MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis

Fan Zhang1, Kang Ik Kevin Cho2, Yingying Tang3

  • 1Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Neuroimage
|December 7, 2020
PubMed

Insights

The MK-Curve method improves Diffusion Kurtosis Imaging (DKI) analysis by correcting artifacts, enhancing detection of white matter abnormalities in individuals at clinical high risk for psychosis (CHR) and correlating with symptom severity.

Area of Science:

  • Neuroimaging
  • Biophysics
  • Radiology

Background:

  • Diffusion Kurtosis Imaging (DKI) measures brain microstructural properties, offering insights beyond Diffusion Tensor Imaging (DTI).
  • DKI parameters like mean kurtosis (MK) can detect subtle white matter abnormalities, particularly in early-stage pathologies.
  • Artifacts in DKI data can reduce sensitivity to true microstructural changes.

Purpose of the Study:

  • To evaluate the MK-Curve method's utility in improving the identification of white matter abnormalities in group comparisons.
  • To assess the impact of MK-Curve correction on the sensitivity and statistical significance of group differences in DKI parameters.
  • To investigate the clinical relevance of corrected DKI parameters by comparing their correlation with clinical characteristics.

Main Methods:

  • Comparison of group differences in DKI parameters between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs), with and without MK-Curve correction.
  • Analysis of correlations between corrected and uncorrected DKI parameters and clinical variables in the CHR group.
  • Utilized the proposed mean-kurtosis-curve (MK-Curve) method for artifact correction in DKI data.

Main Results:

  • MK-Curve correction significantly increased the effect sizes and statistical significance of group differences, particularly for mean kurtosis (MK).
  • Corrected DKI parameters showed stronger correlations with clinical variables in CHR individuals, indicating enhanced clinical relevance.
  • Widespread reductions in MK and fractional anisotropy (FA) were observed in the CHR group, overlapping and correlating with functional decline and symptom severity.

Conclusions:

  • The MK-Curve method effectively corrects artifacts in DKI data, improving the sensitivity for detecting white matter abnormalities.
  • Corrected DKI parameters, especially MK, demonstrate greater clinical relevance in characterizing neurobiological alterations associated with psychosis risk.
  • The findings support the application of MK-Curve correction for enhanced detection and characterization of subtle group differences in neuroimaging studies.

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