Identifying neurobiological markers as predictors of antidepressant treatment using diffusion tensor imaging: A...

It was found that a significant number of patients with major depressive disorder (MDD) did not respond to the treatment, leading to high ongoing costs and disease burden. The main objective of this study was to find neurobiological indicators that can predict the effectiveness of antidepressant treatment using diffusion tensor imaging (DTI). A group of 103 patients who were experiencing their first episode of MDD were included in the study. After 2 weeks of SSRI treatment, the group of patients was split into two categories: ineffectiveand effective. The FMRIB Software Library (FSL) was used for diffusion data preprocessing to obtain tensor-based parameters such as FA, MD, AD, and RD. Tract-Based Spatial Statistical (TBSS) voxel-wise statistical analysis of the tensor-based parameters was carried out using the TBSS procedure in FSL. We conducted an investigation to determine if there were notable variations in neuroimaging attributes among the three groups. Compared to HC, the effective group showed significantly higher AD and MD values in the left CgH. Correlating neuroimaging characteristics and clinical manifestations revealed a significant positive correlation between CgH-l FA and clinical 2-week HAMD-17 total scores and a significant positive correlation between CgH-r FA and clinical 2-week HAMD-17 total scores. Functional damage to the cingulum bundle in the hippocampal region may predispose patients to MDD and predict antidepressant treatment outcomes. More extensive multicenter investigations are necessary to validate these MRI findings that indicate treatment effectiveness and assess their potential significance in practical therapeutic decision-making.