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Longitudinal changes in diffusion tensor imaging in hemodialysis patients. | LitMetric

Introduction: Hemodialysis patients have increased white matter and gray matter pathology in the brain relative to controls based on MRI. Diffusion tensor imaging is useful in detecting differences between hemodialysis and controls but has not identified the expected longitudinal decline in hemodialysis patients. In this study we implemented specialized post-processing techniques to reduce noise to detect longitudinal changes in diffusion tensor imaging parameters and evaluated for any association with changes in cognition.

Methods: We collected anatomical and diffusion MRIs as well as cognitive testing from in-center hemodialysis patients at baseline and 1 year later. Gray matter thickness, white matter volume, and white matter diffusion tensor imaging parameters were measured to identify longitudinal changes. We analyzed the diffusion tensor imaging parameters by averaging the whole white matter and using a pothole analysis. Eighteen hemodialysis patients were included in the longitudinal analysis and 15 controls were used for the pothole analysis. We used the NIH Toolbox Cognition Battery to assess cognitive performance over the same time frame.

Findings: Over the course of a year on hemodialysis, we found a decrease in white matter fractional anisotropy across the entire white matter (p < 0.01), and an increase in the number of white matter fractional anisotropy voxels below pothole threshold (p = 0.03). We did not find any relationship between changes in whole brain structural parameters and cognitive performance.

Discussion: By employing noise reducing techniques, we were able to detect longitudinal changes in diffusion tensor imaging parameters in hemodialysis patients. The fractional anisotropy declines over the year indicate significant decreases in white matter health. However, we did not find that declines in fractional anisotropy was associated with declines in cognitive performance.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014772PMC
http://dx.doi.org/10.1111/hdi.13133DOI Listing

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