Hi, recap first: if you remember we have downloaded the NYU dataset in previous videos of this
series, and we have imported that into CONN and preprocessed that data and denoised it.
We are now ready to run first-level analyses, which is basically to say we are ready to
compute functional connectivity measures for all of these subjects.
In this example today we are going to show how to run seed-based correlations.
Seed-based correlations is basically when you have one or more seeds or regions that
you care about, and you want to compute the functional connectivity between these seeds
and the rest of the brain.
And to do that in CONN you just create a new analysis, you just click here on ROI-to-ROI
or Seed-to-Voxel analyses.
This is the default setting, we are going to rename this analysis to something more
representative, we are going to compute seed-based correlations for network ROIs, I am going
to name it networks seed-based correlations.
Ok, and now this is where we define the type of analysis that we want to run, and here
we are going to looking at the results in real time.
We are going to use the functional connectivity weighted measure, we going to keep this to
both ROI-to-ROI and Seed-to-Voxel analyses.
These options are also fine, we are going to be looking at correlation-bivariate measures,
and we are not going to be using any weighting because this is pure resting state.
And now we are going to change the seeds.
By default it comes with all of the seeds selected.
In this example I just want to select the ones that are the "network" seeds.
This is a number of networks regions that come by default in the CONN toolbox and they
define the Default Mode network and a few other networks of interest.
And those are the regions that I want to use as seeds, for example if I click here on the
MPFC seeds this is part of the Default Mode network I should be seeing here typical default
mode network activation.
This is the functional connectivity between this seed and the rest of the brain for this
subject, and you can browse through different subjects and you would expect to see something
that looks like the Default Mode network.
This is PCC, MPFC medial prefrontal cortex, and this is lateral parietal.
This is the Default Mode network.
And that should show relatively consistently across all the subjects.
If you click anywhere here you can actually see the activation timeseries for that voxel.
This is the timeseries here at the bottom, and at the top you will see the seed that
you have selected.
So the areas that show high correlation basically means that these timeseries are very similar
and the BOLD activity covaries between the two regions.
Ok, and this is everything that you need to do for seed-based correlations.
Now that you are ready to run the analysis again click 'Done' and click 'Start'.
And this will take a little bit so just wait for a few minutes and you will have your results
CONN has taken us to the second-level results tab, this tab, where you can actually look
at the seed-based correlation maps that we have just created across all of the subjects, and
do any type of analysis across subjects that we might want to do.
I will talk about this, how you define what type of second-level analysis you want to
do and I will talk about this in more detail on another video, but today I just want to
show you how to look at, just create a few figures looking at these seed-based correlations
across all of the subjects just looking at the average effect across all of the subjects.
So in order to do that just select 'All Subjects' here, select the 'rest' condition, and then
select the seed that you care about.
Here you can look at different seeds.
This is all of the seeds that we have computed.
For example this is the Salience anterior Insula seed, and these are Default Mode network
seeds.
And you can select several of them if you want to just look at all of them simultaneously.
Ok, and then to look at these results in more detail you click here on 'results explorer'
and what this is doing is actually computing this second-level analysis in SPM, in this
case it is just computing a one-sample t-test looking at the average effect across all of
the subjects and looking at the statistics of that, giving you which regions are significantly
associated, significantly positively correlated with the MPFC region, DMN.MPFC region, and
which regions might be negatively correlated with that region.
And it is doing that using the combination of a heigh threshold that you define here,
and a cluster threshold that you define here.
We are looking at only positive results, only regions that show positive correlations but
you can change that to 'two-sided' to look at regions that show either positive or anti-
correlations with MPFC, and we are looking at parametric statistics.
Once you define the thresholds here you can then select any of the display options here
to look at those and create displays for those results.
For example this is a slice display where you can look at the individual slices for
this individual results.
So these are again MPFC average results across all of the subjects, and you can look at individual
slices, and this is how it looks.
Ok, another useful display is this 'surface' display, where you are taking these results
and you are projecting those results onto the cortical surface, and then displaying
that on an inflated version of that surface.
You can actually switch, so this is basically how it looks, this is the MPFC activation,
and you can choose which surface you want to display that on.
This is the actual gray matter surface, you want to typically use some semi-inflated
or inflated version of this for better being able to look into the gyra on these regions.
Ok, and that is all.
In today's video we have computed seed-based correlations for a number of seeds, and for
all of our subjects, and then we have created a couple of figures showing just the average
SBC maps across all of our subjects, using a one-sample t-test and thresholding that
using statistical thresholds.
Thank you very much for listening