Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis

Abstract : Background: Neuroimaging studies help us better understand the pathophysiology and symptoms of Parkinson's disease (PD). In several of these studies, diffusion tensor imaging (DTI) was used to investigate structural changes in cerebral tissue. Although data have been provided as regards to specific brain areas, a whole brain meta-analysis is still missing. Methods: We compiled 39 studies in this meta-analysis: 14 used fractional anisotropy (FA), 1 used mean dif-fusivity (MD), and 24 used both indicators. These studies comprised 1855 individuals, 1087 with PD and 768 healthy controls. Regions of interest were classified anatomically (subcortical structures; white matter; cortical areas; cerebellum). Our statistical analysis considered the disease effect size (D ES) as the main variable; the heterogeneity index (I 2) and Pearson's correlations between the D ES and co-variables (demographic, clinical and MRI parameters) were also calculated. Results: Our results showed that FAD ES and MD-D ES were able to distinguish between patients and healthy controls. Significant differences, indicating degenerations, were observed within the substantia nigra, the corpus callosum, and the cingulate and temporal cortices. Moreover, some findings (particularly in the corticospinal tract) suggested opposite brain changes associated with PD. In addition, our results demonstrated that MD-D ES was particularly sensitive to clinical and MRI parameters, such as the number of DTI directions and the echo time within white matter. Conclusions: Despite some limitations, DTI appears as a sensitive method to study PD pathophysiology and severity. The association of DTI with other MRI methods should also be considered and could benefit the study of brain degenerations in PD.
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Cyril Atkinson-Clement, Serge Pinto, Alexandre Eusebio, Olivier Coulon. Diffusion tensor imaging in Parkinson's disease: Review and meta-analysis. Neuroimage-Clinical, Elsevier, 2017, 16, pp.98 - 110. ⟨10.1016/j.nicl.2017.07.011⟩. ⟨hal-01776157⟩

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