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Repetitive Transcranial Magnetic Stimulation (rTMS) is an evidence-based treatment for depression. However, the patterns of response to this treatment modality are inconsistent. Whilst many people see a significant reduction in the severity of their depression following rTMS treatment, some patients do not. To support and improve patient outcomes, recent work is exploring the possibility of using Machine Learning to predict rTMS treatment outcomes. Our proposed model is the first to combine functional magnetic resonance imaging (fMRI) connectivity with deep learning techniques to predict treatment outcomes before treatment starts. Furthermore, with the use of Explainable AI (XAI) techniques, we identify potential biomarkers that may discriminate between rTMS responders and non-responders. Our experiments utilize 200 runs of repeated bootstrap sampling on two rTMS datasets. We compare performances between our proposed feedforward deep neural network against existing methods, and compare the average accuracy, balanced accuracy and F1-score on a held-out test set. The results of these experiments show that our model outperforms existing methods with an average accuracy of 0.9423, balanced accuracy of 0.9423, and F1-score of 0.9461 in a sample of 61 patients. We found that functional connectivity measures between the Subgenual Anterior Cingulate Cortex and Centeral Opercular Cortex are a key determinant of rTMS treatment response. This knowledge provides psychiatrists with further information to explore the potential mechanisms of responses to rTMS treatment. Our developed prototype is ready to be deployed across large datasets in multiple centres and different countries., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Healthy first-degree relatives of patients with major depression are at an elevated risk of developing depression, and regional cerebral blood flow (CBF) alterations are observed in patients with depression. Therefore, in a 33-month follow-up study, we used arterial spin labeling-magnetic resonance imaging (ASL-MRI) to investigate quantitative CBF before and after the diagnosis of depression in healthy young adults with and without first-degree relatives with major depression (FH + and FH-, respectively). In cross-sectional and longitudinal CBF comparisons, CBF in the right amygdala was increased or decreased. Additionally, a significant correlation was observed between the altered CBF in the right amygdala and the scores on the 17-item Hamilton Depression Rating Scale (HDRS) in the FH + group. Furthermore, logistic regression and receiver operating characteristic curve analyses showed that increased CBF in the right amygdala at baseline predicted the subsequent onset of depression in the FH + group. Our results suggest that among healthy young adults with a familial risk of depression, those who exhibit increased CBF in the amygdala are susceptible to developing this disease.

Purpose: To determine hippocampal subfields volume loss in depression, which was simulated by a rat chronic unpredictable mild stress (CUMS) model. As different cellular and molecular characteristics in hippocampal subfields, these subfields are regarded as differentially vulnerable to processes associated with stress., Materials and Methods: Twenty male Wistar rats were exposed to various stressors until the model was successfully established. The effects of physical exercise on recovery of hippocampal volume in depressed rats were simulated using the wheel running test (WRT). These rats hippocampal volumes were dynamically measured using T 2 -weighted images (T 2 WIs) at 7T structural magnetic resonance imaging (MRI)., Results: After 4 weeks of CUMS (CUMS-4W), the behavioral tests showed that the rat model of depression was successfully established (P < 0.001). In this process, the bilateral CA1 volume was significantly atrophic after 2 weeks of CUMS (CUMS-2W) compared with controls (left: 21.09 ± 2.31 vs. 26.16 ± 3.83 mm 3 , P < 0.001; right: 21.05 ± 2.36 vs. 26.12 ± 3.78 mm 3 , P < 0.001), whereas the other subfields did not show a similar change (all P > 0.05). The volume of CA3, dentate gyrus (DG), and subiculum displayed atrophy after CUMS-4W (CA3: left:12.23 ± 1.10 mm 3 , right: 12.20 ± 1.14 mm 3 ; DG: left:8.16 ± 0.58 mm 3 , right: 8.18 ± 0.92 mm 3 ; subiculum: left: 4.30 ± 0.52 mm 3 , right: 4.29 ± 0.44 mm 3 ; all P < 0.05). The rats' (CUMS-4W) hippocampal DG volume was restored (left: 10.67 ± 1.60 mm 3 , right: 10.71 ± 1.58 mm 3 ), and the depression-like behaviors of these rats improved after WRT-4W (P < 0.05)., Conclusion: In general, volume loss was demonstrated in various rat hippocampal subfields during the development and recovery from depression, which were detected by ultrahigh-field MRI., Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1456-1463., (© 2017 International Society for Magnetic Resonance in Medicine.)

The reduction of hippocampal volume remains controversial in depression because of the variability among individuals in clinical studies. Here, a reliable experimental rat model of depression, established by chronic unpredictable mild stress (CUMS), was used. Thirty rats were randomly divided into two groups (CUMS group and control group). Hippocampal volume was dynamically measured every 2 weeks in a 56-day chronic stress procedure using structural magnetic resonance imaging, and the correlation between the hippocampal volume and the learning and memory changes was investigated. Our results demonstrated that CUMS rats showed significantly smaller volumes of the bilateral hippocampus compared to that of the controls, changing dramatically with the development of CUMS procedure. The left hippocampal volume was reduced earlier and more markedly than the right one from the 2nd week to the 8th week of the CUMS procedure (on the 8th week: left: approximately 15.3 %; right: approximately 8.4 % reduction). Additionally, the hippocampal volume of CUMS rats was significantly negatively correlated with the learning and memory changes. Of note, it showed that the more obviously the hippocampal volume reduced, the more severely the learning and memory damaged. In conclusion, the hippocampal volume decreased gradually and dynamically and was correlated with the impairment of the learning and memory in depression.

Background: Transient depressive state (TDS) is a transient, negative emotional state caused by certain events or situations. Because of the similarity in depressive symptoms between depression and TDS that arise within 2 weeks of their onset, it is difficult to distinguish TDS from depression. The aims of the present study were to investigate the application value of selected serum indicators in the differential diagnosis of geriatric depression and TDS in the early stage and to provide evidence for treatment. Patients and methods: In this study, a total of 274 elderly patients were divided into the depression group (n=144) and the TDS group (n=130). All participants' serum samples were collected, and 9 selected serum indicators were analyzed. Afterward, 90 patients with depression and 90 patients with TDS were used to build the diagnostic model. A binary logistic regression analysis was used to establish regression models, and the area under the receiver operating characteristic (ROC) curve was drawn. Finally, another 54 patients with depression and 40 patients with TDS were used to validate our model. Results: For the 9 screening serum indicators, the 3 serum indicators selected to build the regression model were BDNF (P=0.001), IL-1946 (P<0.001), and cortisol (P<0.001). The regression equation was Y = 1/[1 + e-(-16.258 - 0.018 (BDNF) + 0.256 (IL-1946) + 0.093 (Cortisol))], and the ROC curve of combined detection was 0.926. The diagnostic rate of the logistic model was 89.36%. Conclusion: The logistic regression model and ROC curves based on serum levels of BDNF, IL-1β, and cortisol could distinguish depression from TDS in early stage, which could provide assistance to the differential diagnosis of geriatric depression and TDS. [ABSTRACT FROM AUTHOR]

The occurrences of dilated Virchow-Robin spaces (dVRSs) and inflammatory factors were closely related with development of depression and antidepressants. The present study indicated accumulation of inflammatory factors in dVRSs played a practical role in behaviors and the treatment-resistant in rats with depression. Eighty-one male Wistar rats were exposed to chronic unpredictable mild stress (CUMS) until the model was successfully established. According to the detection of dVRSs on 7 T structural magnetic resonance imaging (MRI), rats were divided into two groups (45 CUMSnon-dVRSs rats and 36 CUMSdVRSs rats). Subsequently, different antidepressant treatment (nontreat, aspirin, fluoxetine, fluoxetine+aspirin) was given to the two groups, respectively. Sucrose preference test (SPT), forced swim test (FST) and morris water maze (MWM) was used to test the behaviors of rats. ELISA and Western blot was used to determine the levels of inflammatory factors. Compared with the negative control (NC) group, there were obvious behavioral abnormalities and increased inflammatory factors in the CUMSnon-dVRSs rats. Interestingly, these changes were more significant in the CUMSdVRSs rats. In addition, the efficacy of fluoxetine was only found in CUMSnon-dVRSs rats rather than CUMSdVRSs rats. Meanwhile, behaviors and levels of inflammatory factor in hippocampus were ameliorated in the aspirin group of CUMSdVRSs rats, and these effects were significantly enhanced in the fluoxetine+aspirin group. The dVRSs in the hippocampus might exacerbate depression-like behaviors and the effects of antidepressant treatment in depressed rats by increasing the levels of inflammatory factors. [ABSTRACT FROM AUTHOR]

Depression is a serious and potentially life-threatening mental disorder with unknown etiology. Emerging evidence shows that brain-derived neurotrophic factor (BDNF) and microRNAs (miRNAs) play critical roles in the etiology of depression. However, the molecular mechanisms are not fully understood. Expression of miR-182 and BDNF in the hippocampus were analyzed in a chronic unpredictable mild stress (CUMS) model. Male Wistar rats received bilateral intra-hippocampal infusions of BDNF- and miR-182-expressing (miR-182) or miR-182-silencers (si-miR-182) lentiviral vectors (LV). miR-182 upregulation was correlated with decreased BDNF expression in the hippocampus of a CUMS model. Accordingly, an anti-depressant like effect was observed when LV-BDNF or LV-si-miR-182 was injected into the hippocampus. Moreover, BDNF and its target gene cyclic AMP responsive element binding protein 1 (CREB1) decreased following LV-miR-182 injection and increased upon LV-si-miR-182 injection in rat hippocampus and cultured neuronal cells. In contrast, miR-182 overexpression exacerbated depression-like behaviors and decreased BDNF. Further, luciferase reporter evidence confirmed BDNF was a miR-182 target. Taken together, the current results reveal a potential molecular regulation of miR-182 on BDNF and the pronounced behavioral consequences of this regulation. [ABSTRACT FROM AUTHOR]

Background: Stressful life events (SLEs) are well-established proximal predictors of the onset of depression. However, the fundamental causes of interindividual differences in depression outcomes are poorly understood. This study addressed this depression susceptibility mechanism using a well-powered sample of adults living in China.Methods: Healthy participants with SLEs (n = 185; mean = 47.51 years, 49.73% female), drawn from a longitudinal study on the development of depression, underwent diffusion tensor imaging, interleukin-6 (IL-6) level measurement, and trimonthly standardized clinical and scale evaluations within a two-year period.Results: Receiver operating characteristic analyses indicated that reduced feeder connection and HIP.R nodal efficiency improved the predictive accuracy of post-SLEs depression (ORfeeder = 0.623, AUC = 0.869, P < 0.001; ORHIP = 0.459, AUC = 0.855, P < 0.001). The successfully established path analysis model confirmed the significant partial effect of SLEs-IL-6-white matter (WM) network differences-depression (onset and severity) (x2/8 = 1.453, goodness-of-fit [GFI] = 0.935, standard root-mean-square error of approximation [SRMR] = 0.024). Females, individuals with lower exercise frequency (EF) or annual household income (AHI) were more likely to have higher IL-6 level after SLEs (βint-female⁎SLEs = -0.420, P < 0.001; βint-exercise⁎SLEs = -0.412, P < 0.001; βint-income⁎SLEs = -0.302, P = 0.005).Limitations: The sample size was restricted due to the limited incidence rate and prospective follow-up design.Conclusions: Our results suggested that among healthy adults after SLEs, those who exhibited abnormal IL-6-WM differences were susceptible to developing depression. Females, lower AHI or EF might account for an increased risk of developing these abnormal IL-6-WM differences. [ABSTRACT FROM AUTHOR]

Background: Although several pharmacological treatment options for depression are currently available, a large proportion of patients still do not achieve a complete remission or respond adequately to the initial antidepressant prescribed for reasons that remain relatively unknown. This study explored the application of serum biomarkers to the predict the efficacy of escitalopram for treating depression, to guide clinical drug selection.Method: In this study, 306 patients suffering from depression were treated with escitalopram (10 mg) for 6 weeks. After 6 weeks of treatment, the patients were divided into an escitalopram-sensitive group (ES, n = 172) and an escitalopram-insensitive group (EIS, n = 134) according their HAMD-24 scores after 6 weeks of treatment. Serum samples from all participants were collected on the first day, and 10 different serum biomarkers were analysed. Data from 100 patients in the ES group and 100 patients in the EIS group were then used to build a logistic regression model, and a receiver operating characteristic (ROC) curve was drawn. To validate the accuracy of our model, another 72 patients in the ES group and 34 patients in the EIS group were studied.Results: Of the 10 selected serum biomarkers, 4 were screened to build the regression model. BDNF, FGF-2, TNF-α and 5-HT. The regression equation was Z = 1/[1 + e-(-5.065+0.145 (BDNF)+0.029 (FGF-2)-0.368 (TNF-α)+0.813 (5-HT))], and the 4 biomarkers-combined detection achieved an AUC (area under the ROC curve) of 0.929 and a predictive accuracy of 88.70%.Limitation: Decision support tools based on our combined biomarker prediction models hold comparatively great promises; however, they need to be validated on a much larger scales than current studies provide.Conclusion: The logistic regression model and ROC curves based of the serum biomarkers used in this study provide a more reliable means to predict the efficacy of escitalopram in patients with depression, and provide clinical evidence for drug selection. [ABSTRACT FROM AUTHOR]