Alteration of putaminal fractional anisotropy in Parkinson’s disease: a longitudinal diffusion kurtosis imaging study

Research output: Contribution to journalArticle

Abstract

Purpose: In Parkinson’s disease (PD), pathological microstructural changes occur that may be detected using diffusion magnetic resonance imaging (dMRI). However, there are few longitudinal studies that explore the effect of disease progression on diffusion indices. Methods: We prospectively included 76 patients with PD and 38 healthy controls (HC), all of whom underwent diffusion kurtosis imaging (DKI) as part of the prospective Swedish BioFINDER study at baseline and 2 years later. Annualized rates of change in DKI parameters, including fractional anisotropy (FA), mean diffusivity (MD), and mean kurtosis (MK), were estimated in the gray matter (GM) by placing regions of interest (ROIs) in the basal ganglia and the thalamus, and in the white matter (WM) by tract-based spatial statistics (TBSS) analysis. Results: When adjusting for potential confounding factors (age, gender, baseline-follow-up interval, and software upgrade of MRI scanner), only a decrease in FA in the putamen of PD patients (β = − 0.248, P < .01) over 2 years was significantly different from the changes observed in HC over the same time period. This 2-year decrease in FA in the putamen in PD correlated with higher l-dopa equivalent dose at baseline (Spearman’s rho = .399, P < .0001). Conclusion: The study indicates that in PD microstructural changes in the putamen occur selectively over a 2-year period and can be detected with DKI.

Details

Authors
Organisations
External organisations
  • Skåne University Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Neurology

Keywords

  • Diffusion kurtosis imaging, Parkinson’s disease, Tract-based spatial statistics, Tractography
Original languageEnglish
Pages (from-to)247-254
JournalNeuroradiology
Volume60
Issue number3
Publication statusPublished - 2018
Publication categoryResearch
Peer-reviewedYes

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