General Workflow

Input data

The default inputs to XCP-D are the outputs of fMRIPrep and Nibabies. The xcp_d can aslo process ABCD-DCAN and minimal processing of HCP data which require input-type flag ( ‘dcan’ and ‘hcp’ for ABCD-DCAN and HCP respectively).

Processesing Steps

0. Data is read in. See Running XCP-D:Inputs for information on input dataset structures.

1. Skip volumes [Optional]: xcp_d allows the first N number of volumes to be skipped or deleted before processing. These volumes are usually refered to as dummy scans. It can be added to the command line with -d X where X is in seconds. For example, if your TR is .72, and you want to remove the first 2 volumes, you should enter: -d 1.44. Most default scanning sequences include dummy volumes that are not reconstructed. However, some users still perfer to remove the first reconstructed few volumes.

2. Confound regressors selection: The confound regressors configurations in the table below are implemented in xcp_d with 36P as the default. In addition to the standard confound regressors selected from fMRIPrep outputs, a custom confound timeseries can be added as described below in:ref:Running XCP-D:Custom Confounds. If you are passing custom confound regressors, and you want none of the regressors here, the option will be custom.

   Confound

   Pipelines	Six Motion Estimates	White Matter	CSF	Global Signal	ACompCor	AROMA
   24P	X, X2, dX, dX2
   27P	X, X2, dX, dX2	X	X	X
   36P	X, X2, dX, dX2	X, X2, dX, dX2	X, X2, dX, dX2	X, X2, dX, dX2
   acompcor_gsr	X, dX			X	10 com, 5WM,5CSF
   acompcor	X, dX				10 com, 5WM,5CSF
   aroma_gsr	X, dX	X	X	X		X
   aroma	X, dX	X	X			X

   For more information about confound regresssors selection, please refer to Ciric et. al. 2017

   After the selection of confound regressors, the respiratory effects can optionally be filtered out from the motion estimates with band-stop filter to improve fMRI data quality. Please refer to Fair et. al. 2020 for more information. These band-stop parameters are age specific (see table below) and can be added to the command line arguments (see Running XCP-D:Command Structure).

   Respiratory Filter

   Age Range	Cutoff Range (Breaths per Minute)
   < 1 year	30 to 60
   1 to 2 years	25 - 50
   2 - 6 years	20 - 35
   6-12 years	15 - 25
   12 - 18 years	12 - 20
   19 - 65 years	12 - 18
   65 - 80 years	12 - 28
   > 80 years	10 - 30

3. Despiking [Optional]: Despiking is a process in which large spikes in the BOLD times series are truncated. Despiking reduces/limits the amplitude or magnitude of the large spikes but preserves those data points with an imputed reduced amplitude. Despiking is done before regression and filtering to minimize the impact of spike. Despiking is applied to whole volumes and data, and different from temporal censoring. It can be added to the command line arguments with --despike.

4. Temporal Censoring: Temporal Censoring is a process in which data points with excessive motion outliers are identified/flagged. The censored data points are removed from the data before regression. This is effective for removing spurious sources of connectivity in fMRI data but must be applied very carefully. The Framewise displacement (FD) threshold is used to identify the censored volumes or outliers which are obtained from the FMRIPREP nuissance matrix. The default FD threshold implemented in xcp_d is 0.3 mm. This can be modifeid in the commmand line argument with --fd-threshold X where X is the FD threshold in mm. Any volume with FD above the threshold will be flagged as an outlier before the regession. Please refer to Satterthwaite et al. 2013 and Power et. al. 2012 for more information. There is also a process of masking out the non-contiguous segments of data points between outliers. The number of contiguous volumes required to survive masking is set to 5 by default and and can be modifeid by the users in the command line arguments; here, if there are 1-4 volumes between volumes that are masked for censoring, those 1-4 volumes are also masked for censoring.

5. Confound Regression: At this stage, the BOLD data is denoised by regressing the confound regressors from the BOLD data. If there are any volumes or timepoints flagged, as outliers during censoring step, these volumes are excluded from the regression. In addition, if there are custom confound regressors, these are combined with the confound regressors selected, if any, in step 2.

6. Bandpass filtering: XCP-D implements a Butterworth bandpass filter to filter BOLD signal after regression. The bandpass filter parameters are set to 0.009 to 0.08 Hz with order of 2 by default and can be modified in the command line. If there are any flagged volumes or timepoints during Temporal Censoring, these volumes are interpolated before bandpass filtering.

7. Functional timeseries and connectivity matrices: XCP-D implements a module that extracts voxelwise timeseries with the brain atlases. The local mean timeseries within each brain atlas’s region of interest (ROI) is extracted. Currently, static connectivity is estimated using the Pearson correlation between all ROIs for a particular atlas. The following atlases are implemented in XCP-D:

1. Schaefer 100,200,300,400,500,600,700,800,900,1000

2. Glasser 360

3. Gordon 333

4. Tian Subcortical Atlas Tian et al

8. Resting-state derivatives: For each BOLD data, the resting-state derivatives are computed. These includes regional homogeneity (ReHo) and amplitude of low-frequency fluctuation (ALFF).

9. Residual BOLD and resting-state derivatives smoothing: A smoothing kernel of 6mm is implemented as default for smoothing residual BOLD, ReHo and ALFF. Kernel size can be modified in the command line arguments.

10. Quality control. The quality control (QC) in XCP-D estimates the quality of BOLD data before and after regression and also estimates BOLD-T1w coregistration and BOLD-Template normalization qualites. The QC metrics include:

    1. Motion parameters summary: mean FD, mean and maximum RMS

    2. Mean DVARs before and after regression and its relationship to FD

    3. BOLD-T1w coregistration quality - Dice, Jaccard, Coverage and Cross-correlation indices

    4. BOLD-Template normalization quality - Dice, Jaccard, Coverage and Cross-correlation indices

Outputs

XCP-D generates four main types of outputs for every subject.

First, XCP-D generates an HTLM “executive summary” that displays relevant information about the anatomical data and the BOLD data before and after regression. The anatomical image viewer allows the user to see the segmentation overlaid on the anatomical image. Next, for each session, the user can see the segmentation registered onto the BOLD images. Alongside this image, pre and post regression “carpet” plot is alongside DVARS, FD, the global signal. The number of volumes remaining at various FD thresholds are shown.

Second, XCP-D generates an HTML “report” for each subject and session. The report contains a Processing Summary with QC values, with the BOLD volume space, the TR, mean FD, mean RMSD, and mean and maximum RMS, the Correlation between DVARS and FD before and after processing, and the number of volumes censored. Next, pre and post regression “carpet” plots are alongside DVARS and FD. An About section that notes the release version of XCP-D, a Methods section that can be copied and pasted into the user’s paper, which is customized based on command line options, and an Error section, which will read “No errors to report!” if no errors are found.

Third, XCP-D outputs processed BOLD data, including denoised dense unsmoothed and smoothed timeseries in MNI2006 and fsLR32k spaces, parcellated time series, functional connectivity matrices, and ALFF and ReHo (smoothed and unsmoothed).

Fourth, the anatomical data (processed T1w processed and segmentation files) are copied from fMRIPrep. If both images are not in MNI2006 space, they are resampled to MNI space. The fMRIPrep surfaces (gifti files) in each subject are also resampled to standard space (fsLR-32K).

See Outputs for file details about xcp_d outputs.