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Published on: September 8, 2021
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.
Abstract:
Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.
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.

