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DOI: 10.1055/s-0045-1814098
Diffusion Tensor Imaging to Analyze White Matter Tract Abnormalities in Major Psychiatric Disorders
Autor*innen
Abstract
Background
Diffusion tensor imaging (DTI) has emerged as a valuable tool in studying depression-related brain changes. DTI is a type of magnetic resonance (MR) imaging that maps the diffusion of water molecules to visualize white matter tracts in the brain. By measuring the diffusion of water molecules in brain tissue, DTI provides information about the structural organization of white matter fibers and the integrity of neural pathways.
Methods
A total of 30 cases with the first episode of severe depression were included in the study. Age- and gender-matched 30 healthy controls were also included. A structured clinical interview was conducted using the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria for the diagnosis of major depression and the Hamilton Rating Scale for Depression was calculated. A 1.5 Tesla (T) MR System was used to determine the fractional anisotropy (FA) and apparent diffusion coefficient (ADC) values of 10 main white matter fiber tracts. Mann–Whitney U test was applied and p-values of all the 10 white matter fiber tracts were calculated.
Results
DTI analysis found that FA values were significantly lower (p < 0.05) in patients compared with healthy controls in the fornix (p = 0), cingulum hippocampus (p = 0.011), inferior fronto-occipital fasciculus (p = 0.013), and superior longitudinal fasciculus (p = 0.026). ADC values were significantly higher in the fornix (p = 0.040) and reduced in anterior thalamic radiation (p = 0.011).
Conclusion
We concluded that various microstructural changes occur in these white matter tracts, mainly in the fronto-limbic system and play a key role in the pathophysiology of depression. This information can help in developing methods of preventing such alterations, which can reduce the morbidity of depressive patients.
Publikationsverlauf
Artikel online veröffentlicht:
29. Dezember 2025
© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)
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