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The causes of major depressive disorder (MDD), as one of the most common psychiatric disorders, still remain unclear. Neuroimaging has substantially contributed to understanding the putative neuronal mechanisms underlying depressed mood and motivational as well as cognitive impairments in depressed individuals. In particular, analyses addressing changes in interregional connectivity seem to be a promising approach to capture the effects of MDD at a systems level. However, a plethora of different, sometimes contradicting results have been published so far, making general conclusions difficult. Here we provide a systematic overview about connectivity studies published in the field over the last decade considering different methodological as well as clinical issues. A systematic review was conducted extracting neuronal connectivity results from studies published between 2002 and 2015. The findings were summarized in tables and were graphically visualized. The review supports and summarizes the notion of an altered frontolimbic mood regulation circuitry in MDD patients, but also stresses the heterogeneity of the findings. The brain regions that are most consistently affected across studies are the orbitomedial prefrontal cortex, anterior cingulate cortex, amygdala, hippocampus, cerebellum and the basal ganglia. The results on connectivity in MDD are very heterogeneous, partly due to different methods and study designs, but also due to the temporal dynamics of connectivity. While connectivity research is an important step toward a complex systems approach to brain functioning, future research should focus on the dynamics of functional and effective connectivity.

While first gene-drug pairs have emerged to be clinically actionable in the treatment of major depressive disorders (MDD) (e.g., CYP2D6 and TCAs/SSRIs), genomic studies have not yet been successful in identifying replicable and valid biomarkers of pharmacological treatment outcome. While some trials suggest that candidates such as CYP2D6, CYP2C19, CYP1A2, SLC6A4 and HTR2A polymorphisms may improve the prediction of response/remission, these results should be interpreted cautiously and required confirmation in larger samples. This presentation will cover state of the art of pharmacogenomics for MDD as well as the emerging field of pharmacotranscriptomics and functional genomics analyses in MDD. Specifically, pharmacotranscriptomics in combination with genomics may be a promising avenue in overcoming some of the current limitations in treatment response prediction research. More recently, the combined genetic effect of polygenic risk scores has shown promising results in predicting treatment response. Importantly, adequately large and well phenotyped clinical trials are required to be conducted with pharmacogenomics/-transcriptomics prospectively in mind.

This study aimed to assess the impact of and variation on venlafaxine (VEN) at steady state in patients from Trinidad and Tobago of Indian and African descent with major depressive disorder. Patients were phenotyped with dextromethorphan, genotyped for and , and metabolic ratios for VEN obtained at 2-week intervals. Of 61 patients, 55 were genotyped and phenotyped and 47 completed 8 weeks of VEN treatment. The majority of patients had metabolic ratios for VEN that were consistent with those for dextromethorphan and genotype-predicted phenotype using activity scores. One subject presented with a novel no-function allele, . No correlations were observed with genotype. genotype analysis provides valuable information to individualize drug therapy with VEN.

Major depressive disorder (MDD) is common and disabling, but its neuropathophysiology remains unclear. Most studies of functional brain networks in MDD have had limited statistical power and data analysis approaches have varied widely. The REST-meta-MDD Project of resting-state fMRI (R-fMRI) addresses these issues. Twenty-five research groups in China established the REST-meta-MDD Consortium by contributing R-fMRI data from 1,300 patients with MDD and 1,128 normal controls (NCs). Data were preprocessed locally with a standardized protocol before aggregated group analyses. We focused on functional connectivity (FC) within the default mode network (DMN), frequently reported to be increased in MDD. Instead, we found decreased DMN FC when we compared 848 patients with MDD to 794 NCs from 17 sites after data exclusion. We found FC reduction only in recurrent MDD, not in first-episode drug-naïve MDD. Decreased DMN FC was associated with medication usage but not with MDD duration. DMN FC was also positively related to symptom severity but only in recurrent MDD. Exploratory analyses also revealed alterations in FC of visual, sensory-motor, and dorsal attention networks in MDD. We confirmed the key role of DMN in MDD but found reduced rather than increased FC within the DMN. Future studies should test whether decreased DMN FC mediates response to treatment. All R-fMRI indices of data contributed by the REST-meta-MDD consortium are being shared publicly via the R-fMRI Maps Project.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Postpartum depression (PPD), now referred to as perinatal depression, is a prevalent and debilitating mood disorder that reduces health-related quality of life (HRQoL) and psychosocial functioning. Esketamine, which is efficacious in adults with treatment-resistant depression and individuals with depression and suicidality, is also analgesic in pain management during childbirth labour. Herein, we investigate the efficacy of prophylactic esketamine in reducing the incidence of PPD. We performed a systematic review (i.e., PubMed, Scopus, and Ovid databases; inception to January 22, 2024) of randomized controlled trials that investigated the use of esketamine for PPD. We delimited our search to studies that prespecified the prevention of PPD with esketamine as the primary outcome. A meta-analysis was performed on PPD incidence rates using a random effects model. Our analysis consisted of seven studies that met our eligibility criteria. We found that esketamine was significantly associated with a decreased incidence of PPD diagnosis within one week of childbirth (OR = 0.30, 95% CI = [0.15, 0.60], p = 0.0047). We also observed that esketamine was significantly associated with a decreased incidence of PPD diagnosis between 4 to 6 weeks post-delivery (OR = 0.33, 95% CI = [0.18, 0.59], p = 0.0034). Our results indicate that esketamine may have preventive antidepressant effects during the postpartum period. The aforementioned points have both mechanistic and clinically meaningful implications for the treatment of PPD.

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes. Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment. Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy. The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

In 2025, the ‘Prevalence of minimum dietary diversity’ among infants and young children (IYC) aged 6–23 months and females aged 15–49 years was adopted as an additional Sustainable Development Goal 2: Zero Hunger indicator. Previous studies, mainly in high‐income countries, have reported that children's diets bear weak to moderate resemblance of their mothers' diets. Therefore, this study assessed i) the rank correlation between Minimum Dietary Diversity for Women (MDD‐W) and MDD‐IYC prevalence at country‐level and ii) the associations and concordance of nutritious and unhealthy food group consumption among mother‐child dyads using nationally representative survey data from 11 low‐ and middle‐income countries. MDD‐W was significantly higher than MDD‐IYC in each survey, but the indicators nonetheless rank correlated very strongly across countries. Discordance favoured mothers for pulses, nuts and seeds; flesh foods; vitamin A‐rich fruits and vegetables (F&V); other F and fried and salty foods, while the opposite was observed for dairy products, eggs, and sweet drinks. Higher maternal dietary diversity was strongly associated with higher diversity in nutritious food group consumption among children in each country. Lastly, mothers consuming five or more out of 10 nutritious food groups—in other words, achieving MDD‐W—best discriminated whether children achieved MDD‐IYC or not. In conclusion, MDD‐IYC and MDD‐W data provide complementary insights for targeted and context‐specific food and nutrition policies and programmes, such as behavioural change and nutrition education interventions and food environment regulations, needed to improve dietary diversity and moderation of unhealthy food groups among both IYC and females of childbearing age. MDD‐W and MDD‐IYC—adopted as additional SDG 2 indicators in 2025—were strongly rank‐correlated in 11 low‐ and middle‐income countries. Higher maternal dietary diversity was consistently associated with greater consumption of nutritious food group among infants and young children aged 6–24 months. Mothers were, however, more likely to consume pulses, nuts and seeds; flesh foods; vitamin A‐rich fruits and vegetables (F&V); other F and fried and salty foods, while their offspring more often consumed dairy products, eggs, and sweet drinks. These findings support targeted and context‐specific nutrition actions that enable diverse and moderate diets for all.

Background and Aim: Major depressive disorder (MDD) is one of the most prevalent mental illnesses worldwide and involves cognitive dysfunction that may negatively impact clinical and social outcomes. Previous studies have suggested that beta-amyloid peptide (Aβ1-42), DNA methyltransferase (Dnmt3a2), and urinary Alzheimer-associated neuronal thread protein (AD7c-NTP) are associated with cognitive impairment. However, there are no relevant studies in MDD. The aim of this study was to assess the correlation between serum Aβ1-42, Dnmt3a2, and urinary AD7c-NTP and cognitive dysfunction in MDD. Materials and Methods: A total of 59 eligible patients were included in the study, including 29 patients with first-episode MDD (FEDs) and 30 patients with recurrent MDD (RMDDs), and 30 matched healthy controls (HCs) were selected. Participants' cognitive functioning was evaluated using the MATRICS consensus cognitive battery (MCCB). The enzyme-linked immunosorbent assay (ELISA) method was used to measure the concentrations of the three proteins. Statistical analysis was completed using Statistical Package for the Social Sciences (SPSS) 20.0. The statistical significance was set as P < 0.05. Results: Serum Dnmt3a2 and urinary AD7c-NTP showed significant differences among the three groups (both P < 0.001), but there were no significant differences in Aβ1-42 levels. Upon examining the results of cognitive testing, we found that serum Aβ1-42 was negatively associated with working memory scores in RMDDs (P = 0.020), but Dnmt3a2 was positively associated with working memory and verbal learning scores in the same cohort (P = 0.012 and P = 0.037, respectively). In contrast, urinary AD7c-NTP was negatively correlated with verbal learning scores in FEDs (P = 0.013). Conclusions: Serum Dnmt3a2 and Aβ1-42 levels may be associated with cognitive impairment in RMDDs and may act as potential biomarkers of cognitive impairment. Although urinary AD7c-NTP was closely related to cognitive dysfunction in FEDs, this relationship did not hold in RMDDs.

The objective of this review was to evaluate the efficacy of selective serotonin reuptake inhibitors (SSRIs) and SSRIs compared with other treatment modalities in preventing relapse after an episode of major depressive disorder (MDD). An Ovid MEDLINE and PsycINFO search (from 1987 to August 2017) was conducted using the following terms: selective serotonin reuptake inhibitors, antidepressants, depression, prevention, prophylaxis, relapse and MDD. Using predefined criteria, two authors independently selected and reached consensus on the included studies. Sixteen articles met the criteria: 10 compared the relapse rate of selective SSRIs with placebo or other SSRIs; one discussed the effectiveness of SSRIs plus psychotherapy, two compared SSRI versus tricyclic antidepressants (TCAs), two were mainly composed of TCAs plus psychotherapy, and one compared SSRIs and serotonin norepinephrine reuptake inhibitors (SNRIs). According to the included studies, the relapse risk in adults was lower when SSRIs were combined with psychotherapy. Results comparing SSRIs and SNRIs were inconclusive. TCAs may be equally as effective as SSRIs. Atypical antidepressants (mirtazapine and St John’s Wort) had no significant difference in efficacy and remission rates compared with SSRIs. Escitalopram appeared to fare better in efficacy than other SSRIs, owing to a higher prophylactic efficacy and lower side effects; however, according to the current data, this difference was not significant. To conclude, this review provides evidence that continuing SSRIs for 1 year reduces risk of MDD and relapse. Furthermore, the combination of SSRIs and cognitive behavioural therapy may effectively reduce relapse. Escitalopram appeared to yield better results and fewer side effects than did other SSRIs or SNRIs. The effectiveness in reducing relapse of SSRIs was similar to that of TCAs and atypical antidepressants.