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On this page

  • Domain overview
    • Image processing
    • Cortical surface reconstruction
    • Morphometry
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    • Regions of interest (ROIs)
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    • Effects of scanner instance and software version
    • High density, phased array head coils
    • Siemens normalized sMRI DICOMs
  1. Imaging data
  2. Scan types
  3. Documentation
  4. Imaging
  5. Structural MRI

Structural MRI

Domain overview

Please scroll horizontally to view the number of variables and events of administration for the displayed tables.

The Structural MRI (SMRI) data conatins T1-weighted (T1w) 3D structural images and T2-weighted (T2w) 3D structural images.

Image processing

  • corrected for gradient nonlinearity distortions (Jovicich et al. 2006)
  • T2w images registered to T1w images using mutual information (Wells et al. 1996)
  • Intensity non-uniformity correction based on tissue segmentation and sparse spatial smoothing
  • Resampled with 1 mm isotropic voxels into rigid alignment with an atlas brain

Cortical surface reconstruction

  • FreeSurfer v7.1.1
  • Skull-stripping (F. Segonne et al. 2004)
  • White matter segmentation, initial mesh creation (Dale, Fischl, and Sereno 1999)
  • Correction of topological defects (B. Fischl, Liu, and Dale 2001; Florent Segonne, Pacheco, and Fischl 2007)
  • Surface optimization (Dale, Fischl, and Sereno 1999; Dale and Sereno 1993; Bruce Fischl and Dale 2000)
  • Nonlinear registration to a spherical surface-based atlas (Bruce Fischl, Sereno, and Dale 1999)

Morphometry

  • Subcortical regional volume
  • Cortical volume
  • Cortical thickness (Bruce Fischl and Dale 2000)
  • Cortical area (Chen et al. 2012; Joyner et al. 2009)
  • Sulcal depth (Bruce Fischl, Sereno, and Dale 1999)

Image intensity measures

  • T1w and T2w intensity measures in white matter (-0.2 mm from gray/white boundary)
  • T1w and T2w intensity measures in gray matter (+0.2 mm from gray/white boundary)
  • Normalized T1w and T2w cortical gray/white intensity contrast (Westlye et al. 2009)

Regions of interest (ROIs)

  • Subcortical structures labeled with atlas-based segmentation (Bruce Fischl et al. 2002)
  • Cortical regions labeled with the Desikan atlas-based classification (Desikan et al. 2006)
  • Cortical regions labeled with the Destrieux atlas-based classification (Destrieux et al. 2010)
  • Fuzzy-cluster parcels, based on genetic correlation of surface area (Chen et al. 2012); Note, all fuzzy clusters are based on genetic clustering of surface area data, even when applied to other phenotypes such as thickness and volume.

Methods

Image processing and analysis methods corresponding to ABCD Release 2.0.1 are described in Hagler et al., 2019, NeuroImage. Image processing and analysis methods for the Adolescent Brain Cognitive Development Study (doi: 10.1016/j.neuroimage.2019.116091). Changes to image processing and analysis methods in Release 4.0 are documented in Structural MRI Release Notes. No significant changes were made to the processing pipeline for Release 3.0, 5.0, or 6.0.

Notes

Effects of scanner instance and software version

Multisite longitudinal MRI imaging data are potentially susceptible to influences of scanning parameters, scanner manufacturer, scanner model, and software version, which vary across sites, and sometimes across time. For more information, please refer to “Effects of scanner instance and software version” in Imaging Overview Data Documentation.

High density, phased array head coils

The ABCD acquisition sites use either 32 channel head or 64 channel head/neck coils, depending on availability. Standard correction methods, such as those used by FreeSurfer, are limited when compensating for steep spatial intensity variation, leading to inaccurate brain segmentation or cortical surface reconstruction. For example, brain tissue farther from the coils, such as the temporal and frontal poles, typically have lower intensity values resulting in focal underestimation of the white matter surface or the elimination of large pieces of the cortical surface reconstruction. Furthermore, brain tissue close to coils with extremely high intensity values may be mistaken for non-brain tissue (e.g., scalp). To overcome this, the ABCD minimally processed structural MRI (sMRI) files include an improved intensity inhomogeneity correction, using a smoothly varying bias field optimized to standardize image intensities within all white matter voxels.

Siemens normalized sMRI DICOMs

Siemens scanners provide an intensity normalization procedure to correct for bias fields. As a result, Siemens scanners have two sets of DICOMs for each sMRI data series (i.e. T1, T2, T1_NORM, and T2_NORM). The non-normalized images with large intensity ranges (e.g. with a 64 channel head coil) may be clipped at 4095, impairing cortical reconstruction. The ABCD pipeline uses only the normalized sMRI (T1_NORM and T2_NORM), but both versions are included in the source data DICOMs and raw data NIfTI files.

Reference: Hagler et al. (2019)

References

Chen, Chi-Hua, E. D. Gutierrez, Wes Thompson, Matthew S. Panizzon, Terry L. Jernigan, Lisa T. Eyler, Christine Fennema-Notestine, et al. 2012. Science 335 (6076): 1634–36. doi:10.1126/science.1215330.
Dale, Anders M., Bruce Fischl, and Martin I. Sereno. 1999. NeuroImage 9 (2): 179–94. doi:10.1006/nimg.1998.0395.
Dale, Anders M., and Martin I. Sereno. 1993. Journal of Cognitive Neuroscience 5 (2): 162–76. doi:10.1162/jocn.1993.5.2.162.
Desikan, Rahul S., Florent Ségonne, Bruce Fischl, Brian T. Quinn, Bradford C. Dickerson, Deborah Blacker, Randy L. Buckner, et al. 2006. NeuroImage 31 (3): 968–80. doi:10.1016/j.neuroimage.2006.01.021.
Destrieux, Christophe, Bruce Fischl, Anders Dale, and Eric Halgren. 2010. NeuroImage 53 (1): 1–15. doi:10.1016/j.neuroimage.2010.06.010.
Fischl, B., A. Liu, and A. M. Dale. 2001. IEEE Transactions on Medical Imaging 20 (1): 70–80. doi:10.1109/42.906426.
Fischl, Bruce, and Anders M. Dale. 2000. Proceedings of the National Academy of Sciences 97 (20): 11050–55. doi:10.1073/pnas.200033797.
Fischl, Bruce, David H. Salat, Evelina Busa, Marilyn Albert, Megan Dieterich, Christian Haselgrove, Andre Van Der Kouwe, et al. 2002. Neuron 33 (3): 341–55. doi:10.1016/S0896-6273(02)00569-X.
Fischl, Bruce, Martin I. Sereno, and Anders M. Dale. 1999. NeuroImage 9 (2): 195–207. doi:10.1006/nimg.1998.0396.
Hagler, Donald J., SeanN. Hatton, M. Daniela Cornejo, Carolina Makowski, Damien A. Fair, Anthony Steven Dick, Matthew T. Sutherland, et al. 2019. NeuroImage 202 (November): 116091. doi:10.1016/j.neuroimage.2019.116091.
Jovicich, Jorge, Silvester Czanner, Douglas Greve, Elizabeth Haley, Andre Van Der Kouwe, Randy Gollub, David Kennedy, et al. 2006. NeuroImage 30 (2): 436–43. doi:10.1016/j.neuroimage.2005.09.046.
Joyner, Alexander H., Cooper Roddey J., Cinnamon S. Bloss, Trygve E. Bakken, Lars M. Rimol, Ingrid Melle, Ingrid Agartz, et al. 2009. Proceedings of the National Academy of Sciences 106 (36): 15483–88. doi:10.1073/pnas.0901866106.
Segonne, F., A. M. Dale, E. Busa, M. Glessner, D. Salat, H. K. Hahn, and B. Fischl. 2004. NeuroImage 22 (3): 1060–75. doi:10.1016/j.neuroimage.2004.03.032.
Segonne, Florent, Jenni Pacheco, and Bruce Fischl. 2007. IEEE Transactions on Medical Imaging 26 (4): 518–29. doi:10.1109/TMI.2006.887364.
Wells, William M., Paul Viola, Hideki Atsumi, Shin Nakajima, and Ron Kikinis. 1996. Medical Image Analysis 1 (1): 35–51. doi:10.1016/S1361-8415(01)80004-9.
Westlye, Lars T., Kristine B. Walhovd, Anders M. Dale, Thomas Espeseth, Ivar Reinvang, Naftali Raz, Ingrid Agartz, Douglas N. Greve, Bruce Fischl, and Anders M. Fjell. 2009. NeuroImage 47 (4): 1545–57. doi:10.1016/j.neuroimage.2009.05.084.
 

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