Neuroimaging methods are used with increasing frequency in clinical practice and basic research. Designed for students and professionals, this course will introduce the basic principles of neuroimaging methods as applied to human subjects research and introduce the neuroscience concepts and terminology necessary for a basic understanding of neuroimaging applications. Topics include the history of neuroimaging, an introduction to neuroimaging physics and image formation, as well as an overview of different neuroimaging applications, including functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, perfusion imaging, and positron emission tomography imaging. Each will be reviewed in the context of their specific methods, source of signal, goals, and limitations. The course will also introduce basic neuroscience concepts necessary to understand the implementation of neuroimaging methods, including structural and functional human neuroanatomy, cognitive domains, and experimental design.
Magnetic Resonance Spectroscopy
Loading...
Del curso dictado por Johns Hopkins University
Fundamental Neuroscience for Neuroimaging
401 calificaciones
De la lección
Experimental design and special applications in neuroimaging
This week will introduce experimental design in functional neuroimaging and special methods in neuroimaging, including functional connectivity MRI, diffusion tensor imaging and spectroscopy imaging.
Conoce a los instructores
Arnold BakkerAssistant Professor
Psychiatry and Behavioral Sciences
In this module, we're going to discuss yet another specialized application of
magnetic resonance imaging known as "magnetic resonance spectroscopy."
We've seen on several occasions now that precessions or spins are in
a low energy state when they are aligned
parallel to the principal axis of the static magnetic field,
or in a high energy state when their
anti-parallel aligned to the principal axis of the static magnetic field.
And we can change a spin from a low energy state to
a high energy state by introducing electromagnetic energy.
This is known as "excitation."
We've also seen that the most efficient way to introduce
that electromagnetic energy is by using the Larmor frequency,
the radio frequency pulse based on the Larmor frequency formula,
which basically states that the Larmor frequency depends
on the gyromagnetic ratio of the particle that you're trying to image,
as well as the static magnetic field.
Precessions that are in a high energy state or that are excited from
a low energy state to a high energy state actually
causes small magnetic field changes at the nucleus,
typically in the opposite direction of the magnetic field,
the static magnetic field.
This change is the local effective magnetic field,
and actually causes the emitting frequency
the resonant frequency of the particle to shift a tiny little bit,
because we see that the Larmor frequency depends
on the gyramagnetic ratio multiplied by the static magnetic field.
By introducing that radio frequency pulse to excite that particle,
we actually change the local magnetic properties,
which then in turn changes the resonant frequency just a little bit.
And this is referred to as a "chemical shift."
Chemical shift is the change in resonant frequency that
results from a small change in the local magnetic field,
again, as a result of that excitation pulse that we've introduced.
The size of the difference or the value of that difference of
the resonant frequency gives us information about the local molecular group,
which that nucleus is part of.
So, magnetic resonance spectroscopy then is an imaging approach that
aims to quantify the local presence of certain molecules,
certain chemical compounds based on the shift in that resonant frequency.
The chemical shift as expressed in parts per million.
As we can clearly see from the Larmor frequency formula,
the Larmor frequency heavily depends on the strength of the magnetic field.
If we want to compare the frequency distribution or the presence of
certain molecular compounds within a brain area across different studies,
it would be useful to have a metric
that does not depend on the strength of the magnetic field,
so that we can compare concentrations from
a three Tesla scanner with concentrations from a seven Tesla scanner.
Therefore, parts per million is used as the unit to express
the quantity or the density of
that molecular compound that's available in that area of the brain.
And parts per million is calculated by dividing the change in the resonant frequency that
results from the introduction of
that excitation pulse by the frequency of the spectrometer,
the frequency of the measuring device that is used to quantify these measurements.
Now, note that one is expressed in hertz while the other is expressed in megahertz,
so there's an order of magnitude of difference between the two,
which results in the parts per million part.
So chemical shift imaging,
which is another term for magnetic resonance spectroscopy then,
is nothing more than taking a measurement from a particular area of the brain.
And rather than determine the location of water protons,
which is what we typically do for structural and functional imaging,
we take an entire spectrum measurement of the radio frequency that can be measured,
that can be obtained from that area.
And that's referred to as a "spectrum."
The value of the difference of the resonance frequencies gives
information about the molecular group that the nucleus is part of.
So, by quantifying these peaks,
by labeling these peaks of frequencies that we see,
we can determine the local molecular groups that
are available in that particular area of the brain.
Again, this is expressed in parts per million.
The frequency in the sample is subtracted from the frequency in TMS,
which is used as a baseline compound,
setting the range to zero, essentially.
And that difference is divided by the frequency of this spectrometer,
the device that is used to make these quantifications.
And that's how we get one of these spectra distributions,
as you can see on the right hand side.
Spectra can be obtained from different types of nuclei,
but typically or commonly protons are used, again,
because they're highly abundant in the brain,
and they're very sensitive to these types of changes.
They're very sensitive to these types of measurements.
When we look at molecules of interest,
they typically have a very low concentration in the brain.
But because we're using proton-based quantification,
we know that water is also going to be one of those compounds that we can observe.
Water, as we've seen, is very abundant in the brain.
And the water signal in these spectra is much,
much greater than the compounds that we're typically interested in.
So the water signal must be suppressed,
because it's an order of magnitude greater than what we would like to focus on.
Water suppression is used for exactly that purpose.
Chemical shift selective suppression or "chesss" is one of those approaches.
And it essentially uses a very specialized pulse sequence
to saturate the water signal and remove it from the read out.
By doing so, we can focus the read out attention or
the read out range on the molecules that we're interested in.
as you can see in the right hand image.
So, the spectra provides a detection of brain metabolites,
molecules that we're interested in.
And the area under the curve for
a particular metabolite gives us the concentration in that local area of the brain.
So, again, we're focusing on a sub-region of the brain,
a small area of the brain.
This voxel size is typically much larger than what
we would use in structural or functional imaging,
but it certainly doesn't encompass the entire brain.
We're looking at sub-regions of the brain to
quantify the amount or the density of metabolites that are locally available there.
Certain spectroscopy pulse sequences
are more sensitive to metabolites than other metabolites.
So, typically, within a spectroscopy session,
different pulse sequences are used to quantify these different types of metabolites.
And as we've clearly seen from the Larmor frequency formula,
the fields strength heavily determines
the Larmor frequency and therefore our ability to detect these compounds.
So, higher field strengths typically result in much better measurements,
particularly for spectroscopy imaging.
This type of imaging can be very useful and important,
because changes in metabolites very often precede structural brain changes.
For example, in the situation of nerve degenerative disorders or a stroke,
we can see changes in the metabolite profile before we can see changes in
the structural composition as we would see in structural MRI.
Just to show you a few examples of
common metabolites that can be observed with the spectroscopy imaging,
NAA is a very commonly seen one.
And you can see it's the highest peak in a normal brain.
It's a marker of neuronal and axonal viability and density,
so it's often used as a marker of
structural integrity of the brain area that you're trying to image.
And a decreased concentration of NAA is associated
with white matter disease or malignant neoplasm.
So, on the right hand side,
I'm showing a typical example of one of those spectra.
And we can clearly see that the NAA signal is
the largest signal of all the metabolites that are quantified there.
Creatine or CR is another common metabolite observed.
As you can see that the second from the left peak is creatine.
It represents molecules that contain creatine and phosphocreatine.
It's considered a marker of energetic systems in intracellular metabolism.
And reduce levels of CR are observed,
again, in cases that show brain tumors.
So, that's another marker that can be used in cases of tumors and cancers.
Choline, CHO, it represents choline and choline-containing compounds.
It's a marker of cellular membrane turnover reflecting cellular proliferation.
Increased CHL seen in infarction and inflammation,
but it is somewhat nonspecific,
because you can see it both in
infarctions situation or inflammation from all sorts of different sources.
So, that can be the result of many different issues in the brain.
Lactate is typically a very low peak in a normal brain,
you see there on the right hand side of the NAA peak.
It's a marker of a metabolism that results from cerebral hypoxia,
ischemia, seizures and metabolic disorders, again.
And it frequently occurs in cysts,
normal pressure hydrocephalus, and,
again, in certain brain tumors.
So, again, it's another marker that can be used to assess brain health.
Lipids, "lip" for short,
is very difficult to detect.
Typically, you see two peaks of lip that have to be compared and combined.
It's considered a marker of cellular membrane breakdown and necrosis,
as in metastasis or malignant tumors.
So, again, you see this again as a change marker that's very common in brain tumors.
On the right hand side, I'm showing a table of some others,
and the reason that you would see changes in that metabolite marker associated with it.
Clinically, spectroscopy is most commonly used for brain tumors and metabolic disorders.
As we've clearly seen from some of the examples of the metabolites,
some of them frequently co-occur or
increased or decreased in the context of brain tumors.
So, spectroscopy is somewhat sensitive to the presence of brain tumors,
as well as metabolic disorders.
Here in the example,
you can see that the white square is superimposed on a tumor in this particular brain.
And as we compare the spectra to the one that we've seen before from a normal brain,
we can clearly see that the CHO peak is even higher than the NAA peak.
And we had just seen that NAA typically is the highest peak in a spectra.
So, here, we see a spectroscopy spectra
that shows us that there is something abnormal happening in this particular brain area.
And, again, in this case, it concerns a tumor.
In research, there's many more applications for spectroscopy imaging.
Here, I'm showing you an example where measurements
were taken from three different areas in the brain,
as indicated in the far left image by the white squares.
And on the right hand side,
I'm showing the resulting table where they've quantified
the concentration of metabolites in these different brain areas,
comparing them between patients with seizure disorder and a group of healthy control.
And you can see the differences in the concentration of some of these metabolites,
differentiating the patients from the control populations here.
There's many other applications that can be used when it comes to research.
So, clearly, I just showed you an example of
comparing a patient group and a control group
in the concentration of certain metabolites in particular areas of the brain.
It can also be applied in terms of correlation with
structural volume or the growth of a tumor.
It can be used as a correlation with white matter integrity,
and in some cases, it can be used in correlation with functional state.
For example, like an APGAR score has been shown to be
correlated with certain metabolite concentrations in a particular area of the brain.
On the right hand side, I'm showing an example of a correlation between
white matter integrity derived from diffusion tensor imaging,
which we've discussed in one of the previous modules,
and CR in the right temporal stem.
And you clearly see a positive correlation between these measures here.
So, magnetic resonance spectroscopy is a way to
quantify particular molecules most
often bring metabolites within a particular area of the brain.
It's not so much about the spatial location of these things.
The voxel size from which you take these measurements is usually preset.
But it gives you a measurement of the chemical compounds that are locally available.
So, this concludes our discussion of the special applications of
magnetic resonance imaging that we've talked about that
included functional connectivity analyses,
diffusion tensor imaging, and magnetic resonance spectroscopy.
Explora nuestro catálogo
Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.
Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.
© 2019 Coursera Inc. Todos los derechos reservados.
