Abstract Background Major depressive disorder (MDD) has different clinical presentations in males and females. However, the neuroanatomical mechanisms underlying these sex differences are not fully understood. Objective The purpose of present study was to explore the sex differences in brain cortical thickness (CT) and surface area (SA) of MDD and the relationship between these differences and clinical manifestations in different gender. Methods High-resolution T1-weighted images were acquired from 61 patients with MDD and 61 healthy controls (36 females and 25 males, both). The sex differences in CT and SA were obtained using the FreeSurfer software and compared between every two groups by post hoc test. Spearman correlation analysis was also performed to explore the relationships between these regions and clinical characteristics. Results In male patients with MDD, the CT of the right precentral was thinner compared to female patients, although this did not survive Bonferroni correction. The SA of several regions, including right superior frontal, medial orbitofrontal gyrus, inferior frontal gyrus triangle, superior temporal, middle temporal, lateral occipital gyrus, and inferior parietal lobule in female patients with MDD was smaller than that in male patients (P < 0.01 after Bonferroni correction). In female patients, the SA of the right superior temporal (r = 0.438, P = 0.008), middle temporal (r = 0.340, P = 0.043), and lateral occipital gyrus (r = 0.372, P = 0.025) were positively correlated with illness duration. Conclusion The current study provides evidence of sex differences in CT and SA in patients with MDD, which may improve our understanding of the sex-specific neuroanatomical changes in the development of MDD.

Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

<h4>Background</h4>Depression is one of the most common mental illnesses worldwide and is a major burden for those affected. As conventional therapies do not always work and are also associated with side effects, alternative treatment methods are urgently indicated. In the past, exercise has established itself as a seemingly good alternative treatment method. The aim of this work is to provide a state of the art review and to check whether there are new findings since the publication of the article by Wegner and colleagues 10 years ago.<h4>Methods</h4>A systematic literature search was conducted. This included searching for appropriate literature in databases such as PsycINFO, PsychARTICLES, PubMed, CINAHL Complete, SocINDEX, SPORTDiscus and Psyndex. Effect sizes calculation and evaluation of the methodological characteristics (AMSTAR 2) were carried out. Finally, the neurobiological explanations for the effect of exercise on depression are discussed.<h4>Results</h4>Eleven meta-analyses met the inclusion criteria, with the total sample consisting of 16.255 participants and 229 individual studies. The most frequently implemented intervention was aerobic exercise, while the intervention in the control groups was usually no treatment, waiting list, or attention/activity placebo. The pooled results indicate a moderate clinical effect, suggesting the positive effect of exercise and physical activity in reducing depressive symptoms (SMD = -0.61, 95% CI [- 0.78; -0.43], p = <0.01).<h4>Conlcusion</h4>The medium but consistently positive effects that were found in the present study place exercise/ physical activity as a promising and helpful alternative for adults with depression. The positive effect of exercise and physical activity could potentially be explained by neurological changes. However, the exact mechanisms underlying the antidepressant effects are still unclear.

<h4>Background</h4>Postpartum depression, anxiety, and reduced health related quality of life (HRQoL) are common in the first year after childbirth, a period characterized by marked hormonal fluctuations, sleep disruption, and psychosocial role transitions that increase vulnerability to emotional distress, and have substantial consequences for maternal and child well-being. Aerobic exercise is increasingly recommended as a low-risk behavioral strategy, as it has been shown to improve mood, reduce stress, and modulate neurobiological pathways associated with depression, yet its effectiveness and optimal prescription parameters for postpartum mental health remain unclear. This study evaluated the effects of aerobic exercise on depressive symptoms, anxiety, and HRQoL, and characterized corresponding dose response patterns.<h4>Methods</h4>PubMed, Embase, CENTRAL, Web of Science, and Google Scholar were searched from inception to 31 October 2025. Randomized controlled trials lasting at least four weeks and comparing structured aerobic exercise with inactive or minimal control conditions were included. Outcomes were depressive symptoms, anxiety, and HRQoL assessed using validated instruments. Random effects meta-analyses generated standardized mean differences (SMDs). Dose response relationships were examined using model-based methods.<h4>Results</h4>Seventeen trials involving 2,865 women met inclusion criteria. Aerobic exercise significantly reduced depressive symptoms (SMD - 0.37, 95% CI - 0.60 to -0.14), with larger improvements in women with baseline depression and in interventions delivered postpartum. Dose response analysis showed a U-shaped pattern, with maximal benefit at approximately 570 MET minutes per week. No significant effect was found for anxiety (SMD - 0.11, 95% CI - 0.31 to 0.09). Aerobic exercise improved HRQoL (SMD 0.45, 95% CI 0.29 to 0.60), and an inverted U-shaped dose response indicated optimal gains near 420 MET minutes per week.<h4>Conclusion</h4>Aerobic exercise reduces postpartum depressive symptoms and improves HRQoL. Optimal effects were observed at approximately 570 MET minutes per week for depression and 420 MET minutes per week for HRQoL, suggesting that a weekly volume of 400 to 600 MET minutes may be a practical target in postpartum care. Evidence remains insufficient to confirm anxiolytic effects.

Abstract Depression and disturbed sleep are intimately and bidirectionally related. During adolescence, the incidence of both insomnia and major depression increases simultaneously, in a gender‐specific manner. The majority of depressed adolescents suffer from different types of subjective sleep complaints. Despite these complaints, the results from polysomnographic studies in depressed adolescents remain inconsistent. In general, similar features to those seen among adults with depressive disorder (e.g. abnormalities in rapid eye movement sleep and difficulties in sleep onset) have been reported, but expressed to a lesser degree. The inconsistency in findings may be linked with maturational factors, factors related to the stage of illness and greater heterogeneity in the clinical spectrum of depression among adolescents. The exact neurobiological mechanisms by which sleep alterations and depression are linked during adolescence are not fully understood. Aberrations in brain maturation, expressed at different levels of organization, for example gene expression, neurotransmitter and hormone metabolism, and activity of neuronal networks have been suggested. The circadian systems may change in adolescent depression beyond that observed during healthy adolescent development (i.e. beyond the typical circadian shift towards eveningness). A number of therapeutic approaches to alleviate sleep disruption associated with depression have been proposed, but research on the efficacy of th