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.
Functional Connectivity MRI Studies
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Del curso dictado por Johns Hopkins University
Fundamental Neuroscience for Neuroimaging
247 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 the module on fMRI experiments, we saw that an fMRI experiment
is basically taking a measurement from every single voxel in our volume.
And looking at the bulk response within that voxel over time.
So here we see an example of that time series that we then obtain from each and
every single voxel in the brain, over time.
The goal then is to look at that time series and
compare it with the on and off conditions of our experimental task.
We've talked about the finger tapping experiment where we comparing period
of tapping with periods of rest.
And the goal then is to look at the time series activation of the bolt signal.
And look for areas of the brain where during the tapping condition
the activation is high and during the rest condition the activation is low.
If we find such a brain area, the assumption then is that that brain area
shows a high degree of correlation between the task conditions and the activation.
And therefore must be critically important or
at the very least involved in the performance of that task.
Functional connectivity MRI is essentially a special variant of fMRI.
It is based on the premise that the brain activation is observed under any
condition, even in the absence of any external task demands to or stimuli.
Examining the brain during rest when it's not engaged in any type of cognitive or
motor task can be very useful in assessing the functional organization of the brain.
Completely separate and independent from cognitive or behavioral performance.
Functional connectivity MRI is based on the Hebbian principle that states that
when cells fire together or fire in synchrony, they must wire together.
In this particular case, the assumption is that when two cells or
two fractals in the brain show a similar pattern of activation,
a correlated pattern of activation.
That those two voxels must be functionally connected,
that they must contribute to the same task or be part of the same brain network.
So a functional connectivity MRI experiment then
collects time series from every voxel in the volume.
And looks for
correlations between the time series, in different locations in the brain.
So here I show an example of three different voxels from which we measure
a time series.
If those time series show a high degree of overlap,
the assumption is that these three voxels are part of the same brain network, or
are at least functionally connected to each other.
Now, the correlation could obviously not only be a spatial correlation, but
also a temporal correlation.
If there's a small time offset between the time series activation, we can
still consider those to be functionally correlated, just separated in time.
So in a functional connectivity MRI experiment,
you essentially collect over time a number of time series for
each voxel, for each location in the brain, over time.
And look for correlations both spatially and temporally to determine
what areas of the brain show high degrees of correlation of activation.
And therefore are considered to be functionally connected with each other.
In this analysis,
we can take what's called a voxel to voxel connectivity approach.
Where we look at the time series from a single voxel in the brain, and
look to all other voxels in the brain for a correlated levels of activation.
So we pick one single voxel in our volume as the source.
And then do correlations of every other voxel in the brain, their activation with
that source voxel to look at voxel to voxel connectivity.
So on the right-hand side here,
you see an example of such a resulting correlation map.
Where the color of the voxel indicates the degree of correlation
with your originally selected voxel.
An alternative approach is to use a seed based connectivity analysis.
In a seed based connectivity analysis a region of interest is selected,
either based on anatomy or functional task performance.
The area is defined by usually overlaying a sphere, and
then taking the average measurement of the time series within that sphere.
So usually a sphere consists of a number of voxels that have time series
associated with it.
You take the average of that time series within that region of interest,
within that seed.
And then you're looking for other areas in the brain that show
activation that is correlated with the activation in your seed region.
On the right hand side, I'm showing an example of functional connectivity
analysis based on the seeds on the left motor cortex and the right motor cortex.
And clearly, you can see here, a very strong correlation and
activation between these two pieces of cortex.
The bottom graph shows a measurement from the left motor cortex,
compared to the left visual cortex.
And here, as expected, we see a low degree of correlation between the time series.
And the assumption there is that these are functionally not, or
at least functionally less connected with each other.
And that the left motor and
the right motor cortex show a high degree of functional connectivity.
You can base these seeds on anatomical regions by overlaying
an atlas on top of your structural scan as we've seen in one of the previous modules.
Performing a segmentation of the structural volume,
identifying brain areas or structures of interest.
And then calculating the average time series for
that brain region, that anatomically defined brain region.
The blue and red arrow here indicate the left and
the right motor cortex respectively.
And the top right shows the time series, the average time series
that is observed in terms of functional connectivity between these areas.
The very bottom then shows a scatter plot of all the individual measures from all
the individual subjects of the correlation between these two brain structures.
Showing a high degree of correlation and activation between the right and
the left motor cortex.
A slightly different approach is by looking at functional connectivity data
and trying to determine how many independent components or
independent networks of correlations can be observed.
This is referred to as independent component analysis
of resting state fMRI data, or functional connectivity MRI data.
So you're looking for
networks of brain areas that show a high degree of correlation.
That are dissociable from other networks that in and
of themselves show high degrees of correlation.
And you're looking then for the maximum number of independent or
orthogonal network components that you can discover that away.
Ones that are very commonly observed are listed here.
And they include higher order visual motor processing, lower order visual processing.
Motor control as you would expect to see in a button pressing task or
a finger tapping task, a vigilance network which has to do with attention or
attending to the task at hand.
Error monitoring and inhibition, response inhibition,
often shows a network of brain regions associated with that.
And finally brain areas that have been associated with visual monitoring.
A very specific example of one of those networks that you can observe
using independent component analysis Is called the default mode network.
The default mode network consists of a number of brain regions
with highly correlated activation when a person is not engaged in a task but
instead they're resting.
Very often, you will see involvement of the posterior singular cortex,
as you can see on the right-hand side image.
The medial pre-frontal cortex as well as areas in the temporal lobe.
The posterior singular cortex is very often considered a critical hub
of the default mode network.
This default mode network is relevant,
because it tends to be less active during the performance of an external task.
So again, during rest activation in this network is high
when a person is engaged in an externally started initiated task.
Activation of the default mode network tends to be lower.
The default mode network is developmentally established.
In very young children, this correlated activity is typically not observed.
But as children get older particularly between the ages of 9 and 12, we see
this correlated activation emerge in the brain in the same brain areas.
And that persists throughout adulthood.
There's also great similarity between the rodent and
the primate brain In regions involved in the default mode network.
Or brain activation patterns that we see during rest.
The areas that you see here in the rat default mode network
are very similar to those observed in the monkey, which again,
show a great degree of overlap, with the human default mode network.
So there's great similarity and overlap between the rodent and
the primate brains in the default mode network.
And it's a very robust observation, it's a very robust finding.
The default mode network is a functionally connected network.
But in the case of the default mode network,
it also shows a great deal of overlap with structural connectivity.
The default mode network is thought to support comprehension of information and
learning and memory.
But it is also thought to be critically important for and for
the neurological basis of self, self reference ability,
our ability to obtain autobiographical information, etc.
It's also thought to support our ability in thinking about the past and the future.
So it allows us to use information from our memory to think about the past, but
also extrapolate us to what might happen in the future.
And finally it's also thought to be in support of the theory of mind,
social cognition as well as emotion.
So our ability to refer to ourselves, to evaluate our own performance.
To evaluate our own standing relative to those around us in terms of social
cognition and to imagine the perspective of the other person.
So the default mode network seems to be involved in a number of different
critical functions.
And a lot of study is still continuing to figure out exactly
how the default mode network supports these behaviors and these abilities.
In terms of its relation to tasks,
the default mode network seems to be correlated
with successful memory encoding when the default mode network is deactivated.
So, again, the default mode network tends to be active during rest and needs
to be deactivated in order to engage in an external stimulus or an external task.
The level of deactivation of the default mode network seems to be correlated
with successful encoding.
Deactivation of the default mode network also seems to be correlated with the task
difficulty.
In that the more difficult, or the more demanding the external task is,
the greater the deactivation of the default mode network.
And finally, activation of the default mode network
after we learn a particular piece of information tends to improve retention and
later recall off that information.
So in terms of connectivity studies,
the basic premises that we collect function MRI data during rest.
Typically this is done by asking the participant to stare at a plus sign on
the screen, not fall asleep, but also not think about anything specific.
Try to be as restful as possible.
We then use the resulting data to look for correlation patterns between
time series obtained from each of the voxels within that volume.
And use either brain structures or clusters of activation resulting from
task-based fMRI to look for brain networks associated with that structure.
Or that cluster of activation resulting from the task.
We can assess connectivity as a function of task-based performance.
And finally, we can compare connectivity between participant groups.
And just to give you an example of those last two categories.
Here I'm showing an example of a study where they compared connectivity
with the superior temporal cortex between cases with Alzheimer's disease,
cases with frontotemporal dementia and healthy control subjects.
And what we see here, we see altered functional connectivity of the superior
temporal cortex with the cuneal cortex to supracalcarine cortex.
The intracalcarine cortex, and the lingual gyrus as a function of Alzheimer's disease
or frontotemporal dementia when compared to healthy control subjects.
In this slide, I'm showing an example of relating
connectivity measures with task performance outside of the scanner.
So by observing differences in default mode network deactivation or changes in
default mode network deactivation between young and older adults.
We see that that's strongly correlated here with task performance.
In that younger adults show a greater deactivation in the default mode network,
which is then associated with better performance on the memory task.
Older adults show less deactivation of the default mode network,
which is then associated with poor performance on the task.
So here's an example where functional connectivity data is
used to compare two groups, old and
young adults, as well as correlated with behavioral performance on a task.
Connectivity studies are used in a great number of different contexts.
And connectivity and default mode network changes have been reported in a wide range
of diseases and disorders.
Including Alzheimer's disease, autism, depression, schizophrenia, aging,
epilepsy, Parkinson's disease, obsessive compulsive disorder, and anorexia nervosa.
So, all these studies, essentially look at brain areas that are critically
important or associated with the disease.
And then look for network changes that result from
connectivity measures and connectivity differences between those brain areas and
others in these patient groups.
These types of data are also used to examine individual differences.
There have been a number of studies that have shown
that individual variations in default mode network deactivation or
individual variations in functional connectivity between critical brain areas
is associated with individual differences in task performance.
And in some cases even personality or other types of behaviors.
So functional connectivity studies are a special variant of task based
fMRI studies.
And significantly contribute to our ability to study the brain
using these different approaches.
And functional connectivity studies are particularly important for
network approaches.
In the next module, we're going to talk about structural connectivity studies
based on diffusing tenture imaging.
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