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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
BRIEF COMMUNICATION

Abstract

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.

Keywords

Simulations Single-subject Fractional anisotropy Neuroimaging Overlap 

Notes

Acknowledgements

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

Funding

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.

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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

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