A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis.

BACKGROUND: In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment. METHODS: A total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks' taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients. RESULTS: This study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856. CONCLUSION: The study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

Altered functional brain activity in first-episode major depressive disorder treated with electro-acupuncture: A resting-state functional magnetic resonance imaging study.

Background: Previous studies have found electroacupuncture could improve the clinical symptoms of first-episode major depressive disorder (MDD), but the exact neural mechanism of action needs to be further elucidated. Methods: Twenty-eight first-episode MDD patients were randomly divided into 14 electro-acupuncture stimulation (EAS) groups and 14 sham-acupuncture stimulation (SAS) groups, and clinical symptoms were assessed and functional magnetic resonance imaging (fMRI) scans were done in both groups. Amplitude of low-frequency fluctuations (ALFF) was used to observe the changes between the pre-treatment and post-treatment in the two groups, and the altered brain areas were selected as region of interest (ROI) to observe the FC changes. Meanwhile, the correlation between the altered clinical symptoms and the altered ALFF and FC of brain regions in the two groups was analyzed. Results: The EAS significantly decreased the HAMD-24 and HAMA-14 scores of MDD than SAS group. The imaging results revealed that both groups were able to increase the ALFF of the left middle temporal gyrus and the left cerebellar posterior lobe. When using the left middle temporal gyrus and the left posterior cerebellar lobe as ROIs, EAS group increased the FC between the left middle temporal gyrus with the left superior frontal gyrus, the left middle frontal gyrus, and the left hippocampus, and decreased the FC between the left posterior cerebellar lobe and the left calcarine gyrus, while SAS group only increased the FC between the left middle temporal gyrus with the left superior frontal gyrus. The alternations in clinical symptoms after EAS treatment were positively correlated with the altered ALFF values in the left middle temporal gyrus and the altered FC values in the left middle temporal gyrus and the left middle frontal gyrus. Conclusion: EA demonstrates modulation of functional activity in the default mode network (DMN), sensorimotor network (SMN), cognitive control network (CCN), limbic system, and visual network (VN) for the treatment of the first-episode MDD. Our findings contribute to the neuroimaging evidence for the efficacy of EAS.