there was no on/off design here.
In contrast, one could also employ a hierarchical design
where basically the BOLD blocks, fast BOLD block design
is embedded in shorter blocks,
in longer blocks of the PET acquisition.
It really just depends basically on which kind of question
you want to answer.
As a result, we really saw a nice overlap between
the different parameters of neuronal activation
as shown for instance on the right,
this is the spatial overlap of all three
different parameters that show significant activation.
And most of these were also sensitive to subtle changes
in cognitive demands.
Which means that we found changes in glucose metabolism
between the easy and hard task level for
functional PETs, for BOLD imaging
and some effects also for CBF, but not as strong.
Still, I would like to highlight that these are
complementary aspects of neural activation.
Because the BOLD signal basically emerged from
a very high increase in cerebral blood flow,
but less pronounced increase in the
metabolism of oxygen.
On the other hand, glutamate release is actually very
highly involved in neuronal activation and this
causes an increase in cerebral blood flow,
but this also lead to an increased
use of glucose in the neuronal cells.
So basically, we are imaging the same phenomenon,
namely neural activation, from different points of view.
We also assessed the test-retest reliability
of this approach,
all of these approaches and compared this directly.
The strongest reliability giving us the
interclass correlation coefficient was given for
glucose metabolism at resting state.
Was also quite good for cerebral blood flow.
It was not available for both because we didn't use
the calibrated bolt.
On the other hand, for task effects,
we saw a good test-retest variability.
That was actually done with a temporal delay of four weeks.
And was still good for glucose metabolism
and cerebral blood flow, very similar.
But it was not that good
for BOLD imaging.
And actually, this low number is very much in line
with the recent meta analysis,
which has been done on numerous BOLD tasks.
So it could be related to the fact that BOLD
is more a relative signal, whereas the other two parameters
can be quantified absolutely.
Let's come back to our study design.
We did an identification of brain regions and the networks
that are active during these tasks.
And in the next step,
we wanted to assess the interaction between these networks.
And we did this in a collaboration with Luke (indistinct)
and Michael (indistinct) from Australia.
So in the first step we used the method that's called
metabolic connectivity mapping,
and this combines functional connectivity
and metabolism.
I will explain this in a second.
So basically this gives an estimate
of directional connectivity.
But the gold standard really for estimating this
is dynamic causal modeling.
So we use this method as well
to validate the MCM results.
As I said, metabolic connectivity mapping
relates the spatial patterns of glucose metabolism
and connectivity.
In the first step, one computes seed-to- voxel connectivity.
This is all known, basically correlating fMRI time series,
and then one gets a connectivity value between two regions,
but this is undirected connectivity.
So we can now include the information of glucose metabolism
to identify the target region.
Because it is known that the energy demands
mostly emerge post-synaptically.
And this can be used to imply some directionality
to these regions.
So basically, one uses the pattern,
the spatial pattern of (indistinct) pattern,
of functional connectivity and correlates this
with the spatial pattern of the underlying
glucose metabolism.
So if connectivity between region Y
to region X is really causal,
then the pattern of functional connectivity
should match that of glucose metabolism
because of the strong coupling
between the different signals.
We assessed this for the three major brain regions
that were active during the task,
namely, the occipital cortex, intraparietal sulcus
and frontal eye field.
And what we saw that the connection from IPS to FEF was
very significant and this was stable for rest (indistinct),
so it didn't really change across conditions.
However, for the other connections,
we saw a strong increase in the association of
glucose metabolism and connectivity.
And this was mostly for the feet forward connections
from the occipital cortex to other regions.
We did not see this for the feedback connection,
so it was a specific finding.
And interestingly, once the task started,
this basically reached the plateau.
So there was no change from the easy to the hard task level.
So when increasing cognitive demands,
it didn't change any more.
And also we confirmed this with the dynamic causal modeling.
I just want to highlight that the DCM model used
completely independent data basically
than used in the MCM and still we observed
a high correspondence between these models.
So this means that the non-reconfiguration of functional
networks during task performance are indeed dependent
or related to glucose metabolism.
And importantly, the transition from rest
to the task carries actually the major metabolic cost.
It would be interesting to assess these task specific
changes also in disorders such as Alzheimer's disease,
because it has already been shown that
baseline effects are altered and
show alterations in the association of glucose metabolism
and connectivity.
So this result I showed before was for the first PET scan.
However, we wanted to know what happens now if the task
is trained for several weeks; in this case four weeks.
So we did a second PET scan after this training period.
We split up the groups into training and control.
Control group did no learning, so it was a passive
comparison basically.
The first step was, again, to investigate the
energy demands regionally.
This was basically the same as done before
and confirm the previous results in a larger sample.
And then we extended metabolic connectivity mapping
to the voxel level.
So basically,
we are not only able to assess this on a region
by region basis, but now we get entire whole brain maps
of metabolic connectivity mapping showing the influence
of one voxel to a certain brain region.
And finally, we wanted to disentangle the contribution
of connectivity and metabolism in simulations.
Showing the behavioral data,
we saw some changes.
We saw most of the changes for the hard task level
and some changes also in the control group.
This is expected because
the control buttons were more difficult to use.
So there is some intrinsic training
even for the control group.
However, the behavioral effect changes were much larger
for the training group and also the interaction
was highly significant.
So we observed about a threefold increase in performance
for the training group.
And we saw even a further increase after the training
was discontinued.
And we did some testing of the cognitive domains
and saw similar effects for mental rotation
and visual search, but not for spatial planning.
Coming to the imaging effects,
we used the occipital cortex as a target region
and we saw that the input of the salience network
to the occipital cortex really changed
after training.
So at resting state, we saw that the influence
of the salience network to occipital cortex increased
for the insula and dorsal anterior cingulate cortex.
And interestingly, during task execution,
this decreased for the very same connections.
And also this difference between
resting state and task specific MCM values
correlated very well with performance
at second PET scan, PET-MRI scan,
and also correlated with the overall training success
and also with the performance of the
mental rotation paradigm.
So now we wanted to disentangle the effects
or contributions from metabolism and connectivity
to these results.
So basically, since MCM is based on a spatial correlation,
we removed voxels based on the amplitude of either
connectivity or metabolism and then recalculated
the training effects.
More specifically, the interaction of group by time.
And we saw that at rest,
and this was mostly dependent on glucose metabolism.
So basically, if we remove voxels up to 90%,
then we see that the training effects basically diminish
for both regions.
On the other hand, task specific changes were more driven
by factional connectivity,
because if we remove the voxels here,
showing in the dash lines, the training effects
are rapidly decreased.
Interestingly, we could remove up to 90% of voxels randomly
and there was basically no change in the effects at all,
which basically supports the specificity of
the resting and task specific effects.
So what does this all mean?
The salience network is highly involved in error monitoring
and also
representing prediction error between different task
representations across different brain regions.
At resting state, there will be,
basically the salience network was not involved
without the training and therefore we see
very little and randomly
influence or communication between these regions.
During task performance, this will result in a
high prediction era because the neural representations
of the occipital cortex and the salience networks
do not match because subjects hasn't trained yet.
So there is a high level of cognitive effort
required to manage the task.
During the training,
it has been shown that
physiological processes such as synaptic tagging
and capture occur.
Synaptic tagging is basically trial and error
of transiently establishing synaptic connections
and see which connections would improve performance.
And then if these connections are,
I'm sorry, the synaptic connections are
known to improve, then they are captured.
And especially this capturing is very
energy demanding, and it's been shown that this doubles
the energy consumption,
and it happens basically by anchoring
glutamatergic AMPA receptors for instance.
So this will then explain the increase
in the association between glucose metabolism
and BOLD connectivity
after the training at resting state.
We would refer to this as a skill Ingram,
which is a formation of synaptic connections
which support task performance.
Because during task performance, this skill Ingram
can be retrieved
and then
the prediction error decreases because the neural
representations between the different brain regions
has better match so there's less cognitive effort required
to solve the task efficiently.
I would now like to switch to something different.
A more basic investigation.
Namely the investigation of the
underlying metabolic demands of BOLD deactivations.
We did is in collaboration with Swedish group (indistinct).
And they employed a working memory task
in a hierarchical design.
And as shown previously, for basically several other tasks,
increases in BOLD signal during task performance
were also accompanied by increases in glucose metabolism.
This is really a stable finding across most of the tasks.
However,
in regions that showed decreases in the BOLD signal,
especially in the posterior cingulate cortex,
they showed that there was either no change
or even an increase in glucose metabolism.
So that was a mismatch basically.
And this could mean
that the decrease in the BOLD signal really is an
energy demanding process
and it could imply an active suppression of task
irrelevant activity.
So we wanted to further investigate this
in more detail.
So here is the same data shown for the working memory task
at the same mismatch basically
just presented in voxel space, not in the surface anymore.
And we compare this to our "Tetris" task.
And interestingly, we saw that decreases in the BOLD signal,
shown in green, are actually accompanied
also by decreases in glucose metabolism, shown in blue,
and the overlap is shown in white,
which means that we have most of the overlap
in midline core regions of the default mode network.
And interestingly, we also saw in our previous work
decreases in glucose metabolism in these regions
during eyes open and finger tapping conditions.
So we were asking is this really just dependent on the task?
And we had a closer look at this
and we think that this is dependent more on the task
specific networks because
"Tetris," shown in these bar graphs here in blue,
showing the percentage of voxels activated
within each network.
"Tetris" mostly activates visual and dorsal
attention networks.
Whereas working memory, shown in green,
highly activates frontoparietal network.
So the difference between these qualitative difference
is shown here with the open bars.
Conversely, the deactivation between this task
was also different with "Tetris" showing,
as I showed before, highest deactivations in
core regions of the default mode network,
whereas working memory mostly deactivated in
lateral temporal regions.
So this is a first qualitative hint
explaining these differences between tasks.
However, we went further on and wanted to explain this
in a quantitative manner.
So we used the regression analysis and entered
values of glucose metabolism,
into regression model to explain glucose metabolism
of the PCC by that of the frontoparietal network
and those attention and visual networks,
because these three networks showed the biggest difference
between working memory and "Tetris."
And indeed, what we found is that
these two networks, frontoparietal and dorsal tension
networks explained the glucose metabolism
in a posterior cingulate in an opposite manner,
which means that the higher the metabolism
in the dorsal attention network,
and the lower the metabolism in the frontoparietal network
is the more decrease you have in glucose metabolism
in the posterior cingulate cortex.
This could be explained, for instance, because
all deactivations may emerge from
a decreased level of glutamate signaling,
which also would be followed by decreased energy demands.
However, it could also be that
all deactivations are related to an increase in
GABA activity.
And this is a energy demanding process so this would imply
an increase in glucose metabolism as well.
And this is exactly these two effects that we see during
"Tetris" task and working memory.
So we speculate that
maybe the connections between the dorsal attention networks
that is activated during "Tetris"
to the posterior cingulate cortex might be more
glutamatergic.
Various projections from the frontoparietal network,
which is activated during working memory to the PCC
might be more of GABA nature.
And on a behavioral level, as I mentioned before,
this could be explained
by the high external tension that is acquired
to solve the "Tetris" task, whereas working memory
also has some high internal demands.
Another thing that you could do with
combined fPET-MRI imaging is to investigate
immediate drug effects in pharmaco imaging.
So that's been previously shown that the BOLD signal
changes during acute administration of SSRIs
with intravenous injection.
However, we could not really replicate this
in a larger sample using lots of different models
such as that using plasma concentration of (indistinct)
or even serotonin transport binding effects.
So we thought that maybe
more stable signals such as glucose metabolism,
cerebral blood flow obtained with PET and ASL
might be better suited to assess these effects.
Since the acute SSR effects follow an unknown
spatial temporal pattern,
we used intersubject correlations to solve this problem.
So basically, this means that the time course of the PET
and the ASL were correlated between subjects for each voxel.
And we assume that if this correlation across subjects
is non random, then we would see a specific pattern.
And this was indeed the case.
So we saw higher correlations. Higher than average.
For instance, in the striatum in the occipital cortex
and also temporal regions.
And interestingly, comparing Verum with placebo effects,
we saw that these effects in the occipital cortex
were significant in the striatum; at least reach the trend.
So this might be an example of a promising investigation
of pharmacological effects.
And the final thing I would like to present is that
functional PET can also be used for imaging dopamine
neurotransmission.
And we call this the Synthesis Model
because the radioligand FDOPA's used which is surrogate
for imaging
dopamine synthesis.
Because, during stimulation, dopamine is of course released,
and this will increase also dopamine synthesis to refill
the synaptic vesicles.
And we argue that this will also
result in an increased radioligand uptake
because there is really lots of
previous work showing
such an association because for instance,
neuronal firing increases the activity of the enzymes
responsible for dopamine synthesis, such as
tyrosine hydroxylase and AADC.
Furthermore, the amphetamine induced dopamine release
also increases dopamine synthesis.
And this is also true the other way around.
If dopamine synthesis is decreased,
then also the amphetamine induced dopamine release
will be decreased.
So I think this is strong support showing that stimulus
induced changes in dopamine synthesis
probably reflect dopamine release to some extent.
And we use this model in functional PET imaging
while subjects completed the monetary incentive delay task.
This is a reward task basically where
you can win or lose money based on
your individual reaction time.
And exactly this reaction time was manipulated to
create blocks with high probability of gain or high
probability of loss.
We also corrected for radioactive metabolites
as published previously.
Task identification was done with a general linear model
as also as described before, including regresses
for baseline, gain blocks, loss blocks,
and also head motion.
And with this GLM approach, again, we could separate
baseline effects from task specific effects here shown for
the gain condition.
And quantification was then carried out
with the Patlak plot, specifically for the ventral striatum
as this was our target region involved in reward processing.
So as a result, we saw that task specific changes
in dopamine synthesis
were really higher in men
for monetary gain as compared to monetary loss.
Interestingly, for women, we saw the exact opposite pattern.
Mainly that dopamine synthesis was higher
during monetary loss than monetary gain.
We also saw that these changes in dopamine synthesis
were correlated with parameters of reward sensitivity
in men, but punishment sensitivity in women.
We also used the fMRI for direct comparison.
And as published previously, we saw a very strong,
robust activation of the contrast gain versus loss
in the BOLD signal.
However, there was no significant difference
between men and women, and this is very much in line
what has been published previously.
And we are aware of the low sample size, but still
if this holds true, this would have some important
implications because the findings here from
the dopamine system would explain the well known behavioral
differences between men and women during reward processing.
And it's also important for psychiatric disorders because
there are several disorders showing alterations
in the dopamine system in reward processing,
and also differences in the prevalence between
men and women.
So this approach was sensitive to task changes
of about 100% compared to baseline,
but also when using specific control conditions,
such as monetary gain versus loss.
If we now assume that these changes solely are
cost or related to changes in K3,
thereby reflecting AADC activity,
this would result in rather large changes.
However, we have to keep in mind
that only 50% are available for dopamine synthesis
of the FDOPA in humans.
And further, 75% are only stored in the vesicles.
So this will result in an estimation of dopamine synthesized
by the task of about 50% to 100%.
This is rather crude estimate,
but still it is perfectly in line with
animal studies doing micro dialysis
and showing exactly this
release of dopamine during reward.
So to summarize: with functional PET,
you can image metabolism and neurotransmitter action
during baseline and task in a single scan.
As I said, it's perfect,
a very promising approach
for simultaneous imaging with PET-MR.
It's regionally specific for both simple and complex tasks.
It's also sensitive because we can obtain differences
between task loads reliably.
And it also has some good test, re-test reproducibility.
So I hope I could make the point
that these tasks specific changes
between the different imaging parameters really carry
complementary information.
So you can obtain different information
on basic energy demands, such as glucose metabolism,
cerebral blood flows, also BOLD imaging, and of course
connectivity,
for instance, to derive directional connectivity
with the combination of glucose metabolism
and functional connectivity.
You could also go for neurotransmitter estimation.
We tried to explain the value observed,
deactivations seen with the BOLD signal on a metabolic basis
and many more
hopefully promising applications coming up in the future.
Of course, there are also some limitations.
As with every PET scan, there is radioactive
burden.
But this is just basically how it is with PET.
There is not much way around this.
But it might come up soon with
more sensitive scanners.
So basically this would allow us to decrease
the radiation dosage.
The biggest limitation probably for functional PET now
is still the temporary resolution, because it's related
to the radioactive counts that can be obtained
in a certain frame.
And we are still working on this to further
improve this.
As an outlook, it would be very interesting to assess
all of these effects in disease cohorts because,
as I said, with just one scan you can obtain
two or three conditions.
And if you run an FDG scan anyway in your patients,
it might be worth to add a little bit of extra effort
and get more information out of it,
because really, it's not so difficult to implement.
All you need basically is an infusion pump
and stimulation equipment.
Of course, you can come up with some
very
sophisticated paradigms.
You could also try to adapt these of fMRI that are already
existing and established.
And then for the analysis, basically, you only need GLM.
So it's really feasible.
Our current work deals with, for instance,
substitution of anterior blood sampling
and also, of course, as I said,
increasing temporal resolution
to make this approach basically
even more feasible and more applicable
also in different settings.
And we are very happy to support you if you want to
implement this.
We have been doing this
all over the place.
Supporting people with the design,
with the implementation of the setting
and the infusion setting with the analysis.
So if you have any questions to this,
please do not hesitate to contact us.
I would like to conclude with this and thank
the neuroimaging labs, especially the head,
Professor Rupert Lanzenberger, and of course the team
for making all of this possible.
I would like to thank the funding institutions for
providing support and also, of course,
the corporation partners.
And finally,
I would like to thank you for listening to the talk.
Thank you very much and goodbye.
- Hi, and welcome to this presentation.
Today, I'll be speaking about the recent developments
in the application of simultaneous PET/MR
to the study of human brain function.
My name is Shama Jamadar,
and I'm a senior research fellow
at the Turner Institute for Brain and Mental Health
and Monash Biomedical Imaging
at Monash University in Australia.
In this presentation, I'll be talking about PET/MR
from the perspective of cognitive neuroscience
and how the method can improve our understanding
of the neural basis of cognition.
I'll start with a very brief discussion
of how we can measure brain function using PET and MRI,
and then I'll introduce the fPET approach,
which will be discussed at length
in the rest of the presentation.
Then, I'll discuss briefly some of the early work we did
in designing experimental protocols
for simultaneous fPET and fMRI,
and then the application of PET/MR
to the study of brain connectivity.
Lastly, I'll offer a critique
of where I think we are in applying PET/MR
to the study of brain function
and discuss some future directions.
So let's get started.
What's the benefit of PET and MRI in cognitive neuroscience?
From a neuroimaging perspective,
MRI and PET have complimentary strengths and weaknesses.
While MRI provides excellent spatial resolution,
the spatial resolution of PET systems have a natural limit.
On the other hand, PET is remarkably flexible
in the range of molecular targets that can be imaged,
providing the capacity
to measure different physiological properties
of energy metabolism, neurotransmitter dynamics,
protein and inflammatory markers, and so on.
So, from the perspective of functional neuroimaging,
some of the markers of neuronal activity
can be physiologically closer to the process of interest
than fMRI signals.
Both modalities benefit significantly
from a large body of literature.
So a PubMed search for the term functional MRI
yields over 700,000 hits
with 16,000 in 2021 alone.
And, similarly, a search for PET
yields over 140,000 hits
with 12,000 in 2021 alone.
And so investigators using both methods
have got a vast body of knowledge upon which to build upon.
Now, some of the following will be old news to some of you,
but I'm going to very briefly cover the basics
of each signal and each method,
both to provide context
but to also highlight how each approach
is complementary to the other.
So, when we're talking about fMRI,
we're usually talking about BOLD fMRI,
or blood oxygenation level dependent fMRI.
The BOLD response is used to infer neuronal activity
indirectly from changes in blood oxygenation.
The method relies upon the concept
of neurovascular coupling.
Importantly, it's not a quantitative measure
of brain function.
The fMRI signal cannot be compared across brain regions,
subjects, in the same subject across time,
or between scanners.
BOLD relies on the ratio of oxy
and deoxygenated hemoglobin in the blood.
Oxygenated hemoglobin has little magnetic properties,
and so it's got little effect on the magnetic field.
In contrast, deoxygenated hemoglobin is paramagnetic,
and so it disrupts the relaxation of spins
in the transverse plane.
If the concentration of deoxygenated hemoglobin changes,
the BOLD signal will change,
and this is the basis of fMRI.
When neurons are not active,
oxygenated hemoglobin is converted
to deoxygenated hemoglobin at a constant rate,
but, when they do become active,
they use oxygen in the hemoglobin,
and so it becomes deoxygenated.
This changes the ratio of oxy
and deoxygenated hemoglobin in a region
such that increase in neuronal activity
will also increase the T2 star signal of the MRI.
So there's a complex cascade of events
that contributes to the BOLD response.
In essence, following the presentation of stimulus,
which elicits a neuronal response,
there's an increase in the flow of oxygenated blood,
and so there's also a decrease in the amount
of deoxygenated blood in that region.
This causes the BOLD signal to increase.
When the neuronal activity ceases,
there's a decrease in blood flow and volume.
When the blood volume returns to baseline,
the BOLD signal also returned to baseline.
So it's important to note just how indirect fMRI
is a measure of neuronal activity.
The indirect nature of the fMRI response
has implications for how we can use it.
The vascular and indirect nature of the BOLD response
has got significant implications.
In just one example that allows me
to shamelessly plug some of our work,
we simply looked at how estimates of functional connectivity
are influenced by individual differences in hemoglobin.
So simply splitting the group into males and females
and then into low and high hemoglobin groups,
we found that the functional connectome
differed significantly between high
and low hemoglobin groups.
Most concerningly, this influenced the correlation
between connectivity and cognition
across multiple cognitive tests.
So, for all the cognitive tests
that we tested in this study,
despite no differences in scores
between low and high hemoglobin groups,
there was a significant decrease
in the cognition-connectivity relationship.
So, in other words,
functional connectivity measured with fMRI
is highly confounded by simple differences
in hemoglobin levels between individuals.
Secondly, the way that we use fMRI is, in and of itself,
fundamentally affected by the nature of the BOLD response,
so much so that the people who work regularly with fMRI,
I'm one of those people,
sometimes forgets that our modus operandi
is actually quite idiosyncratic.
So, for example,
we know that the BOLD response can't be compared
between brain regions and between individuals,
and so we just don't do this.
We just don't design experiments that do this.
Similarly, direct comparison of fMRI responses
between groups just often isn't done.
So, for example, comparing the BOLD response to a task
between older and younger people
often isn't performed
because we know that cerebrovascular disease
is more common in older than in younger people.
So all of this is old news.
So you can see from the dates on this
that this is stuff that we've known for a long time,
but it's sometimes worthwhile to stop and take stock
of how our dominant method has influenced
the type of questions that we are asking in a discipline.
Okay, so let's now move on to PET.
When we consider the cascade of events
that occur during neuronal activity,
there are numerous indices that we could use
to infer neuronal activity.
As we've just seen,
BOLD measures an indirect outcome from the activity,
namely the change in the concentration
of deoxygenated hemoglobin
that occurs as a result of changes in oxygen consumption
and cerebral blood flow.
We can use PET traces to measure specific components
of the oxygen consumption process,
like blood flow, volume, oxygen, metabolism, and so on.
It's also possible to measure
specific neurotransmitter molecules, like dopamine,
and measure its synthesis, uptake, and release.
So, as you can appreciate,
there's a very wide range of targets
to measure neuronal activity,
and the capacity of PET to image them
is limited only by the range of the radio traces to do so.
So, today, we'll focus on one of the primary methods
to image neuronal activity using PET, FDG.
Glucose is one of the primary sources
of energy in the brain,
and regional glucose uptake primarily reflects activity
at the excitatory synapses.
So glucose uptake can be considered an index
of neuronal activity
that's physiologically closer to the biological process
of interest in the BOLD response.
You'll recall that one of the disadvantages of BOLD fMRI
and its semi-quantitative nature
is that BOLD can't be compared across brain regions
within the same individual across times
or between individuals.
So there's a whole bunch of neurovascular confounds
that limits these comparisons,
but, in contrast, FDG-PET doesn't have these disadvantages
because it can be quantified.
PET imaging produces quantitative radioactivity measurements
throughout the brain.
And, when it comes to glucose uptake,
there are a few different ways that it might be used.
I'll briefly mention two now,
but I'll cover the third in greater detail
later in the talk.
A single static PET image might be acquired
at a specific time point post injection,
or the full time course of radioactivity can be measured
and the cerebral metabolic rate of glucose calculated.
So, at this point, I think it's necessary to stop
and define exactly what I mean
when I use the term static and dynamic.
So, as an MRI person, I probably use these terms
in a different way to a PET person.
As such, I find it really important
to define these terms whenever I use them nowadays.
When I use the term static,
what I refer to is a measure
that essentially takes a snapshot of glucose uptake
over the course of a scan.
So that is resulting in one or a small number of images
with a frame duration in the order of minutes.
Where it gets confusing is where dynamic PET
has been acquired in order to calculate a metabolic rate.
However, if that metabolic rate is calculated
either over the scan duration
or over a frame duration of several minutes,
then it's assuming that the uptake
during that time is stationary.
So, therefore, I use the term static
or static snapshot to describe this.
This contrasts with what we would get
in fMRI, EEG, and infusion PET,
where you have a very short frame duration
or repetition time,
and you're able to estimate a time course
of uptake or activity.
I often use the word dynamic in that case.
However, depending on the analysis method,
that too could also be a stationary metric.
So I'll be careful to define the terms on each use,
but I recommend that you take a look at these papers,
particularly this one by Liegeois et al. in 2017,
where they have a nice mathematical exploration
of these concepts with regards to fMRI.
Lastly, it's important to consider
what brain function is being measured.
In an fMRI scanner, we can put people in the scanner
and measure their brain activity
to multiple instances of the same or similar stimuli.
So take, for example, a faces experiment,
where we might be interested in activation
of the fusiform face area.
The assumption is that the neural response
to a face stimulus at the start of the experiment
is essentially the same as a face stimulus presented
at the end of the experiment.
The multiple presentations of the stimulus
allows us to use signal averaging,
which assumes that the neural response
to multiple presentations of stimuli
is statistically linear.
Rough linearity of the BOLD response has been demonstrated.
However, PET's a completely different beast.
First off, standard approaches need to wait
until the radio tracer has been taken up
and transported throughout the body
before it can be measured.
So people are injected with the radio tracer,
they might wait in an uptake room
for, say, 10, 20, 30 minutes,
and then they're taken into a scanner.
They're positioned, given instructions on what to do,
and then the scan starts.
So, if we were then to present them
with a face stimulus in the scanner,
the measured response is actually the integral
of all the brain activity
that's occurred during the uptake and the scan periods.
And, unlike fMRI, the signal at the beginning of the scan
is different to the signal at the end of the scan.
So it's worth bearing these issues in mind
when interpreting the FDG-PET scan
as an index of brain function.
So, from a cognitive neuroscience,
what are the benefits of the simultaneous acquisition?
Well, I'm guessing, as a participant in this workshop,
you've got a pretty good idea,
but let's just briefly take stock about
what are the specific advantages
for the study of human brain function?
The true benefit of the simultaneous acquisition
is that you can examine multiple physiological components
of the same neural function at the same time.
So, rather than having differences in state or behavior
between testing sessions,
you're measuring the exact same neural function
from multiple perspectives.
So, in my case, I use it to measure hemodynamic
and glucose dynamic components of neuronal activity,
but it's also possible
to look at other physiological targets,
like neurotransmitter systems, oxygen, and so forth.
The simultaneous acquisition,
just like with other systems, like EEG-fMRI,
ensures that the brain activity
is measured in each modality,
significantly improving your inferential power.
So let's move on to the fPET approach.
One method that's been developed with simultaneous PET/MR
is the so-called functional FDG-PET method.
It's called functional PET, or fPET,
to highlight the similarities with fMRI.
fPET uses the constant infusion technique,
where FDG is administered by infusion
over the course of the scan period.
And the goal here is to maintain a constant plasma supply
of radioactive glucose
so that dynamic changes in uptake
in response to a stimulus or task can be measured.
So I won't spend time discussing
the acquisition procedure here,
but we recently published
a Journal of Visualized Experiments video article
demonstrating the procedure,
and you can find that either on the JoVE website
or on the Monash Biomedical Imaging website.
In 2014, the MGH Group published a proof-of-concept study
demonstrating the potential of the fPET method.
They showed that a constant plasma supply of FDG
allows measurement of dynamic changes in glucose uptake
in response to a checkerboard stimulus.
And here, you can see the model fit within the visual cortex
in response to alternating blocks
of flickering checkerboard,
which they measured with FDG-PET
in one-minute frame durations.
They didn't acquire simultaneous fMRI, just structural MRI.
So this landmark study really set the stage
for a number of studies that have continued to develop
and refine the fPET methodology.
Much of this work has been conducted in PET/MRI,
although the fPET approach itself
can be deployed on other PET systems.
A lot of work is focused
on the additional information that can be gained
by acquiring fPET simultaneously with fMRI.
And Andreas will speak a lot more
about task-related fPET-fMRI,
particularly in regards to modeling the data.
So I'm gonna focus more on the application of fPET and fMRI
in the resting state.
But, first, I'll just highlight this first study
that my group did in this area,
just to see if we could design a protocol
that would yield simultaneous BOLD and FDG signal
in the visual cortex.
We designed this embedded block design,
which is based on the standard block design used in fMRI.
Essentially, there are two block designs in the protocol,
a slow design with an on/off period
of between 5 to 10 minutes,
which was intended to give an fPET contrast
between task and rest.
Embedded within each on period
was a standard 32 16-second on/off block design for fMRI,
which was there to provide contrast between task and rest.
We assume that the fast on/off period would be too fast
to influence the fPET signal too much,
and indeed, in this experiment, it was.
We used a one-minute frame duration,
and even the five-minute duration was a bit too fast
to distinguish between task and rest.
Down here, you can see the joint ICA component
that has a component from each modality
that is maximally related to the other.
Also, it's important to note that the embedded design
isn't the only approach to task design for stimulation,
simultaneous fPET and fMRI.
This design is fully constrained,
so we know exactly when people
are performing each component of the task.
It's probably a bit of a holdover from my training at EEG,
where everything has to be millisecond accurate.
Other groups, however, have used much less constrained tasks
to great success with fPET and fMRI.
One question that's been explored since that original paper
is how we can improve the temporal resolution
from the one minute reported in that paper.
So the Vienna Group published this nice paper in 2018,
focusing on manipulating the experimental design
and task duration.
On the basis of this study,
they concluded that an optimal task duration of five minutes
with 30-second frame rates was best.
We've also looked at whether or not
a bolus and infusion protocol
might provide an improved temporal resolution.
So we compared bolus infusion and bolus plus infusion
in a proof-of-concept study.
So, down the bottom here,
you can see the experimental design
and at the top
is the hypothesized plasma radioactivity levels.
Note that, for the infusion protocols,
we turned the pump off at 55 minutes,
assuming that plasma supply would remain stable
for a few minutes after.
In this initial study,
we used a 50% bolus and a 50% infusion.
Although, note that the previous study that I discussed
on the previous slide by Rischka et al.
used a 20% bolus.
And so here are the plasma radioactivity curves.
They basically resemble our hypotheses
except that there was an obvious drop
when the infusion pump was switched off.
And so our first lesson from this study
was that we need to infuse for as long as we scan.
Down the bottom, you can see the gray matter signal
for each of the three protocols.
You can see, qualitatively at least,
that the infusion protocol has got a really low signal.
It never reaches the levels of signals obtained
in the other two protocols.
The bolus approach has got the fastest increase in signal
at the beginning of the scan,
but then the slope of the uptake is relatively shallow
over the course of the scan.
The bolus plus infusion protocol
shows a larger signal at the beginning
relative to the infusion protocol,
but it's not as high as the bolus only.
Again, this is as expected
'cause we're only providing 50% of the dose as the bolus.
The slope of the uptake for the bolus plus infusion protocol
is larger over the course of the scan,
and, for at least four of the five subjects,
it ultimately shows an overall larger signal
than both the bolus and infusion only
by the end of the scan.
Finally, on the bottom left, you can see the fMRI response
averaged across the three protocols.
Again, there's nothing groundbreaking here.
A flickering checkerboard is activating visual cortex
and oculomotor network.
When we look at the fPET results for the three protocols,
the first thing that we noticed was that the first block
was virtually inestimable,
unestimable for the infusion protocol
because the signal was so low.
And, by the fifth block,
the signal for the bolus group was all over the place
because it was also starting to get low.
So, here, we're looking at the three middle blocks.
The bolus actually performed not too badly
at this time point,
showing activity in at least the left visual cortex.
Possibly, there's some artifact or false positive
in the medial parietal area around the precuneus.
The infusion protocol also showed visual cortex activity,
but, again, there were some potential false positives
around the rim of the frontal cortex.
Lastly, the fPET signal in the bolus infusion protocol
showed bilateral activity in the visual cortex
with some hints that it might also be coming apparent
in the intraparietal sulcus,
although this is probably a little too inferior for IPS.
Lastly, note that across all three protocols,
the activation pattern for fPET
is more focal than for fMRI.
So, first off,
because we're not comparing apples to apples here,
the fMRI is across 15 subjects
and each of these PET protocols is in five subjects each.
Secondly, the finding that fPET tends to provide
more focal activity than fMRI
is actually a consistent finding across studies.
So I interpret this as reflecting less vascular spreading
or a more strongly localized signal
to the presynaptic neuron in PET than fMRI.
And, on the basis of these results,
the plasma radioactivity, the gray matter signal, and maps,
we decided to use a bolus infusion protocol from now on.
However, note that we've never actually optimized
which combination of bolus and infusion is best.
We've used a 50/50 bolus and infusion for simplicity's sake,
but, as I noted, others are using a 20/80 ratio.
So, in this final slide,
I'll briefly note that my collaborators
at Monash Biomedical Imaging have also explored
whether using MR-informed PET reconstruction
can be useful for improving the temporal resolution of fPET.
They've used my low-dose PET with 100 megabecquerels
as well as some artificially degraded data
to simulate ultra low-dose fPET.
They've then used a number of down-sampling factors
to simulate a faster temporal resolution.
This work suggests that faster sampling could be possible
with signal optimization strategies like this.
So, in summary, FDG-fPET allows measurement
of glucose uptake with a high temporal resolution,
and the bolus plus infusion approach
appears to have a better SNR than infusion only.
So let's move on to the next part of the talk,
PET/MR for the study of brain connectivity.
And here, I'll just pause and let you note
that the first ever resting-state connectivity analysis
was actually conducted almost 40 years ago using FDG-PET.
Network neuroscience has become one of the dominant models
for understanding how human cognition and function
arises from the interaction of multiple neural systems,
including brain structure, anatomical connections,
systems interactions, and coordinated neuronal activity.
And, at least in its first conceptualization
almost 20 years ago, the human connectome was considered
as a comprehensive map of anatomical connections
at micro and meso scales.
So, while the term connectome was originally conceived
as a map of anatomical connections,
it's grown to encompass multiple dimensions of connectivity.
The most prominent one,
and the one that we'll talk about the most today,
is functional connectivity,
which, by and large, is usually used
to infer connectivity measured using fMRI.
At its simplest level, functional connectivity simply refers
to the statistical dependencies from a time series
and is often calculated from a simple Pearson correlation
of time series between brain regions.
Can we calculate a similar measure for PET?
Well, the short answer is yes.
We've already seen that this was done 40 years ago
by Horwitz and colleagues.
Network-based measures of PET
actually predate the fMRI literature
by at least two decades.
And here, you can see the very early network analysis
by Horwitz and colleagues.
And what's really cool about these matrices
is that you can see some of the structure
that we're used to seeing in functional connectomes,
including a predominance of connections along the diagonal,
indicating that regions that are close together
are sharing a common variance.
So this concept of metabolic connectivity
is being proposed as a punitive biomarker for disease.
So, for example, it's been shown to be useful
for distinguishing between different types of dementia,
between different subtypes of Alzheimer's disease,
and also between a relatively rare condition,
Meige syndrome,
which is a type of dystonia of the neck and jaw,
compared to controls.
However, the vast majority of these studies
have used a static acquisition
that acquires a single image per subject.
So this means that the measure of connectivity
is really a measure of across-subject covariance.
So these measures are useful
for distinguishing between groups of subjects,
but, for a measure to be a biomarker,
I'd argue that it needs to be estimable
at the individual level.
So, what do we mean by this?
If we first consider
how we calculate a functional connectome,
you can see that we're acquiring multiple functional images
per subject over time.
So, here, we have a temporal resolution, or TR,
of 2.45 seconds, but this time course could be sub-second
with a multi-band sequence or in the range of milliseconds
if we were to use a technique like EEG and MEG.
We then parcellate the brain into some regions,
extract the time series of fMRI signal for each region,
and then correlate those time series across regions.
We do this for every subject in our sample,
and then we get a group average
and illustrate it in some way.
Now, if we take the scenario of PET
for what I'm calling a static acquisition
or an acquisition where we end up
with a single image per subject,
here, we also parcellate the brain,
but then we calculate covariance across subjects
for each pair of regions.
So, unlike the case for fMRI,
we're not getting a connectome for each individual.
So, if we consider the argument that metabolic covariance
is a biomarker for disease, this can't be the case
because, to be useful, a biomarker needs to be estimable
at the individual level.
So, while it's telling us something interesting
about the group itself, as we've seen
even from those very early PET connectivity papers,
it's just not as useful as a biomarker.
As an example of this approach
in standalone PET without simultaneous PET/MR,
Arnemann and colleagues examined
metabolic covariance networks using static PET.
They found that the young group differed profoundly
from the older group,
showing widespread, high metabolic correlation strength
compared to younger adults.
In addition, the authors also examined
between and within network connectivity.
So, for reference, in fMRI, it's an established finding
that within-network connectivity, along the diagonal,
decreases with increasing age,
and between-network connectivity,
or those regions off the diagonal in the matrix,
increases with increasing age.
So this is interpreted
as reflecting reduced network segregation
and increased network integration with age.
So, in other words, a loss of specialization
and an increase in generalization of network function.
Arnemann and colleagues found that metabolic covariance
showed uniformly high metabolic correlation strength
both within and between networks.
And, in some respect, this can be considered an update
of that very early work that we saw
from Horwitz and colleagues in the 1980s.
So, what about PET/MR measures of brain connectivity?
Some of the first simultaneous MR/PET studies
were conducted in the resting state.
So Riedl and colleagues examined eyes open versus closed
in the resting state,
and they found that there was a remarkable overlap
between increased FDG uptake in eyes open versus closed
and functional connectivity in V1
and all of the major salience network regions.
Importantly, at least for the discussion today,
while this was a PET/MR study
and they used functional connectivity
estimated from fMRI data,
the authors did not calculate
a metabolic connectivity measure based on the FDG data.
In a follow-up of this paper,
the authors used an ICA decomposition
of the static FDG and fMRI data
to look at resting state networks.
They found that the FDG networks showed some similarities
with fMRI networks,
but there were also a lot of networks
that were unique to each modality.
The results are compatible with FDG ICA decomposition
published previously by Biswal and colleagues
from the ADNI data,
where they reported that FDG components
typically show more left-right homolog connectivity
and less anterior-posterior connectivity.
So data from the simultaneous MR/PET
with static acquisitions
is suggesting that metabolic connectivity
may show some similarities
but also differences in functional connectivity.
One cool way that the simultaneous PET/MR approach
has been used to estimate connectivity
is by using the metabolic connectivity mapping approach.
This approach essentially uses the FDG data
to derive a directional functional connectivity,
which is also known as effective connectivity.
Under the assumption that most FDG-related signal
is occurring post-synaptically, i.e., at target neurons,
they assume that an increase in local metabolism
indicates an increase in afferent effective connectivity
from source regions.
So, using this approach,
Riedl et al. studied eyes open versus closed
in the same data presented in the previous slide.
They found that metabolic connectivity mapping
reliably detected stable and bidirectional communication
between earlier and higher visual regions
as well as stable top-down signaling
from the frontoparietal network
encompassing the frontal eye fields.
The MCM approach has since been used quite a bit
in task-related PET/MR by Andreas Hahn and colleagues.
So this is a nice example of an additional way
to use metabolic information
in functional connectivity analysis.
So far we've seen approaches to PET/MR estimates
of brain connectivity that have relied
upon the across-subject covariance approach.
However, it's increasingly being acknowledged
that individual level metric is required
in order to use metabolic connectivity as a biomarker.
One approach to estimating metabolic connectivity
uses a similarity estimation to obtain a surrogate measure
of metabolic connectivity between brain regions.
Studies that use this approach are recognizing
that the individual-level connectomes
are necessary to make inferences on individual variability.
This approach was originally developed
by Wang and colleagues and tested on ADNI data.
In this study, they used intraregional similarities
calculated from KLSE
to estimate the strength of connections
to obtain individual level,
undirected, weighted metabolic connectivity matrices.
Using the method, Wang and colleagues
were able to predict conversion from MCI to AD
in the ADNI data.
And, in fact, the surrogate measure
of individual-level connectivity
outperformed the across-subject covariance approach,
which has previously been highlighted as useful
as a predictive biomarker from this data.
And because we're specifically interested in PET/MR here,
the KLSE approach has also been applied
to simultaneously acquired PET/MR.
Albeit, in this case,
functional MRI was not obtained with the PET-FDG data.
Mertens and colleagues first parcellated the FDG data
using the Schaefer atlas, which is based on fMRI,
and then sorted the regions of interest into networks
based on that atlas.
Metabolic connectivity strength was estimated
as the similarity metric to obtain an undirected,
weighted metabolic connectivity matrix.
They then applied nodal metrics to the subsequent networks
to examine metabolic connectivity in aging,
and they found that metabolic connectivity strength
decreased with age in predefined networks
as well as increased with age between networks.
So this is compatible with what we've seen
in functional connectivity,
but it is the opposite of what we've seen
in network-based measures of across-subject variance,
as discussed with reference
to Arnemann and colleagues in 2018.
So we've seen a number of approaches
to estimating metabolic connectivity
using the static PET data.
We've seen across-subject metabolic covariance,
which is analogous to cortical thickness networks,
which estimate covariance between brain regions
across subjects.
In such networks, positive correlations indicate
that both ROIs are either increase
or decreasing consistently across subjects,
and a negative correlation indicates
that one ROI is increasing
and the other is decreasing consistently across subjects.
The strength of the positive or negative correlation
indicates the consistency of the pattern
across subjects in the samples.
This allows inferences to be drawn about the homogeneity
of change between groups,
e.g., between younger and older adults,
and also homogeneity of associations between regions.
ICA static PET data, as in Savio and Riedl's paper,
offer a similar interpretation,
the covariance of metabolic activity across subject.
Again, there's no time dimension in that analysis.
Metabolic connectivity mapping is a little bit different,
and it's one of the few approaches
for studying the relationship between BOLD and FDG data.
It's valuable for understanding the relationship
between uptake over a scan period and its relationship
with dynamically changing estimates of activity
derived from the hemodynamic response.
The nice thing about
the metabolic connectivity mapping approach highlighted here
is that it uses an assumption
based on the known physiology of neuronal activity
to derive an estimate of directionality
and functional connectivity.
And, at least in the context of the review
that I'm presenting today,
this is a complimentary approach
to metabolic connectivity analysis.
Lastly, we also saw that the metabolic similarity mapping
is the closest thing that we have so far
that we've discussed
that is an individual-level connectome.
These are similar to fMRI-based measures
of functional connectivity
in that there is an individual-level estimate
of connectivity.
They're also promising because their network properties
are showing similarities to functional connectivity.
However, they're different
in that they're a similarity metric
and not a temporal time series or a temporal dependency,
as is the definition of functional connectivity.
And, at this point, we're just going to step aside
from the primary argument of this talk
and just remember that fMRI is not ground truth.
And so this is something that we all know.
It's not controversial.
We all know that fMRI is not the best method
of brain function that we have.
But knowing that,
in the development of metabolic connectivity,
it's very, very easy to fall into the trap
of using what is known from fMRI and fMRI connectivity
as ground truth.
It's tempting because we just know so much more about it.
It does remain possible that metabolic connectivity
shows a different pattern in aging
to functional connectivity, as we saw in Arnemann et al.,
and the fact that Mertens' metabolic similarity approach
yielded information consistent with fMRI
is indicative that we're on the right track,
but it's only that.
It's only indicative
because we don't know what the ground truth is.
Also, it can be really tempting when doing these analyses
to constrain the PET analysis by the fMRI in some way.
So either to use fMRI patterns of activity as a mask
or just using an fMRI-based functional parcellation.
So I wanna be clear
that there's nothing wrong with this per se.
It's just important to remember
how this influences the inferences
that we can draw from the analysis.
We've got a fairly good reason for expecting the fMRI
and fPET to be highly correlated,
but, at the same time, we also expect
that they will be different.
So, first off, because they're just indexing
different physiological properties of the brain.
And, secondly, we're not trying to develop a new fMRI here.
We have fMRI.
It's relatively straightforward, used,
and lots of people like it.
What we're interested in is the new knowledge
that can be gained from the FDG-PET,
in this case, from connectomic analysis of glucose uptake.
So it's for this reason
that I try not to constrain my fPET analyses
either by simultaneously acquired fMRI data
or by atlases derived from fMRI data.
Okay, so, after that aside, let's get back to the data.
So we've said that, while the static PET covariance approach
gives us some interesting information
about the covariance of metabolism across individuals,
it isn't really the same as functional connectivity,
which examines relationships between time series
across brain regions within individuals.
This is where the fPET approach comes into handy.
fPET provides a time series of FDG uptake
over the course of the scan.
So this means that we can analyze it
in a similar way to fMRI.
In our first exploration
of within-subject metabolic connectivity,
we ran an ICA on resting state fPET.
And the goal of that paper wasn't to compare the results
to sPET or to fMRI,
so we just have the fPET components here.
I'm showing the sPET results from Savio and colleagues
on the side here for reference.
So, in this analysis, just like in the previous in sPET,
there were fPET networks that resembled fMRI networks,
including default mode network,
primary and secondary visual networks, and the cerebellum.
However, there were other components
that were not easily mapped onto fMRI components,
including a very frontal component and a medial component
that comes up quite frequently in sPET decompositions.
So, in summary, fPET appears to yield complimentary
and unique components during resting state compared to fMRI.
And while the components in Li et al. are analogous to fMRI
in that they're estimated across subjects
with temporal dimension,
they are indicating, at least qualitatively,
a similar story to the components estimated from sPET.
So let's have a deeper dive into fPET connectivity.
This time, if we analyze the fPET
and the fMRI in the same way.
In this study,
we used a constant infusion administration technique.
We didn't have a bolus here.
And then, we parcellated the brain into 82 ROIs.
We estimated time courses for the fMRI and the fPET
with a 2.45 second TR for MRI
and a 16-second frame duration for fPET.
We then correlated the time courses between regions
and constructed a connectome matrix
for each modality separately.
And then, just to compare the fPET metabolic connectome
to the sPET covariance matrix,
we also calculated that.
And this is what we found.
So static PET showed strong temporo subcortical covariance
within and between hemispheres
and strong fronto subcortical,
frontotemporal, and parieto occipital covariance,
albeit with a smaller magnitude
than the temporal occipital covariance.
fPET connectivity was dominated
by frontoparietal connections,
both within and between hemispheres,
and the low signal that's inherent
in the constant infusion fPET here is really noticeable.
fMRI showed strong connectivity
within anatomical subdivisions
as well as a number of long-range connections
between the frontoparietal, parietal occipital,
and temporoparietal regions.
We then sorted the networks into regions.
Sorry, we then sorted the regions into networks.
And I'll note that these are networks
that have been previously identified using fMRI.
So, in this way, the result is a bit more constrained
by fMRI than the previous result
because we are using that fMRI-derived atlas.
Static PET network graphs
showed strong widespread connectivity
between the dorsal attention,
frontoparietal subcortical sensory motor,
and default mode network.
fPET network graphs showed strongest connectivity
between frontoparietal and dorsal attention networks
and between default mode network,
dorsal attention, frontoparietal,
and visual attention networks.
fMRI graphs showed strongest connectivity
between the dorsal attention and visual networks.
The ventral attention network
showed high interconnectedness with frontoparietal
and sensory motor network,
and the default mode network
showed intermediate connectedness
with frontoparietal and dorsal attention networks.
We looked at the relationship between fPET and fMRI
and sPET and fPET.
And, in the paper, we looked at residuals,
but I quite like this early analysis that we did too.
So that you can see that across the brain, across regions,
fPET and fMRI showed many regions
where the correlation between modalities was non zero.
The strongest fPET-fMRI correlation was obtained
for superior cortex,
so superior frontal, superior parietal regions,
intermediate in temporal cortex, and lower subcortically.
However, for the sPET and fPET correlation,
there were no regions above zero,
even before correcting for multiple comparisons.
This is consistent with our residual analysis,
suggesting that the metabolic covariance
was a poor predictor of metabolic connectivity.
So fPET and fMRI matrices showed a greater level
of shared variance than fPET and sPET,
and supports a contention that fPET metabolic connectivity
is complimentary to fMRI.
It's important to note that direct comparison
between the methods was challenging
because there are such substantial SNR differences
between the methods.
It's also challenging because we do want to avoid
using fMRI as the ground truth here.
Each method should provide a unique, but likely correlated,
index of neural activity.
So work in this area is still continuing.
And while we expected at some level
that the static PET covariance
might be a less than satisfactory reflection
of within-subject PET connectivity,
we really didn't expect to see such a stark difference
between the metrics,
nor did we expect fPET connectivity
to be more similar to fMRI than to sPET covariance.
This led us down the path of exploring the concept
of ergodicity in neuroimaging.
An ergodic process occurs when a group-level result
is generalizable to the individuals within the sample.
Simpson's paradox is a special case of non-ergodicity,
where the within-individual correlation
is in the opposite direction
to the group-level correlation.
In reality, ergodic processes are quite rare
because two strict criteria must be met.
Firstly, the process has to be homogenous
across individuals within a sample,
and, secondly, the statistical parameters
that describe the process must be constant
or stationary over time.
In a recently published commentary
on our static versus functional PET connectivity paper,
Sala and colleagues raised some important points
about the concept of ergodicity for metabolic connectivity.
They argued that ergodicity should be considered
on a continuum rather than being all or none,
which we do agree with.
They also noted that many parameters
may have influenced the ergodicity in our experiment,
and the ones they noted included demographic factors,
pre-processing choices, scanner resolution, and so on.
Again, we do agree with this,
but we also feel that the argument is demonstrating
that the parameters for achieving ergodicity are so strict
that it's questionable whether true ergodicity
could ever be obtained using neuroimaging data.
Therefore, we argue that,
given that ergodicity is so difficult to obtain,
we should, at the very least,
avoid to use metrics like metabolic covariance
if we have another metric that can be estimable
at the individual level.
In the last experiment that I'd like to discuss,
we've recently explored how metabolic connectivity
relates to cognition.
And this work was led by my new postdoc Katharina Voigt
and used the data that I just presented earlier.
We explored the question of whether neural connectivity
maps to cognition in a domain-general way
or in a domain-specific way.
In a PLS analysis of the functional metabolic connectomes,
we found that both modalities
yielded a single cognition connectome latent pair.
The functional connectome loaded strongest on depression,
inhibitory control, and memory retention
with the strongest loadings
within the frontoparietal cortex.
And here, you can see that we found
that both long-range and short-range connections
were significantly related to the cognition.
In fPET, we found something similar.
Metabolic connectivity in the frontoparietal cortex
loaded strongly on measures of executive function,
including inhibitory control and verbal fluency.
While the metabolic connectome in the 99th percentile
was faster than the fMRI connectome,
you can see that both long and short-range connections
were related to cognition.
So we concluded that both metabolic
and functional connectivity is supporting the global view
of cognition connectome mapping
because we did just see that one cognition connectome pair
for each modality.
We also concluded that metabolic and functional connectivity
are explaining unique variance
in the cognition-connectome relationship.
So, in other words, the two modalities
are showing complimentary and unique information
about connectivity in the brain.
So, where to from here?
First off, I'd like to return to the concept of time
for PET/MR.
The terms dynamic and static
are so fraught with this methodology
that I'm sure that I've offended half of the audience
with my use of them.
As I mentioned earlier, when I use the term static PET,
the static is really just referring to the fact
that there is an assumption that glucose uptake
is stationary across the acquisition period.
The image may have been or probably was acquired
with dynamic acquisition in order
to determine the cerebral metabolic rate of glucose.
While fPET is useful in that it's providing
a within-subject time course of uptake,
and we're using that time series to calculate a connectome,
the analysis itself, the ones that I've presented today,
have assumed stationarity as well.
So, if we compare this to a dynamic connectivity measure,
as used in fMRI and EEG,
our analysis would be called static
by these people in that field.
So, in order to move towards what an fMRI
or a different type of neuroscientist
would call a dynamic analysis,
we'd have to employ something
like a sliding window approach to our data.
As an aside, we actually did try to do this
with our early checkerboard data, but we did have no luck.
But we also had a very low dose
and a frame duration of one minute.
So, possibly, this might be possible
with the bolus infusion and the faster frame rates.
Secondly, the signal-to-noise ratio of fPET is low.
It's really low.
And, at least in our cerebral cortex paper,
it was difficult to compare the fPET
to the fMRI connectomes
because the signal was just so much smaller in fPET
compared to fMRI.
We're hoping that the bolus infusion approach
will help here.
Unfortunately, we've been delayed by COVID
in acquiring this data,
but possibly other approaches, like signal optimization,
like the MR-informed PET reconstruction,
and deep neural networks, might help here.
Thirdly, something that is a little bit of a challenge
is that standardized imaging processes
don't yet exist for fPET.
So fMRI and functional connectivity
benefit from very large research bodies
that give us an idea how best to analyze the data,
but fPET is still in its infancy in this area
and much work is required.
Another thing I want to explore
is whether or not we're getting the most
out of our simultaneous acquisition.
So, if we consider the benefits
of a simultaneous multimodal acquisition,
there are three levels of benefit.
First off, we get a simple benefit
of spatial registration.
So PET and MRI being registered on acquisition
is a very useful thing.
At the second level, we have an asymmetric integration.
Here, we might use one method to inform the other.
So we might use the spatial resolution of MRI to inform PET,
or the more direct and quantitative nature of PET
to inform fMRI.
Similarly, we might also consider
using the information from fMRI,
e.g., the fMRI-derived functional atlases,
or fMRI activity masks
to inform or constrain the PET analysis.
The third level of simultaneous multimodal acquisition
is starting to get to a truly synergistic integration,
where we're learning much more
about the physiology of the brain
by capitalizing on the benefits of each method.
This might be a technical integration
or a physiological interaction.
I think, at the moment, at best,
we're currently at level two
in terms of the data that I've discussed today,
and it'll be great to see how we can get
to that third level of benefit in the near future.
I'm also super excited by the possibility of using the fPET
to measure the dynamics of other physiological targets.
I think the ability to track neurotransmitter dynamics
in response to tasks is such an exciting future direction
with this method.
And if we take the models that we're used to seeing
in cognitive psychology, it's really common to see
that these models do make predictions
about the role of different neurotransmitter systems
in behavior or in psychiatric illness.
These models are often based off indirect evidence,
including known disruptions of neurotransmitter systems
in certain disorders,
or it might be on the basis of preclinical challenge models.
I've just screenshotted two examples of this
that I just had in my files
from back when I was doing my thesis,
but this is a very common approach in cognitive psychology.
I think the fPET approach offers the ability
to directly measure some of these dynamics in vivo
in healthy humans, and I think that's so cool,
and I can't wait to see
where we go to with this in the future.
Lastly, I just wanna highlight
that the international community is finding value
in early PET/MR data releases.
So my group have made three such datasets available.
And here, you can see the references to them here.
One nice thing about the visfPET
and the DaCRA-fPET datasets
is that we've made our list mode data available.
The technical validation for each datasets
is published in either Scientific Data or GigaScience.
And those papers demonstrate how the list mode data
can be reconstructed using open source algorithms.
I just wanna finish off by saying that,
while this is a little bit of shameless publicity,
I do urge you in considering
making your data publicly available.
The international neuroimaging community has shown
that great gains in knowledge can be made by making unique
and difficult to acquire data publicly available.
When we started this, we were a bit in the dark
because PET BIDS was still in its draft form,
but we now do have the standard.
And plus, we've developed a sharing format
for list mode data that allows it to be used
without access to the proprietary software.
And I, at least, feel really lucky
to be able to use this cool new technology,
and so I think it's valuable
to share it with others as well.
We've already started to see some cool papers
coming out on the basis of our data,
and I'll very briefly discuss them here.
In the first, Guo and colleagues capitalized
on the simultaneous acquisition
to show that functional connectivity within white matter
is related to glucose uptake and so is unlikely to be noise.
And, in this pre-print, Wang and colleagues
compared effective connectivity approaches of fPET data,
the metabolic connectivity mapping approach,
which does not require a temporal dimension
for the PET data,
and Granger causality, which does.
They found that the two approaches to effective connectivity
differed in their directed connections,
and concluded that each method showed its own strengths
in estimating effective connectivity.
I look forward to see what other new discoveries are made
on the basis of this open PET/MR data.
Okay, so thank you, everybody, for your time.
I just want to acknowledge the significant input
of all these people that have made to the work
that I presented today.
I'm lucky to work with a great team of neuroscientists,
biomedical engineers, clinical radiographers,
and nuclear medicine technologists,
as well as informatics officers.
This work was supported by the Australian Research Council
and a National Health
and Medical Research Council of Australia grant.
The work was conducted at Monash Biomedical Imaging
on the lands of the indigenous Kulin Nations,
and I acknowledge the Elders past, present, and emerging.
Thank you.
- My name is Christin Sander,
and I'm delighted to be a part
of this NIMH PET/MR Symposium.
The title of my talk is
"From Neuroreceptor Binding to Brain Function
with PET and Functional MRI."
Networks in the brain exist at various scales.
Functional networks span the whole-brain level,
and functional MRI is used to investigate
related underlying neuronal networks across the whole-brain.
Now, PET can be used to inform us about brain chemistry
at the synaptic or protein level.
And in today's talk, I will focus on how interventions
such as drug treatment, stimulation,
or behavioral modulation
may alter or change these kinds of networks.
And I will focus specifically
on how we can use combined PET and fMRI
to understand relationships across networks
and across scales of these networks.
While we want to reach across spatial scales
in order to enhance our understanding of the brain,
we also want to integrate temporal scales.
And here it's really important
to understand strengths and limitations
of each of the modalities that we're looking at,
as well as understand
the biological phenomena that we want to study.
EEG and fMRI can really resolve second time scales
for whole-brain functional networks,
and then look at circuit dynamics
or neuronal information flow.
PET, on the other hand, is somewhat slower,
and so we're looking at changes that we can resolve
on the order of minutes or hours or days.
And so, depending
on the biological question we are looking at,
each modality can add their own strength and limitation
to this type of understanding.
Now, neuroreceptor PET imaging is the gold standard
for determining in vivo drug binding to a specific target.
In this image, you can see a parametric map
of a D2/D3 radiotracer
that is in competition with an antipsychotic drug.
The drug is administered at different doses.
And so at each dose, we can determine
receptor occupancy using PET imaging.
Functional MRI allows us to image
the brain's functional response
or activation due to a stimulus.
And in pharmacological MRI, the stimulus is a drug.
And so we can also use functional MRI
to look at an in vivo drug response, similar to PET imaging.
Of course, it gives us a different set of information.
And so here you can see a change in cerebral blood flow
due to the psychostimulant amphetamine
that has been shown using functional MRI.
And in our lab, we are really interested
in understanding how a stimulus, such as a drug or a task,
changes receptor binding,
which then in turn modulates neuronal activity,
and how we can record that
using vascular response as a readout,
by understanding that there is a connection
through energy consumption and metabolism.
And I will cover a set of studies in this talk today
that will touch upon this flow of information chain here
and how we can use PET and MRI
to dissect some of these questions along this pathway.
So this is the outline of my talk.
I will touch upon four main topics,
the first one being pharmacological PET/MRI
and how we can use it to classify drugs in vivo.
Second, I will show some examples of how we can use PET/MRI
to measure endogenous neurotransmitter release.
I will then go into receptor quantification
and potential sources of bias,
and how we can use PET/MRI
to address these kinds of challenges.
And finally, I will show an example
of where we can get insight into receptor adaptations
using PET/MRI.
So we'll start with pharmacological PET/MRI.
Pharmacodynamics describes the effects
of a drug acting at a receptor.
It is characterized by dose-response relationships,
which is what you can see in this graph below
of response plotted versus concentration of drug.
And depending on the response,
we can then classify drugs
into full agonists or antagonists, for example.
This behavior is typically determined
using functional assays in vitro.
In vivo, our best measurement for this
is to use receptor occupancy from PET imaging.
And it is typically assumed that receptor occupancy
is related to tissue function,
or is really representative of tissue function.
However, we also know that receptor binding
does not always equate receptor function.
And so one of the strengths
of using PET and fMRI as a readout,
or pharmacological MRI as a readout,
is that we can have independent readouts
of receptor occupancy
and potentially tissue function from fMRI,
and to understand better
what this relationship actually looks like.
In a series of studies,
we looked at a set of pharmacological challenges
in non-human primates
to evaluate different types of drugs
that bind to the D2/D3 dopamine system.
And we used the radiotracer C11 raclopride
together with functional MRI as a readout.
Specifically, we looked at two antagonists,
a partial agonist, and two agonists,
quinpirole and Ropinirole.
Our study design was to do
a pharmacological intervention during the scan,
and simultaneously acquire functional MRI
together with dynamic PET.
Because we are looking at imaging non-human primates,
we are also injecting an iron oxide contrast agent
that allows us to determine fMRI signal changes
in terms of cerebral blood volume.
Here are more details of our image acquisition
and analysis methods.
We acquire PET images in list mode,
and then reconstruct into time bins
of 30 seconds to one minute.
All images are quantified using kinetic modeling
and a reference tissue model
with cerebellum as the reference region.
To compare with dynamic functional MR measurements,
we also include a time-varying binding term
that allows us to compute dynamic binding potential
and also plot receptor occupancies over time.
Functional MR images are acquired
using PET-compatible non-human primate receive coil,
and we acquire gradient echo-echo plan imaging
with a timing resolution of three seconds.
We then use a general linear model
to analyze the fMRI images
and model the pharmacological challenge
with a gamma-variate function or sigmoidal function.
I also want to point out again
that we use an iron oxide contrast agent,
which allows us to convert fMRI signal changes
to cerebral blood volume.
When using the D2/D3 antagonist raclopride,
as both the challenge and the radio tracer,
we get a robust decrease
in C11 raclopride binding potential.
We also see an increase in cerebral blood volume
due to the drug challenge itself.
And in the time course that you can see below the images,
you can see that the blue curve shows PET-specific binding
that is similar in its time course
to the increase in cerebral blood volume.
And these similar temporal responses
are concordant with a classical occupancy model
in which binding directly relates
to receptor functional changes.
When using a D2/D3 agonist, in this case quinpirole,
we again get an increase in receptor occupancy.
And now we get a localized decrease in CBV in the striatum.
In the temporal responses, we find
that there's diverging time courses between PET and fMRI,
which may indicate receptor internalization mechanisms.
And we will go a little bit more into details on this
in the last section of my talk.
By investigating a variety of dopaminergic drugs,
we find that our results generally hold true.
At baseline, there's a high specific binding
of C11 raclopride in the striatum.
And with both preclinical as well as a clinical antagonist
that's specific to D2/D3 receptors,
we find that there's high occupancy
coupled with an increase in cerebral blood volume
that's localized to the striatum.
With agonists, in this case quinpirole
and the clinical agonist ropinirole,
we find that there is again a decrease in receptor occupancy
that is related to a decrease in cerebral blood volume,
which is the functional response and expected to be negative
with D2 receptors being inhibitory.
We also looked at partial agonists,
and this is the third generation antipsychotic
aripiprazole here.
At about 90% occupancy, which is matched
to the same occupancy as the antagonists,
we find that there's still a positive response
in the cerebral blood volume,
but it's at a lower magnitude
compared to the full acting antagonists.
And this area of partial agonists
is really an interesting area to explore
in how we can add functional MRI
to understand better about the functional effects
when bound to the receptor.
The effects of partial agonists
have also been explored at the serotonin system
in this study led by Dr. Hansen.
In this case, a serotonin receptor partial agonist,
AZ, was given as a within scan challenge
and showed to reduce binding potential.
At the same time, the cerebral blood volume response
was shown to be biphasic,
with an initial negative followed by a positive component,
suggesting that this is really a drug
that's acting as a partial agonist
by having both a negative and a positive response.
So pharmacological PET/MRI allows us
to gain more insight into drug studies
beyond just simple target engagement and receptor occupancy.
With PET, we can ensure and directly get evidence
for the fact that a drug binds to a receptor,
as shown here for examples at the dopamine,
the opioid, and the serotonin system.
When additionally acquiring fMRI, and in this case,
these are all cerebral blood volume responses,
we can then evaluate the drug function
by looking at its time courses, its signaling properties,
and gain more insight beyond occupancy measurements.
And it is a really interesting area
to further explore time courses
and compare both PET time activity curves
and specific binding reductions
with changes in cerebral blood volume or fMRI time courses,
because there's a lot of insights
that can be gained from understanding
concordant or diverging time courses.
This brings me to the second topic of my talk,
which is on measuring endogenous neurotransmitter release
with PET/MRI.
I've touched upon how PET and fMRI
are complementary tools for dynamic functional imaging,
but this table more specifically outlines
some of the parameters that are of interest
when we are doing combined PET and fMRI experiments.
And I'd like to outline a couple
of these parameters more specifically.
PET imaging of course is known
for its molecular sensitivity to its target.
And this allows us to determine pharmacokinetics
and then receptor occupancies, all at tracer-level doses.
PET, compared to fMRI, is slower
and has a temporal resolution on the order of minutes,
whereas functional MRI can resolve images at a second level.
But the pure temporal resolution is different
from what are potentially observable dynamic changes
when related to biology.
And in PET, this is really radiotracer-dependent,
as it depends on the kinetics of the tracer.
And in functional MRI, this also depends on biology
and several other factors.
Another key parameter that's important to keep in mind
is the detectable signal changes from baseline.
This really depends, one, on how fast the changes are,
but also, two, on how reliable the baseline measurement is
and whether you can, for example, repeat certain stimuli
as is typically done in fMRI.
And so a lot of these parameters
influence the kinds of robust measurements
that you can do with PET or fMRI.
And really understanding the parameters
between those two modalities,
in order to get the best experimental design
to understand underlying biology, is really important.
And I'm gonna outline a couple of examples of studies now
that have really made use
of some of these features in PET and fMRI
to understand more about brain networks in general,
In this study led by Dr. Hansen,
serotonin release was measured during visual attention.
And here, autobiographical images with positive valence
were shown during simultaneous PET/MRI acquisition.
They showed that there's changes in binding potential
during the stimulus session,
and that there's increases in cerebral blood flow
during the viewing of these images.
Most interestingly, there was a correlation
between the change in cerebral blood flow
and the change in binding potential
from the 5-HT1B receptor radioligand.
And so this really suggests a link
between serotonin signaling
and visual processing and attention.
And so overall, this PET/fMRI study allowed us
to get a better insight into the role of 5-HT
in overall sensory and behavioral processing.
In another example of looking
at endogenous neurotransmitter release,
Dr. Wey et al. looked at pain-induced
PET and fMRI activations.
Here, pain pressure was used to induce
endogenous new opioid release,
and increases in BOLD fMRI were found,
together with changes
in diprenorphine PET receptor availability.
And in the thalamus, a positive correlation
between BOLD and changes in binding potential
suggests that there's a direct opioid modulation.
However, in a different area, in the striatum,
this correlation was not found.
And so this suggests that maybe
other neurotransmitter systems
drive the BOLD response in the striatum.
In this PET/MR study, the aim was to look at glutamate
release using MR spectroscopy and a radiotracer
that binds to the metabotropic glutamate receptor five.
And indeed, an acute decrease in striatal glutamate
was observed using spectroscopy
after stimulation with an acetylcysteine.
However, no significant changes
in MGluR5 availability were observed.
Nevertheless, the relationship between changes in glutamate
and a baseline binding potential was found,
which is an interesting relationship
that I'm sure is of interest to explore in future studies.
We've been interested to image neurotransmitter release,
and specifically in a model of deep brain stimulation.
The ventral tegmental area is known
as the primary dopamine-producing nucleus,
and plays a role in reinforcement learning and motivation.
So it's been shown in the literature
that VTA stimulation leads to dopamine release,
but it's not known what are the receptor contributions,
what may be the effect of stimulation parameters,
and what are the in vivo circuit effects
or dynamic changes that occur to this stimulation.
So we looked at a monkey model of deep brain stimulation
in order to determine neurochemical dynamics
of microstimulation and the functional networks.
You can see here an image of the DBS implant,
together with the actual microstimulation or DBS electrode.
And our experimental paradigm then looked
at dynamic acquisition of fMRI
and C11 raclopride PET during a stimulation,
which allowed us to look at different parameter
of the stimulation,
and also at different experimental designs.
Overall, we found a really robust fMRI activation
with increases in CBV on the ipsilateral side,
that was mainly shown in ventral striatum and caudate,
and caused by the microstimulation.
The PET data proved to be
somewhat more challenging to analyze.
And so we looked at various new models to implement,
and we, in the end, were able to show
that a forward model implementation
of the full reference tissue model
localizes decreases in binding potential
in the ventral stratum that match the fMRI response.
This model specifically uses a faster rate of convergence
for estimating changes in binding potential,
and really allowed for accounting for very fast changes
like microstimulation in the data.
When using a standard approach with an MRTM2 model,
we mainly saw an apparent increase in binding potential
in low-binding areas that are surrounding the stratum.
So we've seen that PET is highly sensitive
to imaging molecular processes,
but the sensitivity for imaging dynamic changes
is really important.
And it's important to keep in mind
that the test-retest variability in PET
can be on the order of five to 10%.
And so that sometimes effect sizes
need to be quite large over a sustained period
in order to reliably measure changes.
And so this brings me to the next topic of my talk,
which touches upon receptor quantification
and potential sources of bias.
In this recent paper by Dr. Levine,
motion and model bias were investigated
with a set of behavioral challenges
using the radiotracer C11 raclopride.
There's both human task data
as well as simulations that were investigated.
And he showed that motion bias can account for changes
in binding potential of more than 10%.
And it's really important to do correction methods,
such as frame-based motion correction
or reconstruction-based motion correction
in order to account for these sources of bias due to motion.
Model bias can also affect the data,
and it can differentially affect
different anatomical regions, with positive bias
specifically in low binding potential areas.
The magnitude of the bias is dependent
on the choice of your model,
and when using a two parameter reference tissue model,
is dependent on fixing K2 prime.
But he also showed that de-biasing
the contribution of the initial uptake period
can reduce dependence on this parameter.
Furthermore, he went on to show
that the magnitude of neurotransmitter release
can really affect some of these biases.
And sources of bias in general pose a great challenge
when the elicited task-response effect is small.
Often in behavioral paradigms,
a task-based change is less than 5%,
and this is really on the order
of what motion or model bias
can also be affected by.
And so it's important to be aware
of some of these sources of bias.
We have been interested in how physiology
may affect PET or fMRI data,
and specifically the effects of blood flow
on PET radiotracer kinetics
is something that's been really a question
in the PET literature for a long time.
Some radiotracers have even been suggested
to be surrogate markers of cerebral blood flow,
but there's not been experimental data out there
to specifically investigate this.
So we wanted to use combined PET and fMRI
to measure the CBF effects
on PET radiotracers that bind to neuroreceptors.
Using combined PET and arterial spin labeling,
this allowed us to look at both
tracer binding to receptors together with flow.
And we could then also induce changes in flow artificially
using a hypercapnia challenge,
where you breathe in higher CO2 than normal,
and where you really induce large flow changes.
It's possible to control and vary
the timing of the challenge very precisely,
so it's a really good model to investigate.
And so all of these study results that I'm gonna show you
were done here in non-human primates
during a hypercapnia challenge.
Here are results from C11 raclopride.
This is a typical time activity curve
that you see from C11 raclopride.
And this is really the time activity curve
where we did apply hypercapnia,
and we did not see measurable changes
in the time activity curve during the hypercapnea challenge.
We also did not see any residuals
that map with the hypercapnea pattern from kinetic modeling.
And at the same time, we have very robust data
showing that flow increases by more than twofold
based on our arterial spin labeling measurements.
We repeated a similar paradigm
with the radiotracer F18 fallypride,
which is thought to have a high extraction fraction,
therefore would be more susceptible to flow.
But even here we don't see measurable changes
in the time activity curve,
and no specific pattern that relate
to large increases in flow.
These data were also repeated
with changing flow at the very beginning,
during the uptake period of the radiotracer,
and we found very similar results.
So overall, we used PET and MRI
to enable simultaneous measurement
of receptor-specific radiotracer kinetics and blood flow.
And we showed that they did not affect
fallypride or raclipride binding.
We also evaluated a set of human imaging studies
in chronic pain patients and the radiotracer PBR28,
which looks at neuroinflammation.
And here we find that SUV outcome measurements
were not correlated with cerebral blood flow,
as determined from pCASL.
We also found that PBR28 signal increases
were not accompanied by increases in CBF,
and that areas where there was lower perfusion
were not accompanied by decreases in CBF.
When we did a set of measurements in non-human primates,
where we again used a hypercapnea challenge,
we showed that K1 and K2 were not elevated
with higher blood flow, and outcome measures
were also not correlated with blood flow.
This makes sense when looking at the direct relationships
between K1 and the extraction fraction
in comparison to increases in blood flow.
And we show that K1 is not expected to have large changes,
even with large changes in flow.
We also more generally looked at the effects
of kinetic properties and quantification.
Changes in binding potential were quantified here
due to changes in blood flow.
And overall, we found that radiotracers
with large K1 or small K2 values
may be most sensitive to changes in blood flow,
but that K3 and K4 are really robust
to changes in blood flow.
And for CBF increases up to more than twofold,
binding potential values were affected by less than 5%,
which is really within the test-retest variability
of most radiotracers.
This brings me to the fourth topic of my talk,
which is how we can use PET and MRI
to understand more about changes in receptor signaling
and receptor adaptations over time.
I'll start this section with just a little bit
of background on receptor adaptations.
Receptor internalization is a homeostatic
neuroadaptation mechanism at the synapse
that can modulate cell signaling.
And it generally occurs in response
to G-protein-coupled receptor activation by agonist binding.
Initially, a receptor would be desensitized
by binding to its agonist,
and can then undergo internalization,
all within the span of seconds to minutes.
And there may be short-term as well as long-term tolerance
or adaptations that follow from these kinds of mechanisms.
And our understanding of this phenomena
is mainly gained from in vitro evidence.
And in vivo, we've not had direct measurements
of receptor internalization,
so its dynamics are relatively unknown.
Nevertheless, it does affect neuroimaging data,
and that has been shown in several publications,
which is the case both for fMRI as well as for PET data.
So a non-invasive in vivo measurement
of any parts of these receptor internalization mechanisms
could enable the evaluation of drug dynamics,
and allow us to understand
receptor dysfunction in disease in more detail.
Here you can see a dopamine synapse
and receptors at the postsynaptic membrane.
In a classical occupancy model,
a drug can wash in and bind to these receptors,
eliciting a downstream response
that we can read out with an fMRI signal.
Because the drug is now bound to the receptors,
we would expect a decrease in our PET imaging signal.
And in this classical occupancy model,
binding directly follows function.
In an internalization model, though,
the drug also washes in, it binds,
but after it's bound, it can desensitize
and then internalize receptors,
which then may attenuate the functional signaling response,
yet at the same time, the PET signal would show
a similarly decreased PET signal as in the left hand case,
because only available receptors are similarly bound
at the postsynaptic membrane.
And in vitro measurements have shown
that internalization does occur
in response to agonist binding.
So we've looked at this
from a more theoretical point of view,
where we looked at how internalization may affect
both PET and fMRI imaging signals.
And we can express the total number of receptors, Bmax,
as the sum of available receptors,
receptors bound by agonist, and receptors bound by DA.
Both of these compartments are in flux
with the free drug concentrations.
If we then add an additional compartment
that describes internalized receptors,
we can simulate its effects on both a PET and fMRI signals.
And in simulations without receptor trafficking,
we see that there is a classical occupancy model
where CBV and PET occupancy time courses are matched.
And this is what I've shown you in data
with the D2/D3 antagonist in the beginning of my talk.
When internalization does occur,
we find that PET and MR imaging time courses
start to diverge, with fMRI time courses being shortened
and PET displacement being prolonged.
And again, this is the kind of experimental data
that we also see in our experiments
when using a D2/D3 agonist drug challenge.
We've been interested in exploring receptor internalization
in the context of high doses of endogenous dopamine.
Amphetamine type stimulants acutely increase
extracellular dopamine levels.
And internalization is an immediate synaptic response
to adapting to such high concentrations of dopamine.
In vitro internalization of D2 receptors
can occur very fast, within minutes,
and receptors can stay internalized
for more than an hour and up to 24 hours.
It's also been shown that there may be differences
between D1 and D2 receptor internalizations,
with D1 receptors recycling quickly
back to the cell membrane surface.
Overall, this phenomena has really only been explored
at the D2/D3 receptors,
and we have some PET imaging data
that show that receptor availability
may stay depressed for up to two days
due to receptor internalization.
So we designed a study to look at the effects
of repeated amphetamine and receptor internalization.
The goal of the study was to determine
dopamine receptor adaptation longitudinally
due to repeated amphetamine in vivo,
and how this would be reflected
in a modulation of PET and fMRI signals.
Our hypothesis for this was that stimulant drugs
cause rapid internalization of dopamine receptors in vivo,
but that D1 and D2 receptors
may have differential recovery times.
So in order to run this study,
we used the simultaneous PET/MR acquisitions
with the radiotracer C11 raclopride.
This study was done in non-human primates,
and amphetamine was injected
about 40 minutes after radiotracer injection,
and fMRI was simultaneously recorded.
After first amphetamine injection,
we then injected amphetamine a second time
at either three or 24 hours,
allowing us to evaluate the effects of a second injection.
And then, in order to look at D1 versus D2 receptors,
we blocked the D1 receptors using Schering 23390
in an equivalent set of experiments
that allowed us to differentiate
between D1 and D2 receptor signaling.
Our results show that dopamine release occurred
with each amphetamine challenge in the striatum.
Second injection showed a slightly reduced
dopamine release capacity at both three hours and 24 hours.
Simultaneously acquired cerebral blood volume measurements
showed a negative cerebral blood volume response
with the first injection at zero hours.
But when given amphetamine for a second time
at three hours, this negative CBV response
converted to a positive CBV response.
And at 24 hours, we find that there is a mixed
negative and positive CBV component
that is evident on the cerebral blood volume maps.
Time activity curves from C11 raclopride
are shown in this slide here.
With each amphetamine challenge
there's a decrease in binding,
suggesting a release of dopamine
for each of these amphetamine injections.
You can also see that binding potential stays decreased
for the three hour time point,
but returns back to baseline by 24 hours.
And that's also reflected
in the corresponding occupancy measurements.
The corresponding cerebral blood volume time courses
show that there's a short lasting negative CBV response
for the first amphetamine injection,
but that this response is markedly different
at the second injection at the three hour time point,
where the CBV response now shows
a longer lasting positive response.
At 24 hours, there's a mix of a positive
and negative response that you can see here.
We can also visualize the occupancy time courses
together with the CBV time courses,
as shown in these graphs here.
And it is clearly visible that D2 receptor occupancy
remains elevated at three hours,
but then returns back to baseline after 24 hours.
When using a D1 receptor blocker,
we see that dopamine release was slightly attenuated.
But the most interesting part here
is that the CBV response at the three hour mark
was effectively blocked due to the D1 receptor blocking,
suggesting that this positive response at three hours
is mediated by D1 receptors.
The negative responses at zero and 24 hours
were hardly affected by Schering 23390 blocking.
The corresponding C11 raclopride time activity curves
also show that Schering did not significantly affect
C11 raclopride binding
compared to the non-blocking condition.
And finally, we quantified all binding potentials
for each of the time points,
again, showing that Schering had
a somewhat reduced dopamine release,
but did not affect D2 receptor binding otherwise.
We can also clearly visualize the effect
of blocking D1 receptors on Voxel-Wise maps.
Even with a D1 receptor blocker on board,
we still get dopamine release
with each amphetamine injection.
The main striking part here
is that the positive cerebral blood volume component
seems to be entirely eliminated by blocking D1 receptors.
We find that striatal responses are all very similar
in terms of the functional signaling
with each amphetamine injection.
However, we find that the thalamus
behaves a little bit different,
in that the sharing compound was not able
to block a positive component in the thalamus,
whereas it was effectively blocking
all of the positive signaling in the striatum.
So this part is really an interesting phenomena
that would be of value to be explored further.
In this final section of my talk,
I've shown you how we can use simultaneous PET and fMRI
to decipher the role of subtype receptor contributions,
and to learn about their desensitization
and internalization mechanisms.
We've learned that PET decreases
can sometimes represent a combination
of dopamine release and D2/D3 receptor internalization.
And this is important to be aware of
when interpreting both PET and fMRI signals.
The main striking finding in this amphetamine study
was that after three hours,
receptor internalization was still persistent,
but D1 receptors were again available
for functional reactivation.
Overall, this allowed us to look at first in vivo evidence
for different recycling rates
between dopamine receptor subtypes.
And this was enabled by using simultaneous PET and fMRI.
To summarize, I've shown you that we can use
simultaneous PET and fMRI to image neuroreceptor binding
together with brain function in the living brain.
We can use pharmacological PET/MRI
to classify drugs in vivo,
and we can look at endogenous neurotransmitter release
at various receptor systems using PET and fMRI.
At the same time, it's important
to really understand receptor quantification
and the sources of bias that come with it,
and how we can use PET/MRI to validate
or characterize some of these sources of bias.
And finally, I've shown you
that PET/MRI can give us insight into receptor adaptations
and further understand dopamine
and other receptor systems in more detail.
Overall, the value of simultaneous PET/MRI
really lies in integrating
systems biology and systems neuroscience
in order to understand biological mechanisms
in vivo and at the whole brain circuit level.
We can use novel MR and PET techniques
together with novel pharmacological challenges,
as well as models and analysis approaches,
in order to probe receptor systems and their interactions.
And I'd like to close with this slide,
showing that there's really a world of opportunities here
that allow us to look at different targets in PET imaging
for different receptor classes.
And in combination with different contrasts of fMRI
or MR techniques in general,
we can really try to understand the brain
at a more holistic level.
Finally, I'd like to thank
the symposium organizers at the NIMH,
and I'd like to thank my collaborators
as well as the funding sources.
If you have any questions about our studies,
please feel free to contact me,
and I'd be happy to discuss further
in the live Q&A session as well.
- Hello, my name is Dardo Tomasi, and I am a physicist
with experience in PET and MRI
and a staff scientist at the NIH.
I will talk about how the speed
of dopamine increases modulates brain function
and reward in humans, and I will present results
from our simultaneous PET/fMRI study.
I will show you how we are using simultaneous PET/fMRI
to assess dopamine neurotransmission,
brain function, and behavior.
The overarching goal of our study
was to assess the dynamic association
between dopamine D2 receptor occupancy
in the striatum meshed with carbon 11-raclopride base
and brain activity inferred by pharmacological fMRI
in the human brain
and also to assess the relative sensitivity
and specificity of the neurovascular coupling
for a slow drug delivery achieved
with oral administration of methylphenidate versus
rapid drug delivery using intravenous
methylphenidate administration.
I will describe how we are extracting the time course
of extracellular dopamine increases elicit
by methylphenidate from PET data and how we are using it
for modeling pharmacodynamic fMRI responses
to methylphenidate challenges.
And for linking the methylphenidate related functional
connectivity changes to the rate of dopamine increases
in a striatum.
We want understand why some routes
of drug administration are more likely
to be addictive than others.
Data from priority studies suggests
that drug pharmacokinetics and the route
of administration played huge roles in this regard.
For instance, oral methylphenidate doses
are not addictive and used in the treatment
of attention hyperactivity disorder.
However, intravenous methylphenidate is addictive.
The routes of abuse are often taken
by intravenous, smoked, oral, or intranasal routes.
The route of drug administration determines both how fast
and how much drug reaches the general circulation
and ultimately the brain.
The faster drug reaches the brain,
the more likely it will be addictive.
The figure on the left was extracted
from a review by Allain and colleagues.
It show pharmacokinetic profiles of plasma cocaine levels
in humans for different drug administration routes.
Plasma cocaine levels rise sharply and decline rapidly
when cocaine is injected intravenously or smoked.
In contrast, plasma cocaine levels rise
and decline more slowly peaking one to two hours
following intranasal or oral administration.
The figure on the right was extracted from a review by
Volkow and Swanson and shows in purple a time activity curve
demonstrating the first uptake of cocaine in the brain
which peaks four to six minutes
after the intravenous injection and in blue
the perceived high, which parallels the fast cocaine uptake
in the striatum.
In products, faster cocaine injection results in higher
mesolimbic c-fos expression and metabolic activity
and also faster dopamine transporter blockade
and more cocaine self-administration.
For instance, if cocaine was delivered
in over a 92nd period, animals did not escalate their
cocaine self-administration as a function
of cession.
Differently, self-administration increased gradually
if cocaine was administered in five seconds periods.
In humans, however, we do not know much about why
some routes of administrations are more addictive
than others.
For instance, we do not know which brain circuits
are sensitive to the speed of dopamine release
and how this relates to the subjective experience
of the drug, which seems to be very relevant
for the misuse potential of a drug.
Drugs like cocaine or methylphenidate block dopamine
and epinephrine transporters in the synapse,
which cause some changes in brain function that lead
to the behaviors that we care about.
But the truth is that we do not know much
about this medium part.
We want to uncover the brain circuitry that is sensitive
to the speed of dopamine release
because we think this may explain the subjective experience
of the drug.
These are some of the questions we want to address
in our study.
We wanted to study the dynamics of dopamine release
and identify brain circuits that are sensitive
to drug administration route.
Also, we aim to study decoupling
between dopamine neurotransmission, brain activation
and functional connectivity, and the relations
with the subjective perception of reward
from methylphenidate.
We wonder if these associations differ
for intravenous and oral administrations.
Simultaneous PET/fMRI is the ideal imaging tool
for this because PET can measure molecular changes
with high sensitivity and specificity
and fMRI can assist brain function
with high spatial temporal resolution.
And also because PET and MRI data collection
occurs simultaneously.
we just completed this study
and this is its timeline.
Subjects first received an oral pill
60 milligrams of methylphenidate or placebo,
and had a baseline MRI.
Then carbon-11 raclopride, the tracer was injected
30 minutes after the oral pill.
And the intravenous medication
either 25, 0.25 milligram per kilogram of methylphenidate
or saline was injected 30 minutes after the tracer.
Simultaneous fMRI data was collected uninterruptedly
for 90 minutes while subjects rated their feelings of high
during the study.
We selected oral doses of methylphenidate and IV doses
of methylphenidate that left to roughly equivalent
levels of dopamine transport occupancy,
such that the main difference
between the IV and oral conditions was the speed
not the magnitude of drug brain delivery.
The study had a double-blind placebo control
within subjects design.
There were three sessions
on separate days for each participant.
The active dose was delivered orally
in one session and intravenously in another session.
And additionally we had a double placebo control session.
Session order was counterbalanced.
Participant and researchers were blind
to the nature of the medication.
We studied 20 healthy adults
from the D.C. area who had prior experience with alcohol use
or stimulant drugs, but did not have drug dependence.
Exclusion criteria included allergy to
methylphenidate, cardiovascular abnormalities,
use of medications that could interact with methylphenidate,
and positive drug toxicology test results.
For each PET study we collected data
in a Siemens biograph scanner using this mode acquisition
after the bolas injection of 16 millicurie
of carbon 11-raclopride.
For PET imagery reconstruction into 48-time windows
we used the three order subset expectation
maximization algorithm within the Siemens platform.
Because this is a PET MRI, not a PET CT scanner,
We used two echo UTE images
and a convolution network to estimate the coefficients
for attenuation correction.
The standardized and take values, SUVs, compute
and calculated after normalization for body weight
and injected dose were especially normalized
to MNI template.
We computed relative SUV time series in a striatum
by normalizing each SUV volume
by its mean in the cerebellum using the three for ROIs.
Our goal was to study dynamic associations
between stable dopamine release and brain activity.
For this purpose, we are using the rate
of dopamine increases in a striatum
as a regressor to understand
if fMRI signals elicit by metal rate
using a general linear model.
We focus on the rate of dopamine increases
because faster dopamine release could overwhelm
this slow neuro adaptation of the dopamine system
and be associated with drug-related euphoria.
Overall, our approach is analogous to the use
of thermodynamic response function
in a standard fMRI studies.
Now changes in binding potential following
a pharmacological challenge
with the methylphenidate transporter block
like methylphenidate or cocaine
are frequently interpreted as dopamine release.
We found that changes in a standardized uptake value
ranges between placebo and methylphenidate conditions
were highly correlated across subjects independently
for IV and oral conditions
with the corresponding changes in binding potential.
This suggests that methylphenidate related changes
in SUVr could be interpreted
as dopamine increases average dopamine increases
during the study.
This made us think of the difference
in SUVr between placebo and methylphenidate condition
as a dynamic marker of dopamine increases.
We are testing this hypothesis using simulations
of raclopride binding with and without methylphenidate.
Specifically, we are simulating the SUVr difference
between placebo and methylphenidate using Alpert's
linear extension of the simplified reference model.
We are using a gamma feed, the red curve
in this plot as a surrogate for the dopamine increases
which is the black curve.
We quantify the speed of the SUVr changes by the time
to peak of the dopamine rate, which is the blue curve
in this plot.
We interpret the time varying SUVr changes
in terms of extracellular dopamine changes
which reflect accumulation of dopamine released
due to the dopamine transporter blockade the methylphenidate
and also dopamine clearance via block dopamine transporters.
In simulations, including noise, and also within subjects
physiological variability, we found that increasingly lower
the methylphenidate doses were associated
with lower dopamine increases the red curve here
and a smaller SUVr changes.
And this suggests that Delta SUVr is highly sensitive to
dopamine increases induced by methylphenidate.
In simulations, we also found that errors
in protocol execution may not be a serious problem.
However, variability across individuals
in raclopride kinetic parameters could result
in significant variants in time to peak.
Our simulations also suggest that time to peak estimated
from SUVr changes is highly sensitive to the time to peak
of the true rate of dopamine increases produced
by methylphenidate.
Now, this is our real data.
These figure show the average SUVr difference
between placebo and methylphenidate experiments
across the 20 participants in our study.
The figures show good agreement
between the gamma feeds and the SUVr changes
across individuals independently for IV
and oral methylphenidate.
This approach allowed us to estimate the average dopamine
grade time courses we are using for the analysis
of the fMRI signals as well as individual
time-to-peak values
which were of course shorter for IV
than for oral methylphenidate.
And we are observing an association between
time-to-peak and reward.
During the scans participants use a bottom box to
record their responses to the question how high
you feel right now in a scale from one to 10?
The figure on the right shows that shorter
time-to-peak estimated from individual feeds
was associated with more intense feelings of high,
both for IV and for oral methylphenidate.
We think that this could be important to understand why
some routes of drug administration are more addictive
than others.
From the simple noninvasive approach to assess the dynamics
of dopamine increases with methylphenidate
we learn that time value SUVr change
induced by methylphenidate reflects the time course
of extracellular dopamine increases.
Modeling the time course of SUVr allowed us to
assess the rate of dopamine increases and its time-to-peak
which was associated with the high
elicited by methylphenidate.
In the next slides I will describe how we are using
the average rate of dopamine increases in containment
as a regressor to understand fMRI signals elicit
by methylphenidate using the general linear.
Only one group has investigated the association
between dopamine activity and brain activation
using simultaneous MRI with pharmacological challenges.
These studies were carried out in monkeys, not in humans.
The MGH group did this first study to assess
the neurovascular coupling to dopamine receptor
occupancy using raclopride pharmacological challenge.
And the study shows a dynamic association
between receptor occupancy using
raclopride pharmacological challenge
and cerebral blood volume in a striatum.
This study, however, was limited by anesthesia
and cannot be repeated in humans due to the
toxicity of the nanoparticles used to push the fMRI signal.
Simultaneous fMRI can be used to assess effects
of drug administration route on brain activation.
Specifically, we wonder if oral NIV methylphenidate
activate different brain circuits.
We hypothesize that methylphenidate-related fMRI
signal changes would correlate with the rate
of dopamine increases in striatum as a function of time
and that this would differ between oral and IV routes.
As I said simultaneously, with PET acquisition
we collected fMRI data using a 12-channel
radiofrequency coil for fMRI.
We monitor blood pressure, heart rate,
blood oxygen level during simultaneous PET MRI
for safety reasons.
And we collected one millimeter isotropic MRI structure
for free surface analysis.
And for bolus MRI we collected 1800 volumes at three seconds
temporal resolution using standard whole brain
ecoplainer imaging.
The pipelines of the human connections project were used
for image per processing
and aCompCor was used to remove artifacts associated
with physiological signals and head motion.
Linear regression was used to access the association
between both signals of dopamine rate extracted
from PET data.
To identify brain regions that were stimulated
by the speed of dopamine release,
as I said, Pete Manza is using the rate of extracellular
dopamine increases.
And has a regressor for a statistical paramedic mapping.
And he found that intravenous but not oral methylphenidate
strongly activated the insulin.
And also the MP singular here, you see the 10 courses
which are the central notes of the resiliency network.
He also found that both for oral and IV methylphenidate
there was a deactivation
in the medial prefrontal cortex, which is hyperactive
in individuals with cocaine use disorder.
Similarly, Pete found that fMRI responses in ventral
striatum decreased and also dorsal is there to increase
after intravenous methylphenidate.
But the signals in these regions were weaker
than those in cortical regions where fMRI signal
to noise is higher.
To summarize, the rate of extracellular dopamine increases
in use by methylphenidate in striatum,
modulated the amplitude of fMRI signals in dorals
anterior simulate insula striatum
and intermediate prefrontal cortex.
Our results are consistent with those
from prior PET MRI studies with nanoparticles in monkeys
that show that pharmacological doses
of raclopride can increase the blood volume in striatum.
We found in all subjects that time varying fMRI responses
in cortical regions were coupled
to the rate of extracellular dopamine increases in use
by methylphenidate in striatum.
These findings are consistent
with our hypothesis that methylphenidate-related
fMRI signal changes in the salience network
and limbic regions would correlate with the rate
of dopamine increases in striatum,
and that piece effects would differ between oral
and IV methylphenidate.
This shows how simultaneous PET MRI can be used to
assess the influence of drugs of abuse on brain activity.
This slide highlights the only pharmacological PET MRI study
on functional connectivity.
This preclinical study by Rasmus Birn and colleagues
from University of Wisconsin Madison
investigated the effect of methylphenidate
on functional connectivity on three awakes monkeys
using the Fallypride tracer.
The study reports an association between methylphenidate
related changes in binding potential in striatum
and increase cortical functional connectivity
in these monkeys.
To study the association between a spontaneous
brain activity in the low frequency band
and the rate of dopamine increases in striatum,
we first bypass filter the pre-processed time series to
remove ultra low frequency components
of methylphenidate effects on both fMRI signals.
Then we created time series of voxelwise
voxel correlations using the cluster that demonstrated
significant association with dopamine rate using a sliding
window approach.
Based on our findings on the activation
of the resiliency network and the findings
of increased stato cortical connectivity in monkeys
after IV methylphenidate,
we hypothesized that IV methylphenidate
would increase functional connectivity between the
caldate and the anterior singular.
When analyzing this data, Pete Manza
found that IV methylphenidate indeed increased significantly
the connectivity between the covate
and the anterior singulum which is consistent
with the findings in awake monkeys from Rasmus Birn
and colleagues.
Differently, oral methylphenidate
did not change the functional connectivity
within the dorsal striatum and the anterior singulum.
Simultaneously MRI can also be used to study the effect
of drug administration route
on a spontaneous brain activity.
Specifically, we wondered if oral and
intravenous administration
of methylphenidate would alter the fractional amplitude
of low-frequency fluctuations in the brain.
We hypothesized that oral methylphenidate would stimulate
inhibitory D2 receptors and reduce spontaneous activity.
In this case, we use the sliding window approach to
compute FR fractional amplitude of low-frequency
fluctuations to which we fitted our dopamine regressor.
To identify brain regions in which methylphenidate
alters spontaneous brain activity
we compare the fractional amplitude
of low-frequency fluctuations in the last 30 minutes
against that of the first 30 minutes of this study.
We found that intravenous methylphenidate
strongly reduced spontaneous brain activity in
occipital, parietal, and and temporal cortices
and also in the motor strength.
Also for oral methylphenidate
the decreases were weaker than those
for intravenous methylphenidate.
This spatial pattern of decreases in a spontaneous brain
activity is consistent with the distribution of a straight
of dopamine increases, which was not homogeneous
in the brain.
Specifically, dopamine increases were larger
in posterior regions of the dorsal striatum
than in anterior, dorsal or ventral striatum regions.
These posterior regions of the dorsal striatum
are structurally connected to posterior brain regions
that show a significant decrease in the spontaneous
brain activity following intravenous administration
in the present study.
Differently, anterior, dorsal or ventral regions
of the striatum which did not show significant
dopamine increases in our study are connected
to anterior brain regions that did not show
significant changes in a spontaneous brain activity.
Therefore, the spatial distribution of decrease
in cortical activity with methylphenidate is consistent
with the spatial distribution of changes
in binding potential in striatum caused by methylphenidate.
To assess the role of dopamine in the reduction
of brain activity we map the statistical significance
for the slope of the linear association
between the dynamic fractional amplitude
of low-frequency fluctuations and the dopamine
rate regressor extracted from PET data.
We found a linear association between the rate
of dopamine increases in the reduction
of spontaneous brain activity in occipital,
parietal, and temporal core
and in the motor acro subjects.
Plots show for seven traditional brain networks
the negative correlation between the fractional amplitude
of low-frequency fluctuations and the rate
of dopamine increases.
The associations between dopamine rate
and the fractional amplitude of low-frequency fluctuations
differentiated the networks for oral and IV routes.
For instance, in somatomotor cortex, the linear association
between dopamine rate and fALFF was very different
for oral and IV methylphenidate.
We also found for the first time an association
between peak high ratings and a spontaneous brain activity
across participants.
Such that the lower the amplitude
of low-frequency fluctuations in sensory motorcortices
the greater the peak high ratings.
This association was maximal in the cuneus.
This is very interesting and intriguing to us because we see
for the first time an association between high ratings
and brain responses to methylphenidate outside the striatum.
In summary, we found that methylphenidate
increased functional connectivity
between the striatum and the resiliency network consistent
with prior studies in awake monkeys.
Methylphenidate reduces spontaneous brain activity
in sensorimotor cortices that are structurally
connected to posterior regions of the striatum where
methylphenidate induced significant dopamine released.
We also found that a spontaneous activity
in this cortical regions was coupled to dopamine
increases induced by methylphenidate in striatum
as a function of time.
These findings are consistent with our hypothesis
that dopamine increases will stimulate inhibitor
the two receptors and weight spontaneous brain activity.
And with the notion that methylphenidate enhances alertness
and vigilance which should decrease
low-frequency fluctuations in the brain.
This is also consistent with the role of dopamine
in the promotion of wakefulness.
Overall, this study also shows how simultaneous
PET/fMRI can be used to study the influence
of drugs of abuse on functional connectivity.
Our old Siemens biographic scanner is now being replaced
by a new GE PET MRI scanner in the clinical center.
And in the future we will use this scanner to investigate
further the effect of the rate of dopamine increases
on brain activation, functional connectivity,
and behavior using slow and fast
intravenous methylphenidate infusions.
I want to end by thinking everyone's contribution
to this study, especially those of Pete and Nora.
This study was one of the most complex studies
we have done at the NIH.
Our study wouldn't have been possible
without the effort and smart contributions from post docs,
post bacs, and staff from the Laboratory
of Neuroimaging and the significant contributions
from NIH Clinical Center personnel
and collaborators from other institutions.
Thank you very much for watching.