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Spatial distribution bias in subject-specific abnormalities analyses.

Andrew B Dodd1, Josef M Ling1, Edward J Bedrick2

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

Brain Imaging and Behavior
|February 15, 2018
PubMed
Summary
This summary is machine-generated.

Subject-specific abnormality (SSA) analysis in neuroimaging can be spatially biased depending on the z-transformation method used. Using an independent third group for z-transformation avoids this bias, ensuring reliable lesion load assessment.

Keywords:
Fractional anisotropyNeuroimagingOverlapSimulationsSingle-subject

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Area of Science:

  • Neuroimaging
  • Biostatistics
  • Medical Image Analysis

Background:

  • Renewed interest in algorithms for subject-specific abnormality (SSA) assessment in neuroimaging.
  • Methods extended to detect spatial overlap of extrema across individuals within a cohort.
  • Statistical validity of spatial overlap assessment requires rigorous evaluation.

Purpose of the Study:

  • Evaluate potential spatial bias in SSA distribution using common z-transformation algorithms.
  • Assess impact of different z-transformation methods on spatial localization of abnormalities.
  • Determine conditions under which SSA overlap analysis is statistically valid.

Main Methods:

  • Simulated data and Diffusion Tensor Imaging (DTI) data from 50 healthy controls were used.
  • Evaluated four z-transformation algorithms: leave-one-out (LOO), independent sample (IDS), EZ-MAP, and DisCo-Z.
  • Assessed spatial bias in the distribution of SSA extrema and summed results.

Main Results:

  • Z-transformation methods using reference group moments (LOO, DisCo-Z) introduced spatial bias in the comparison group.
  • Methods using an independent third group (EZ-MAP, IDS) showed no spatial bias.
  • No bias was observed when results were summed across all elements; bias driven by sampling error.

Conclusions:

  • Spatial bias in SSA overlap analysis is algorithm-dependent and influenced by sampling error.
  • Avoid SSA overlap analysis or use an independent third group for z-transformation.
  • Established group-wise comparisons remain a preferred method for lesion load assessment.