So let's recap what has happened so far.
In the previous video we downloaded the NYU test-retest dataset.
This is a pure resting state dataset.
And we created a new CONN project where we imported both the functional and the structural
data for this project and we have preprocessed it.
Preprocessing involves realignment, slice-timing correction, outlier identification, normalization,
segmentation, and smoothing.
And now we are going to look at the results of these preprocessing steps and just make
sure that everything looks fine, and how to evaluate that, and then we are going to move
on to Denoising.
So let's do that.
First, this is the functional data.
Now it looks a little bit blurry, and this is because it has been spatially smoothed.
Another thing that you should notice is that if preprocessing worked fine then the data
should be in MNI space.
So we repeat the previous step of just displaying the 'single-slice for all subjects', now we
should be seeing what appears to be actually the same slice across multiple subjects because
the different brains are in the same spatial location, so the same slice should correspond
to the same place in the brain across multiple subjects.
And this is what we are seeing here.
Another way to evaluate this same thing is to look at this display in 'functional tools'
select the one that is called 'slice viewer with MNI boundaries' and then select 'dataset
1' just to display the unsmoothed functional data.
Then you are going to look at the functional data overlaid with the MNI boundaries.
MNI boundaries represent the 25% boundaries of where the gray matter should be or would
be expected to be if the brain was actually on MNI space, and you should see a reasonably
good match between the MNI boundaries of the brain and the actual functional data after
it has been normalized.
So this is how it looks, and you can repeat all of these plots for every subject and this
is what the Quality Assurance plots, if you click here, allows you to do, basically create
all of these quality assurance plots in a simpler manner and evaluate them in a very
simple manner.
But we will see that another day, today I just want to quickly browse through how the
data looks after normalization.
This is how the structural looks, the structural also has been normalized and that means that
it that the skull has been stripped and all of the brain are in the same space.
If you look across brains, again you can select the 'structural tools', 'display single-slice
for all subjects', to look at the same slice across multiple subjects, and that should
roughly represent the same position in the brain for all of the subjects.
You can also select 'slice viewer with MNI boundaries' to display for that individual
subject the MNI boundaries and how they look with respect to the actual structural data.
And that should also give you a very good match between those boundaries on the template
and the actual structural data for that subject after it has been normalized.
Ok, so this is both the structural and functional data normalized.
In addition to actually normalizing, and correcting for motion, and all of these things to the
functional and structural data, there are other three sources of information that have
been changed in your CONN project automatically by the preprocessing step.
The first one is in 'ROIs'.
CONN has created now three ROIs for you, they are called 'Grey Matter', 'White Matter',
and 'CSF', and they contain these three tissue classes for each subject.
This is a mask of gray matter voxels for subject one, this is subject two, subject three, and
all of these have been created by the segmentation step as part of preprocessing.
You can again use 'display single-slice for all subjects' and that that should give you
a good match between the gray matter voxels across multiple subjects.
This is what we are seeing here.
And the same for white matter and CSF.
These are the other ROIs that come by default in CONN, and this is the Harvard-Oxford atlas
ROIs, and this is a set of network ROIs derived from Independent Component Analyses.
These are not subject-specific ROIs, these are actually in MNI space as well.
Ok, this is the first thing that has been added by the preprocessing steps, these three
masks.
Another thing that has been added to your project automatically by preprocessing are
these first-level covariates.
The first one is called 'realignment' and that contains the actual subject movement
parameters estimated across time, so these are timeseries representing how the subject
moved within the scanner, and these are different of course for each subject, and these have
been estimated by realignment.
Then you have these Quality Assurance timeseries that represent two things: the first thing
is how the global signal -the BOLD signal- in the brain is changing from one scan to
the next.
This is a measure to identify potential outliers, places where there are a lot of changes in
the BOLD signal between one scan and the next are good indicators that something went wrong
with those scans.
And the second timeseries is the Framewise Displacement.
This is a measure of how much the subject moved from one scan , from one acquisition,
to the next.
So also, again, high values represent a lot of motion between two adjacent scans and that
is a good indicator of potential outliers.
And 'scrubbing' is actually the identification of potential outliers in these two timeseries.
This is the result of ART, the outlier identification step.
It basically identifies a few scans that may have been potential outliers because they
have either a lot of motion in the subject or a lot of global signal changes.
And the last thing that has been added to your project automatically by preprocessing
has been these second-level covariates.
You have now a number of Quality Assurance covariates that represent different aspects
of the quality of the data for each subject.
This is the number of valid scans, how many valid scans remain after the outlier identification
step has identified potentially-invalid scans.
This is the opposite, the number of invalid scans.
So basically you want this to be roughly constant or relatively similar across all of the subjects.
If you see a subject that has a very low value compared to others that is a potentially problematic
subject to take into account.
Then you also have 'MaxMotion' and 'MeanMotion', these are two measures that have to do with
how much each subject moved; these are a number per subject, these are your 25 subjects here,
'MeanMotion' is how much the subject has moved on average, while 'MaxMotion' is what was
the maximum displacement of that subject in the scanner.
And then you have two Quality Assurance measures that tell you information about the global
signal changes, what is the maximum global signal change across the scanner and what
is the mean global signal change across the scanner.
So again, if you see outlier subjects that moved a lot or had a lot of global signal
changes those are potential identification of subjects that may have some problem in
the data and you may want to look at them in more detail, or you may want to use these
covariates as control covariates to try to correct for potential differences between
subjects in the amount of motion or in the amount of global signal changes.
Ok, so all of this has been automatically done by 'Preprocessing'.
At this step the only thing that may remain if you have a more complex design is to define
your experimental conditions.
If you have a pre-post design, or if you have a task-design, where you may have different
tasks within the scanner, you may want to specify them here.
This is a pure resting-state data and we have a single session, so we don't have anything
else to do right now, we are defining a single condition in our data, we are calling it the
'rest' condition, this is by default, and it encompasses the entire timeseries.
We are ready to go.
We have already looked at the structural and functional data and they seem fine, no reason
to be concerned so far, so we can move on to the 'Denoising' step.
To do that we just click 'Done' here, and then click 'Start'.
And what this is going to do is it is going to perform a little bit of erosion on your
white matter and CSF masks, it is going to extract the ROI signals for each subject for
all of the ROIs that we have defined, that includes both the white matter, CSF, and grey
matter, but also the Harvard-Oxford atlas ROIs as well as the network ROIs that we have
defined.
And this is going to get all of our data ready for denoising, this process takes about less
than a minute per subject, so in this cases it is going to take about 15 minutes.
So let's speed time a little bit and move on to the Denoising step.
So here we are at 'Denoising'.
Denoising is where we clean up the data to get it ready for functional connectivity analyses.
Unlike all of the preprocessing steps which are really common steps that are applied to
all functional data or fMRI data whether it is for functional connectivity analyses or
for functional activation analyses, Denoising is very specific to functional connectivity
analyses.
These are a set of techniques that have been developed over the last few years, and have
been proven to improve the quality and the replicability of functional connectivity measures,
and they have been used almost exclusively in the context of functional connectivity
analyses.
And what denoising does is to address a number of residual physiological and motion effects
that are present and very salient and very clear on the data even after all of the preprocessing
steps.
So let me show you that with this example.
If we take this data that came from preprocessing and look at the BOLD signal across multiple
voxels and we compute functional correlations between every pair of voxels, and we just
compute a histogram for the distribution of those correlations we will get something like
this gray histogram.
This is a histogram that shows that most voxels are highly correlated, everything is correlated
with everything in the brain, so basically the mean is close to point eighty-something
or .9 and every voxel shows very high correlations with everybody else.
And if you look at that across subjects, different subjects are going to show different distributions.
These are all the gray distributions that we are looking at.
Some subjects show incredibly high correlations between every voxel and every voxel, while
other subjects do not, and that changes a lot the shape and size of those distributions.
And why is this happening?
This is happening mostly because of residual physiological and subject motion effects.
There are a number of residual effects that are global in nature, and when you have a
global effect that is affecting your BOLD signal you are going to have a lot of bias
in your functional correlation measures because the correlations are going to reflect the
contribution of these global effects, rather than the more subtle functional connectivity
effects that you want to look at.
So you really need to remove these very large and very salient global effects from the signal
before being able to move on successfully to getting clean and meaningful functional
connectivity measures.
One of the methods, well there are many methods that have been developed and there are many
methods that are available in the CONN toolbox to do so, but the method that comes by default
with all of the settings is called anatomical CompCor, and what it is doing is extracting
a representative noise signal from white matter regions and a representative noise signal
from CSF, and it is going to be removing anything that correlates with those noise components
from the BOLD signal for every voxel.
And it is also going to remove anything that correlates with the realignment parameters,
these are the subject motion parameters that were estimated during realignment, and also
it is going to remove anything that correlates with the scrubbing covariate, so basically
this effectively takes out those outlier scans.
This is called 'scrubbing' and it is removing the influence or the effect of those individual
scans that have been deemed to be potential outliers.
And all of these are removed in a single linear regression step, and the residuals of that
linear regression are going to be your clean signal, without the effect of all of these
noise effects.
And you can see that the effect that that has on the functional connectivity measures
is a very beneficial one, because now if you look at this across different subjects, this
is the distribution after denoising of the voxel-to-voxel correlations, that looks now
very similar across all of the subjects, and it looks reasonably centered around zero,
which is what you would expect.
So basically you see distributions around zero and distributions that are very similar
across subjects.
All of these huge differences between subjects influenced by noise effects have been removed.
So this is how you evaluate whether denoising is doing what you would want it to do.
You look across subjects and if these distributions look reasonably centered and they look reasonably
similar across subjects then you are fine.
You can also look at these 'carpet plots'.
This is showing the BOLD signal timeseries, this is the time dimension, and this is voxels,
and you can see these patterns.
When you see these patterns that means that there are still some residual global effects
on the signal.
This is before denoising, and this is after denoising.
So, basically, after denoising you should not see any patterns, and this is again an
indication that everything is working fine.
And you can do that across multiple subjects, to see whether that is working as you would
expect.
You can also click on 'Quality Assurance plots', and see these histograms automatically computed
and compared superimposed across all of the subjects, to see whether that is working as
you would expect.
And this is the distribution of those voxel-to-voxel correlations before denoising, and this is
the distribution after denoising.
So in this case, you typically want to have distributions here that are similar across
all of the subjects, and centered.
And this is roughly what we get.
We get one example here, this is subject 24, that seems to be a little bit different from
the rest of the distributions, so we are going to pay a little bit more attention to that
subject and see what is going on with that.
Just as an example, it is not a really big departure from the rest of the subjects, but
just to show you what to do in those cases.
Let's say that we do care about that, this is a little departure from other subjects,
it shows a little bit of bias towards positive values, and if you look at the carpet plots
they show a little bit of a pattern here in the 'after denoising' plots.
So you might want to worry about that, it is not really huge but if you worry about
that how would you fix it? typically you can try changing the denoising parameters to improve
that.
The most typical thing that works in these cases is to increase the number of white matter
or CSF components that you extract from the white matter and CSF regions.
Let's go for example to change this and instead of 5 components let's extract 10 and see whether
that has an effect.
And there we go, you see that now after denoising this is a little bit better centered, so let's
see if whether that generalizes to other subjects or not.
So let's create the Quality Assurance plots again and look at the distribution of the
voxel-to-voxel correlations across all of the subjects.
And there we go, now we see that that individual subject that was a bit off from the rest seems
to have been corrected, so this looks perfectly fine, and this is a good indication that your
data is possibly as good as it can be and it is all ready for doing functional connectivity
analyses.
Another thing that I am going to change here, just to show you how to and because this is
something that I typically do, I typically care about the high frequency of the signal,
just because I am interested about what is going on at those higher frequencies, so even
though the default band-pass filtering options in many functional connectivity analyses is
set to focus on the frequency range of 0.01 Hz to 0.10 Hz approximately, I want to change
that and use just a high-pass filter, so I can look at higher frequencies as well, so
I just change this to 'inf', I am going to change that to 0.01 to 'inf', and that is
the range that I want to look at of higher frequency.
That also gives me much more degrees of freedom from my data so that improves a little bit
the quality of the denoising and connectivity estimation.
So that is all that is needed here, when you are satisfied with how everything looks here
you can just move on and click 'Done', and then click 'Start'.
And that is going to apply this denoising procedure to all of your subjects, and it
is going to take just about 10 seconds per subject, so overall it is going to take about
5 minutes on this particular dataset.
And that is going to take us to first-level analyses and second-level results, where we
are actually going to start computing functional connectivity measures, and looking at those
measures across multiple subjects and seeing how those look.
So, thank you very much for listening and see you in the next video