An evaluation of Z-transform algorithms for identifying subject-specific abnormalities in neuroimaging data
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.
KeywordsSimulations Single-subject Diffusion tensor imaging Neuroimaging Variability
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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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