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Harmonization of White and Gray Matter Features in Diffusion Microarchitecture for Cross-Sectional Studies

  • Prasanna ParvathaneniEmail author
  • Shunxing Bao
  • Allison Hainline
  • Yuankai Huo
  • Kurt G. Schilling
  • Hakmook Kang
  • Owen Williams
  • Neil D. Woodward
  • Susan M. Resnick
  • David H. Zald
  • Ilwoo Lyu
  • Bennett A. Landman
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 31)

Abstract

Understanding of the specific processes involved in the development of brain microarchitecture and how these are altered by genetic, cognitive, or environmental factors is a key to more effective and efficient interventions. With the increasing number of publicly available neuroimaging databases, there is an opportunity to combine large-scale imaging studies to increase the power of statistical analyses to test common biological hypotheses. However, cross-study, cross-sectional analyses are confounded by inter-scanner variability that can cause both spatially and anatomically dependent signal aberrations. In particular, scanner-related differences in the diffusion-weighted magnetic resonance imaging (DW-MRI) signal are substantially different in tissue types like cortical/subcortical gray matter and white matter. Recent studies have shown effective harmonization using the ComBat technique (adopted from genomics) to address inter-site variability in white matter using diffusion tensor imaging (DTI) microstructure indices like fractional anisotropy (FA) or mean diffusivity (MD). In this study, we propose (1) to apply the correction at voxel level using tract-based spatial statistics (TBSS) in FA, (2) to correct variability across scanners with different gradient strengths in DTI, and (3) to apply the ComBat technique to advanced DW-MRI models, i.e., neurite orientation dispersion and density imaging (NODDI), to correct for variability of orientation dispersion index (ODI) in gray matter using gray matter-based spatial statistics tool (GSBSS). We show that the biological variability with age is retained or improved while correcting for variability across scanners.

Keywords

Harmonization NODDI Brain microstructure Gray matter surface-based analysis 

Notes

Acknowledgements

This work was supported in part by the Intramural Research Program, National Institute on Aging, NIH and by NIH R01EB017230 & R01MH102266 & Grant UL1 RR024975-01 & Grant 2 UL1 TR000445-06 & Grant UL1 RR024975-01. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We thank the participants of the studies.

References

  1. 1.
    O’connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, Boellaard R, Bohndiek SE, Brady M, Brown G (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14(3):169CrossRefGoogle Scholar
  2. 2.
    Thompson PM, Andreassen OA, Arias-Vasquez A, Bearden CE, Boedhoe PS, Brouwer RM, Buckner RL, Buitelaar JK, Bulayeva KB, Cannon DM (2017) ENIGMA and the individual: predicting factors that affect the brain in 35 countries worldwide. Neuroimage 145:389–408CrossRefGoogle Scholar
  3. 3.
    Fortin J-P, Parker D, Tunc B, Watanabe T, Elliott MA, Ruparel K, Roalf DR, Satterthwaite TD, Gur RC, Gur RE (2017) Harmonization of multi-site diffusion tensor imaging data. Neuroimage 161:149–170CrossRefGoogle Scholar
  4. 4.
    Fortin J, Cullen N, Sheline Y, Taylor W, Aselcioglu I, Cook P, Adams P, Cooper C, Fava M, McGrath P (2017) Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167:104–120CrossRefGoogle Scholar
  5. 5.
    Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, Soussan M, Frouin F, Frouin V, Buvat I (2018) A post-reconstruction harmonization method for multicenter radiomic studies in PET. J Nuclear Med jnumed. 117.199935Google Scholar
  6. 6.
    Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31(4):1487–1505CrossRefGoogle Scholar
  7. 7.
    Shock NW (1984) Normal human aging: the Baltimore longitudinal study of agingGoogle Scholar
  8. 8.
    Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC (2012) NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage 61(4):1000–1016CrossRefGoogle Scholar
  9. 9.
    Huo Y, Carass A, Resnick SM, Pham DL, Prince JL, Landman BA (2016) Combining multi-atlas segmentation with brain surface estimation. In: Book combining multi-atlas segmentation with brain surface estimation, NIH Public AccessGoogle Scholar
  10. 10.
    Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41CrossRefGoogle Scholar
  11. 11.
    Lombaert H, Sporring J, Siddiqi K (2013) Diffeomorphic spectral matching of cortical surfaces. Inf Process Med Imaging 23:376–389CrossRefGoogle Scholar
  12. 12.
    Parvathaneni P, Rogers BP, Huo Y, Schilling KG, Hainline AE, Anderson AW, Woodward ND, Landman BA (2017) Gray matter surface based spatial statistics (GS-BSS) in diffusion microstructure. In: Book gray matter surface based spatial statistics (GS-BSS) in diffusion microstructure. Springer, pp 638–646Google Scholar
  13. 13.
    Winkler AM, Ridgway GR, Webster MA, Smith SM, Nichols TE (2014) Permutation inference for the general linear model. Neuroimage 92:381–397CrossRefGoogle Scholar
  14. 14.
    Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44(1):83–98CrossRefGoogle Scholar
  15. 15.
    Winkler AM, Ridgway GR, Douaud G, Nichols TE, Smith SM (2016) Faster permutation inference in brain imaging. NeuroImage 141:502–516CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Prasanna Parvathaneni
    • 1
    Email author
  • Shunxing Bao
    • 2
  • Allison Hainline
    • 4
  • Yuankai Huo
    • 1
  • Kurt G. Schilling
    • 3
  • Hakmook Kang
    • 4
  • Owen Williams
    • 7
  • Neil D. Woodward
    • 5
  • Susan M. Resnick
    • 7
  • David H. Zald
    • 6
  • Ilwoo Lyu
    • 2
  • Bennett A. Landman
    • 1
    • 2
    • 3
    • 5
  1. 1.Electrical EngineeringVanderbilt UniversityNashvilleUSA
  2. 2.Computer ScienceVanderbilt UniversityNashvilleUSA
  3. 3.Vanderbilt University Institute of Imaging Science, Vanderbilt UniversityNashvilleUSA
  4. 4.BiostatisticsVanderbilt UniversityNashvilleUSA
  5. 5.Department of Psychiatry and Behavioral SciencesVanderbilt University School of MedicineNashvilleUSA
  6. 6.Department of Psychology and PsychiatryNashvilleUSA
  7. 7.National Institutes of HealthBethesdaUSA

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