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Depression has been widely accepted as a major psychiatric disease affecting nearly 350 million people worldwide. Research focus is now shifting from studying the extrinsic and social factors of depression to the underlying molecular causes. Microglial activity is shown to be associated with pathological conditions, such as psychological stress, pathological aging, and chronic infections. These are primary immune effector cells in the CNS and regulate the extensive dialogue between the nervous and the immune systems in response to different immunological, physiological, and psychological stressors. Studies have suggested that during stress and pathologies, microglia play a significant role in the disruption of neuroplasticity and have detrimental effects on neuroprotection causing neuroinflammation and exacerbation of depression. After a systematic search of literature databases, relevant articles on the microglial regulation of bidirectional neuroimmune pathways affecting neuroplasticity and leading to depression were reviewed. Although, several hypotheses have been proposed for the microglial role in the onset of depression, it is clear that all molecular pathways to depression are linked through microglia-associated neuroinflammation and hippocampal degeneration. Molecular factors such as an excess of glucocorticoids and changes in gene expression of neurotrophic factors, as well as neuro active substances secreted by gut microbiota have also been shown to affect microglial morphology and phenotype resulting in depression. This review aims to critically analyze the various molecular pathways associated with the microglial role in depression.

Cognitive impairments reported across psychiatric conditions (ie, major depressive disorder, bipolar disorder, schizophrenia, and posttraumatic stress disorder) strongly impair the quality of life of patients and the recovery of those conditions. There is therefore a great need for consideration for cognitive dysfunction in the management of psychiatric disorders. The redundant pattern of cognitive impairments across such conditions suggests possible shared mechanisms potentially leading to their development. Here, we review for the first time the possible role of inflammation in cognitive dysfunctions across psychiatric disorders. Raised inflammatory processes (microglia activation and elevated cytokine levels) across diagnoses could therefore disrupt neurobiological mechanisms regulating cognition, including Hebbian and homeostatic plasticity, neurogenesis, neurotrophic factor, the HPA axis, and the kynurenine pathway. This redundant association between elevated inflammation and cognitive alterations across psychiatric disorders hence suggests that a cross-disorder approach using pharmacological and nonpharmacological (ie, physical activity and nutrition) anti-inflammatory/immunomodulatory strategies should be considered in the management of cognition in psychiatry.

Machine learning methods hold promise for personalized care in psychiatry, demonstrating the potential to tailor treatment decisions and stratify patients into clinically meaningful taxonomies. Subsequently, publication counts applying machine learning methods have risen, with different data modalities, mathematically distinct models, and samples of varying size being used to train and test models with the promise of clinical translation. Consequently, and in part due to the preliminary nature of such works, many studies have reported largely varying degrees of accuracy, raising concerns over systematic overestimation and methodological inconsistencies. Furthermore, a lack of procedural evaluation guidelines for non-expert medical professionals and funding bodies leaves many in the field with no means to systematically evaluate the claims, maturity, and clinical readiness of a project. Given the potential of machine learning methods to transform patient care, albeit, contingent on the rigor of employed methods and their dissemination, we deem it necessary to provide a review of current methods, recommendations, and future directions for applied machine learning in psychiatry. In this review we will cover issues of best practice for model training and evaluation, sources of systematic error and overestimation, model explainability vs. trust, the clinical implementation of AI systems, and finally, future directions for our field.

Patients with Major Depressive Disorder experience significantly reduced subjective Quality of Life (QOL), including impaired social and emotional functioning and greater fatigue and physical pain. Mounting evidence suggests that cognitive dysfunction (e.g., deficits in memory, executive function) contributes independently to the onset of reduced QOL, however the domain-specific nature of this relationship has not been investigated. The present study examined the relationship between specific cognitive domains (e.g., attention, spatial cognition) and specific deficits in mental and physical QOL in subjects with lifetime MDD, as well as acutely depressed, remitted and healthy participants. Data were obtained (N = 387) from the Cognitive Function and Mood Study (COFAMS), a cross-sectional study of emotional, functional and cognitive status in individuals with mood disorders. Participants' (acutely depressed n = 93, remitted n = 170, and healthy control n = 124) QOL was assessed with the 36-Item Short Form Health Survey (SF-36) and cognitive functioning was evaluated with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), the Colorado Assessment Tests (CATs) and the Psychology Experiment Building Language (PEBL). Analyses revealed that poor immediate and delayed memory were associated with reduced mental QOL in individuals with lifetime MDD, acutely depressed, and healthy controls. In contrast, cognitive functioning was not associated with mental QOL in remitted patients. No cognitive domains were significantly related to physical QOL in any participant group. The result suggests that deficits in immediate and delayed memory may contribute to reduced mental QOL in acute MDD, whereas cognition does not appear to play a role in physical QOL. Memory should be considered important cognitive treatment targets for MDD patients suffering specifically from reduced mental QOL. •Cognitive deficits in MDD are associated with mental, but not physical, quality of life.•There is an altered role of cognitive dysfunction in mental QOL across the course of illness in MDD•Executive function is related to mental QOL in depressed participants•Immediate and delayed memory are associated with mental QOL in healthy individuals and individuals with lifetime MDD

Environmental Enrichment (EE) has received considerable attention for its potential to enhance cognitive and neurobiological outcomes in animal models. This bibliometric analysis offers a comprehensive evaluation of the EE research spanning from 1967 to 2024, utilizing data extracted from Scopus and analyzed through R and VOSviewer. The volume of publications, citation patterns, and collaborations were systematically reviewed, highlighting important contributions and emerging trends within the field of animal research. Core concepts of EE research are mapped, revealing key themes such as neuroplasticity, cognitive function, and behavioral outcomes. A significant increase in EE research is demonstrated, particularly after the year 2000, reflecting growing scientific and public interest in EE paradigms. This analysis provides insights into the global contributions and collaborative networks that have shaped EE studies over time. The role of EE in advancing the understanding of neurobiological, neurodevelopmental, and neurodegenerative processes is underscored. Influential contributors, leading countries, and high-impact journals in the field of EE are identified, offering a valuable resource for researchers seeking to understand or extend the current knowledge base. The strategic selection of keywords and rigorous data curation methods ensure that the findings accurately reflect the most impactful aspects of EE research in animals. This study serves as an essential reference for future explorations and applications of EE across disciplines. By providing a clear and structured overview of the field, this paper aims to serve as a foundation for ongoing and future research initiatives, encouraging more robust investigations and applications of EE to enhance cognitive and neurological health globally.

We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of more data, larger machine learning studies consistently show much weaker performance than the numerous small-scale studies. Here, we systematically investigated this effect focusing on one of the most heavily studied questions in the field, namely the classification of patients suffering from Major Depressive Disorder (MDD) and healthy controls based on neuroimaging data. Drawing upon structural MRI data from a balanced sample of N = 1868 MDD patients and healthy controls from our recent international Predictive Analytics Competition (PAC), we first trained and tested a classification model on the full dataset which yielded an accuracy of 61%. Next, we mimicked the process by which researchers would draw samples of various sizes (N = 4 to N = 150) from the population and showed a strong risk of misestimation. Specifically, for small sample sizes (N = 20), we observe accuracies of up to 95%. For medium sample sizes (N = 100) accuracies up to 75% were found. Importantly, further investigation showed that sufficiently large test sets effectively protect against performance misestimation whereas larger datasets per se do not. While these results question the validity of a substantial part of the current literature, we outline the relatively low-cost remedy of larger test sets, which is readily available in most cases.

Psychiatric disorders are highly prevalent and disabling conditions of increasing public health relevance. Much recent research has focused on the role of cytokines in the pathophysiology of psychiatric disorders; however, the related family of immune proteins designated chemokines has been relatively neglected. Chemokines were originally identified as having chemotactic function on immune cells; however, recent evidence has begun to elucidate novel, brain-specific functions of these proteins of relevance to the mechanisms of psychiatric disorders. A systematic review of both human and animal literature in the PubMed and Google Scholar databases was undertaken. After application of all inclusion and exclusion criteria, 157 references were remained for the review. Some early mechanistic evidence does associate select chemokines with the neurobiological processes, including neurogenesis, modulation of the neuroinflammatory response, regulation of the hypothalamus-pituitary-adrenal axis, and modulation of neurotransmitter systems. This early evidence however does not clearly demonstrate any specificity for a certain psychiatric disorder, but is primarily relevant to mechanisms which are shared across disorders. Notable exceptions include CCL11 that has recently been shown to impair hippocampal function in aging - of distinct relevance to Alzheimer's disease and depression in the elderly, and pre-natal exposure to CXCL8 that may disrupt early neurodevelopmental periods predisposing to schizophrenia. Pro-inflammatory chemokines, such as CCL2, CCL7, CCL8, CCL12, and CCL13, have been shown to drive chemotaxis of pro-inflammatory cells to the inflamed or injured CNS. Likewise, CX3CL has been implicated in promoting glial cells activation, pro-inflammatory cytokines secretion, expression of ICAM-1, and recruitment of CD4+ T-cells into the CNS during neuroinflammatory processes. With further translational research, chemokines may present novel diagnostic and/or therapeutic targets in psychiatric disorders.

Anxiety and health related Quality of Life (HRQoL) have emerged as important mental health measures in obstetric care. Few studies have systematically examined the longitudinal trajectories of anxiety and HRQoL in pregnancy. Using a linear growth modeling strategy, we analyzed the course of State-Trait Anxiety Inventory (STAI)- and Short Form (36) Health Survey (SF-36) scores between the 12th and the 36th week of gestation, in a sample of 355 women. We additionally analyzed the impact of depressive symptoms and a chronic medical condition (asthma), on STAI and SF-36 trajectory curves. STAI scores remained stable throughout pregnancy. A previous history of anxiety increased the overall STAI scores. Asthma and depressive symptoms scores had no impact on the STAI trajectory. Physical SF-36 scores decreased over the course of pregnancy, whereas mental SF-36 trended towards improvement. Asthma reduced physical SF-36 overall. While high depressive symptoms decreased the overall mental SF-36, they were also significantly associated with mental SF-36 improvements over time. Anxiety symptoms are stable during pregnancy and are not modulated by depressive symptoms or asthma. Physical HRQoL declines in pregnancy. In contrast, mental HRQoL appears to improve, particularly in women with high initial levels of depressive symptoms.

Background In patients with major depressive disorder (MDD), antidepressant response and remission rates are low, highlighting the need for new treatment approaches. Recently, the abundant literature linking inflammatory processes and depressive symptoms have led to the hypothesis that selecting treatment for MDD based on the patient’s inflammatory status could be a promising strategy to improve outcomes in patients suffering from MDD. The aim of the randomised control trial we propose is to investigate the antidepressant efficacy of the combined treatment of MDD with antidepressant medication plus anti-inflammatory medication in individuals with raised inflammation levels. For the first time, this study will prospectively test the efficacy of an antidepressant plus anti-inflammatory augmentation based on baseline inflammatory maker levels in MDD using a randomised controlled trial design. Methods This study proposes to measure blood C-reactive protein (CRP) levels before the initiation of treatment in 200 participants with MDD. Study participants are then assigned into one of two study strata: either into the ‘Depression with inflammation’ stratum (CRP levels > 3 mg/L); or into the ‘Depression without inflammation’ stratum (CRP levels ≤ 3 mg/L). Within each of the two study strata, participants randomly receive either antidepressant medication alone (vortioxetine) plus anti-inflammatory medication (celecoxib) or vortioxetine plus placebo for six weeks. At the end of the treatment period, participants have the opportunity to continue vortioxetine alone for a six-month post-trial period. Clinical outcomes are measured at baseline, fortnightly during the treatment period and at the three-month and six-month post-trial visits. The primary outcome is change in MADRS score, with a primary endpoint of a score reduction by 50% from baseline to six weeks (end of augmentation treatment with celecoxib). Secondary clinical outcomes are changes in the cognitive dimensions of depression (cognitive function, emotion processing and social cognition). Biological outcome measures (levels of CRP and other inflammatory markers) are measured at baseline, after six weeks of treatment and at the six-month post-trial visit. Discussion The current study will generate novel evidence for biomarker-based personalised antidepressant treatment selection based on patient inflammatory status before treatment. Trial registration Australian New Zealand Clinical Trials Registry (ANZCTR), ACTRN12617000527369p . Registered on 11 April 2017.

Major depressive disorder (MDD) is associated to affected brain wiring. Little is known whether these changes are stable over time and hence might represent a biological predisposition, or whether these are state markers of current disease severity and recovery after a depressive episode. Human white matter network (“connectome”) analysis via network science is a suitable tool to investigate the association between affected brain connectivity and MDD. This study examines structural connectome topology in 464 MDD patients (mean age: 36.6 years) and 432 healthy controls (35.6 years). MDD patients were stratified categorially by current disease status (acute vs. partial remission vs. full remission) based on DSM-IV criteria. Current symptom severity was assessed continuously via the Hamilton Depression Rating Scale (HAMD). Connectome matrices were created via a combination of T1-weighted magnetic resonance imaging (MRI) and tractography methods based on diffusion-weighted imaging. Global tract-based metrics were not found to show significant differences between disease status groups, suggesting conserved global brain connectivity in MDD. In contrast, reduced global fractional anisotropy (FA) was observed specifically in acute depressed patients compared to fully remitted patients and healthy controls. Within the MDD patients, FA in a subnetwork including frontal, temporal, insular, and parietal nodes was negatively associated with HAMD, an effect remaining when correcting for lifetime disease severity. Therefore, our findings provide new evidence of MDD to be associated with structural, yet dynamic, state-dependent connectome alterations, which covary with current disease severity and remission status after a depressive episode.