Page Last Updated: May 18, 2026
Functional MRI🔗
Head motion is a serious issue in neuroimaging, especially for resting state fMRI, as it creates brain-wide artifactual effects such as inflated short-distance connectivity and attenuated long-distance connectivity (Power et al. 2012). Researchers employ a variety of strategies to mitigate head motion during acquisition and processing (Power et al. 2014; 2015; Satterthwaite et al. 2013; Siegel et al. 2017; Gratton et al. 2020). This includes motion censoring (discarding individual frames that are proximal to motion events within a run) and/or excluding entire runs due to high motion. These strategies may lead to the exclusion of some participants from further analysis due to lack of sufficient data.
Levels of head motion differ according to demographic factors such as sex, race/ethnicity, and SES (Cosgrove et al., 2022). Therefore, mitigation strategies for head motion introduce questions around fairness and differential exclusions across demographic groups. In addition, motion censoring causes sessions to vary by the amount of data remaining. Such variability may continue to inflate findings especially in the presence of conditions that may correlate with the motion artifact like autism or ADHD (Eggebrecht, 2017). The amount of data remaining influences the variation in the connectivity calculations by affecting the degrees of freedom. Therefore, even after motion censoring, issues concerning fairness may persist when examining factors that might be affected by motion like sex, race/ethnicity, SES, and BMI (Cosgrove et al., 2022). One strategy that avoids this confound is to strictly control the degrees of freedom, where functional connectivity measures are calculated with the exact same amount of data. Researchers should assess whether control of artifactual effects of head motion effects can be achieved by alternative means that mitigate this impact. Examples of such strategies could include data augmentation approaches such as sampling from other datasets, data processing strategies like the include use of ICA-based denoising (Pruim et al., 2015a; 2015b), use of bootstrap aggregation (Ramduny et al., 2024), or the creation of “pseudo-rest” by removing task signals from the task data (Fair et al. 2007), or post-hoc approaches like propensity weighting.
Researchers interested in examining brain-behavior associations or multivariate predictions should follow strategies such as those in Eggebrecht 2017 to: 1) assess how missing data impacts dependent, independent variables and covariates, 2) examine the association between the degrees of freedom and non-FC variables, 3) use trimmed FC measures when needed to mitigate artifacts due to data quality.
Avoid V02 Derivatives Processed with Infant FreeSurfer
Recommendation: Use M-CRIB-S derivatives for all neonatal (V02; 0–1 month) analyses.
V02 data were processed in Infant fMRIPrep via two separate surface reconstruction workflows, Infant FreeSurfer and M-CRIB-S (details). Expert review and BrainSwipes QC consistently showed higher-quality surfaces from M-CRIB-S. This is expected, as M-CRIB-S uses T2w images, which are much higher contrast than the T1w (on which FreeSurfer relies) in neonates. Goncalves et al., 2025 report optimal performance for M-CRIB-S ≤5 months and Infant FreeSurfer ≥3 months.
Note: M-CRIB-S outputs can still be affected by poor T1w quality as it uses an externally generated brain segmentation from BIBSNet fed into Infant fMRIPrep as an external derivative. BIBSNet uses both T1w and T2w (when available), and low-quality T1w data may degrade segmentation quality.
Signal Intensity Clipping Artifact
A subset of Philips fMRI scans exhibit signal intensity clipping, where voxel intensities >4095 are capped due to a reconstruction scaling error. This produces hyperintense regions that can distort BOLD registration and impact downstream measures such as functional connectivity. The issue was identified during pilot data collection and fixed at most sites before the main study. Residual cases remain at VAN and CCH (patch implemented Oct 2024). Approximately ~20% of scans at these sites show some clipping, with ~6% classified as severe. Severe cases fail raw data QC, so are naturally filtered out from inclusion in downstream processing steps.
Users should practice caution in sensitive analyses and consider including clipping metrics as covariates. The presence and severity of clipping can be estimated based on raw data QC metrics. Potential clipping is indicated if: (1) the fraction of max intensity voxels in the brain mask (brain_fvox_max) is > 0.001 AND (2) the med/max image intensity ratio (brain_median/brain_max) is > 0.5 (> 0.8 indicates severe clipping).
Overview & Acquisition🔗
Functional MRI (fMRI) measures brain activity via the blood oxygen level–dependent (BOLD) signal. The HBCD Study includes resting-state fMRI (rs-fMRI), with head motion monitored in real time using FIRMM to estimate usable data during acquisition (Dosenbach et al., 2017). A target of 7.5 minutes of usable low-motion data is acquired across runs per session.
- rs-fMRI data is acquired at 2 mm isotropic resolution with a repetition time (TR) of 1725 ms and multiband (MB) factor of 4
- A minimum of 2 runs are collected (during sleep for infants <30 months old), each lasting 7.5 minutes
- FIRMM is used to monitor head motion in real time, quantified by framewise displacement (FD)
- Additional runs are acquired as needed to obtain at least 7.5 minutes of low-motion data (FD < 0.3 mm)
- Each run includes forward and reverse phase-encoding spin-echo EPI images for distortion correction
Processing & Derivatives🔗
For structural and functional MRI processing, file selection is based on raw data quality control metrics, including:
- Overall passing QC score (
QC= 1) - Motion score below a defined threshold (
QU_Motion≤ 2 for the current release) - If multiple scans are present for a given modality (T1w/T2w), the scan with the highest QC metrics is used
All processing streams utilized both the T1w and T2w if they were present. Processing was still executed with only a single modality present as well, with certain requirements depending on the surface reconstruction method utilized within Infant fMRIPrep (see details):
- M-CRIB-S (T2w-based): requires T2w
- Infant FreeSurfer (T1w-based): requires both T1w and T2w
HBCD structural and functional MRI data are processed through a sequence of BIDS App pipelines. At a high level, BIBSNet generates brain tissue segmentations and masks for T1w/T2w images. These are fed into Infant-fMRIPrep to generate confound files and motion-corrected data (in MNI space, registered to age-specific volumetric atlases) as well as fs_LR32k surface space. Outputs are then fed into XCP-D to run nuisance regression/denoising, parcellate the fMRI data, and compute summary measures.
Infant fMRIPrep🔗
Infant-fMRIPrep (also known as NiBabies) performs minimal structural and functional MRI processing. It is an adapted version of fMRIPrep optimized for infant data processing, using age-appropriate templates and surface reconstruction methods optimized for early development (Goncalves et al., 2025). Pipeline outputs include visual quality assessment reports, preprocessed derivatives, and confounds used for denoising in subsequent processing steps.
Anatomical Preprocessing
T1w and T2w images are denoised, bias-corrected, and normalized to the MNI Infant template (0–4.5 yr), then to MNI152 for compatibility with adult datasets. Surface reconstruction is performed via one of the following methods:
| M-CRIB-S | T2w-based method for neonates (Adamson et al., 2020). Infant fMRIPrep runs a modified MCRIBReconAll workflow that uses the BIBSNet-derived brain segmentation. Optimal age range per Goncalves et al., 2025: ≤ 5 months |
| Infant FreeSurfer | T1w-based method for infants 0-2 years old (Zöllei et al., 2020). Infant fMRIPrep executes infant_recon_all with its default configuration. Optimal age range per Goncalves et al., 2025: ≥ 3 months |
Functional Processing
- Motion and distortion correction using fieldmap-based estimation.
- Alignment of functional to anatomical space via boundary-based registration.
- Confound estimation: framewise displacement (FD) and DVARS for motion, CompCor physiological noise regressors, global signals (mean CSF, white matter, and whole brain), and derived regressors (e.g. motion outlier flags for frames exceeding 0.5 mm FD or 1.5 standardized DVARS thresholds)
- Resampling of BOLD data to subject and fsLR-space surfaces, with grayordinates (91k) for surface-based analyses.
Overview
- JSON files excluded for brevity from file trees below
- See How To Read File Trees for additional guidance
- T1w-related files will only be present in the derivatives if a T1w was acquired
hbcd/
└── derivatives/
└── nibabies-{HASH}/
└── sub-[ID]/
├── figures/
├── ses-[V0X]/
│ ├── anat/
│ ├── fmap/
│ ├── func/
│ └── log/
│
└── sub-[ID]_ses-[V0X]_hash-{HASH}.html
# Label Values Legend
HASH: 0f306a2f , 2afa9081
Anatomical Folder Details
...
└── ses-[V0X]/
└── anat/
# Primary volumetric outputs & segmentations
├── *_desc-preproc_{T1w|T2w}.nii.gz
├── *_space-MNI152NLin6Asym_res-2_desc-preproc_T2w.nii.gz
├── *_space-MNI152NLin6Asym_res-2_desc-brain_mask.nii.gz
├── *_space-T2w_desc-ribbon_mask.nii.gz
├── *_space-{STD_SPACE}_dseg.nii.gz
├── *_space-T2w_desc-{aparcaseg|aseg}_dseg.nii.gz
├── *_space-{STD_SPACE}_label-{CSF|GM|WM}_probseg.nii.gz
# Transforms
├── *_from-{SPACE}_to-T2w_mode-image_xfm.h5
├── *_from-T2w_to-{SPACE}_mode-image_xfm.h5
# Surface & CIFTI outputs
├── *_hemi-{L|R}_desc-cortex_mask.label.gii
├── *_space-fsLR_den-91k_{METRIC}.dscalar.nii
├── *_hemi-{L|R}_{METRIC}.shape.gii
├── *_hemi-{L|R}_{inflated|sphere}.surf.gii
├── *_hemi-{L|R}_{SURF}.surf.gii
├── *_hemi-{L|R}_space-dhcpAsym_den-32k_{SURF}.surf.gii
└── *_hemi-{L|R}_space-{dhcpAsym|fsaverage}_reg_sphere.surf.gii
...
# Label Values Legend
File Prefixes (anat/, fmap/ files): sub-[ID]_ses-[V0X]_hash-{HASH}_run-[X]
METRIC: curv , sulc , thickness
SPACE: fsnative , MNI152NLin6Asym , MNIInfant+1 , T1w
STD_SPACE: MNI152NLin6Asym_res-2 , T2w
SURF: midthickness , pial , white
Fieldmap & Functional Folder Details
...
└── ses-[V0X]/
├── fmap/
│ └── *_fmapid-auto[X]_desc-{coeff|epi|preproc}_fieldmap.nii.gz
│
└── func/
# BOLD & masks
├── *_desc-{preproc_bold|brain_mask}.nii.gz
├── *_space-{STD_SPACE}_boldref.nii.gz
├── *_space-{STD_SPACE}_desc-{preproc_bold|brain_mask}.nii.gz
# Motion-corrected or coregistered outputs
├── *_desc-{hmc|coreg}_boldref.nii.gz
├── *_from-orig_to-boldref_mode-image_desc-hmc_xfm.txt
├── *_from-boldref_to-T2w_mode-image_desc-coreg_xfm.txt
├── *_from-boldref_to-auto*_mode-image_xfm.txt
# Surface & CIFTI outputs
├── *_space-fsLR_den-91k_bold.dtseries.nii
├── *_hemi-{L|R}_space-fsnative_bold.func.gii
# Confounds
└── *_desc-confounds_timeseries.tsv
# Label Values Legend
File Prefixes (func/): sub-[ID]_ses-[V0X]_hash-{HASH}_task-rest_dir-PA_run-[X]
STD_SPACE: MNI152NLin6Asym_res-2 , T2w
M-CRIB-S & FreeSurfer🔗
Infant fMRIPrep and XCP-D derivative folder/filenames include unique hash IDs to indicate distinct processing parameters used for a given pipeline. In the case of HBCD data, the hash IDs correspond to which surface reconstruction method was used for processing within Infant fMRIPrep.
M-CRIB-S & FreeSurfer are alternative surface reconstruction methods supported by Infant fMRIPrep, optimized for different age ranges (see details above).
| Method | Hash ID | Description | Visits (Age Range in Months) |
|---|---|---|---|
| M-CRIB-S | 0f306a2f | T2w-based method for neonates | V02 (0-1 m) |
| Infant FreeSurfer | 2afa9081 | T1w-based method for infants 0-2 years old | V02 (0-1 m), V03 (3-9 m), V04 (9-15 m) |
Downstream XCP-D derivatives include a second hash ID (0ef9c88a) indicating the XCP-D processing configuration. This value is identical for all HBCD data because the XCP-D parameters were fixed. Below we summarize the processing workflows and resulting derivative folder names.
The M-CRIB-S and FreeSurfer derivative folders are generated from the intermediate FreeSurfer-like folders produced by Infant fMRIPrep during surface reconstruction. When M-CRIB-S is used, Infant fMRIPrep still creates a FreeSurfer-structured folder containing the M-CRIB-S results mapped to the standard recon-all layout; these appear in the release under freesurfer-0f306a2f/.
hbcd/
└── derivatives/
└── freesurfer-{HASH}/
└── sub-[ID]_ses-[V0X]/
├── label/
│ ├── {lh|rh}.{ATLAS}.annot
│ ├── {lh|rh}.{ATLAS}.auto.nomask.annot
│ └── {lh|rh}.cortex.label
│
├── mri/
│ ├── T2.mgz
│ ├── {ATLAS}+aseg.mgz
│ ├── aseg*.mgz
│ ├── {brain|brainmask}.mgz
│ ├── {lh|rh}.ribbon.mgz
│ ├── norm.mgz
│ ├── orig.mgz
│ └── ribbon.mgz
│
├── stats/
│ ├── aseg.stats
│ ├── brainvol.stats
│ ├── {lh|rh}.{ATLAS}.stats
│ └── {lh|rh}.curv.stats
│
├── surf/
│ ├── {lh|rh}.{white,pial,midthickness}
│ ├── {lh|rh}.{inflated,sphere}*
│ ├── {lh|rh}.smoothwm*
│ ├── {lh|rh}.{area,area.mid,area.pial}
│ └── {lh|rh}.{curv,sulc,thickness,volume}
│
└── scripts/*
# Label Legend
HASH: 0f306a2f | 2afa9081
HEM: lh | rh
ATLAS: aparc | aparc+DKTatlas
hbcd/
└── derivatives/
└── mcribs-0f306a2f/
└── sub-[ID]_ses-V02/
├── RawT2/sub-[ID]_ses-V02.nii.gz
├── RawT2RadiologicalIsotropic/sub-[ID]_ses-V02.nii.gz_symlink_s3_object
├── SurfReconDeformable/
│ └── sub-[ID]_ses-V02/
│ ├── meshes/
│ │ ├── {internal|pial|white|pial+internal|white+internal}.vtp
│ │ ├── pial-{lh|rh}.vtp
│ │ ├── pial-{lh|rh}-reordered.vtp
│ │ └── white-{lh|rh}.{CortexMask.curv|Normals.surf|RegionId.curv|vtp}
│ ├── recon/
│ │ ├── cortical-hull-dmap.nii.gz
│ │ └── regions.nii.gz
│ └── temp/
│ ├── brain-mask.nii.gz
│ ├── ventricles-dmap.nii.gz
│ ├── t2w-image.nii.gz_symlink_s3_object
│ ├── cerebrum-{lh|rh}-dmap.nii.gz
│ ├── cerebrum-{lh|rh}-hull-[X].vtp
│ ├── cerebrum-{lh|rh}-iso.vtp
│ ├── {STRUCT}-mask-[X].nii.gz
│ ├── {cerebrum-lh|cerebrum-rh|pial|white}-[X].vtp
│ ├── {cerebrum-lh|cerebrum-rh|pial|white}-[X]-output_[X].vtp
│ │
│ └── {pial|white}-foreground.nii.gz
├── TissueSeg/
│ ├── sub-[ID]_ses-V02_all_labels.nii.gz
│ ├── sub-[ID]_ses-V02_all_labels_manedit.nii.gz_symlink_s3_object
│ ├── sub-[ID]_ses-V02_brain_mask.nii.gz
│ └── sub-[ID]_ses-V02_t2w_restore.nii.gz_symlink_s3_object
├── TissueSegDrawEM/sub-[ID]_ses-V02/N4/sub-[ID]_ses-V02.nii.gz_symlink_s3_object
├── freesurfer/ # M-CRIB-S–specific outputs
│ └── sub-[ID]_ses-V02/
│ └── mri/
│ └── {brain|orig}.mgz_symlink_s3_object
├── logs/sub-[ID]_ses-V02.log
└── command.txt
# Label Values Legend
STRUCT: brain | cerebrum-{lh/rh} | corpus-callosum | cortex | {deep-gray|gray|white}-matter | ventricles
When downloaded, the symlink files present within the M-CRIB-S derivatives (mcribs-0f306a2f/), appended with *_symlink_s3_object, appear as text files that contain the S3 object path instead of the actual file content. If needed, you may restore these files as symlinks via the following terminal command, which restores all symlink files within your locally downloaded directory and renames them without *_symlink_s3_object to match the original sourcedata filenames:
To simply print the commands needed to restore individual symlinks without making any changes to the local data, run the following command:
XCP-D🔗
XCP-D performs functional MRI post-processing and noise regression from Infant-fMRIPrep derivatives, producing cleaned and parcellated data (see parcellation atlases) ready for analysis.
Anatomical Processing
Native-space T2w images are transformed into standard MNI152NLin6Asym space (1 mm³ resolution).
Morphometric surfaces (fsLR-space) from Infant fMRIPrep are copied to the XCP-D derivatives. HCP-style midthickness, inflated, and very-inflated surfaces are generated from the white-matter and pial surface meshes and mapped to fsLR space.
Functional Processing
For each BOLD run, XCP-D performs a series of cleanup and quality-control steps:
- First 4 volumes (dummy scans) are removed.
- Motion correction: Framewise displacement (FD) is calculated per Power et al. (2014); volumes with FD > 0.3 mm flagged as high-motion outliers.
- Nuisance regression: 36 confound regressors (motion, tissue, and global signals plus derivatives) regressed out following the 36P strategy.
- Despiking and filtering: Data despiked, temporally filtered (0.01–0.08 Hz), and smoothed (6 mm FWHM).
- Censoring: High-motion volumes are interpolated and later censored to minimize motion artifacts.
- Amplitude of Low-Frequency Fluctuations (ALFF) and Regional Homogeneity (ReHo) metrics computed from cleaned data.
- Parcellated time series are extracted for each atlas and pairwise functional connectivity is calculated as the Pearson correlation between regional time series.
- Postprocessed derivatives are concatenated across runs.
Below is a high-level summary of the file structure of XCP-D derivatives. See Supplement: Detailed XCP-D Derivatives Guide for a detailed guide to the anatomical and functional files as well as the MRI Derivatives Quick Start Guide below.
# JSON files excluded for brevity How To Read File Trees → hbcd/ └── derivatives/ └── xcp_d-{HASH}/ └── sub-[ID]/ └── ses-[V0X]/ │ ├── anat/ │ # File Prefix: sub-[ID]_ses-[V0X]_hash-{HASH}_run-[X] │ ├── *_space-MNI152NLin6Asym_desc-preproc_T2w.nii.gz │ ├── *_space-fsLR_seg-{PARC}_stat-mean_desc-{METRIC}_morph.tsv │ ├── *_hemi-{L|R}_space-fsLR_den-32k_{SURF}.surf.gii │ └── *_space-fsLR_den-91k_{METRIC}.dscalar.nii │ ├── func/ │ # File Prefix: sub-[ID]_ses-[V0X]_hash-{HASH}_task-rest │ ├── *_desc-abcc_qc.hdf5 │ ├── *_{motion|outliers}.tsv │ ├── *_space-fsLR_den-91k_desc-{denoised|denoisedSmoothed}_bold.dtseries.nii │ ├── *_space-fsLR_seg-{PARC}_den-91k_stat-mean_timeseries.ptseries.nii │ ├── *_space-fsLR_seg-{PARC}_stat-mean_timeseries.tsv │ ├── *_space-fsLR_seg-{PARC}_stat-pearsoncorrelation_relmat.tsv │ │ ├── *_dir-PA_run-[X]_desc-abcc_qc.hdf5 │ ├── *_dir-PA_run-[X]_space-fsLR_den-91k_desc-linc_qc.tsv │ ├── *_dir-PA_run-[X]_{design|motion|outliers}.tsv │ ├── *_dir-PA_run-[X]_space-fsLR_den-91k_desc-{denoised|denoisedSmoothed}_bold.dtseries.nii │ ├── *_dir-PA_run-[X]_space-fsLR_den-91k_stat-{alff|reho }_boldmap.dscalar.nii │ ├── *_dir-PA_run-[X]_space-fsLR_den-91k_stat-alff_desc-smooth_boldmap.dscalar.nii │ │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_den-91k_stat-coverage_boldmap.pscalar.nii │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_den-91k_stat-mean_timeseries.ptseries.nii │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_den-91k_stat-pearsoncorrelation_boldmap.pconn.nii │ │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_stat-{alff|reho}_bold.tsv │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_stat-coverage_bold.tsv │ ├── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_stat-mean_timeseries.tsv │ └── *_dir-PA_run-[X]_space-fsLR_seg-{PARC}_stat-pearsoncorrelation_relmat.tsv │ ├── figures/* ├── sub-[ID]_ses-[V0X]_hash-{HASH}_executive_summary.html └── sub-[ID].html # ── Label Legend ───────────────────────────────────────────── HASH : 0f306a2f+0ef9c88a , 2afa9081+0ef9c88a METRIC : curv , sulc , thickness PARC : 4S-{156|256|...|1056}Parcels , Glasser , Gordon , MIDB , MyersLabonte , HCP (func/ only) , Tian (func/ only) SURF : midthickness , pial , white , inflated , vinflated
MRI Derivatives Quick Start Guide🔗
Below is a summary of key MRI derivatives used for structural morphology and resting-state functional MRI (rsfMRI) functional connectivity analyses. Key derivatives, produced by the XCP-D pipeline, include volumetric and surface-based time series for each participant. The data release also includes dense and parcellated time series with at least 2.5 minutes of low-motion data (FD>0.3), functional connectivity matrices, regional homogeneity values, and amplitude of low-frequency fluctuation values.
Curvature, Sulcal Depth, & Cortical Thickness
File: anat/*_space-fsLR_den-91k_{curv|sulc|thickness}.dscalar.nii
Recommended for: Vertex-wise cortical morphology analyses (e.g., folding, curvature, thickness comparisons).
These CIFTI scalar files contain surface-based structural metrics derived from reconstructed L/R cortical surfaces, aligned to the fsLR template (~64k vertices per hemisphere).
- Curvature: Characterizes cortical folding and morphology; often used as a covariate in morphometric analyses.
- Sulcal depth: Complements curvature to describe cortical shape and folding complexity.
- Cortical thickness: Distance between pial and white matter surfaces (mm); typically averaged within ROIs or compared across participants to study development, aging, or group effects.
Parcellated Structural Measures
File: anat/*_space-fsLR_seg-{PARC}_stat-mean_desc-{curv|sulc|thickness}_morph.tsv
Recommended for: Region-based (ROI-level) analyses such as group comparisons or developmental modeling.
Tabulated summaries of cortical metrics (curvature, sulcal depth, thickness) within anatomical regions defined by parcellation atlases. These files provide regional averages for statistical modeling or visualization.
Midthickness, Pial, and White Matter Surfaces
File: anat/*_hemi-{L|R}_space-fsLR_den-32k_{midthickness|pial|white}.surf.gii
Recommended for: Visualizing cortical anatomy or mapping functional data to anatomical space.
3D surface models representing the midthickness, gray–white matter boundary, and pial surfaces for each hemisphere. Useful for rendering structural data, computing surface-based metrics, or visualizing functional overlays.
Dense Timeseries
File: func/*_task-rest_space-fsLR_den-91k_desc-{denoised|denoisedSmoothed}_bold.dtseries.nii
Recommended for: Voxelwise or seed-based FC analyses, timeseries analysis via sliding windows or markov chains, etc.
CIFTI dense time series containing fully preprocessed, temporally filtered, and nuisance-regressed BOLD data. These files combine the left and right surfaces, aligned to the standard fsLR surface template, with the subcortical volume annotated by subcortical structure. Each greyordinate (~96k total) represents a vertex or voxel with pre-processed resting-state functional MRI time-series.
Parcellated Timeseries
File: func/*_task-rest_space-fsLR_seg-{PARC}_stat-mean_timeseries.tsv
Recommended for: ROI-to-ROI connectivity or network analyses using mean BOLD signals per region.
Tabulated mean BOLD time series for each region in the
parcellation atlases.
Also available as CIFTI .ptseries.nii files, where columns = regions and rows = timepoints.
Connectivity Matrices
File: func/*_task-rest_space-fsLR_seg-{PARC}_stat-pearsoncorrelation_relmat.tsv
Recommended for: Quick inspection, validation, or as input to network and graph analyses.
Tab-delimited matrices of pairwise Pearson correlations between atlas regions, computed from parcellated time series using all available low-motion data (motion censored with a framewise displacement threshold of 0.3 mm). These matrices form the foundation for ROI-to-ROI connectivity analyses.
Motion Detection and Confound Files
File: func/*_task-rest_dir-PA_run-{X}_{design|motion|outliers}.tsv
Required for: Motion assessment and filtering low-quality data prior to group analyses.
Includes framewise displacement values and nuisance regressor design files. Design files contain one column per regressor (e.g., motion parameters, high-motion outlier volume indicators). See the XCP-D documentation for details.
See Parcellations & Atlases in the XCP-D documentation for more details.
| Atlas | Description |
|---|---|
Glasser |
Multimodal anatomical atlas (population-level) Derived from multimodal MRI data (Glasser et al., 2016) Surface-based morphology, population-level structure |
Gordon |
Functional atlas (333 ROIs) rs-fMRI boundary detection (120 young adults, ~14 min per subject; Gordon et al., 2016) Functional network mapping, group-level FC analyses |
HCP |
Multimodal cortical atlas (360 ROIs) Combined task, resting-state, and diffusion MRI (210 young adults; Glasser et al., 2013) Cross-modal structural–functional alignment |
MIDB |
Precision functional atlas (individualized) Derived from ABCD data using a 75% probability threshold (Hermosillo et al., 2024) Individualized functional network mapping |
Myers-Labonte |
Infant probabilistic functional atlas 50% probability threshold; infant population (Myers et al., 2023) Infant functional network mapping |
Tian |
Subcortical parcellation atlas High-resolution subcortical segmentation (Tian et al., 2020) Subcortical connectivity analyses |
4S{X}56Parcels |
Multimodal atlas (multi-resolution) Schaefer cortical parcellations (100–1000 parcels) supplemented with subcortical and cerebellar regions (AtlasPack) Cross-modality alignment across XCP-D, QSIPrep, and ASLPrep |
Quality Control Summary Statistics🔗
We evaluated the impact of data quality on functional connectivity. Average functional connectivity matrices were computed using the Gordon-parcellated time series available in the V02 XCP-D derivatives. Data were included based on varying thresholds of BrainSwipes QC scores. Functional connectivity patterns were not substantially altered with the inclusion of lower-quality data, indicating robustness to mild quality variation
Connectivity matrices as data quality improves (left -> right) based on QC thresholds of 0.1, 0.5, and 0.9:
References🔗
Adamson, C. L., Alexander, B., Ball, G., Beare, R., Cheong, J. L. Y., Spittle, A. J., Doyle, L. W., Anderson, P. J., Seal, M. L., & Thompson, D. K. (2020). Parcellation of the neonatal cortex using Surface-based Melbourne Children’s Regional Infant Brain atlases (M-CRIB-S). Scientific Reports, 10(1), 4359. https://doi.org/10.1038/s41598-020-61326-2
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