Brain Imaging and Behavior

, Volume 12, Issue 2, pp 437–448 | Cite as

An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data

  • Andrew R. Mayer
  • Andrew B. Dodd
  • Josef M. Ling
  • Christopher J. Wertz
  • Nicholas A. Shaff
  • Edward J. Bedrick
  • Carlo Viamonte
Original Research

Abstract

The need for algorithms that capture subject-specific abnormalities (SSA) in neuroimaging data is increasingly recognized across many neuropsychiatric disorders. However, the effects of initial distributional properties (e.g., normal versus non-normally distributed data), sample size, and typical preprocessing steps (spatial normalization, blurring kernel and minimal cluster requirements) on SSA remain poorly understood. The current study evaluated the performance of several commonly used z-transform algorithms [leave-one-out (LOO); independent sample (IDS); Enhanced Z-score Microstructural Assessment of Pathology (EZ-MAP); distribution-corrected z-scores (DisCo-Z); and robust z-scores (ROB-Z)] for identifying SSA using simulated and diffusion tensor imaging data from healthy controls (N = 50). Results indicated that all methods (LOO, IDS, EZ-MAP and DisCo-Z) with the exception of the ROB-Z eliminated spurious differences that are present across artificially created groups following a standard z-transform. However, LOO and IDS consistently overestimated the true number of extrema (i.e., SSA) across all sample sizes and distributions. The EZ-MAP and DisCo-Z algorithms more accurately estimated extrema across most distributions and sample sizes, with the exception of skewed distributions. DTI results indicated that registration algorithm (linear versus non-linear) and blurring kernel size differentially affected the number of extrema in positive versus negative tails. Increasing the blurring kernel size increased the number of extrema, although this effect was much more prominent when a minimum cluster volume was applied to the data. In summary, current results highlight the need to statistically compare the frequency of SSA in control samples or to develop appropriate confidence intervals for patient data.

Keywords

Simulations Single-subject Diffusion tensor imaging Neuroimaging Variability 

Notes

Acknowledgements

This work was supported by the National Institutes of Health (grant numbers 1R01MH101512-01A1 and 1R01NS098494-01A1 to A.M.). The funding agencies had no involvement in the study design, data collection, analyses, writing of the manuscript, or decisions related to submission for publication. We would also like to thank Diana South and Catherine Smith for their assistance with data collection.

Compliance with ethical standards

Conflicts of interest

The authors declare that there are no conflicts of interest.

Human studies and informed consent

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Supplementary material

11682_2017_9702_MOESM1_ESM.docx (1.2 mb)
ESM 1 (DOCX 1217 kb)

References

  1. Bigler, E. D., Abildskov, T. J., Petrie, J., Farrer, T. J., Dennis, M., Simic, N., et al. (2013). Heterogeneity of brain lesions in pediatric traumatic brain injury. Neuropsychology, 27(4), 438–451.CrossRefPubMedGoogle Scholar
  2. Birmingham, A., Selfors, L. M., Forster, T., Wrobel, D., Kennedy, C. J., Shanks, E., et al. (2009). Statistical methods for analysis of high-throughput RNA interference screens. Nature Methods, 6(8), 569–575.CrossRefPubMedPubMedCentralGoogle Scholar
  3. Booth, B. G., Miller, S. P., Brown, C. J., Poskitt, K. J., Chau, V., Grunau, R. E., et al. (2016). STEAM — Statistical template estimation for abnormality mapping: A personalized DTI analysis technique with applications to the screening of preterm infants. NeuroImage, 125, 705–723.CrossRefPubMedGoogle Scholar
  4. Bouix, S., Pasternak, O., Rathi, Y., Pelavin, P. E., Zafonte, R., & Shenton, M. E. (2013). Increased gray matter diffusion anisotropy in patients with persistent post-concussive symptoms following mild traumatic brain injury. PloS One, 8(6), e66205.CrossRefPubMedPubMedCentralGoogle Scholar
  5. Ceritoglu, C., Oishi, K., Li, X., Chou, M. C., Younes, L., Albert, M., et al. (2009). Multi-contrast large deformation diffeomorphic metric mapping for diffusion tensor imaging. NeuroImage, 47(2), 618–627.CrossRefPubMedPubMedCentralGoogle Scholar
  6. Commowick, O. & Stamm, A. (2012). Non-local robust detection of DTI white matter differences with small databases. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 476–484). Berlin Heidelburg: Springer.Google Scholar
  7. Commowick, O., Fillard, P., Clatz, O., & Warfield, S. K. (2008). Detection of DTI white matter abnormalities in multiple sclerosis patients. Medical Image Computer Assist Interventions, 11(Pt 1), 975–982.Google Scholar
  8. Cox, R., & Glen, D. (2006). Efficient, robust, nonlinear, and guaranteed positive definite diffusion tensor estimation. In Seattle: Proceedings of the International Society for Magnetic Resonance and Medicine, 14th Scientific Meeting.Google Scholar
  9. Ding, Z., Gore, J. C., & Anderson, A. W. (2005). Reduction of noise in diffusion tensor images using anisotropic smoothing. Magnetic Resonance in Medicine, 53(2), 485–490.CrossRefPubMedGoogle Scholar
  10. Eklund, A., Nichols, T., Andersson, M., & Knutsson, H. (2015). Empirically investigating the statistical validity of SPM, FSL and AFNI for single subject fMRI analysis. In (pp. 1376–1380). IEEE 12th International Symposium on Biomedical Imaging (ISBI).Google Scholar
  11. Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences of the United States of America, 113(28), 7900–7905.CrossRefPubMedPubMedCentralGoogle Scholar
  12. Friston, K. J., Holmes, A., Poline, J. B., Price, C. J., & Frith, C. D. (1996). Detecting activations in PET and fMRI: Levels of inference and power. NeuroImage, 4(3 Pt 1), 223–235.CrossRefPubMedGoogle Scholar
  13. Ge, Y., Law, M., & Grossman, R. I. (2005). Applications of diffusion tensor MR imaging in multiple sclerosis. Annals of the New York Academy of Sciences, 1064, 202–219.CrossRefPubMedGoogle Scholar
  14. Gebhard, T., Koerte, I., & Bouix, S. (2015). Sample size estimation for outlier detection. In N. Navab, J. Hornegger, W. M. Wells, & A. F. Frangi (Eds.), Medical image computing and computer-assisted intervention - MICCAI 2015 (pp. 743–750). Switzerland: Springer International Publishing.CrossRefGoogle Scholar
  15. Hayasaka, S., Phan, K. L., Liberzon, I., Worsley, K. J., & Nichols, T. E. (2004). Nonstationary cluster-size inference with random field and permutation methods. NeuroImage, 22(2), 676–687.CrossRefPubMedGoogle Scholar
  16. Jones, D. K., & Cercignani, M. (2010). Twenty five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine, 23(7), 803–820.CrossRefPubMedGoogle Scholar
  17. Kim, N., Branch, C. A., Kim, M., & Lipton, M. L. (2013). Whole brain approaches for identification of microstructural abnormalities in individual patients: Comparison of techniques applied to mild traumatic brain injury. PloS One, 8(3), e59382.CrossRefPubMedPubMedCentralGoogle Scholar
  18. Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M. C., et al. (2009). Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage, 46(3), 786–802.CrossRefPubMedPubMedCentralGoogle Scholar
  19. Landman, B. A., Yang, X., & Kang, H. (2012). Do we really need robust and alternative inference methods for brain MRI? In (pp. 77–93). Springer.Google Scholar
  20. Lindquist, M. A. (2008). The statistical analysis of fMRI data. Statistical Science, 23(4), 439–464.CrossRefGoogle Scholar
  21. Lucchinetti, C., Bruck, W., Parisi, J., Scheithauer, B., Rodriguez, M., & Lassmann, H. (2000). Heterogeneity of multiple sclerosis lesions: Implications for the pathogenesis of demyelination. Annals of Neurology, 47(6), 707–717.CrossRefPubMedGoogle Scholar
  22. Mac Donald, C. L., Dikranian, K., Bayly, P., Holtzman, D., & Brody, D. (2007). Diffusion tensor imaging reliably detects experimental traumatic axonal injury and indicates approximate time of injury. The Journal of Neuroscience, 27(44), 11869–11876.CrossRefPubMedPubMedCentralGoogle Scholar
  23. Mayer, A. R., Bedrick, E. J., Ling, J. M., Toulouse, T., & Dodd, A. (2014). Methods for identifying subject-specific abnormalities in neuroimaging data. Human Brain Mapping, 35(11), 5457–5470.CrossRefPubMedGoogle Scholar
  24. Mori, S., & van Zijl, P. C. (2007). Human white matter atlas. The American Journal of Psychiatry, 164(7), 1005.CrossRefPubMedGoogle Scholar
  25. Nichols, T., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: A comparative review. Statistical Methods in Medical Research, 12(5), 419–446.CrossRefPubMedGoogle Scholar
  26. Nichols, T. E., & Holmes, A. P. (2002). Nonparametric permutation tests for functional neuroimaging: A primer with examples. Human Brain Mapping, 15(1), 1–25.CrossRefPubMedGoogle Scholar
  27. Parrish, T. B., Gitelman, D. R., LaBar, K. S., & Mesulam, M. M. (2000). Impact of signal-to-noise on functional MRI. Magnetic Resonance in Medicine, 44(6), 925–932.CrossRefPubMedGoogle Scholar
  28. Pasternak, O., Koerte, I. K., Bouix, S., Fredman, E., Sasaki, T., Mayinger, M., et al. (2014). Hockey concussion education project, part 2. Microstructural white matter alterations in acutely concussed ice hockey players: A longitudinal free-water MRI study. Journal of Neurosurgery, 120(4), 873–881.CrossRefPubMedPubMedCentralGoogle Scholar
  29. Rosenberg, G. A. (2012). Neurological diseases in relation to the blood-brain barrier. Journal of Cerebral Blood Flow and Metabolism, 32(7), 1139–1151.CrossRefPubMedPubMedCentralGoogle Scholar
  30. Saad, Z. S., Glen, D. R., Chen, G., Beauchamp, M. S., Desai, R., & Cox, R. W. (2009). A new method for improving functional-to-structural MRI alignment using local Pearson correlation. NeuroImage, 44(3), 839–848.CrossRefPubMedGoogle Scholar
  31. Schwarz, C. G., Reid, R. I., Gunter, J. L., Senjem, M. L., Przybelski, S. A., Zuk, S. M., et al. (2014). Improved DTI registration allows voxel-based analysis that outperforms tract-based spatial statistics. NeuroImage, 94, 65–78.CrossRefPubMedPubMedCentralGoogle Scholar
  32. Shaker, M., Erdogmus, D., Dy, J., & Bouix, S. (2017). Subject-specific abnormal region detection in traumatic brain injury using sparse model selection on high dimensional diffusion data. Medical Image Analysis.Google Scholar
  33. Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1), 83–98.CrossRefPubMedGoogle Scholar
  34. Suri, A. K., Fleysher, R., & Lipton, M. L. (2015). Subject based registration for individualized analysis of diffusion tensor MRI. PloS One, 10(11), e0142288.CrossRefPubMedPubMedCentralGoogle Scholar
  35. Watts, R., Thomas, A., Filippi, C. G., Nickerson, J. P., & Freeman, K. (2014). Potholes and molehills: Bias in the diagnostic performance of diffusion-tensor imaging in concussion. Radiology, 272(1), 217–223.CrossRefPubMedPubMedCentralGoogle Scholar
  36. White, T., Schmidt, M., & Karatekin, C. (2009). White matter ‘potholes’ in early-onset schizophrenia: A new approach to evaluate white matter microstructure using diffusion tensor imaging. Psychiatry Research, 174(2), 110–115.CrossRefPubMedPubMedCentralGoogle Scholar
  37. Woo, C. W., Krishnan, A., & Wager, T. D. (2014). Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations. NeuroImage, 91, 412–419.CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Andrew R. Mayer
    • 1
    • 2
    • 3
  • Andrew B. Dodd
    • 1
  • Josef M. Ling
    • 1
  • Christopher J. Wertz
    • 1
  • Nicholas A. Shaff
    • 1
  • Edward J. Bedrick
    • 4
  • Carlo Viamonte
    • 5
  1. 1.The Mind Research Network/Lovelace Biomedical and Environmental Research InstituteAlbuquerqueUSA
  2. 2.Neurology and Psychiatry DepartmentsUniversity of New Mexico School of MedicineAlbuquerqueUSA
  3. 3.Department of PsychologyUniversity of New MexicoAlbuquerqueUSA
  4. 4.Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public HealthUniversity of ArizonaTucsonUSA
  5. 5.Radiology DepartmentUniversity of New Mexico School of MedicineAlbuquerqueUSA

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