Diffusion MRI

WarningData consideration: Potential body-size dependent effects in ABCD diffusion MRI (dMRI) data

We have identified a potential body-size dependent effect in dMRI data acquired on Siemens scanners running the MGH sequence on VE11 software. This issue affects ABCD dMRI data, and not HBCD dMRI data. The impact of this confound on derived ABCD dMRI data is still being evaluated.

Technical Details. The nominal 90°/180° flip angles in the protocol are reduced for all ABCD dMRI scans acquired using VE11 software, with larger participants showing greater reduction. This appears to be due to the sequence requesting RF transmitter coil voltages that exceed hardware limits.

Such reduced flip angles are expected to reduce the measured signal amplitude. Most diffusion metrics are based on relative signal intensities (e.g., Fractional Anisotropy [FA], Mean Diffusivity [MD]), so would not be directly biased by systematic reductions in signal. However, when the signal approaches the noise floor (which may be the case for high b-values acquisitions; e.g., Restriction Spectrum Imaging [RSI], Diffusion Kurtosis Imaging [DKI]) the increased relative noise can introduce an indirect bias into the measured signal. This body-size dependent bias and or any variable correlated with it may propagate to derived diffusion metrics, however, the full extent of this effect on traditional longitudinal and population level analyses is yet to be determined.

Scope and Next Steps. While we have established that a potential bias exists, it has not been fully characterized yet. We do not believe that this is a problem on other software versions, such as the CMRR sequence run on Siemens XA30, or other vendors. Thus, it should not affect the HBCD study, which uses CMRR sequences on Siemen’s platforms. However, any dataset collected on a VE11 Siemens scanner using the standard product diffusion sequence may show the same body-size-dependent reduction in flip angle (since the MGH sequence was based primarily on Siemens product libraries). At this stage we do not have sufficient evidence to support recommendations for changes to analyses, such as exclusion criteria, covariates or harmonization strategies.

Updates and More Information. We will provide updates as our investigations progress.

Domain overview

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The Diffusion MRI (DTI) data contains imaging field maps, non-diffusion weighted images with opposite phase encode polarity, and multi-b-value, multi-direction diffusion weighted images.

Image processing

Diffusion tensor imaging (DTI) analysis

mr_y_dti

  • Conventional DTI methods (Basser et al., 1994; Le Bihan et al., 2001; Pierpaoli et al., 1996)
  • Two modeling approaches
    • DTI inner shell (DTIIS): b values > 1000 excluded from tensor fitting. DTI inner shell may be useful when comparing results to other, single-shell diffusion studies, particularly for MD, LD, or TD.
    • DTI full shell (DTIFS): all b values used in the tensor fitting DTI full shell is recommended for most applications.
  • Measures of microstructural tissue properties
    • Fractional anisotropy (FA)
    • Mean diffusivity (MD)
    • Longitudinal (or axial) diffusivity (LD)
    • Transverse (or radial) diffusivity (TD)

Restriction Spectrum Imaging (RSI)

mr_y_rsi

  • Linear estimation approach allowing for mixtures of “restricted”, “hindered”, and “free” diffusion pools within individual voxels (White et al., 2013; White et al., 2014)
  • Takes advantage of multiple b-value acquisition
  • Two signal fractions modeled as fiber orientation density (FOD) functions
    • Longitudinal diffusivity constant for both fractions, with a value of 1.0 x 10-3 mm2/s
    • Restricted fraction (e.g. intracellular): transverse diffusivity modelled as 0
    • Hindered fraction (e.g. extracellular): transverse diffusivity modelled as 0.9 x 10-3 mm2/s
  • One signal fractions modeled as isotropic free water diffusion
    • Free fraction (e.g., CSF): isotropic diffusivity modeled as 3.0 x10-3 mm2/s
  • Measures derived from the RSI model fit (see Supplementary Tables)
    • Restricted normalized isotropic (RNI, previously N0)
      • The 0th order spherical harmonic coefficient of the restricted fraction divided by the Euclidean norm (square root of the sum of squares) of all model coefficients
    • Restricted normalized directional or “neurite density” (RND, previously ND)
      • Norm of the 2nd and 4th order spherical harmonic coefficients of the restricted fraction divided by the norm of all model coefficients
    • Restricted normalized total (RNT, previously NT)
      • Norm of the 0th, 2nd, and 4th order spherical harmonic coefficients of the restricted fraction divided by the norm of all model coefficients
    • Hindered normalized isotropic (HNI, previously N0_s2)
      • The 0th order spherical harmonic coefficient of the hindered fraction divided by the norm of all model coefficients
    • Hindered normalized directional (HND, previously ND_s2)
      • Norm of the 2nd and 4th order spherical harmonic coefficients of the hindered fraction divided by the norm of all model coefficients
    • Hindered normalized total (HNT, aka NT_s2)
      • Norm of the 0th, 2nd, and 4th order spherical harmonic coefficients of the hindered fraction divided by the norm of all model coefficients
    • Free normalized isotropic (FNI)
      • The 0th order spherical harmonic coefficient of the free water fraction divided by the norm of all model coefficients

Regions of interest (ROIs)

  • Subcortical structures labeled with atlas-based segmentation (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)

  • White and gray matter values sampled near gray/white boundary (Elman et al., 2017)

  • Major white matter tracts labelled using AtlasTrack (Hagler et al., 2009)

    • Voxels containing primarily gray matter or cerebral spinal fluid excluded from analysis

  • Atlas files and additional AtlasTrack documentation available here.

  • Subcortical structures labeled with atlas-based segmentation (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)

  • White and gray matter values sampled near gray/white boundary (Elman et al., 2017)

  • Major white matter tracts labelled using AtlasTrack (Hagler et al., 2009)

    • Voxels containing primarily gray matter or cerebral spinal fluid excluded from analysis

  • Atlas files and additional AtlasTrack documentation available here.

Methods

Image processing and analysis methods corresponding to ABCD Release 2.0.1 are described in Hagler et al. (2019). Changes to image processing and analysis methods in prior releases are documented in Imaging Overview Data Documentation. No significant changes were made to the processing pipeline for Release 5.0 or Release 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 diffusion imaging, parameters obtained from different scanner manufacturers/models have been found to vary substantially. For more information, please refer to the “Effects of scanner instance and software version” in Imaging Overview data documentation.

Key references:

  • Dick, A. S., Lopez, D. A., Watts, A. L., Heeringa, S., Reuter, C., Bartsch, H., Fan, C. C., Kennedy, D. N., Palmer, C., Marshall, A., Haist, F., Hawes, S., Nichols, T. E., Barch, D. M., Jernigan, T. L., Garavan, H., Grant, S., Pariyadath, V., Hoffman, E., Neale, M., Stuart, E. A., Paulus, M. P., Sher, K. J., & Thompson, W. K. (2021). NeuroImage, 239, 118262. https://doi.org/10.1016/j.neuroimage.2021.118262
    Dick et al. (2021)
  • Dudley, J. A., Maloney, T. C., Simon, J. O., Atluri, G., Karalunas, S. L., Altaye, M., Epstein, J. N., & Tamm, L. (2023). Neuroinformatics, 21(2), 323–337. https://doi.org/10.1007/s12021-023-09624-8
    Dudley et al. (2023)
  • Fortin, J., Parker, D., Tunç, B., Watanabe, T., Elliott, M. A., Ruparel, K., Roalf, D. R., Satterthwaite, T. D., Gur, R. C., Gur, R. E., Schultz, R. T., Verma, R., & Shinohara, R. T. (2017). NeuroImage, 161, 149–170. https://doi.org/10.1016/j.neuroimage.2017.08.047
    Fortin et al. (2017)
  • Holland, D., Kuperman, J. M., & Dale, A. M. (2010). NeuroImage, 50(1), 175–183. https://doi.org/10.1016/j.neuroimage.2009.11.044
    Holland et al. (2010)
  • Palmer, C. E., Pecheva, D., Iversen, J. R., Hagler, D. J., Sugrue, L., Nedelec, P., Fan, C. C., Thompson, W. K., Jernigan, T. L., & Dale, A. M. (2022). Developmental Cognitive Neuroscience, 53, 101044. https://doi.org/10.1016/j.dcn.2021.101044
    Palmer et al. (2022)

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