The XCP-D workflow takes fMRIPRep, NiBabies, DCAN and HCP outputs in the form of BIDS derivatives. The outputs are required to include at least anatomical and functional outputs with at least one preprocessed BOLD image. In these examples, we use an fmriprep output directory.
Command Structure =============== The exact command to run in xcp_d depends on the Installation method and data that needs to be processed. We start first with the the bare-metal Manually Prepared Environment (Python 3.8+) installation, as the command line is simpler. xcp_d can be executed on the command line, processesing fMRIPrep outputs, using the following command-line structure, for example: :: $ xcp_d <fmriprep_dir> <outputdir> --cifti --despike --head_radius 40 -w /wkdir --smoothing 6
However, we strongly recommend using Installation:Container Technologies. Here, the command-line will be composed of a preamble to configure the container execution followed by the xcp_d command-line options as if you were running it on a bare-metal installation.
If you are computing locally, we recommend Docker. See Installation:Docker Installation for installation questions. :: $ docker run --rm -it -v /fmriprepdata:/data/ -v /tmp/wkdir:/wkdir -v /tmp:/scrth -v /tmp/xcpd_ciftiF/:/out pennlinc/xcp_d:latest /data/fmriprep /out --cifti --despike --head_radius 40 -w /wkdir --smoothing 6
If you are computing on a HPC (High-Performance Computing), we recommend Singularity. See Installation:Singularity Installation for installation questions. :
$ singularity run --cleanenv xcp_d.simg \
path/to/data/fmri_dir path/to/output/dir \
--participant-label labelRelevant aspects of the $HOME directory within the container. By default, Singularity will bind the user's $HOME directory on the host into the /home/$USER directory (or equivalent) in the container. Most of the time, it will also redefine the $HOME environment variable and update it to point to the corresponding mount point in /home/$USER. However, these defaults can be overwritten in your system. It is recommended that you check your settings with your system's administrator. If your Singularity installation allows it, you can work around the $HOME specification, combining the bind mounts argument (-B) with the home overwrite argument (--home) as follows: :
$ singularity run -B $HOME:/home/xcp --home /home/xcp \
--cleanenv xcp_d.simg <xcp_d arguments>Therefore, once a user specifies the container options and the image to be run, the command line options are the same as the bare-metal installation.
Command-Line Arguments =============== .. argparse:: :ref: xcp_d.cli.run.get_parser :prog: xcp_d :nodefault: :nodefaultconst:
XCP-D can implement custom confound regression (i.e., denoising). Here, you can supply your confounds, and optionally add these to a confound strategy already support in XCP-D. Here we document how to regress task block effects as well as the 36 parameter model confounds.
- Regression of task effects from the BOLD timeseries is performed in 3 steps:
- Create a task event timing array
- Convolve task events with a gamma-shaped hemodynamic response function (HRF)
- Regress out the effects of task via a general linear model implemented with xcp_d
First, for each condition (i.e., each separate contrast) in your task, create an Nx2 array where N is equal to the number of measurements (volumes) in your task fMRI run. Values in the first array column should increase sequentially by the length of the TR, with the first index = 0. Values in the second array column should equal either 0 or 1; each volume during which the condition/contrast was being tested should = 1, all others should = 0.
For example, for an fMRI task run with 210 measurements and a 3 second TR during which happy faces (events) were presented for 5.5 seconds at time = 36, 54, 90 seconds etc:
[ 0. 0.]
[ 3. 0.]
[ 6. 0.]
[ 9. 0.]
[ 12. 0.]
[ 15. 0.]
[ 18. 0.]
[ 21. 0.]
[ 24. 0.]
[ 27. 0.]
[ 30. 0.]
[ 33. 0.]
[ 36. 1.]
[ 39. 1.]
[ 42. 1.]
[ 45. 0.]
[ 48. 0.]
[ 51. 0.]
[ 54. 1.]
[ 57. 1.]
[ 60. 1.]
[ 63. 0.]
[ 66. 0.]
[ 69. 0.]
[ 72. 0.]
[ 75. 0.]
[ 78. 0.]
[ 81. 0.]
[ 84. 0.]
[ 87. 0.]
[ 90. 1.]
[ 93. 1.]
[ 96. 1.]
[ 99. 0.]]Next, the BOLD response to each event is modeled by convolving the task events with a canonical HRF. This can be done by first defining the HRF and then applying it to your task events array with numpy.convolve.
import numpy as np
from scipy.stats import gamma
# HRF function
def hrf(times):
""" Return values for HRF at given times """
# Gamma pdf for the peak
peak_values = gamma.pdf(times, 6)
# Gamma pdf for the undershoot
undershoot_values = gamma.pdf(times, 12)
# Combine them
values = peak_values - 0.35 * undershoot_values
# Scale max to 0.6
return values / np.max(values) * 0.6
# Compute HRF with the signal
hrf_times = np.arange(0, 35, TR) # TR = repetition time, in seconds
hrf_signal = hrf(hrf_times)
N=len(hrf_signal)-1
tt=np.convolve(taskevents[:,1],hrf_signal) # taskevents = the array created in the prior step
realt=tt[:-N] # realt = the output we need!- TR: a variable equal to the repetition time
- taskevents: the Nx2 array created in the prior step
xcp_d.If you have multiple conditions/contrasts per task, steps 1 and 2 must be repeated for each such that you generate one taskevents Nx2array per condition, and one corresponding realt numpy array. The realt outputs must all be combined into one space-delimited ${subid}_${sesid}_task-${taskname}_desc-custom_timeseries.tsv file. A task with 5 conditions (e.g. happy, angry, sad, fearful, and neutral faces) will have 5 columns in the custom .tsv file. Multiple realt outputs can be combined by modifying the example code below.
import pandas as pd
# Create an empty task array to save realt outputs to
taskarray = np.empty(shape=(measurements,0)) # measurements = the number of fMRI volumes
# Create a taskevents file for each condition and convolve with the HRF, using the code above
## code to compute realt
# Write a combined custom.tsv file
taskarray = np.column_stack((taskarray, realt))
df = pd.DataFrame(taskarray)
df.to_csv("{0}_{1}_task-{2}_desc-custom_timeseries.tsv".format(subid,sesid,taskid),index = False, header = False, sep=' ')The space-delimited *desc-custom_timeseries.tsv file for a 5 condition task may look like:
0.0 0.0 0.0 0.0 0.0
0.0 0.0 0.0 0.0 0.3957422940438729
0.0 0.0 0.0 0.0 0.9957422940438729
0.0 0.0 0.0 0.0 1.1009022019820307
0.0 0.0 0.0 0.0 0.5979640661963432
0.0 0.0 0.0 0.0 0.31017195439257517
0.0 0.0 0.0 0.0 0.7722398821320118
0.0 0.0 0.0 0.0 0.9755486196351566
0.0 0.0 0.0 0.0 0.9499183578181378
0.0 0.0 0.0 0.0 0.8987971115721047
0.0 0.0 0.0 0.0 0.8750149365335346
0.0 0.0 0.0 0.0 0.47218635162456457
0.0 0.0 0.0 0.0 -0.1294234695774829
0.3957422940438729 0.0 0.0 0.0 -0.23488535934344593
0.9957422940438729 0.0 0.0 0.0 -0.12773843588350925
1.1009022019820307 0.0 0.0 0.0 -0.04421213464698274
0.5979640661963432 0.0 0.0 0.0 -0.011439970324577234
-0.08557033965129775 0.0 0.0 0.0 -0.0023929581477495315
-0.22350241191186113 0.0 0.0 0.0 -0.00042413222490445587
0.27038871169699874 0.0 0.0 0.0 -6.512750410688139e-05
0.9519542916217947 0.0 0.0 0.0 -8.104611114467545e-06
1.0895273591615604 0.0 0.0 0.3957422940438729 0.0
0.5955792126597081 0.0 0.0 0.9957422940438729 0.0
-0.0859944718762022 0.0 0.0 1.1009022019820307 0.0
-0.223567539415968 0.0 0.0 0.5979640661963432 0.0
-0.12536168695798863 0.0 0.0 -0.08557033965129775 0.0
-0.04378800242207828 0.0 0.0 -0.22350241191186113 0.0
-0.011374842820470351 0.3957422940438729 0.0 -0.12535358234687416 0.0
-0.002384853536635064 0.9957422940438729 0.0 -0.04378800242207828 0.0
-0.00042413222490445587 1.1009022019820307 0.3957422940438729 -0.011374842820470351 0.0
-6.512750410688139e-05 0.5979640661963432 0.9957422940438729 -0.002384853536635064 0.0
0.39573418943275845 -0.08557033965129775 1.1009022019820307 -0.00042413222490445587 0.0
0.9957422940438729 -0.22350241191186113 0.5979640661963432 -6.512750410688139e-05 0.0
1.1009022019820307 -0.12535358234687416 -0.08557033965129775 -8.104611114467545e-06 0.0
0.5979640661963432 -0.04378800242207828 -0.22350241191186113 0.0 0.0Last, supply the ${subid}_${sesid}_task-${taskid}_desc-custom_timeseries.tsv file to xcp_d with -c option. -c should point to the directory where this file exists, rather than to the file itself; xcp_d will identify the correct file based on the subid, sesid, and taskid. You can simultaneously perform additional confound regression by including, for example, -p 36P to the call:
singularity run --cleanenv -B /my/project/directory:/mnt xcpabcd_latest.simg \
/mnt/input/fmriprep /mnt/output/directory participant --despike \
--lower-bpf 0.01 --upper-bpf 0.08 --participant_label $subid -p 36P -f 10 -t emotionid -c /mnt/taskarray_file_dir Custom Parcellations =============== While XCP-D comes with many built in parcellations, we understand that many users will want to use custom parcellations. We suggest running XCP-D with the -cifti option, and then using the Human Connectome Project wb_command to generate the time series:
wb_command -cifti-parcellate {SUB}_ses-{SESSION}_task-{TASK_run-{RUN}_space-fsLR_den-91k_desc-residual_bold.dtseries.nii your_parcels.dlabel \
{SUB}_ses-{SESSION}_task-{TASK_run-{RUN}_space-fsLR_den-91k_desc-residual_bold.ptseries.niiAfter this, if one wishes to have a connectivity matrix:
wb_command -cifti-correlation {SUB}_ses-{SESSION}_task-{TASK_run-{RUN}_space-fsLR_den-91k_desc-residual_bold.ptseries.nii \
{SUB}_ses-{SESSION}_task-{TASK_run-{RUN}_space-fsLR_den-91k_desc-residual_bold.pconn.niiMore infomration can be found at the HCP documentation
Logs and crashfiles are outputted into the <output dir>/xcp_d/sub-<participant_label>/log directory. Information on how to customize and understand these files can be found on the nipype debugging page.
Support and communication. The documentation of this project is found here: https://xcp-abcd.readthedocs.io/.
All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/PennLINC/xcp_d/issues.