Page Last Updated: May 17, 2026

Structural MRI (sMRI)🔗

Overview & Acquisition🔗

HBCD protocols for structural MRI were informed by recent large-scale developmental neuroimaging studies including ABCD, HCP Lifespan, and BCP (Howell et al., 2019). These studies laid critical foundation for the development of well-validated, high-resolution protocols harmonized across all three major scanner vendors (Casey et al., 2018). In addition, the findings emphasized the importance of T2w acquisition in infants due to suboptimal grey/white T1w contrast resulting from incomplete myelination (Howell et al., 2019, Myers et al., 2023).

Key features of the HBCD T1w and T2w structural imaging protocols include:

0.8 mm isotropic resolution
Compressed SENSE reconstruction enabling high acceleration and shorter acquisition times (<9 min total for T1w & T2w;)
Embedded navigators for motion tracking (White et al., 2010, Tisdall et al., 2016, Andersen et al., 2019)
Harmonization approach similar to the ABCD Study:
- T1w: contrast-relevant parameters matched as closely as possible across vendors
- T2w: vendor-specific parameters selected to achieve comparable contrast and SNR, accounting for differences in 3D T2w pulse sequence implementation

Processing & Derivatives🔗

Structural MRI data is used in several processing pipelines. In addition to the derivatives listed below, sMRI is also critical to Infant fMRIPrep and XCP-D, which generate structural-specific derivatives within an anat/ subfolder (including T1w/T2w images processed to correct for motion and distortions and surface reconstructions). These derivatives are described on the fMRI page.

BIBSNet is a deep learning model optimized for infant MRI brain tissue segmentation (Hendrickson et al. 2024). The BIBSNet pipeline generates native-space brain segmentations and brain masks (as well as volumes.tsv files with ROI volume statistics), which are fed into Infant fMRIPrep for use in anatomical preprocessing and surface reconstruction.

hbcd/
└── derivatives/ 
    └── bibsnet/
        └── sub-[ID]/
            └── ses-[V0X]/
                └── anat/
                    ├── sub-[ID]_ses-[V0X]_space-{T1w|T2w}_desc-aseg_dseg.nii.gz (+JSON)
                    ├── sub-[ID]_ses-[V0X]_space-{T1w|T2w}_desc-aseg_volumes.tsv (+JSON)         
                    └── sub-[ID]_ses-[V0X]_space-{T1w|T2w}_desc-aseg_brain-mask.nii.gz (+JSON)

 How To Read File Trees →

MRIQC extracts image quality metrics (IQMs) for each T1w/T2w and generates visual .html reports. The BME-X pipeline performs motion correction, resolution enhancement, denoising, and harmonization of MR images.

hbcd/
└── derivatives/
    ├── mriqc/                            
    │   ├── sub-[ID]/
    │   │   └── ses-[V0X]/
    │   │       ├── anat/
    │   │       │   └── sub-[ID]_ses-[V0X]_run-[X]_{T1w|T2w}.json
    │   │       └── func/
    │   │           └── sub-[ID]_ses-[V0X]_run-[X]_{T1w|T2w}.json
    │   └── sub-[ID]_ses-[V0X]_run-[X]_{T1w|T2w}.html
    │
    └── bme-x/                  
        └── sub-[ID]/
            └── ses-[V0X]/
                └── anat/
                    |__ sub-[ID]_ses-[V0X]_run-[X]_desc-{enhanced|preproc}_{T1w|T2w}.nii.gz (+JSON)
                    |__ sub-[ID]_ses-[V0X]_run-[X]_space-{T1w|T2w}_desc-brain_mask.nii.gz (+JSON)
                    |__ sub-[ID]_ses-[V0X]_run-[X]_{T1w|T2w}.nii.gz (+JSON)

 How To Read File Trees →

References🔗

Andersen, M., Björkman-Burtscher, I. M., Marsman, A., Petersen, E. T., & Boer, V. O. (2019). Improvement in diagnostic quality of structural and angiographic MRI of the brain using motion correction with interleaved, volumetric navigators. PLoS One, 14(5), e0217145. https://doi.org/10.1371/journal.pone.0217145

Casey, B. J., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., Orr, C. A., Wager, T. D., Banich, M. T., Speer, N. K., Sutherland, M. T., Riedel, M. C., Dick, A. S., Bjork, J. M., Thomas, K. M., … ABCD Imaging Acquisition Workgroup. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001

Hendrickson, T. J., Reiners, P., Moore, L. A., Lundquist, J. T., Fayzullobekova, B., Perrone, A. J., Lee, E. G., Moser, J., Day, T. K. M., Alexopoulos, D., Styner, M., Kardan, O., Chamberlain, T. A., Mummaneni, A., Caldas, H. A., Bower, B., Stoyell, S., Martin, T., Sung, S., … Feczko, E. (2024). BIBSNet: A deep learning Baby image brain segmentation network for MRI scans. In bioRxivorg. https://doi.org/10.1101/2023.03.22.533696

Howell, B. R., Styner, M. A., Gao, W., Yap, P.-T., Wang, L., Baluyot, K., Yacoub, E., Chen, G., Potts, T., Salzwedel, A., Li, G., Gilmore, J. H., Piven, J., Smith, J. K., Shen, D., Ugurbil, K., Zhu, H., Lin, W., & Elison, J. T. (2019). The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development. NeuroImage, 185, 891–905. https://doi.org/10.1016/j.neuroimage.2018.03.049

Myers, M. J., Labonte, A. K., Gordon, E. M., Laumann, T. O., Tu, J. C., Wheelock, M. D., Nielsen, A. N., Schwarzlose, R., Camacho, M. C., Warner, B. B., Raghuraman, N., Luby, J. L., Barch, D. M., Fair, D. A., Petersen, S. E., Rogers, C. E., Smyser, C. D., & Sylvester, C. M. (2023). Functional parcellation of the neonatal brain. In bioRxivorg. https://doi.org/10.1101/2023.11.10.566629

Tisdall, M. D., Reuter, M., Qureshi, A., Buckner, R. L., Fischl, B., & van der Kouwe, A. J. W. (2016). Prospective motion correction with volumetric navigators (vNavs) reduces the bias and variance in brain morphometry induced by subject motion. Neuroimage, 127, 11-22. https://doi.org/10.1016/j.neuroimage.2015.11.054

White, N., Roddey, C., Shankaranarayanan, A., Han, E., Rettmann, D., Santos, J., Kuperman, J., & Dale, A. (2010). PROMO: Real-time prospective motion correction in MRI using image-based tracking. Magnetic Resonance in Medicine, 63(1), 91–105. https://doi.org/10.1002/mrm.22176