Diffusion tensor imaging technique for studying brain microstructural changes in Parkinson’s disease patients

Diffusion tensor imaging (DTI) was used to observe degeneration processes at the microstructural and functional levels in the brains of patients with Parkinson’s disease (PD). Two tensor-based unscented Kalman filter (UKF) was used for analyses of eight regions: the substantia nigra, putamen, caudate nucleus, globus pallidus, primary motor cortex, preprimary motor cortex, supplementary motor area (SMA), presupplementary motor area (pre-SMA), and whole brain of patients with PD (n = 14) and controls (n = 12). We analyzed eight DTI metrics in the entire brain and eight brain regions separately for each hemisphere using univariate and multivariate statistical analysis and their correlation with the clinical parameters. The most affected brain regions in patients with PD were the substantia nigra, pre-SMA, globus pallidus, and caudate nucleus. These results suggest that DTI is an adequate tool for evaluating structural and functional alterations, including inflammation, reduced fiber length, changes in neurite density, axonal growth, demyelination, and axonal damage or loss, in the studied brain regions of patients with PD. The results also revealed a generalized brain degeneration process. In conclusion, DTI can be applied for in vivo studies of the degenerative process and could be considered a complementary method in future studies to improve the accuracy of PD diagnosis.

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Publication Details

Journal
Journal of Applied Biomedicine
Published
2025-05-30
DOI
https://doi.org/10.1016/j.bbe.2025.05.010
Citations
1
Primary Topic
Parkinson's Disease Mechanisms and Treatments
Type
article
Field-Weighted Citation Impact
1.39
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article

Diffusion tensor imaging technique for studying brain microstructural changes in Parkinson’s disease patients

Leszek Królicki, Beata Toczyłowska, Urszula Fiszer, Małgorzata Michałowska et al.
1 citations
Journal of Applied Biomedicine
Parkinson's Disease Mechanisms and Treatments
1.39
article

Diffusion tensor imaging technique for studying brain microstructural changes in Parkinson’s disease patients

Leszek Królicki, Beata Toczyłowska, Urszula Fiszer, Małgorzata Michałowska, Piotr Ładyżyński, Małgorzata Chalimoniuk
article en
1 citations

Abstract

Diffusion tensor imaging (DTI) was used to observe degeneration processes at the microstructural and functional levels in the brains of patients with Parkinson’s disease (PD). Two tensor-based unscented Kalman filter (UKF) was used for analyses of eight regions: the substantia nigra, putamen, caudate nucleus, globus pallidus, primary motor cortex, preprimary motor cortex, supplementary motor area (SMA), presupplementary motor area (pre-SMA), and whole brain of patients with PD (n = 14) and controls (n = 12). We analyzed eight DTI metrics in the entire brain and eight brain regions separately for each hemisphere using univariate and multivariate statistical analysis and their correlation with the clinical parameters. The most affected brain regions in patients with PD were the substantia nigra, pre-SMA, globus pallidus, and caudate nucleus. These results suggest that DTI is an adequate tool for evaluating structural and functional alterations, including inflammation, reduced fiber length, changes in neurite density, axonal growth, demyelination, and axonal damage or loss, in the studied brain regions of patients with PD. The results also revealed a generalized brain degeneration process. In conclusion, DTI can be applied for in vivo studies of the degenerative process and could be considered a complementary method in future studies to improve the accuracy of PD diagnosis.

Journal of Applied BiomedicineVol. 45(3)
Medical University of Warsaw (PL), Postgraduate School of Molecular Medicine (PL), Grochowski Hospital (PL), Institute of Biocybernetics and Biomedical Engineering (PL), Józef Piłsudski University of Physical Education in Warsaw (PL), Polish Academy of Sciences (PL)
Openalex Percentile: Top 19%
Parkinson's Disease Mechanisms and Treatments
1.39
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