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.
Structural MRI Studies
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Del curso dictado por Johns Hopkins University
Fundamental Neuroscience for Neuroimaging
338 calificaciones
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De la lección
Principles and Methods of Neuroimaging
This week will introduce the principles of neuroimaging and applications in structural and functional neuroimaging.
Conoce a los instructores
Arnold BakkerAssistant Professor
Psychiatry and Behavioral Sciences
[NOISE] In this module
we'll be discussing
some examples of
structural MRI studies.
In the previous two modules, we've discussed how the MRI signal is generated.
We've talked about protons spinning around their axis, being knocked
out of their aligned position by the magnetic field of the MRI scanner.
When you remove that stimulation pulse, the proton spins back into its
original position, emitting energy that can be measured by radio frequency coils.
So you see this procession here in this image, and you can measure two aspects of
that procession, the longitudinal and the transverse relaxation.
And if you do that and map that out, you can identify tissue classes
because CSF grey matter and white matter have different relaxation times providing
you with a measurement of what the type of tissue is at that particular location.
We've then talked about the introduction of gradients in the x, y, and
z direction that allow you to give spatial specificity to those measurements,
exciting one slice of the brain at a time, doing a readout for
that particular slice, and then moving on to the next slice in the volume.
So in the middle you see here an example of these stacked slices and
each slice is a particular thickness.
That thickness is defined as the volumetric pixel thickness.
And if you do a series of these slices, one after another, put them together
in a two or three dimensional volume and that provides the basis for
your structure MRI scan of which you see an example here on your far right.
Now what types of studies are the structural MRI scans used for?
Well very fundamentally,
we can start with the qualitative evaluation of the structural MRI scan.
On the far left side, you see an example of a structural
MRI scan of a patient who had a cortical infarction as highlighted by the arrow.
So qualitatively, you can evaluate the size and location of the cortical
infarction that you see there and compare these with control patients or
other patients who've had a similar type of insult to the brain.
Next to it you see an example of a brain scan with white matter hyperintensities.
These areas of increased signal
in the white matter of the brains of these patients.
You can quantify them simply by counting the number, or
also account for where in the brain you see these types of hyperintensities.
And conclude something potentially about the symptoms that a patient might
be experiencing as a consequence of these types of white matter hyperintensities.
The third image from the left shows an example of a brain scan with traumatic
brain injury in this particular case, a veteran.
You see these small areas in the posterior aspect of the brain of shearing, or
basically minuscule holes that are in the white and gray matter tissue that
are the consequence of brain injury in this particular case, a shock injury.
On the far right you see the example of periventricular white matter lesions,
these are very common in aging.
They occur at the end of the ventricles on the corners of the ventricles, and
also appear as these white matter hyperintensities,
that in some cases can have some clinical correlates associated with them.
So here you're just looking at a qualitative evaluation of the brain scan
that you get from a structural scan that can be used to either to diagnose
the presence of an abnormality, or in some cases even a disease syndrome.
Like Wilson's disease, that is characterized by yyperintensities in
the gray and the white matter that you can observe with these T2 or T1 brain scans.
But often for research studies, we want to take a more quantitative approach.
We want to be able to quantify certain aspects, [COUGH] of the brain anatomy.
And one of the simplest approaches is to do a volumetric analysis to determine
the volume of a particular structure of the brain that you're interested in.
On the left hand side, you can see a highlighted section of the medial temporal
lobe, in this particular case, the hippocampus and the surrounding cortices.
You can then, as you can see at the bottom of the screen,
use a manual segmentation approach in which you use a computer program to
manually delineate the structure that you're most interested in,
as you can see in the outlines of the different colors.
And then essentially count the number of voxels,
the scan elements that are within that outlined structure.
Multiply that by the volume of the voxel, that depends on the resolution of
the scan and the thickness of the slice, which will then give you a volume for
that particular structure that you're interested in.
So on the left hand side, you see an example of these outlines.
On the top right, you see a segmentation approach that used these overlays, these
nontransparent colors to define certain structures in the center of the brain.
And at the bottom right,
you see an example graph of what the results from that could look like.
That in the caudate, the hippocampus, and putamen,
you could potentially see a pattern of differences between controls and
the patient population that you're interested in.
So this is a very common approach.
This is very frequently used to look at brain
structural differences between patients and control cases.
These methods have also advanced a little bit further into
automated segmentation methods that can be used to answer the same question.
On the top left you see an example of a segmentation method that separates out
the white matter from the gray matter and the CSF.
And similar methods can then be used to quantify
each of these tissue classes that you see in the top left.
And on the bottom left and on the right, you see an example of an automated
segmentation technique in which realignment methods and
automated segmentation are used to overlay labels on a large
collection of brain structure that we anatomically can define
in an individual subject or even in a group of subjects.
Similar to what I described before, these labels can then be used to generate volume
for each of those different structures both on the left and on the right, and
be used for comparison between subjects either patient groups in controls or
any type of other comparison that you're interested in.
Different methods are employed for T1 weighted MRI scans versus T2
weighted MRI scans depending on the type of question you want to ask for
your particular experiment.
There are a number of software programs that can be employed
that take this automated approach each with its own benefits and downsides.
But there are a large number of software packages that can be used to accomplish
this very easily using a structural brain scan.
Another approach that can be taken is to look at the cortical thickness.
We have talked about the cortical folding that occurs in the brain.
And if you look at those folds, you can determine the thickness of the gray matter
which is the cell bodies of the neurons.
And separate it out from the white matter which is largely the axon
bundles that communicate information from one location to another.
So the gray matter is typically considered the computational processing unit
of the brain.
So if you want to quantify that specifically, you could look
at the cortical thickness of that gray matter by separating out the white matter.
So here you see an example of how that's approached.
You basically find the boundary between the white matter and
gray matter, as well as the gray matter and the outer boundary of the brain.
And you create vertices to determine the average
thickness of that cortical layer over a particular length of structure.
So you could do that for one cortical folding, or you can do it for
a stretch of cortex to determine what the average cortical thickness for
that region is, quantify that and
similarly compare different subject groups on their level of cortical thickness.
Here on the left-hand side you see an example of scatter plots on
the bottom that are quantified in the bar graphs that you see on top.
In this particular case we're dealing with the study cortical thickness in
the cingulate, an the area of the brain.
And you see the mean cortical thickness there is very different between what they
label super ages, middle aged healthy control subjects and elderly controls.
And you see a very significant difference in these cingulate
in the cortical thickness that these different groups show.
So, this is a subset of a structural volumetric approach that focuses
exclusively on the thickness of the gray matter, that you can then use for
your particular study.
Particularly neurodegenerative disorders like Alzheimer's disease and
Parkinson's disease, you will see a number of studies focusing on cortical
thickness or cortical thinning that is associated with the disease.
In the approaches that we've talked about so far, we've looked at a particular area
in the brain and then quantify either the volume or
the thickness of that area to be able to make comparisons between subject groups.
Voxel-based morphometry is a similar but slightly different approach.
It focuses on the question of where in the brain
there are differences between gray matter density or gray matter concentration.
If you don't know for
a particular patient group where you would expect the change in cortical volume or
cortical thickness, you can use an approach like voxel-based morphometry.
Which essentially asks the question, where in the brain are there difference
in grey matter or white matter density or concentration between a patient group
A and control group B or really any compassion that you can make.
The approach that is taken there, you can see on the left-hand side at the bottom
of the screen where the original brain scan is segmented,
normalized into a central standard space.
Segmented into grey matter, white matter and CSF,
compare to template models or priors to determine where in the brain
differences in density of white matter and grey matter can be observed.
On the right hand side, you can see an example of such result where you see these
highlighted in red clusters or areas where there is white matter or
grey matter differences in volume or in density,
is you will, between patient groups and control groups.
So here we're focusing on the location of the volumetric differences
rather than a preset region of interest that you're focusing on.
You can do a similar approach with cortical thickness,
obviously, where you're asking the question where in the brain
are there cortical thickness differences between patient group A and
patient group B or between the patient group and the control group.
And you get similar types of results showing areas of cortical thinning,
usually identified by colder colors, or
cortical thickening, usually indicated by the warmer colors,
if that's observable between your control and your patient groups.
So, on the left-hand side here you just see an example of such results.
All these approaches can obviously also be used in longitudinal structural studies.
You can do a longitudinal assessment of cortical and
subcortical structure volumes.
If you have an area that you're particularly interested in
like the hippocampus or the striatum, you can look longitudinally at the volume
change by using these manual or automated segmentation methods.
You can do the same for
cortical thickness, where you assess cortical thickness over time.
The longitudinal assessment of tissue class,
to see if the proportion of white matter, gray matter and CSF changes over time.
As well as structural atrophy basically determining if a structure declines
in volume over time, which is most over used by neurodegenerative disorders,
like Alzheimer's disease and Parkinson's disease.
And finally, you can do an assessment of structural morphology.
So here on the right I just want to show you an example of a thickness study that
is in children with autism, compared to typically developing children.
And in the occipital lobe, you can see over their life span and
their early life span, that there is a little bit of a difference between
the cortical thickness in the occipital lobe that occurs over time.
Although they're certainly overlapping.
And there's clearly an age in their teens there were both
the healthy typically developing children and
the autism children are very similar in their cortical thickness.
But the overall pattern over their lifetime certainly seems
to differ from typical developing children.
So now just a word about structural morphology.
Structural morphology is essentially a refinement of the volumetric technique.
It is possible of course that the volume of a particular structure does not change
between a patient and a control population.
But the shape changes either over time or
between groups in a way that does not effect the overall volume.
So if the shape changes you could possibly detect without being able to detect any
volume differences.
So, structural morphology is an analysis method to compare shape
differences between groups of subjects or shape over time.
On the left-hand side, you see an example of shape differences between two groups.
At the top, you see the extent or the magnitude of the displacement or
the magnitude of the change.
And on the bottom is indicated in these different colors of purple, red, and
green whether or not the deflection or
the change is inward, outward, or the areas where there are no differences.
So here you can imagine a situation where the overall volume doesn't change because
in certain areas you have inward deflections and
other areas have outward deflections that overall cancel each other out.
But they're certainly shape changes that happened between subject groups
that may be relevant to the symptomatology that these patients or
these subjects are displaying.
On the right-hand side I'm showing an example of longitudinal morphometry.
So you could look over longitudinal period of time if there are shape changes to
a particular structure that could be relevant.
In this particular case,
we're dealing with patients with early Alzheimer's disease.
And if you look, the red colors indicate significant shape changes over time.
So, this is 0, 6, 12, 18, and 24, month scans.
And the red indicates the earliest change.
And you'll see the red progressing from the bottom left-hand side to the top right
side showing a very clear shaped pattern of changes overtime that could have very
significant implications for the behavior that these patients or
the impairments that these patients are showing.
And this is an example of the shape change in the entorhinal cortex specifically
in patients with early Alzheimer's disease.
An additional factor that we could introduce in these types of studies is
the correlation or the connection between these brain changes, structural, volume,
thickness, or morphology, with behavioral measures.
So you can do qualitative structural analysis, volumetric, cortical,
voxel based morphometry, longitudinal analysis or structural morphology.
And you could look at all the results from those studies in the context of certain
task performance, or a pathological vulnerability, or an intervention outcome.
Does the cortical thickness change as a function of antidepressant treatment?
Does the structure or voxel based morphometry change or
predict some type of pathological vulnerability?
Are patients who are experiencing cortical thinning in mid-life at a greater
likelihood of developing Alzheimer's disease than those that do not?
Or, more fundamentally, we can look at the correlation between certain brain
structure and performance on particular tasks.
Are people who have a large hippocampus,
particularly good at memory tasks, or are people who have
a particularly small frontal lobe poor at tests of executive function?
So we can take all the results from these different types of structural scans and
put them into context of the behavior that that person displays.
And there's a great many examples that we can bring to bear that
studies are exactly using this way.
I just highlighted a few examples here of interest, title of the paper on the top
left shows that brain structures differ between musicians and non musicians.
So they compared two groups of expert levels basically,
in this particular case musicians.
And showed very different brain patterns or volume of structures
between these groups, that might be relevant to their musical ability.
On the bottom left there's an example of a paper that showed that aerobic exercise
training increases brain volume in aging humans.
So at the bottom you see an example of the location of the areas in blue and
the reddish color that are increasing over time as a result
of the aerobic exercise regimen that's been introduced to these older adults.
So here we're talking about brain volume increases.
And finally, on the right hand side,
I'm showing an example of abnormal cortical thickness and brain behavior
correlation patterns in individuals with heavy prenatal alcohol exposure.
So here's an example where you use MRI structural scanning as an outcome
measure to determine what the consequences of a particular intervention,
in this particular case, exposure to prenatal alcohol.
And at the bottom,
you see a correlation between structural changes in the brain, and performance
on the work list learning task that seem to be relevant to this patient category.
So here's some examples where we take the results from various types of structural
imaging scans and relate them to the behavior or the syndrome of behaviors
that certain subjects are displayed, that are interest to the scientist.
But there's a number of limitations that must be kept in mind when
using structural MRI scans in your research.
There's a number of reasons why the structure of the brain, or
the volume of the brain, can change.
Some of those are important to our research questions.
Like developmental questions or atrophy questions in terms of neurodegeneration,
Alzheimer's disease and Parkinson's disease.
But there's also a number of causes
of structural brain changes that might not be relevant to your research study.
I've just shown you an example where exercise increases
the volume of a particular brain structure, and if that's not
your research question it'd be important to keep in mind as a covariant.
Substance use can also very significantly affect the volume of the brain.
This is both positive in terms of prescribe medication that will affect
brain structure.
For example, a certain anti depressant medication will positively affect
the volume of certain brain areas.
But also negatively when there's a substance abuse,
which often has negative consequences for bringing volume and cortical thickness,
such as alcohol or drug abuse.
Inflammation or edema, which is essentially a swelling or absorption of
fluid into the tissue will cause the brain to increase in size overall and
these might also not be relevant to your particular research question.
But they certainly could have a significant defect on the outcome.
If you have an infection somewhere in your body,
it could have an inflammation effect in the brain that could alter the volume
of the brain structure that you're interested in.
In a way that's not relevant to your research question.
And finally, I want to mention gliosis, as I mentioned in a previous module,
the brain cells consists of either glial cells or neurons.
And the glial cells support the neurons and perform these housekeeping tasks.
When the glia is affected by disease or some other type of process,
that could very significantly affect the overall volume of that area,
without affecting the neurons.
So you might be measuring a gliosis disease or a gliosis change,
rather than a neuronal change.
So if you think that the volume is a consequence
of a neurodegenerative disorder, but in fact it only affects the glia, you would
potentially draw the wrong conclusion on the basis of that volumetric difference.
So there's many reasons why the brain can change in size and these things must be
kept in mind when you're doing this type of research using structural methods.
In the next module, I'll be talking about some examples of functional MRI studies
and how functional MRI is used to answer scientific questions.
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