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

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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.

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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.

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Authors and Affiliations

  1. The Mind Research Network/Lovelace Biomedical and Environmental Research Institute, Pete & Nancy Domenici Hall, 1101 Yale Blvd. NE, Albuquerque, NM, 87106, USA

    Andrew R. Mayer, Andrew B. Dodd, Josef M. Ling, Christopher J. Wertz & Nicholas A. Shaff

  2. Neurology and Psychiatry Departments, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA

    Andrew R. Mayer

  3. Department of Psychology, University of New Mexico, Albuquerque, NM, 87131, USA

    Andrew R. Mayer

  4. Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, 85724, USA

    Edward J. Bedrick

  5. Radiology Department, University of New Mexico School of Medicine, Albuquerque, NM, 87131, USA

    Carlo Viamonte

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  1. Andrew R. Mayer
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  2. Andrew B. Dodd
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Correspondence to Andrew R. Mayer.

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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.

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Mayer, A.R., Dodd, A.B., Ling, J.M. et al. An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data. Brain Imaging and Behavior 12, 437–448 (2018). https://doi.org/10.1007/s11682-017-9702-2

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  • DOI: https://doi.org/10.1007/s11682-017-9702-2

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