XCP-D

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Overview & Processing

XCP-D ingests minimally preprocessed fMRIPrep outputs to generate denoised BOLD images, parcellated time series, functional connectivity matrices, and quality assessment reports (Mehta et al., 2024).

ABCC data were processed using XCP-D v0.13.0 with the following parameters:

  • --mode abcd
    Applies the ABCD-specific preprocessing workflow, including ABCD-tailored default settings for denoising, censoring, and quality control.

  • --motion-filter-type notch
    Applies a notch filter to the motion parameters to remove respiratory-frequency artifacts from head motion estimates prior to framewise displacement (FD) calculation.

  • --create-matrices 300 600 all
    Generates functional connectivity matrices (correlation matrices) using only time points with at least 300 seconds, 600 seconds, or all available uncensored data retained after motion censoring.

  • --atlases Gordon HCP MIDB
    Parcellates the BOLD signal using three atlases:

    • Gordon — Gordon’s 333-ROI cortical parcellation derived using boundary detection on resting-state fMRI data (Gordon et al., 2016)
    • MIDB — MIDB’s 80-ROI precision brain atlas derived from ABCD data, thresholded at 75% probability (Hermosillo et al., 2024)
    • HCP — HCP’s 19-ROI CIFTI subcortical parcellation as defined in the HCP minimal preprocessing pipelines (Glasser et al., 2013)

  • --band-stop-min ${band_stop_min} / --band-stop-max ${band_stop_max}
    The following min / max filter frequencies were applied to address respiratory artifacts present in ABCD multiband acquisitions, which can distort framewise alignment estimates and impact motion censoring (Fair et al., 2020):

    • ses-00A: 18.582 / 25.726
    • ses-02A: 15 / 25
    • ses-04A & ses-06A: 14.4 / 24

Derivatives

As with fMRIPrep, XCP-D derivatives are organized under anat/ and func/ subfolders. The derivative file contents are explained below - also see the main XCP-D documentation.

abcd/
└── derivatives/
    └── abcc-xcp_d_v0.13.0/
        └── sub-<SUBJECT_ID>/
            └── ses-<SESSION_ID>/
                ├── anat/ 
                ├── func/
                ├── figures/
                ├── sub-<LABEL>_ses-<LABEL>.html 
                └── sub-<LABEL>_ses-<LABEL>_executive_summary.html
TipAT A GLANCE
  • Denoised BOLD timeseries
    Surface-based (fsLR 91k), in CIFTI format, generated per task and run
  • Parcellated timeseries
    Atlas-based (Gordon, HCP, MIDB), in TSV and CIFTI formats
  • Functional connectivity matrices
    Pearson correlation matrices for Gordon, HCP, and MIDB atlases, in TSV and CIFTI formats
  • ALFF and ReHo maps
    Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo), in CIFTI format
  • Anatomical surfaces
    fsLR 32k meshes and morphometry maps (curvature, sulcal depth, thickness)
  • Quality control
    Motion parameters, outlier detection, ABCC QC metrics (HDF5), and visual reports

Structural Outputs

Structural derivative files are stored under anat/ as displayed below.

Note: The standard BIDS file prefix sub-<label>_ses-<label> is replaced with * below for readability. See further annotated notes following the file tree.

anat/

# Preprocessed T1w and T2w images in MNI152NLin2009cAsym standard space
├── *_space-MNI152NLin2009cAsym_desc-preproc_<T1w|T2w>.nii.gz

# fsLR 32k cortical surfaces for L/R hemispheres [1]
├── *_hemi-<L|R>_space-fsLR_den-32k_desc-hcp_inflated.surf.gii
├── *_hemi-<L|R>_space-fsLR_den-32k_desc-hcp_midthickness.surf.gii
├── *_hemi-<L|R>_space-fsLR_den-32k_desc-hcp_vinflated.surf.gii
├── *_hemi-<L|R>_space-fsLR_den-32k_pial.surf.gii
├── *_hemi-<L|R>_space-fsLR_den-32k_white.surf.gii

# Dense scalar maps (fsLR 91k grayordinate space) [2]
├── *_space-fsLR_den-91k_curv.dscalar.nii
├── *_space-fsLR_den-91k_sulc.dscalar.nii
├── *_space-fsLR_den-91k_thickness.dscalar.nii

# Atlas-based summary statistics [3]
├── *_space-fsLR_seg-<ATLAS>_stat-mean_desc-curv_morph.tsv
├── *_space-fsLR_seg-<ATLAS>_stat-mean_desc-sulc_morph.tsv
└── *_space-fsLR_seg-<ATLAS>_stat-mean_desc-thickness_morph.tsv

[1] Cortical surfaces include inflated, very-inflated, midthickness, pial, white 
[2] Dense scalar maps include cortical curvature, sulcal depth, thickness 
[3] Atlas values (<ATLAS>) include MIDB and Gordon

Functional Outputs

Functional derivative files are stored under func/ as shown below.

Values for <TASK> in the filetrees below include:

  • mid: Monetary Incentive Delay task
  • sst: Stop Signal task
  • nback: N-Back working memory task
  • rest: Resting-state fMRI

Also note that most outputs are provided both per-run and concatenated across runs. Run-specific files include run-<LABEL> in the filename and are omitted below for readability.

Dense Timeseries & Confounds

Primary denoised BOLD outputs in fsLR grayordinate space (.dtseries.nii) accompanied by confound files (motion.tsv, outliers.tsv).

func/
├── *_task-<TASK>_space-fsLR_den-91k_desc-denoised_bold.dtseries.nii
├── *_task-<TASK>_space-fsLR_den-91k_desc-denoisedSmoothed_bold.dtseries.nii

├── *_task-<TASK>_motion.tsv
├── *_task-<TASK>_outliers.tsv
├── *_task-<TASK>_run-<label>_design.tsv
  • Primary input for most analyses
  • Includes nuisance regression, bandpass filtering, and motion censoring
  • Provided with and without surface-based spatial smoothing
  • Available as both per-run and concatenated outputs
  • Accompanied by run-level nuisance regressors and motion metrics used during denoising, including:
    • Motion parameters and framewise displacement (motion.tsv)
    • Censored timepoints (outliers.tsv)
    • Nuisance regressor design matrices (design.tsv)

Dense Scalar Maps

Voxel-wise (grayordinate-level) functional metrics derived from the denoised timeseries.

├── *_task-<TASK>_space-fsLR_den-91k_stat-alff_boldmap.dscalar.nii
├── *_task-<TASK>_space-fsLR_den-91k_stat-alff_desc-smooth_boldmap.dscalar.nii
├── *_task-<TASK>_space-fsLR_den-91k_stat-reho_boldmap.dscalar.nii
  • ALFF: Amplitude of low-frequency fluctuations (unsmoothed and smoothed variants)
  • ReHo: Regional Homogeneity, reflecting local BOLD synchrony across neighboring grayordinates

Parcellated Outputs

Atlas-based summaries derived from dense grayordinate data.

├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_den-91k_stat-mean_timeseries.ptseries.nii
├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_stat-mean_timeseries.tsv

├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_stat-pearsoncorrelation_relmat.tsv     
├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_den-91k_stat-pearsoncorrelation_boldmap.pconn.nii

├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_den-91k_stat-coverage_boldmap.pscalar.nii
├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_stat-coverage_bold.tsv

├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_stat-alff_bold.tsv
├── *_task-<TASK>_space-fsLR_seg-<ATLAS>_stat-reho_bold.tsv

Includes:

  • Mean parcel timeseries (.ptseries.nii, .tsv)
  • Functional connectivity (Pearson correlation matrices; .pconn.nii, .tsv)
  • Parcel-wise coverage metrics (.pscalar.nii, .tsv)
  • Parcel-averaged ALFF and ReHo summaries

Parcellated timeseries are extracted from dense grayordinate data using standard atlas templates. All parcellations include subcortical regions from the standard HCP 91k-grayordinate template. Available parcellations (and corresponding atlas-<ATLAS> labels) include:

  • Gordon — Gordon’s 333-ROI cortical parcellation, derived using boundary detection on resting-state fMRI data from 120 young adults (Gordon et al., 2016)
  • MIDB — MIDB’s 80-ROI precision brain atlas from ABCD data and thresholded at 75% probability (Hermosillo et al., 2024)
  • HCP — HCP’s 19-ROI CIFTI subcortical parcellation, as defined in the HCP minimal preprocessing pipelines (Glasser et al., 2013)

Quality Control Metrics

Summary metrics for assessing data quality and inclusion.

├── *_task-<TASK>_space-fsLR_den-91k_desc-linc_qc.tsv
└── *_task-<TASK>_desc-abcc_qc.hdf5
  • LINC QC (.tsv): Run-level metrics (e.g., retained frames, mean FD, DVARS)
  • ABCC QC (.hdf5): Aggregated metrics across tasks, including motion summaries and atlas coverage, for downstream filtering and participant exclusion

HTML QC Reports

The .html files provide subject-level visual summaries designed for rapid, systematic quality control (QC) review (source image files stored in figures/).

  • sub-<LABEL>_ses-<LABEL>.html
    • Standard XCP-D subject-level report, structured similarly to the fMRIPrep report
    • Includes registration figures, BOLD carpet plots, motion summaries, and connectivity outputs
  • sub-<LABEL>_ses-<LABEL>_executive_summary.html
    • ABCC-specific executive summary report that provides a streamlined review of the most critical QC checkpoints
    • Includes anatomical normalization quality, per-task BOLD pre/post-processing summaries, and motion censoring profiles

References

Fair, D. A., Miranda-Dominguez, O., Snyder, A. Z., Perrone, A., Earl, E. A., Van, A. N., Koller, J. M., Feczko, E., Tisdall, M. D., Van Der Kouwe, A., Klein, R. L., Mirro, A. E., Hampton, J. M., Adeyemo, B., Laumann, T. O., Gratton, C., Greene, D. J., Schlaggar, B. L., Hagler, D. J., … Dosenbach, N. U. F. (2020). NeuroImage, 208, 116400. https://doi.org/10.1016/j.neuroimage.2019.116400
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., Van Essen, D. C., & Jenkinson, M. (2013). NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Cerebral Cortex, 26(1), 288–303. https://doi.org/10.1093/cercor/bhu239
Hermosillo, R. J. M., Moore, L. A., Feczko, E., Miranda-Domínguez, Ó., Pines, A., Dworetsky, A., Conan, G., Mooney, M. A., Randolph, A., Graham, A., Adeyemo, B., Earl, E., Perrone, A., Carrasco, C. M., Uriarte-Lopez, J., Snider, K., Doyle, O., Cordova, M., Koirala, S., … Fair, D. A. (2024). Nature Neuroscience, 27(5), 1000–1013. https://doi.org/10.1038/s41593-024-01596-5
Mehta, K., Salo, T., Madison, T. J., Adebimpe, A., Bassett, D. S., Bertolero, M., Cieslak, M., Covitz, S., Houghton, A., Keller, A. S., Lundquist, J. T., Luo, A., Miranda-Dominguez, O., Nelson, S. M., Shafiei, G., Shanmugan, S., Shinohara, R. T., Smyser, C. D., Sydnor, V. J., … Satterthwaite, T. D. (2024). Imaging Neuroscience, 2, 1–26. https://doi.org/10.1162/imag_a_00257