Running XCP-D
Warning
XCP-D may not run correctly on M1 chips.
Execution and Input Formats
The XCP-D workflow takes fMRIPRep, NiBabies, DCAN and HCP outputs in the form of BIDS derivatives. In these examples, we use an fmriprep output directory.
The outputs are required to include at least anatomical and functional outputs with at least one preprocessed BOLD image. Additionally, each of theseshould be in directories that can be parsed by the BIDS online validator (even if it is not BIDS valid - we do not require BIDS valid directories). The directories must also include a valid dataset_description.json.
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 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 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.
Command-Line Arguments
xcp_d postprocessing workflow of fMRI data
usage: xcp_d [-h] [--version]
[--participant_label PARTICIPANT_LABEL [PARTICIPANT_LABEL ...]]
[-t TASK_ID] [-m] [-s] [--nthreads NTHREADS]
[--omp-nthreads OMP_NTHREADS] [--mem_gb MEM_GB]
[--use-plugin USE_PLUGIN] [-v]
[--input-type {fmriprep,dcan,hpc,nibabies}]
[--smoothing SMOOTHING] [--despike]
[--nuissance-regressors {27P,36P,24P,acompcor,aroma,acompcor_gsr,aroma_gsr,custom} | -p {27P,36P,24P,acompcor,aroma,acompcor_gsr,aroma_gsr,custom}]
[-c CUSTOM_CONFOUNDS] [-d DUMMYTIME]
[--disable-bandpass-filter | --bandpass_filter BANDPASS_FILTER]
[--lower-bpf LOWER_BPF] [--upper-bpf UPPER_BPF]
[--bpf-order BPF_ORDER] [--motion-filter-type {lp,notch}]
[--band-stop-min BPM] [--band-stop-max BPM]
[--motion-filter-order MOTION_FILTER_ORDER] [-r HEAD_RADIUS]
[-f FD_THRESH] [-w WORK_DIR] [--clean-workdir]
[--resource-monitor] [--notrack] [--warp-surfaces-native2std]
fmri_dir output_dir analysis_level
Positional Arguments
- fmri_dir
the root folder of a preprocessed fMRI output.
- output_dir
the output path for xcp_d
- analysis_level
the analysis level for xcp_d, must be specified as “participant”.
Named Arguments
- --version
show program’s version number and exit
Options for filtering BIDS queries
- --participant_label, --participant-label
a space delimited list of participant identifiers or a single identifier (the sub- prefix can be removed)
- -t, --task-id
select a specific task to be selected for the postprocessing
- -m, --combineruns
this option combines all runs into one file
Options for cifti processing
- -s, --cifti
postprocess cifti instead of nifti this is set default for dcan and hcp
Options to for resource management
- --nthreads
maximum number of threads across all processes
- --omp-nthreads
maximum number of threads per-process
- --mem_gb, --mem_gb
upper bound memory limit for xcp_d processes
- --use-plugin
nipype plugin configuration file. for more information see https://nipype.readthedocs.io/en/0.11.0/users/plugins.html
- -v, --verbose
increases log verbosity for each occurence, debug level is -vvv
Input flags
- --input-type
Possible choices: fmriprep, dcan, hpc, nibabies
The pipeline used to generate the preprocessed derivatives. The default pipeline is ‘fmriprep’. The ‘dcan’, ‘hcp’, and ‘nibabies’ pipelines are also supported. ‘nibabies’ assumes the same structure as ‘fmriprep’.
Parameters for postprocessing
- --smoothing
smoothing the postprocessed output (fwhm)
- --despike
despike the nifti/cifti before postprocessing
- --nuissance-regressors
Possible choices: 27P, 36P, 24P, acompcor, aroma, acompcor_gsr, aroma_gsr, custom
Nuisance parameters to be selected, other options include 24P and 36P acompcor and aroma. See Ciric et. al (2007) for more information about regression strategies. This parameter is deprecated and will be removed in version 0.3.0. Please use
-p
or--nuisance-regressors
.- -p, --nuisance-regressors
Possible choices: 27P, 36P, 24P, acompcor, aroma, acompcor_gsr, aroma_gsr, custom
Nuisance parameters to be selected. See Ciric et. al (2007).
- -c, --custom_confounds
Custom confound to be added to nuisance regressors.
- -d, --dummytime
first volume in seconds to be removed or skipped before postprocessing
Filtering parameters and default value
- --disable-bandpass-filter, --disable_bandpass_filter
Disable bandpass filtering. If bandpass filtering is disabled, then ALFF derivatives will not be calculated.
- --bandpass_filter
Whether to Butterworth bandpass filter the data or not. If bandpass filtering is disabled, then ALFF derivatives will not be calculated. This parameter is deprecated and will be removed in version 0.3.0. Bandpass filtering is performed by default, and if you wish to disable it, please use –disable-bandpass-filter`.
- --lower-bpf
lower cut-off frequency (Hz) for the butterworth bandpass filter
- --upper-bpf
upper cut-off frequency (Hz) for the butterworth bandpass filter
- --bpf-order
number of filter coefficients for butterworth bandpass filter
- --motion-filter-type
Possible choices: lp, notch
Type of band-stop filter to use for removing respiratory artifact from motion regressors. If not set, no filter will be applied.
If the filter type is set to “notch”, then both
band-stop-min
andband-stop-max
must be defined. If the filter type is set to “lp”, then onlyband-stop-min
must be defined.- --band-stop-min
Lower frequency for the band-stop motion filter, in breaths-per-minute (bpm). Motion filtering is only performed if
motion-filter-type
is not None. If used with the “lp”motion-filter-type
, this parameter essentially corresponds to a low-pass filter (the maximum allowed frequency in the filtered data). This parameter is used in conjunction withmotion-filter-order
andband-stop-max
. Age Range (years)
Recommended Value (bpm)
< 1
30
1 - 2
25
2 - 6
20
6 - 12
15
12 - 18
12
19 - 65
12
65 - 80
12
> 80
10
When
motion-filter-type
is set to “lp” (low-pass filter), another commonly-used value for this parameter is 6 BPM (equivalent to 0.1 Hertz), based on Gratton et al. (2020).- --band-stop-max
Upper frequency for the band-stop motion filter, in breaths-per-minute (bpm). Motion filtering is only performed if
motion-filter-type
is not None. This parameter is only used ifmotion-filter-type
is set to “notch”. This parameter is used in conjunction withmotion-filter-order
andband-stop-min
. Age Range (years)
Recommended Value (bpm)
< 1
60
1 - 2
50
2 - 6
35
6 - 12
25
12 - 18
20
19 - 65
18
65 - 80
28
> 80
30
- --motion-filter-order
number of filter coeffecients for the band-stop filter
Censoring and scrubbing options
- -r, --head_radius
head radius for computing FD, default is 50mm, 35mm is recommended for baby
- -f, --fd-thresh
framewise displacement threshold for censoring, default is 0.2mm
Other options
- -w, --work_dir
path where intermediate results should be stored
- --clean-workdir
Clears working directory of contents. Use of this flag is notrecommended when running concurrent processes of xcp_d.
- --resource-monitor
enable Nipype’s resource monitoring to keep track of memory and CPU usage
- --notrack
Opt-out of sending tracking information
Experimental options
- --warp-surfaces-native2std
If used, a workflow will be run to warp native-space (
fsnative
) reconstructed cortical surfaces (surf.gii
files) produced by Freesurfer into standard (fsLR
) space. These surface files are primarily used for visual quality assessment. By default, this workflow is disabled. Filename
Description
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_pial.surf.gii
The gray matter / pial matter border.
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_smoothwm.surf.gii
The smoothed gray matter / white matter border for the cortex.
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_midthickness.surf.gii
The midpoints between wm and pial surfaces. This is derived from the FreeSurfer graymid (
mris_expand
with distance=0.5 applied to the WM surfs).<source_entities>_space-fsLR_den-32k_hemi-<L|R>_inflated.surf.gii
An inflation of the midthickness surface (useful for visualization). This file is only created if the input type is “hcp” or “dcan”.
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_desc-hcp_midthickness.surf.gii
The midpoints between wm and pial surfaces. This is created by averaging the coordinates from the wm and pial surfaces.
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_desc-hcp_inflated.surf.gii
An inflation of the midthickness surface (useful for visualization). This is derived from the HCP midthickness file. This file is only created if the input type is “fmriprep” or “nibabies”.
<source_entities>_space-fsLR_den-32k_hemi-<L|R>_desc-hcp_vinflated.surf.gii
A very-inflated midthicknesss surface (also for visualization). This is derived from the HCP midthickness file. This file is only created if the input type is “fmriprep” or “nibabies”.
see https://xcp-d.readthedocs.io/en/latest/generalworkflow.html
Running xcp_d
via Docker containers
If you are running xcp_d
locally, we recommend Docker.
See Container Technologies for installation instructions.
In order to run Docker smoothly, it is best to prevent permissions issues associated with the root file system. Running Docker as user on the host will ensure the ownership of files written during the container execution.
A Docker container can be created using the following command:
docker run --rm -it \
-v /dset/derivatives/fmriprep:/fmriprep:ro \
-v /tmp/wkdir:/work:rw \
-v /dset/derivatives:/out:rw \
-v /dset/derivatives/freesurfer:/freesurfer:ro \ # Necessary for fMRIPrep versions <22.0.2
pennlinc/xcp_d:latest \
/fmriprep /out participant \
--cifti --despike --head_radius 40 -w /work --smoothing 6
Running xcp_d
via Singularity containers
If you are computing on an HPC, we recommend using Singularity. See Container Technologies for installation instructions.
If the data to be preprocessed is also on the HPC or a personal computer, you are ready to run xcp_d.
singularity run --cleanenv xcp_d.simg \
path/to/data/fmri_dir \
path/to/output/dir \
--participant-label label
Relevant 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.
Custom Confounds
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 supported in XCP-D.
Task Regression
Here we document how to regress task block effects as well as the 36 parameter model confounds.
However, this method is still under development.
- 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
Create a task event array
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.]
Convolve task events with the HRF
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
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!
The code block above contains the following user-defined variables:
TR
: a variable equal to the repetition timetaskevents
: the Nx2 array created in the prior step
The code block above produces the numpy array realt
,
which must be saved to a file named ${subid}_${sesid}_task-${taskid}_desc-custom_timeseries.tsv
.
This tsv file will be used in the next step of 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 Nx2 array 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='\t',
)
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 0.0
Command Line XCP-D with Custom Confounds
Last, 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 (assuming you have cifti files),
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.nii
After 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.nii
More information can be found at the HCP documentation
Troubleshooting
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-d.readthedocs.io/.
All bugs, concerns and enhancement requests for this software can be submitted here: https://github.com/PennLINC/xcp_d/issues.
If you have a question about using xcp_d
, please create a new topic on NeuroStars with the “xcp_d” tag.
The xcp_d
developers follow NeuroStars, and will be able to answer your question there.