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Variability in diffusion kurtosis imaging: impact on study design, statistical power and interpretation.

Filip Szczepankiewicz1, Jimmy Lätt, Ronnie Wirestam

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Statistical power analysis for Diffusion Kurtosis Imaging (DKI) reveals that including more subjects is more efficient than longer scan times. Variability differs across brain structures, impacting DKI parameter interpretation.

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Area of Science:

  • Neuroimaging
  • Biophysics
  • Statistical Analysis

Background:

  • Diffusion Tensor Imaging (DTI) offers limited microstructural insights.
  • Diffusion Kurtosis Imaging (DKI) provides advanced quantification of tissue microstructure.
  • Understanding DKI parameter variability is crucial for study design and result interpretation.

Purpose of the Study:

  • To perform statistical power analysis on four DKI parameters: mean diffusivity, fractional anisotropy, mean kurtosis, and radial kurtosis.
  • To characterize variability in DKI parameters across different white matter structures.
  • To guide the design and interpretation of DKI studies.

Main Methods:

  • Employed statistical power analysis to determine required group sizes for a statistical power of 0.9.
  • Quantified the contributions of imaging/processing noise versus inter-subject variability.
  • Evaluated the impact of covariates, such as structure size, on group mean differences.
  • Analyzed data from healthy subjects in the superior cingulum, corticospinal tract, and corpus callosum.

Main Results:

  • Heterogeneous variability was observed across and within white matter structures, affecting statistical power.
  • Inter-subject variability was a greater contributor to total variance than imaging noise.
  • Including more subjects is more efficient for increasing statistical power than extending scan times.
  • Significant correlations were found between DKI parameters and structure size in the cingulum and corpus callosum.

Conclusions:

  • DKI parameter variability is structure- and parameter-dependent, necessitating careful consideration for inferring pathogenesis.
  • Prioritizing subject numbers over scan duration enhances statistical power in DKI studies.
  • Structure size is an important covariate to consider for controlling confounding effects or reducing variance in DKI analyses.