View source for Freesurfer BOLD files

Freesurfer, like SPM, can perform functional analyses on fMRI data. These analyses generally employ the general linear model (GLM) to assess the degree to which the blood-oxygen dependent (BOLD) signal correlates with different predictors (e.g., group, experimental condition, task or even nuisance regressors such as motion).

For Freesurfer and SPM, these files will start off as NIfTI (.nii) files. Each file contains a header, which contains important information directing the software how to interpret the data, and one or more 3D matrices of raw data, representing the signal at each voxel at a particular point in time, as measured by the scanner during the experiment. The data for a single run (e.g., a 5-minute scan of an experimental task) can be stored as a series of individual 3D volume files, with each file representing a single scan of the brain like a single frame of a 5-minute movie. Alternately, multiple volumes can be concatenated together and stored in a single 4D file. Whereas SPM operates on either 3D or 4D data, Freesurfer seems to require 4D data.

The header information is important because it provides the context required to interpret the data matrix. For example, an experiment looking at hippocampal processing might use a 32 x 32 x 24 array of 1mm voxels and look at a relatively small subcortical volume. Another experiment looking at global cortical processing might use a 32 x 32 x 24 array of 3mm voxels to look at the entire brain volume. Without knowing the voxel size, it would be impossible for any piece of software to interpret the data matrix. The NIfTI header includes information about how big the voxels are, the orientation, and many other important bits of information, though not all software makes use of all the header information.

== File Fixing ==
Freesurfer expects the BOLD data to be organized into individual folders for each experimental run, and that each file is called "f.nii". Moreover, Freesurfer (but not SPM) appears to make use of the TR value stored in the NIfTI header. In order to ensure that the filename requirement is met and that the TR value is correctly set, a script called <code>set_TR.m</code> can be found in the ubfs Scripts/Matlab folder. Another issue, specific to our scanner, is the fact that the Toshiba scanner at the CTRC does not (currently) do anything to account for the magnetic field inhomogeneity that invariably accompanies the first several seconds of a functional run. Other scanners (e.g., Siemens) automatically discard the first several seconds of data. Because the BOLD data from the CTRC will have this magnetic field inhomogeneity artifact, we must manually drop the affected volumes and also remember to adjust the onsets of the experimental events accordingly.

===Golden Rule===
'''Never''' directly modify the BOLD data in the raw files. Instead, copy the raw data to your working directory and work on copies of the data. This avoids the potentially catastrophic possibility of procedures being applied multiple times to the same data, resulting in data loss or mangling.

Note that certain operations are fine to apply to BOLD data because they don't change the data itself, but instead just make changes to the ''NII'' file headers that are easily detected and can easily be reversed. The only allowable change that you might consider doing to the raw data, however, is to set the TR.

=== Determining which volumes are important ===
Before you drop any volumes (the next step), you should be aware of the volumes that are associated with any events. For some of our studies, this may be constant across participants, and therefore something that a lab member familiar with the study can tell you. However, a MATLAB function called <code>critVols</code> can be found in the ubfs/Scripts/Matlab folder that can also provide this information. Copy it to your MATLAB path if you haven't already done so. This function requires the participant's PsychToolBox MATLAB runtime files -- the same files that are used to generate .par files. The simplest way to run the script is to call it with no parameters:
 v=critVols(); %assume the default TR of 2.047
The function assumes a TR of 2.047, which is correct for the LDT and Multisensory Imagery study. 
If you are using it for any other study, you may need to specify the TR:
 v=critVols('TR', 2.00); %overrides the default TR
As usual, there is a built-in help
 help critVols

After you run this function on a set of .mat files, you will get a list of start and end volumes for each of the associated runs:
 v{:}

Below is the information for the 6th run for participant 202, who participated in the LDT study: 
 ans = 
 
         run: 6
     subject: '0202'
    startvol: 5
      endvol: 242

This would tell us that nothing happened during volumes 1:4 and during the volumes from 242:end. You will almost certainly want to drop the first 4 volumes. Depending on your requirements, you may wish to drop the volumes at the end as well, but consult with a knowledgeable lab member first, if you have any question (it is essential that all data sets are treated the same way within a single study).

===Fixing Files Using Drag and Drop with MRICron ===
NIfTI (.nii) files collected from DENT may have the incorrect number of volumes listed in the file header. Though the run often terminates before the 256 volume maximum has been reached, their system nonetheless indicates that the 4D volumes have 256 frames. If you open up the headers using fsledithd, you will receive a warning, and fsl will automatically pad the end of the 4D volumes with empty data frames (volumes containing only zeroes) until they reach the requisite file size. It turns out that the files with partial data frames are problematic for the motion correction for reasons that you will appreciate when you follow these directions.

====Copy the RAW BOLD Files====
The first step will be to copy the 4D BOLD data from the master file archive into separate directories. The original files will not be uniformly named, with names like <code>334_BOLD_run_1_4_1.nii</code>. Copy each file into its own 3-digit subdirectory to indicate the run number, and rename each file to <code>f.nii</code>, e.g.:
*001
**f.nii
*002
**f.nii
* etc.

====Expand the BOLD Files====
You will need to use the terminal for this part. In each of the run directories, use <code>fslsplit</code> to convert the single 4D f.nii files into a series of 3D files that will be named <code>vol####.nii.gz</code>. 
 fslsplit f.nii
At this point, if there is a mismatch between the number of time points in the file header and the amount of data stored in the file, a number of empty volumes will be generated, up to the number of volumes indicated in the file header. For example, if the file header indicates that there are 256 volumes but the MRI stopped recording after 210 volumes, the files named vol0210.nii.gz onwards will be dummy volumes. Note that the file numbers start at 0000. This is important to bear in mind: <strong>The file number will be one less than the volume index</strong>. For example, volume 1 is numbered vol0000.nii.gz. Likewise, the 256th volume is numbered vol0255.nii.gz.

After you've expanded the files, delete the original f.nii file:
 rm f.nii

====View Data Frames in MRICron====
Installed on your workstation is a handy MRI file viewer called MRICron. Launch it (it may try to reload the last file you viewed -- just let it do whatever it wants). Now that you have the MRICron viewer window open, you can drag and drop .nii files directly into the viewer. If you have the Linux File Manager window open to a directory containing your expanded 4D volumes, you can drag any of these files into MRICron to find where the dummy data starts. Participants in the imagery study usually finish somewhere around volume 200-215, so you can test random files somewhere in that range. You're looking for the volume with the highest number where you can see an entire volume of BOLD data. If you want to see what I'm talking about, try loading in vol0255.nii.gz and you will probably see that the entire MRI volume just appears as a solid gray.  If the MRI was stopped part-way through acquisition, you may also find the brain is partly cut-off. That volume is no good either, but that means you have probably found the first volume that you want to delete (load the volume immediately previous in the sequence and you should find that it contains a complete brain volume).

Now that you have figured out which volumes are the dummy volumes, you can select them in the file manager window and delete them. For example, if you determine that vol0209.nii.gz is good, but vol0210.nii.gz is a dummy volume, go ahead and delete all volumes from vol0210.nii.gz up to the end of the series (i.e., all the way to vol0255.nii.gz)

Finally, while we're here we can also drop the first for volumes, just to remain consistent with how we processed data from the CTRC. Go ahead and delete vol0000.nii.gz to vol0003.nii.gz (remember, the fourth volume is numbered 0003).

====Merge Volumes====
Back to the terminal window, we are going to use fslmerge to recombine the remaining volumes back into a single f.nii.gz file:
 fslmerge -t f.nii vol*
 rm vol* #deletes the individual volume files
 '''#OPTIONAL: Also set the TR for the corrected file. Check with an expert before doing the next two steps'''
 TR_DURATION=2.047  # the duration of the TR 
 3drefit -TR ${TR_DURATION} f.nii.gz # While we're here, we can make sure the TR is precisely correct

Now we're good to go: we have a series of files named ''f.nii.gz'' that all have the first 4 volumes dropped, have the correct number of time points in the header, and have the correct TR.

===Fixing Files in the Terminal===
It turns out there are several useful utilities associated with AFNI and FSL that can let us do everything we need to do in the terminal, without running MATLAB. This is helpful if you want to automate the process with a script, but note also that the method described below doesn't handle 4D volumes where early stopping affects motion correction. Automation would require computing when the last TR of real data was acquired so that padded volumes could automatically be dropped.

===== Do I need to drop any volumes? =====
Run the following command to examine the .nii header for one of your BOLD files:
 fslhd <filename.nii>
The first few lines of the output will look like this:
 filename       f.nii.gz
 
 sizeof_hdr     348
 data_type      FLOAT32
 dim0           4
 dim1           64
 dim2           64
 dim3           29
 '''dim4           220'''
 dim5           1
 dim6           1
 dim7           1
 vox_units      mm
 time_units     s

The key line here is the number of elements in the time dimension (nt). Here we see there are 220 volumes in this file.
Now let's compare to the original data, as it came off the scanner. Go to the raw data on ubfs and run the same command. In this case, we get the following output:
 
 filename       1500-fMRI_BOLD.nii.gz
 
 sizeof_hdr     348
 data_type      FLOAT32
 dim0           4
 dim1           64
 dim2           64
 dim3           29
 '''dim4           220'''
 dim5           1
 dim6           1
 dim7           1
 vox_units      mm
 time_units     s

'''Egads!''' The new file and the original data both have 220 volumes, meaning that the new file has not yet had the first 4 volumes dropped, and so you're going to need to check out the procedures below.

====Setting the TR in the Terminal Using 3drefit====
The AFNI utility, <code>3drefit</code> can do all sorts of things, including the trivial task of modifying the TR field in your BOLD data when you use the <code>-TR</code> switch:
 BASEFILENAME=f.nii.gz #the name of the file to modify
 TR_DURATION=2.047 #the duration of the TR
 3drefit -TR ${TR_DURATION} ${BASEFILENAME}

Running the above command will produce output like the following:
 ++ 3drefit: AFNI version=AFNI_2011_12_21_1014 (Jan  8 2015) [64-bit]
 ++ Authored by: RW Cox
 ++ Processing AFNI dataset f.nii
  + loading and re-writing dataset f.nii (./f.nii in NIFTI storage)
 ++ 3drefit processed 1 datasets
====Dropping Volumes Using dropvols.sh====
Upon learning that fslroi can be used to easily drop volumes in a single step, I wrote a script found in /ubfs/Scripts/Shell/dropvols.sh. You should also find that it's on your path (/opt/scripts). If not, let someone know. The script will drop the first n volumes from the named file.
 '''dropvols.sh'''
 Usage: dropvols.sh <filename> <vols>
Example usage:
 dropvols.sh f.nii 5 #will drop the first 5 volumes from f.nii
Note that even though the original file was a .nii file, the modified file will be gzip compressed, and saved as f.nii.gz (it may be possible to prevent it from gzipping the output, but it's really not worth the effort).

====<span style="color:gray">Deprecated: Dropping Volumes in the Terminal Using fslsplit and fslmerge</span>====
These steps are deprecated as of May, 2020 when I accidentally discovered that a single FSL utility, fslroi, can do this all in one shot. I leave these instructions for posterity.

<span style="color:blue">Dropping the volumes with fslsplit</span>

The FSL utility <code>fslsplit</code> will break up a 4D BOLD volume into a series of 3D BOLD volumes. The default behavior is to produce files named vol'''0000'''.nii.gz to vol'''n-1'''.nii.gz from a 4D volume with n time points. For example, if your BOLD volume was 220 time points, this command would produce <code>vol0000.nii.gz, vol0001.nii.gz, ... , vol0219.nii.gz</code>

Conversely, the FSL utility <code>fslmerge</code> does the reverse.

We can chain these two commands together, deleting any unwanted volumes in between. Suppose we wish to drop the first 4 volumes. These correspond to vol0000.nii.gz, vol0001.nii.gz, vol0002.nii.gz and vol0003.nii.gz. Fortunately, the BASH shell uses regular expressions which have a [https://www.regular-expressions.info/numericranges.html convenient shorthand] for specifying wildcard characters in a range of values. In this case, we will want to delete all the files that start with "vol000" followed by the numbers 0 to 3, followed by ".nii.gz". This works out to the following pattern:
 vol000[0-3].nii.gz

Now we can put this all together:
 BASEFILENAME=f.nii.gz <span style="color:gray">'''#the file we will drop volumes from'''</span>
 DELETE_TO=3 <span style="color:gray">'''#we wish to drop the first 4 volumes'''
 '''#However, since file numbering starts at 0, we stop at 3 (0000,0001,0002,0003) rather than 4'''</span>
 mkdir old <span style="color:gray">'''#a directory to store the backup out of the way'''</span>
 cp ${BASEFILENAME} old/ <span style="color:gray">'''#back up the original file just in case'''</span>
 fslsplit ${BASEFILENAME} <span style="color:gray">'''#break up the original file'''</span>
 rm ${BASEFILENAME} <span style="color:gray">'''#delete the original file'''</span>
 rm vol000[0-${DELETE_TO}].nii.gz <span style="color:gray">'''#delete the files corresponding to first 4 volumes'''</span>
 <span style="color:gray">'''#merge all the remaining vol*.nii.gz files into a single 4D volume'''
 '''#it will gzip the file, producing f.nii.gz. That's ok if it's gzipped.'''</span>
 fslmerge -t f.nii vol* 
 rm vol* <span style="color:gray">'''#delete all the individual 3D BOLD volumes'''</span>
 <span style="color:gray">'''#inspect the new f.nii.gz file using fslhd to confirm the header information is what you expect.''' 
 '''If so, delete the backup'''</span>
 rm -rf old

===Fixing Files in MATLAB (Deprecated)===
Originally, we were using a series of MATLAB scripts to modify the TR and drop volumes from our BOLD datasets. The sections below describe this process for posterity. These methods will still work; they just don't lend themselves as well to command-line scripting.

'''Remember to account for the dropped volumes when creating your [[Par-Files|.par files]]'''

==== Combining critVols with nii_4D_drop_vols ====
Specifying the initial volumes to drop is easy, as it is almost always just the first 4 volumes, 1:4 (1 up to, but not including, the first critical volume). This is usually the case, but make sure you check the startvol from critVols and drop one less than that (for example, if it starts at volume 5, drop 4 volumes). The end volumes might be a bit trickier. Finding the total volumes of each f.nii file is explained above. 

Let's make a variable called <code>tail</code> that lists the indices of the tail end of the run:
 nvols=256; %determined using fslhd
 endvol=242+4; %retains 4 volumes after the last event occurs
 tail=(endvol+1:nvols)-endvol;
initial_drop=5-1 %subtract 1 from the startvol
 nii_4D_drop_vols('./f.nii', [1:initial_drop, tail]);

It is not necessary, but I (rebecca) like to make a little matrix in matlab specifying all these data points. I'll make a column for the bold file numbers, one for start vol, endvol, nvol, and use this. Keeps things more organized and prevents you from having to reference crtiVols and then a reference sheet or fslhd for the nvol every time.

==== Fixing the TR header field using set_TR.m ====
The first requirement is that the source files for a participant be organized into numbered folders as described in the section below concerning file organization. If this requirement is met, you can use the MATLAB set_TR.m function to simultaneously rename the source files and set the TR to the correct value (you will, of course, have to know what the correct TR value is!). Data coming from the CTRC currently have the TR header field intialized to a value of 1.0, corresponding to a 1-second TR. SPM doesn't make use of the TR value from the header field, so this isn't a problem. However Freesurfer '''does''' use this value, so you may want to ensure that the header contains the correct TR value.

If you wish to simply inspect the values in a .nii file header, you can use a command-line FSL utility called <code>fslhd</code>:
 fsledithd 4D.nii

This will echo header information to the screen
 filename       f.nii.gz
 
 sizeof_hdr     348
 data_type      FLOAT32
 dim0           4     <--- 4 Dimensional BOLD
 dim1           64    <---64 voxels in the X (L/R) direction
 dim2           64    <---64 voxels in the Y (Front/Back) direction
 dim3           29    <---29 slices (the Z direction)
 dim4           216   <---216 time points
 dim5           1
 dim6           1
 dim7           1
 vox_units      mm  <--voxel dimensions expressed in millimeters
 time_units     s      <---time units expressed in seconds
 datatype       16
 nbyper         4
 bitpix         32
 pixdim0        0.000000
 pixdim1        3.312500 
 pixdim2        3.312500
 pixdim3        4.100000  <-- voxels are 3.3 * 3.3 * 4.1
 pixdim4        2.047000  <-- time points are 2.047 seconds in duration
 pixdim5        0.000000
 pixdim6        0.000000
 pixdim7        0.000000
 vox_offset     352
 cal_max        0.0000
 cal_min        0.0000
 scl_slope      1.000000
 scl_inter      0.000000
 phase_dim      0
 freq_dim       0
 slice_dim      0
 slice_name     Unknown
 slice_code     0   <-- valid values are 0,1,2,3,4 0="unknown". For slice timing. Should be 4 for CTRC data
 slice_start    0
 slice_end      0
 slice_duration 0.000000
 time_offset    0.000000
 intent         Unknown
 intent_code    0
 intent_name    
 intent_p1      0.000000
 intent_p2      0.000000
 intent_p3      0.000000
 qform_name     Scanner Anat
 qform_code     1
 qto_xyz:1      3.312500  0.000000  0.000000  -106.000000
 qto_xyz:2      0.000000  3.312500  0.000000  -142.328003
 qto_xyz:3      0.000000  0.000000  4.100000  -27.664000
 qto_xyz:4      0.000000  0.000000  0.000000  1.000000
 qform_xorient  Left-to-Right
 qform_yorient  Posterior-to-Anterior
 qform_zorient  Inferior-to-Superior
 sform_name     Scanner Anat
 sform_code     1
 sto_xyz:1      3.312500  0.000000  0.000000  -106.000000
 sto_xyz:2      0.000000  3.312500  0.000000  -142.328003
 sto_xyz:3      0.000000  0.000000  4.100000  -27.664000
 sto_xyz:4      0.000000  0.000000  0.000000  1.000000
 sform_xorient  Left-to-Right
 sform_yorient  Posterior-to-Anterior
 sform_zorient  Inferior-to-Superior
 file_type      NIFTI-1+
 file_code      1
 descrip        FSL5.0
 aux_file

The above output gives information about the voxel dimensions in the x,y and z axes (dx, dy, dz are 3.3125, 3.3125 and 4.1 units, respectively). Those units are interpreted as millimeters, according to the xyz_units field value (2=mm). Similary, for the time dimension field, the value is set to 1 in this header, interpreted as seconds according to the time_units field (8=seconds; 16=milliseconds). Thus, according to this file header, the functional volumes were collected 1-second apart, which is not correct (the TR for this experiment was 2.047). 

These values can be edited using <code>fsledithd</code> by simply changing the dt field value from 1 to 2.047 (if you do so, you would then type ^O (ctrl-O) to save the changes, then ^X (ctrl-X) to exit the editor). However, a MATLAB script exists that will update the fields for multiple files belonging to the same individual.
 
This script, <code>set_TR.m</code> can be found in the ubfs Scripts/Matlab folder. Ensure that this file can be found somewhere in your MATLAB path (e.g., copy it to your Documents/MATLAB folder)

First, open a terminal and start matlab:
 matlab &
Your <code>startup.m</code> script should run automatically, setting your path to include the most commonly used toolboxes in the process. Nonetheless, you should ensure that set_TR and other needed functions are indeed in your path:
 help load_untouch_nii
 help set_TR
If either of these commands return a command not found type of message, ask for help in getting them permanently in your path. It could be simple matter of running a startup script (e.g., <code>startup_spm8</code>).

Assuming that these files are in your path, the next step is to use MATLAB to navigate to the directory containing your participant data. For example, working with a data set on ubfs:
 cd ~/ubfs/cpmcnorg/openfmri/test/1001
Now you are in the directory for participant 1001. There should be a bold subdirectory with one or more numbered directories below it, as described above. Move into the bold directory:
 cd bold
Now you can run the set_TR command:
 set_TR(2.047, [1 2 5 6], '4D.nii')
The above command would look for a file called '4D.nii' in directories 001, 002, 005 and 006, modify the NIfTI header file information to have a TR of 2.047 seconds, and then save the changed files to 'f.nii', leaving the originals unchanged (if your files are already called ''f.nii'', they will be overwritten).

== File Organization==
All FreeSurfer BOLD files for an individual need to have the same name, and be organized in numerically labelled directories. As the example below illustrates, it is not imperative to our usual workflow that these folders be sequentially numbered (e.g., if there is any missing data). By convention, we store the BOLD data in the '''bold''' subfolder in the participant's FreeSurfer folder:
*$SUBJECTS_DIR
**$SUBJECT_ID
***bem
***<span style="color:red">bold</span>
****<span style="color:red">001</span>
*****<span style="color:red">f.nii</span>
****<span style="color:red">002</span>
*****<span style="color:red">f.nii</span>
****<span style="color:red">006</span>
*****<span style="color:red">f.nii</span>
***label
***mri
***scripts
***src
***stats
***surf
***tmp
***touch
***trash

==Handling Multiple Sessions==
When data are acquired over multiple sessions, it is likely that the files will be in very different space. If you plan to analyze all the data simultaneously, you will need to ensure that all your BOLD files are coregistered with each other. 

=== Is There a Coregistration Issue?===
View a template BOLD file from each of your sessions, with the surfaces overlaid. If the surfaces do not line up with the BOLD data, you will have to force the registration to a BOLD file that ''does'' line up with the surfaces:
 #inspect the BOLD/SURF registration for one of the preprocessed files (alternately, could check it for the f.nii file)
 SUBJID=FS_0217
 freeview -v fmcpr.nii.gz -f ${SUBJECTS_DIR}/${SUBJID}/surf/lh.white:edgecolor=green \
 ${SUBJECTS_DIR}/${SUBJID}/surf/rh.white:edgecolor=green \
 -viewport coronal

If there's a problem, it will look something like what is shown in [https://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/MultiModalRegistration_freeview this tutorial]

Assume that your 3D surface was generated from the T1 MPRAGE from one of those sessions. Use the template BOLD file that was acquired closest in time to this MPRAGE image. For example, if a participant was in SESS1 and SESS2, and the SESS2 T1 file was used to generate the surfaces, coregister the SESS1 BOLD files to the first run of SESS2 (in the example below, this is run 007):
 cd 001
 #generate a template BOLD image from f.nii
 mri_convert --mid-frame f.nii.gz template.nii.gz
 
 #What is the reference (target) image to align to?
 TARGET=${SUBJECTS_DIR}/${SUBJID}/bold/007/template.nii.gz
 mri_robust_register --mov template.nii.gz --dst ${TARGET} --lta coregister.lta  --satit

Then apply the ''lta'' coregistration file to the BOLD data:

 mri_convert -at coregister.lta f.nii.gz f.nii.gz

Repeat for the remaining runs from the session that is out-of-alignment.

'''IMPORTANT:''' After you have completed the above steps and the volumes have been roughly aligned, delete all the template.nii.gz files you just created. The preproc-sess command that you will run later on will check whether a template.nii.gz file exists. If this file already exists, the command will skip all kind of important steps that it will infer have already been done! 

 Highly Accurate Inverse Consistent Registration: A Robust Approach
 M. Reuter, H.D. Rosas, B. Fischl.  NeuroImage 53(4):1181-1196, 2010.
 http://dx.doi.org/10.1016/j.neuroimage.2010.07.020
==== Coregister.sh ====
I've written a shell script that coregisters multisession BOLD data to a reference functional file. You will find coregister.sh in the ubfs Scripts/Shell/ folder. It requires that your bold/ directory contains a file called '''runs''' that contains the BOLD subdirectories you wish to coregister. The script will coregister the f.nii.gz files in each of the directories with the f.nii.gz file it find in the first listed directory. For example:
 '''Sample runs file'''
 001
 002
 003
 004
 005
 006
The script takes a list of subjects as a parameter and iterates through all their folders:
 '''sample subjects file (mysubjects.txt)'''
 FS_001
 FS_003
 FS_011
Then to run the script to coregister the functional runs for these three subjects:
 ./coregister.sh mysubjects.txt
[[Category: FreeSurfer]]