Brain Imaging and Behavior

, Volume 12, Issue 6, pp 1828–1834 | Cite as

Spatial distribution bias in subject-specific abnormalities analyses

  • Andrew B. Dodd
  • Josef M. Ling
  • Edward J. Bedrick
  • Timothy B. Meier
  • Andrew R. MayerEmail author


The neuroimaging community has seen a renewed interest in algorithms that provide a location-independent summary of subject-specific abnormalities (SSA) to assess individual lesion load. More recently, these methods have been extended to assess whether multiple individuals within the same cohort exhibit extrema in the same spatial location (e.g., voxel or region of interest). However, the statistical validity of this approach has not been rigorously established. The current study evaluated the potential for a spatial bias in the distribution of SSA using several common z-transformation algorithms (leave-one-out [LOO]; independent sample [IDS]; Enhanced Z-Score Microstructural Assessment of Pathology [EZ-MAP]; distribution-corrected z-scores [DisCo-Z]) using both simulated data and DTI data from 50 healthy controls. Results indicated that methods which z-transformed data based on statistical moments from a reference group (LOO, DisCo-Z) led to bias in the spatial location of extrema for the comparison group. In contrast, methods that z-transformed data using an independent third group (EZ-MAP, IDS) resulted in no spatial bias. Importantly, none of the methods exhibited bias when results were summed across all individual elements. The spatial bias is primarily driven by sampling error, in which differences in the mean and standard deviation of the untransformed data have a higher probability of producing extrema in the same spatial location for the comparison but not reference group. In conclusion, evaluating SSA overlap within cohorts should be either be avoided in deference to established group-wise comparisons or performed only when data is available from an independent third group.


Simulations Single-subject Fractional anisotropy Neuroimaging Overlap 



We would also like to thank Diana South and Catherine Smith for their assistance with data collection.


This work was supported by the National Institutes of Health (grant numbers 1R01MH101512-01A1 and 1R01NS098494-01A1) to A.R.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.

Compliance with ethical standards

Conflict of interest

Mr. Dodd reports no conflicts of interest. Mr. Ling reports no conflicts of interest. Dr. Bedrick reports no conflicts of interest. Dr. Meier reports no conflicts of interest. Dr. Mayer reports no conflicts of interest.

Ethical approval

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

Informed consent was obtained from all individual participants included in the study.


  1. 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.Google Scholar
  2. Hayes, J. P., Miller, D. R., Lafleche, G., Salat, D. H., & Verfaellie, M. (2015). The nature of white matter abnormalities in blast-related mild traumatic brain injury. Neuroimage Clinics, 8, 148–156.CrossRefGoogle Scholar
  3. 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.Google Scholar
  4. 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.CrossRefGoogle Scholar
  5. Mayer, A. R., Dodd, A. B., Ling, J. M., Wertz, C. J., Shaff, N. A., Bedrick, E. J., et al. (2017). An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data. Brain Imaging and Behavior.Google Scholar
  6. Meier, T. B., Bergamino, M., Bellgowan, P. S., Teague, T. K., Ling, J. M., Jeromin, A., et al. (2016). Longitudinal assessment of white matter abnormalities following sports-related concussion. Human Brain Mapping, 37(2), 833–845.CrossRefGoogle Scholar
  7. Miller, D. R., Hayes, J. P., Lafleche, G., Salat, D. H., & Verfaellie, M. (2016). White matter abnormalities are associated with chronic postconcussion symptoms in blast-related mild traumatic brain injury. Human Brain Mapping, 37(1), 220–229.CrossRefGoogle Scholar
  8. Miller, D. R., Hayes, J. P., Lafleche, G., Salat, D. H., & Verfaellie, M. (2016). White matter abnormalities are associated with overall cognitive status in blast-related mTBI. Brain Imaging and Behavior.Google Scholar
  9. 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 Neurosurgery, 120(4), 873–881.CrossRefGoogle Scholar
  10. Seghier, M. L., & Price, C. J. (2016). Visualising inter-subject variability in fMRI using threshold-weighted overlap maps. Scientific Reports, 6, 20170.CrossRefGoogle Scholar
  11. Solmaz, B., Tunc, B., Parker, D., Whyte, J., Hart, T., Rabinowitz, A., et al. (2017). Assessing connectivity related injury burden in diffuse traumatic brain injury. Human Brain Mapping.Google Scholar
  12. Taber, K. H., Hurley, R. A., Haswell, C. C., Rowland, J. A., Hurt, S. D., Lamar, C. D., et al. (2015). White matter compromise in veterans exposed to primary blast forces. The Journal of Head Trauma Rehabilitation, 30(1), E15-E25.CrossRefGoogle Scholar
  13. Ware, J. B., Hart, T., Whyte, J., Rabinowitz, A., Detre, J. A., & Kim, J. (2017). Inter-subject variability of axonal injury in diffuse traumatic brain injury. Journal of Neurotrauma.Google Scholar
  14. 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.CrossRefGoogle Scholar
  15. White, T., Ehrlich, S., Ho, B. C., Manoach, D. S., Caprihan, A., Schulz, S. C., et al. (2013). Spatial characteristics of white matter abnormalities in schizophrenia. Schizophrenia Bulletin, 39(5), 1077–1086.CrossRefGoogle Scholar
  16. White, T., Langen, C., Schmidt, M., Hough, M., & James, A. (2015). Comparative neuropsychiatry: white matter abnormalities in children and adolescents with schizophrenia, bipolar affective disorder, and obsessive-compulsive disorder. European Psychiatry, 30(2), 205–213.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Andrew B. Dodd
    • 1
  • Josef M. Ling
    • 1
  • Edward J. Bedrick
    • 2
  • Timothy B. Meier
    • 3
  • Andrew R. Mayer
    • 1
    • 4
    • 5
    Email author
  1. 1.The Mind Research Network/Lovelace Biomedical and Environmental Research InstituteAlbuquerqueUSA
  2. 2.Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public HealthUniversity of ArizonaTucsonUSA
  3. 3.Department of Neurosurgery, Neuroscience Research CenterMedical College of WisconsinMilwaukeeUSA
  4. 4.Neurology and Psychiatry DepartmentsUniversity of New Mexico School of MedicineAlbuquerqueUSA
  5. 5.Department of PsychologyUniversity of New MexicoAlbuquerqueUSA

Personalised recommendations