Today, I'm going to show you how to run functional connectivity analysis using the CONN toolbox
We're going to be analyzing a resting state data set, in particular
we are going to analyze the NYU test retest dataset, which is publicly available
You're welcome to follow along and do that on your own computer
We're going to go through all of the steps, starting from the actual raw data, functional and structural raw data, and ending with
Seed-to-Voxel connectivity maps for Default Mode network regions. We are going to look at those patterns of connectivity.
So the first thing that we need to do is actually download the data. We're going to look for it actually
You can just look for NYU test-retest download
and search, and you will find the NITRC site where you can download this particular dataset. The NYU
CSC test-retest dataset is part of the 1,000 Connectomes project
and it's publicly available in several locations. You can click on 'see all files' and you will see that
there are six files that you can download. These correspond to different subsets of
subjects and different sessions, because data has been acquired in three different sessions per subject, you have all of the data
available here. But for this particular example we are going to only download one particular sessio: the first session and all of the subjects
So we are going to get data for 25 subjects and a single session. And to do that
we just need to download these two files: the NYU_TRT_session1a and NYU_TRT_session1b
so after you do that you can go to the place where you downloaded the data
and you should have these two files there. These are zipped files, if you
unzip them you will see that they create a folder each of them
and inside of that folder you have the data for the different subjects. For each subject you have an anatomical and a functional
folder containing the anatomical data and the functional data. So this is all that we need, we are going to start Matlab and
what we are going to do here is going to start CONN and create a new project for analyzing the NYU dataset, and
importing all of these functional and anatomical data into the CONN toolbox, and process it and analyze it
So to get started let me just make sure that I have CONN installed in some folder, and I have SPM as well
Ok, I find these so I can so I can just start CONN
The first thing that you want to do when you start a new analysis is to create a new
project, so to do that just click on 'new' and
decide where do you want to start that project. The /data folder is fine for me, I'm going to call it conn_NYU.mat
for this particular project. Ok, and now we are taken to the 'Setup' tab where we can define basic setup information about this experiment.
In particular we have a
continuous acquisition sequence, so this is fine, and the TR
is actually two seconds, so this is fine, the number of sessions is one, and the number of subjects is going to be 25, so we change that
to 25 and hit 'Enter' to feed that information to the CONN toolbox.
Now we are going to enter those actual 25 structural and functional files
To do that you go to 'Structural', and then you find your data. Your data is here in NYU
Remember these folders that contain data for the individual subjects, this is actually the first subject's
anatomical folder, it contains a file that is called mprage_anonymized and one that is called skullstripped
we're going to use the anonymized dataset, so you could actually click here and
enter into the CONN toolbox that particular structural for the first subject, but doing this -and this is the data, this is the
structural data- but doing this for all subjects for 25 subjects is going to be a bit tedious, so we're going to do something else
We're going to look recursively through all of these folders for this particular file: mprage
mprage_anonymized.nii.gz
And now instead of looking at this individual folder for this particular subject
I'm going to go to the root folder and click on 'Find' to find all of the files that have this same name across all of the
folders recursively. CONN is going to find 25 files, so you can actually select those 25 files
and then you select all of the subjects here, and now you have 25 subject selected here and 25 files selected here
so you can just click on 'Select', and that is going to import all of these
structural data files into the CONN toolbox and distribute
them across your 25 subjects
and it's going to
take a second because these are actually compressed
nifti files and CONN wants to uncompress them and look inside of them to be able to display those files for you
When it finishes it tells us that 25 files have been assigned to 25 subjects and if you want you can look at the
individual subjects' data, here this is the first subject structural, second subject... if you want to look at this
across different slices, this is actually the empty anonymization process
removal of
some portion of the skull, and then you can look at the data here
And if you want to look at this in a little bit higher resolution you can click on the 'structural tools' and the 'slice viewer'
And this is going to show this individual subject structural data in a little bit more detail
This is your actual anatomical file
for that subject
Okay, we have the structural data
we're going to enter now the
functional data, and the process is the same, we are going to go to those folders, look at the first subject just to make
sure and remember what is the name of the functional file. They are called lfo.nii.gz
and we are going to recursively search for those file names, so we enter lfo
.nii.gz here, and then go back here to the root folder,
click 'Find', and look for those files across all of the folders, CONN is going to find 25 files
So we just select all of those 25 functional data files, then we select all of the subjects
25 subjects 25 files click 'select'. That is going to import all of these functional files into the CONN toolbox as the functional data
and again it's going to take a little bit because CONN is uncompressing these files to look into them
so it is able to display them and tell you a little bit of information about those files such as the
voxel resolution and the number of acquisitions that you have for each data and things like that
So after this finishes you are going to be able to look at the individual files. They all will be imported here
Okay, it is done, it is telling us that 25 files have been assigned to 25 subjects
So that sounds perfectly fine, let's actually browse through it, this is the first subject, second subject, third subject
for each of these CONN is displaying the first acquisition and the last acquisition, but each of these
functional files actually contains
197 acquisitions
you can click here to actually go to that particular folder if you want and you can click anywhere in the
space here in any particular
voxel to look at the time series the BOLD time series for that individual voxel, so you see that it's actually a time series
containing
197 points/acquisitions. You can click at different positions and look at the actual BOLD signal at all those individual voxels
Ok, one thing that you can do here is select the 'functional tools' and then 'display single slides for all subjects'
Just to see whether our data is actually coregistered and normalized, so we want to look at the same slice
ideally across all of the subjects and if your data is normalized
It's going to look like it's the same slice actually across all subjects
And if is not, then it is going to look like this. When you have not
coregistered or normalized data across multiple subjects
when you are trying to look at the same slice across multiple subjects
you are actually looking at different places in the brain. That just means that your data hasn't been normalized yet
That is one of the things that we are going to do through pre-processing
So we're going to go right to that. We have already
imported the structural data and the functional data
so this is the perfect time to go click on 'pre-processing' to actually pre-process all of these structural and functional data
and preprocessing are a number of steps that are typically done to your data to
get it into MNI space for example to co-register across all of the subjects, to
realign it to correct for potential movement -subject movement-, and do slice timing correction for example that corrects for the different acquisition
times for the different slices in your functional data, to
run ART outlier identification
that actually looks for potential outlier scans, whether that is because the subject may have moved a lot or because there might be some artifacts in the
acquisition
those are all outliers that ART can detect
You typically want to also smooth the data so that you increase a little bit the signal to noise ratio
and you gain a little bit of robustness to anatomical variability in the actual subjects, so
preprocessing steps, you have all of these individual preprocessing steps that you can apply, but you have also all of these things
in bold, which are actually pipelines that are multiple steps that have been
predefined for you.
And the most typical pipeline that most people would want to use is this 'default processing pipeline for volume-based analysis'
This is the pipeline that you want to use if you want to do analysis in MNI space
and you want to do volume-based analyses
Let's just click on that and click on 'Add' to look at the contents of that pipeline, we actually have them here
and these are just the steps that I mentioned: realignment, slice timing correction, ART outlier identification, and then
Normalization and segmentation of the data - I didn't mention segmentation
but this is actually going to
Segment the data into the different tissue classes: white matter, grey matter, and CSF are the typical tissue classes that we care most about
okay, and this is all ready to go if
you can just click on 'Start' and
CONN is going to ask you a few questions depending on the actual steps that you included into
into the pipeline. In the case of the default MNI pipeline it is going to ask us four questions: the first one is what is the
the slice order acquisition for this timeseries, and we know that it is 'interleaved (Siemens)', so I'm going to select that
the second question is how
do we want to
threshold the data in the outlier identification, for potential identification of outliers
I am going to switch this to the 95th percentile
And this is called the 'conservative' settings for ART, and if possible these are the settings that we often recommend
so click 'Ok'. Then CONN is asking us how do want to reslice the data after normalization, and 2mm
sounds perfectly fine
Then how much do you want to smooth the data for spatial smoothing, and 8mm sounds perfectly fine as well
Okay, this all that is needed. Pre-processing takes about 5 minutes per subject
Since we have 25 subjects that means this is going to
take about 2 hours
so this is the perfect time to actually take a break have a
coffee and come back in a couple of hours when this is going to be finished
and we're going to see what that entails, how the data has has been changed by this pre-processing steps and
how that looks in second. Ok, so see you in a few minutes