Journal of Affective Disorders

Background and ObjectivesThe goals of depression screening, which is universally recommended in primary care settings in the U.S., are to identify adolescents with depression and connect them to treatment. However, little is known about how depression screening affects the likelihood of being diagnosed with a mental disorder or accessing mental health care over time. MethodsThis longitudinal cohort study used insurance claims data from 57,732 adolescents who had at least one routine well-visit between 2014 and 2017. Using propensity score matching, we compared adolescents who were screened for depression to similar adolescents who were not screened for depression during the well-visit. Diagnostic and treatment-related outcomes were examined over 6-month follow-up and included depression diagnoses, mood-related diagnoses, antidepressant prescriptions, any mental health-related prescriptions, and psychotherapy. We also examined heterogeneity of associations by sex. ResultsCompared to adolescents who were not screened for depression, adolescents screened for depression were 30% more likely to be diagnosed with depression (RR=1.30, 95% CI=1.11-1.52) and 17% more likely to receive a mood-related diagnosis (RR=1.17, 95% CI=1.08-1.27), but were not more likely to be treated with an antidepressant prescription (RR=1.11, 95% CI=0.82-1.51), any mental health prescription (RR=1.15, 95% CI=0.87-1.53), or psychotherapy (RR=1.13, 95% CI=0.98-1.31). In general, associations were stronger among females. ConclusionsAdolescents who were screened for depression during a well-visit were more likely to receive a diagnosis of depression or a mood-related disorder in the six months following screening. Future research should explore methods for increasing access to treatment and treatment uptake following screening. Clinical Trial Registration (if any)N/A Table of Contents SummaryInsurance claims data were used to explore associations between depression screening during routine well-visits and depression diagnoses, psychiatric prescriptions, and psychotherapy among adolescents. Whats Known on This SubjectDepression screening is increasingly viewed as a key strategy for addressing depression among adolescents. To inform clinical guidelines, the United States Preventive Services Task Force has called for research to examine the diagnostic and treatment-related outcomes of depression screening. What This Study AddsIn a sample of 57,732 adolescents, adolescents who were screened for depression during a well-visit were more likely to receive a depression or mood-related diagnosis over 6-month follow-up, but were not more likely to be treated with medication or psychotherapy.

ObjectiveGiven the episodic nature of bipolar disorder (BD) and the variability in mood episode recurrence across individuals, accurate recurrence prediction is critical. The original COBY recurrence risk calculator (RC) was developed in a longitudinal youth cohort to estimate threshold recurrence risk. However, its accuracy for predicting subthreshold recurrences had not been fully evaluated. The objective of this study was to extend the previously developed COBY mood recurrence RC to predict both threshold and subthreshold mood recurrences and evaluate its performance in an independent sample. MethodAdolescents and young adults with BD-I/II (N= 51; BD-I: 38, BD-II: 13; 14-24 years old) were interviewed with standard instruments at intake and during the follow-up on average every 6 months for a median of 54 weeks. We assessed the degree to which the COBY RC predicted mood recurrence (threshold or subthreshold) in this independent sample. Discrimination was measured using the area under the receiver operating characteristic curves (AUC); calibration and variable importance were also assessed. ResultsThe model demonstrated good prediction of any recurrence within the next six months (any threshold recurrence AUC = 0.72, any subthreshold or worse recurrence AUC = 0.77). Calibration analysis demonstrated the model tended to overestimate risk in the external sample, plausibly attributable to differences in recurrence ascertainment strategy (prospective vs retrospective) or the significant difference in prior remission length, a key predictor. Recalibration greatly improved calibration without loss of discrimination. ConclusionThe mood recurrence RC demonstrated good discrimination for both threshold and subthreshold mood recurrences in an independent young adult cohort, consistent with prior youth and adult validations. Validation now spans across developmental stages and different degrees of severity of mood symptoms opening the opportunity for clinical implementation to provide personalized monitoring and early intervention for people with BD.

IntroductionCognitive impairments are common in depression and often persist beyond mood resolution. However, the relationship between cognitive performance, its neurological underpinnings, and future depression risk is unclear, limiting strategies for primary and secondary prevention. Our objective was to determine whether cognition associates with subsequent depression, including both relapse and first-episode occurrences. Methods2094 UK Biobank participants with previous ICD-10-defined depression currently in remission (RD) (mean(SD) age: 52.4(7.25) years) were age- and sex-matched to 2094 participants without depression history or current antidepressant use. Cognitive scores were compared between groups at the composite (z-score), domain, and task levels. MRI-derived phenotypes assessed brain network structure and functional connectivity. Longitudinal associations with future depression were assessed using logistic regression models controlling for key confounders. ResultsParticipants with RD had a higher risk of future depression (30%) than controls (8.5%). Composite cognitive performance in controls was inversely associated with future depression risk (risk estimated marginal means: 0.48% at -1SD, 0.37% at mean, 0.28% at +1SD). In RD, this relationship was reversed (1.56% at -1SD, 1.80% at mean, 2.08% at +1SD). Executive functioning, processing speed, and reasoning task scores all contributed. Higher grey matter in default mode network regions was associated with better concurrent cognitive performance across all participants, but not with future depression risk. Other MRI findings were limited. ConclusionRD carried a threefold higher risk of future depression than controls. Cognitive performance was a risk marker for future depression in both groups but in opposing directions. Neuroimaging metrics provided little predictive value. What is already known on this topicNeurocognitive impairments are common in depression, even after low mood has resolved and outside of comorbid neurodegenerative processes. However, the specific relationship between cognitive performance and risk of future depression is unclear, including how this relates to previous history of depression. What this study addsCognitive performance is a differential risk marker for future depression in those with previous depression compared to matched controls: without previous history of depression, poorer cognitive scores confer the highest risk; in remitted depression, higher levels of cognitive performance are associated with greater risk of depressive relapse. How this study might affect research, practice or policyFurther research should explore targeting interventions based on specific cognitive profiles, especially in high-risk populations such as those with previous episodes of depression.

BackgroundDepressive (DD) and bipolar (BD) disorders are characterized by biases in anticipating and encoding emotional information. Depressive traits are linked to negative affective biases, while hypomanic features are associated with heightened responsiveness to positive stimuli. The vividness of visual imagery may further modulate these biases. This study examined how lifetime dimensional symptoms of depression and hypomania across diagnoses interact with imagery vividness to modulate brain activation during anticipation and encoding of happy and sad faces. MethodsA total of 155 individuals aged 18-45 years with BD, DD, or healthy control (HC) status completed a cued emotional face-encoding task during functional magnetic resonance imaging (fMRI). Lifetime dimensional symptoms of depression and hypomania were assessed using the MOODS-SR, and imagery vividness was measured with the Vividness of Visual Imagery Questionnaire (VVIQ). Interaction effects between spectrum depression/hypomania and imagery vividness on brain activation during anticipation and encoding of happy versus sad faces were analyzed using the Sandwich Estimator approach in FSL. ResultsHigher spectrum hypomania scores were associated with greater imagery vividness and increased activation for happy versus sad faces in the occipital pole, cuneus, intracalcarine, and lateral occipital cortices during encoding. However, there was reduced activation for happy versus sad faces in the precentral gyrus during anticipation. Depressive symptoms interacted with imagery vividness within the default mode network: more severe spectrum depression and lower vividness were associated with greater activation for happy versus sad faces in the frontopolar cortex during anticipation, but with reduced activation in the angular gyrus during encoding. ConclusionLifetime depressive and hypomanic spectrum symptoms across diagnoses differentially interact with visual imagery to influence anticipation and encoding of emotional faces. Depressive biases were observed in frontopolar-parietal regions, while hypomanic biases appeared in occipital cortices. These findings highlight the value of dimensional approaches to mood psychopathology and identify imagery vividness as a promising transdiagnostic target for intervention.

ObjectiveElectroconvulsive therapy (ECT) is one of the most studied and validated available treatments for severe or treatment-resistant depression. However, little is known about the neural mechanisms underlying the ECT treatment. This systematic review aims to critically review all structural magnetic resonance imaging studies investigating longitudinal cortical thickness (CT) changes after ECT in patients with unipolar or bipolar depression. MethodsWe performed a search on PubMed, Medline, and Embase to identify all available studies published before April 20, 2023. A total of 10 studies were included. ResultsThe investigations showed widespread increases in CT after ECT in depressed patients, involving mainly the temporal, insular, and frontal regions. In five studies, CT increases in a non-overlapping set of brain areas correlated with the clinical efficacy of ECT. The small sample size, heterogeneity in terms of populations, medications, comorbidities, and ECT protocols, and the lack of a control group in some investigations limit the generalizability of the results. ConclusionsOur findings support the idea that ECT can increase CT in patients with unipolar and bipolar depression. It remains unclear whether these changes are related to the clinical response. Future larger studies with longer follow-up are warranted to thoroughly address the potential role of CT as a biomarker of clinical response after ECT. SummationsO_LIThis review summarizes how ECT affects CT in patients with unipolar or bipolar depression. C_LIO_LIThe areas that were predominantly affected by ECT were temporo-insular and frontal regions. An association between the antidepressant effect of ECT and CT changes was reported by half of the included studies. C_LIO_LIIdentifying the possible cortical changes associated with the clinical efficacy of ECT opens new targets to ameliorate ECT protocols. C_LI ConsiderationsThe review is based on studies with small numbers of patients and considerable heterogeneity in terms of patients characteristics and ECT protocols. Most studies cited did not have a randomized design, thus reducing the strength of evidence supporting a causal link between ECT and CT changes.

BackgroundRates of depression have increased worldwide during the COVID-19 pandemic. One known protective factor for depression is social support, but more work is needed to quantify the extent to which social support could reduce depression risk during a global crisis, and specifically to identify which types of support are most helpful, and who might benefit most. MethodsData were obtained from participants in the All of Us Research Program who responded to the COVID-19 Participant Experience (COPE) survey administered monthly from May 2020 to July 2020 (N=69,066, 66% female). Social support was assessed using 10 items measuring emotional/informational support (e.g., someone to confide in or talk to about yourself or your problems), positive social interaction support (e.g., someone to do things with to help you get your mind off things), and tangible support (e.g., someone to help with daily chores if sick). Elevated depression symptoms were defined based on having a moderate-to-severe ([≥]10) score on the Patient Health Questionnaire (PHQ-9). Mixed-effects logistic regression models were used to test associations across time between overall social support and its subtypes with depression, adjusting for age, sex, race, ethnicity, and socioeconomic factors. We then assessed interactions between social support and potential effect modifiers: age, sex, pre-pandemic mood disorder, and pandemic-related stressors (e.g., financial insecurity). ResultsApproximately 16% of the sample experienced elevated depressive symptoms. Overall social support was associated with significantly reduced odds of depression (adjusted odds ratio, aOR [95% CI]=0.44 [0.42-0.45]). Among subtypes, emotional/informational support (aOR=0.42 [0.41-0.43]) and positive social interactions (aOR=0.43 [0.41-0.44]) showed the largest protective associations with depression, followed by tangible support (aOR=0.63 [0.61-0.65]). Sex, age, and pandemic-related financial stressors were statistically significant modifiers of the association between social support and depression. ConclusionsIndividuals reporting higher levels of social support were at reduced risk of depression during the early COVID-19 pandemic. The perceived availability of emotional support and positive social interactions, more so than tangible support, was key. Individuals more vulnerable to depression (e.g., women, younger individuals, and those experiencing financial stressors) may particularly benefit from enhanced social support, supporting a precision prevention approach.

BackgroundChallenges in correctly identifying bipolar II disorder (BD-II) during depressive states have led to poor clinical outcomes. BD-II-specific imaging investigations are lacking. This study addresses current knowledge gaps by comparing white matter (WM) integrity in BD-II and unipolar depression (UD) using fixel-based analysis. MethodFibre density (FD), fibre cross-section (FC), and the combined measure (FDC) within 72 WM tracts were compared among 33 individuals with BD-II, 50 with UD, and 51 healthy controls (HC). The effects of illness characteristics on FBA correlates were also examined. Sensitivity analyses compared these measures among unmedicated participants to check whether medication status affects the results. ResultsParticipants with BD-II and UD showed reduced FD in the left parieto-occipito-pontine (POPT) and striato-occipital (ST-OCC) tracts. Compared to UD, BD-II was associated with lower FD in the left arcuate fascicle (AF) and bilateral superior longitudinal fasciculi I and II (SLF-I and II). In BD-II, illness duration negatively correlated with FD in left AF, left POPT, and right ST-OCC, while the number of lifetime BD-II depressive episodes positively correlated with FDC in left SLF-I. Group differences were significant but less pronounced in unmedicated participants. ConclusionsOur findings demonstrate shared and distinct WM abnormalities in tracts involved in visuomotor and executive processes in BD-II and UD, with BD-II exhibiting more extensive alterations. With BD-II, but not UD, longer illness duration was linked to lower FD, while depression recurrence was associated with higher FDC, suggesting potential degenerative and compensatory neurobiological mechanisms. Longitudinal studies should investigate the joint trajectories of symptomatology and WM alterations.

IntroductionBipolar Disorder (BD) is characterized by marked disruptions in emotional regulation and cognitive control, often accompanied by structural abnormalities in the brains white matter. Diffusion tensor imaging (DTI) offers a window into white matter microstructure through metrics such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). This study aimed to examine white matter tract alterations in individuals with BD compared to healthy controls (HCs) and to explore potential associations between DTI metrics and clinical symptom severity. MethodsWe conducted a voxel-wise whole-brain DTI analysis in a sample of BD patients and age-matched HCs, focusing on FA, MD, RD, and AD values. Group comparisons were performed to identify significant differences in white matter integrity. In addition, we assessed correlations between DTI metrics and psychological assessment scores to investigate links between structural alterations and clinical features. ResultsCompared to HCs, BD patients exhibited significantly lower FA in several major white matter tracts, including the cingulum (olfactory tract), forceps minor of the corpus callosum, fornix, inferior fronto-occipital fasciculus, and superior longitudinal fasciculus. These reductions suggest disrupted microstructural coherence. In parallel, elevated MD, RD, and AD in overlapping regions point to possible myelin degeneration or axonal injury. However, associations between DTI metrics and psychological symptom scores were weak and did not reach statistical significance. Discussion/ConclusionThese findings reinforce the presence of widespread white matter abnormalities in BD, particularly in tracts relevant to emotional and cognitive processing. While structural disruptions were evident, their weak correlations with symptom severity highlight the complex relationship between brain microstructure and clinical expression in BD. Future studies are warranted to clarify the diagnostic and prognostic relevance of DTI-based biomarkers in mood disorders.

IntroductionPerinatal depression affects up to 30% of pregnant and postpartum women, which has increased since the COVID-19 pandemic, making rapidly identifying affected women a high clinical priority. While screening tools like the Edinburgh Postnatal Depression Scale (EPDS) are widely used, brevity is important for busy clinical practice to reduce administration time and patient burden. Current methods to shorten assessments rely on traditional psychometric approaches, rather than machine learning (ML) methods that could optimize predictive accuracy. MethodsWe developed a ML framework using National Clinical Cohort Collaborative (N3C) data to predict full 10-item EPDS scores from shortened question subsets (n=22,924). We evaluated all 2-5 item combinations using linear regression, validating performance across multiple cohorts including postpartum women (n=7,750) and an external non-N3C pregnancy population (n=1,217). For additional validation, we applied our approach to the PHQ-9 (n=398,606) to test generalizability. Binary classification models using clinical thresholds ([≥]13) determined EPDS screening accuracy. Decision curve analysis was performed to assess the clinical utility of our ML method. ResultsThe optimal 2-question EPDS combinations Q4+Q8 (anxiety/sadness) and Q5+Q8 (scared/sadness) both achieved R2=0.70. Binary classification demonstrated strong performance (sensitivity=0.68-0.72, specificity=0.98-0.99). The framework generalized across postpartum subsets, external pregnancy cohorts, and PHQ-9 validation (R2=0.64-0.73). Adding covariates did not improve performance. Decision curve analysis showed our ML approach had superior clinical benefit (0.01-0.03) versus traditional additive scoring. Conclusion/ImplicationsOur ML framework suggests a reduced assessment burden with two EPDS questions maintains predictive accuracy as the full-item EPDS. With [~]3.6 million annual U.S. births, this approach could identify additional positive perinatal depression screens, enhancing screening implementation across clinical settings.

BackgroundSelf-reported trauma exposure has consistently been found to be a risk factor for Major Depressive Disorder (MDD) and several studies have reported interactions with genetic liability. To date, most studies have examined interaction effects with trauma exposure using genome-wide variants (single nucleotide polymorphisms SNPs) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. We sought to re-examine genome-by-trauma interaction effects using genetic measures utilising all available genotyped data and thus, maximising accounted variance. MethodsMeasures of self-reported depression, neuroticism and trauma exposure for 148 129 participants with whole genome SNP data are available from the UK Biobank study. Here, we used a mixed-model statistical approach utilising genetic, trauma exposure and genome-by-trauma exposure interaction similarity matrices to explore sources of variation in depression and neuroticism. FindingsOur approach estimated the heritability of MDD to be approximately 0{middle dot}160 [SE 0{middle dot}016]. Subtypes of self-reported trauma exposure (catastrophic, adult, childhood and full trauma) accounted for a significant proportion of the variance of each trait, ranging from 0{middle dot}056 [SE 0{middle dot}013] to 0{middle dot}176 [SE 0{middle dot}025]. The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction ranged from 0{middle dot}074 [SE 0{middle dot}006] to 0{middle dot}201 [SE 0{middle dot}009]. Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in males than in females. InterpretationThis is the first study to utilise an approach combining all genome-wide SNP data when exploring genome-by-trauma interaction effects in MDD and present evidence that interaction effects are influential in depression manifestation. This effect accounts for greater trait variance within males which points to potential differences in depression aetiology between the sexes. The methodology utilised in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions. Research In ContextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed up to January 30th 2022, with the following terms: ("gene environment interaction" OR "gene environment" OR "genome wide by environment" OR "GWEIS" OR "polygenic environment" OR ("gene" AND "environment")) AND ("polygenic risk score" OR "polygenic score" OR "genomic relationship matrix" OR "GRM") AND ("trauma" OR "environmental adversity" OR "stressful life events") AND ("depression" OR "major depressive disorder" OR "MDD" OR "depressive symptoms"). Date or language restrictions were not applied. We further reviewed the reference lists of identified articles. This search was supplemented by reviewing related articles identified by Google Scholar. We identified 12 relevant articles. Studies to date have not explored genome-by-environment interaction effects in depression using genomic similarity matrices, however, these effects have been explored using individual single nucleotide polymorphisms (SNPs) from genome-wide studies and polygenic scores (PGSs). Some findings suggest genome-by-environment interaction effects increase risk of depression. However, replication attempts have produced either inconsistent or null findings. Taken together, it is evident that findings have failed to provide consistent evidence of substantial interaction effects. These findings may be a result of limited statistical power in analyses due to genome-wide variants and PGSs failing to capture the polygenic nature of depression with sufficient precision. Added value of this studyThis study is the first to explore genome-by-trauma interaction effects on MDD through the estimation of variance components using relationship matrices. Genomic relationship matrices (GRMs) utilise all available genotyped variants, thus, capturing a greater proportion of the trait variance and potentially providing greater power to detect genetic effects in comparison to PGSs. Additional relationship matrices capturing trauma exposure, and genome-by-trauma exposure similarity are computed and included into mixed linear models. We found evidence for substantial genome-by-trauma (including subtypes of trauma) exposure interaction effects on depression manifestation. Estimated genome-by-trauma interaction effects were larger in males than in females. Implications of all the available evidenceOur findings are the first to show substantial genome-by-trauma effects on depression using whole genome methods. These findings highlight that the role of trauma exposure on depression manifestation may be non-additive and different between sexes. Exploring these effects in depth may yield important insight into various mechanisms, which may explain prevalence differences observed between males and females. Future work can build upon the framework we propose to explore genome-by-trauma interaction effects and the underlying molecular sites and mechanisms which are involved in depression manifestation.

Background and purposeBipolar disorder (BD) is a common psychiatric disorder with high morbidity and mortality. Several polymorphisms have been found to be implicated in the pathogenesis of BD, however, these loci have small effect sizes that fail to explain the high heritability of the disease. Here, we provide more insights into the genetic basis of BD by identifying the differentially expressed genes (DEGs) and their associated pathways and biological processes in post-mortem brain tissues of patients with BD.\n\nMethodsEight datasets were eligible for the differential expression analysis. We used six datasets for the discovery of the gene signature and used the other two for independent validation. We performed the multi-cohort analysis by a random-effect model using R and MetaIntegrator package.\n\nResultsThe initial analysis resulted in the identification of 126 DEGs (30 up-regulated and 94 down-regulated). We refined this initial signature by a forward search process and resulted in the identification of 22 DEGs (6 up-regulated and 16 down-regulated). We validated the final gene signature in the independent datasets and resulted in an Area Under the ROC Curve (AUC) of 0.756 and 0.76, respectively. We performed gene set enrichment analysis (GSEA) which identified several biological processes and pathways related to BD including Ca transport, inflammation and DNA damage response.\n\nConclusionOur findings support the previous findings that link BD pathogenesis to abnormalities in glial inflammation and calcium transport and also identify several other biological processes not previously reported to be associated with the development of the disease. Such findings will improve our understanding of the genetic basis underlying BD and may have future clinical implications.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) has shown efficacy for treating depression, but not for all patients. Accurate treatment response prediction could lower treatment burden. Research suggests machine learning trained with electroencephalographic (EEG) data may predict response, but only a limited range of measures have been tested. ObjectivesWe used >7000 time-series features to comprehensively test whether rTMS treatment response could be predicted in a discovery dataset and an independent dataset. MethodsBaseline EEG from 188 patients with depression treated with rTMS (125 responders) were decomposed into the top five principal components (PCs). The hctsa toolbox was used to extract 7304 time-series features from each participant and PC. A classification algorithm was trained to predict responders from the feature matrix separately for each PC. The classifier was applied to an independent dataset (N = 58) to test generalizability on an unseen sample. ResultsWithin the discovery dataset, the third PC (which showed a posterior-maximum and prominent alpha power) showed above-chance classification accuracy (68%, pFDR = 0.005, normalised positive predictive value = 114%). Other PCs did not outperform chance. The model generalized to the independent dataset with above-chance balanced accuracy (60%, p = 0.046, normalised positive predictive value = 114%). Analysis of feature-clusters suggested responders showed more high frequency power relative to total power, and a more negative skew in the distribution of their time-series values. ConclusionThe dynamical properties of PC3 predicted treatment response with moderate accuracy, which generalized to an independent dataset. Results suggest treatment stratification from pre-treatment EEG may be possible, potentially enabling better outcomes than one-size-fits-all treatment approaches.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) has been increasingly used worldwide in the treatment of depression, however, we currently lack the means to reliably predict whether patients will respond to the treatment. Recent research suggests that the neurophysiological measures of beta power and correlation dimension may have predictive potential, however, studies of beta power and correlation dimension to differentiate rTMS group response in individuals with major depression are limited. MethodsFifty treatment-resistant patients with major depressive disorder were recruited. Forty-two participants underwent baseline resting EEG sessions and 5-8 weeks of rTMS treatments and 12 participants were responders to the treatment. Beta power and correlation dimension from baseline resting EEG were compared between responders and non-responders. ResultsResponders demonstrated significantly lower beta power in baseline resting EEG, however, correlation dimension did not show a significant difference between groups. LimitationsThere were a small number of responders in this study. ConclusionBaseline resting beta power may help to differentiate responders from non-responders to rTMS treatment. However, further studies are needed with larger sample sizes.

ImportanceMajor depressive disorder (MDD) and bipolar disorder (BD) are often misdiagnosed during depressive episodes, therefore, exploring biomarkers for differential diagnosis is important. ObjectiveTo identify circadian biomarker signatures in patients peripheral blood that differentiate MDD from BD during depressive states. Design, setting, and participantsThis case-control study recruited patients with MDD and BD at depressive state diagnosed by the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) and Health control (HC) subjects from January 2021 to May 2023. We collected serum samples to detect levels of Period (PER)1/PER2/phosphorylated cAMP reaction element binding protein (pCREB). Main outcomes and measuresParticipants clinical data were evaluated by HAMD-17 and MDQ scales. Blood samples (n = 100) were collected and extracted for serum followed by detecting serum PER1, PER2 and pCREB levels by ELISA. ResultsThere were 100 participants in the cohort, including 40 in the MDD group, 30 in the BD group and 30 in the health control (HC) group. 71% were female in the cohort. The mean (SD) serum PER1 level in the HC group was 12.05 (2.96) ng/mL, in the MDD group was 8.41 (2.96) ng/mL, significantly decreased vs the HCs, and in the BPD group that was 16.05 (3.60) ng/mL, significantly increased vs the HCs (adjusted P < 0.0001). The serum pCREB level in the HC group was 205.2 (49.57) ng/mL, in the MDD group was 192.6 (38.52) ng/mL, and that in the BPD group was 290.0 (72.10) ng/mL, which was significantly increased vs the HCs and vs the MDD group. For the differential diagnosis between MDD and BPD, PER1 & pCREB (AUC, 0.9858) show similar high diagnostic efficiency as combined biomarkers of PER1, PER2 & pCREB (AUC, 0.9906). Conclusions and relevanceThis study reveals the importance of serum PER1, combined use of serum PER1 and pCREB as well as that of serum pCREB, PER1 and PER2 in differentiating MDD from BPD. Key PointsO_ST_ABSQuestionC_ST_ABSCan major depressive disorder (MDD) be distinguished from bipolar disorder (BD) during their depressive episodes by circadian biomarkers in patients peripheral blood? FindingsIn this case-control study of 100 participants, serum PER1 levels were significantly decreased in the MDD group but were significantly increased in the BPD group, serum PER2 levels were significantly elevated in both MDD and BPD groups, and serum pCREB levels were significantly increased in the BPD group, compared with the HC group. Besides, In the MDD group, serum PER1 levels were significantly positively associated with mood disorder questionnaire (MDQ) scores; and serum pCREB levels were significantly negatively associated with MDQ scores. In the BPD group, serum pCREB levels were significantly negatively associated with HAMD-17 scores. The combined use of serum pCREB and PER1 and the three biomarkers (pCREB, PER1 and PER2) had similar diagnostic values in differentiating MDD from BPD. MeaningThe serum PER1 has great value for differentiating MDD from BPD, and combined use of serum biomarkers of pCREB/PER1 or pCREB/PER1/PER2 shows higher efficacy in differentiating MDD from BPD.

This study aimed to develop a classification model predicting incident bipolar disorder (BD) cases in young adults within a 5-year interval, using sociodemographic and clinical features from a large cohort study. We analyzed 1,091 individuals without BD, aged 18 to 24 years at baseline, and used the XGBoost algorithm with feature selection and oversampling methods. Forty-nine individuals (4.49%) received a BD diagnosis five years later. The best model had an acceptable performance (test AUC: 0.786, 95% CI: 0.686, 0.887) and included ten features: feeling of worthlessness, sadness, current depressive episode, selfreported stress, self-confidence, lifetime cocaine use, socioeconomic status, sex frequency, romantic relationship, and tachylalia. We performed a permutation test with 10,000 permutations that showed the AUC from the built model is significantly better than random classifiers. The results provide insights into BD as a latent phenomenon, as depression is its typical initial manifestation. Future studies could monitor subjects during other developmental stages and investigate risk populations to improve BD characterization. Furthermore, the usage of digital health data, biological, and neuropsychological information and also neuroimaging can help in the rise of new predictive models.

Depression affects approximately 5% of adults worldwide, with India reporting a prevalence of 4.5%. Oral medication is a common treatment, but over 50% of patients fail to respond to the first-line antidepressants and often require medication adjustments or augmentation. This highlights the urgent need for predictive models that can guide personalized treatment strategies more quickly. Our study aims to achieve three key objectives: first, to assess the predictive ability of previously identified biomarkers such as frontal theta power and alpha asymmetry, in explaining the response to interventions within our sample; second, to evaluate the utility of whole-person research approach, focusing the gut-brain interactions, in predicting responses; and third, to identify reliable early biomarkers that can predict responses across various phenotypic subtypes. Across two sites, a total of 161 (+45) participants, including 99 (45) treatment-naive patients, enrolled in our study from site 1 (+site 2) which spanned three visits. We aimed to predict antidepressant outcomes at the third visit (4-6 weeks) using data collected from visits one (baseline) and two (7-10 days), and the data from site 2 was solely used for testing the predictive utility. Our predictive models, which incorporated electrophysiological data from both the brain and gut along with clinical information, achieved an cross validation (independent test) performance of 78% (80%) specificity and 84% (71.43%) sensitivity in identifying non-responders to antidepressant treatment administered as per Clinical Practice Guidelines of India. We found that certain electrophysiological features were strongly predictive of treatment outcomes for specific depression subtypes. For example, increased excitation-inhibition ratios in the fronto-central brain regions were predictive for patients with dominant anxiety and sleep symptoms. Similarly, decreased tachygastric gut coupling with the sensory-motor brain region predicted treatment non-response in patients with high levels of negative self-thoughts. Increased connectivity in the right fronto-central region was associated with better outcomes in patients with significant appetite issues. Additionally, higher fronto-central theta power and beta asymmetry were predictive of responses in patients with a composite set of symptoms. Our findings suggest that combining brain and gut electrophysiological markers with clinical phenotyping offers a promising, scalable approach to personalize depression treatment. This approach could guide clinicians in developing more effective and tailored medication strategies, ultimately improving patient outcomes.

BackgroundMajor Depressive Disorder (MDD) is a prevalent mental health condition with significant public health implications. Early detection is crucial for effective intervention, yet current diagnostic methods often fail to identify MDD in its early stages. ObjectiveThis study aimed to develop and validate machine learning models for the early detection of MDD using functional Magnetic Resonance Imaging (fMRI) data. MethodsWe utilized fMRI data from 1,200 participants (600 with early-stage MDD and 600 healthy controls) across three public datasets. Four machine learning models (Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Deep Neural Network (DNN)) were developed and compared. Models were evaluated using accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC), and F1 score. ResultsThe DNN model demonstrated superior performance, achieving 89% accuracy (95% CI: 0.86-0.92) and an AUC-ROC of 0.95 (95% CI: 0.93-0.97) in detecting early-stage MDD. Key predictive features included altered functional connectivity between the dorsolateral prefrontal cortex, anterior cingulate cortex, and limbic regions. The model showed good generalizability across different datasets and identified 78% (95% CI: 71%-85%) of individuals who developed MDD within a 2-year follow-up period. ConclusionsOur AI-driven approach demonstrates promising potential for early MDD detection, outperforming traditional diagnostic methods. This study highlights the utility of machine learning in analyzing complex neuroimaging data for psychiatric applications. Future research should focus on prospective clinical trials and the integration of multimodal data to enhance the clinical applicability of this approach further.

Depression among Black women is a significant public health concern. However, our understanding of their unique experiences and the barriers and facilitators to utilising healthcare services remains limited. To address these issues, we conducted a qualitative evidence synthesis in collaboration with experts by lived experiences. We searched seven databases (ASSIA, MEDLINE, APA PsycInfo, Sociological Abstracts, CINAHL, AMED and EMBASE) from inception to 9th September 2021 and updated to 29th March 2024 with an English language restriction. Study quality and confidence in findings were assessed using the Critical Appraisal Skills Programme (CASP) and Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach. Of 15025 papers screened, 45 were eligible for inclusion. Data were analysed using thematic analysis. Women reported depression stemming from racial and gender-related stressors, social isolation, and a loss of faith; moreover, the Strong Black Woman schema masked depression symptoms. Mistrust of healthcare providers, stigma, religious coping, and pressure to conform to the Strong Black Woman schema hindered healthcare service utilisation. The rapport between women and their healthcare providers, endorsement from faith leaders, and points of crisis enabled service utilisation. Lived experience experts provided reflections and recommendations for practice. HighlightsO_LIRecognition of depression may be hampered by schemas connected to Black womens identity. C_LIO_LITrust between Black women experiencing depression and clinicians is essential for effective care. C_LIO_LITraining which incorporates antiracist principles is needed for competence in discussing issues surrounding race and gender. C_LIO_LI(Re-)consideration of diagnostic criteria to acknowledge differential presentation and the development of culturally adapted treatments are warranted. C_LIO_LICo-producing research with experts by lived experience ensures it is more impactful. C_LI

BackgroundRecent data indicates high prevalence of post-traumatic stress disorder (PTSD) in bipolar disorder (BD). PTSD may play a role in poor treatment outcomes and quality of life for people with BD. Despite this, few studies have examined the pharmacological treatment interventions and outcomes for this comorbidity. This systematic review will bring together currently available evidence regarding the impact of comorbid PTSD on pharmacological treatment outcomes in adults with BD. MethodsA systematic search of Embase, MEDLINE Complete, PsycINFO, and the Cochrane Central Register of Controlled Trials (CENTRAL) will be conducted to identify randomised and non-randomised studies of pharmacological interventions for adults with diagnosed bipolar disorder and PTSD. Data will be screened and extracted by two independent reviewers. Literature will be searched from the creation of the databases until April 1 2021. Risk of bias will be assessed using the Newcastle-Ottawa Scale and the Cochrane Collaborations Risk of Bias tool. A meta-analysis will be conducted if sufficient evidence is identified in the systematic review. The meta-analysis will employ a random-effects model and be evaluated using the I2 statistic. DiscussionThis review and meta-analysis will be the first to systematically explore and integrate the available evidence on the impact of PTSD on pharmacological treatments and outcome in those with BD. The results and outcomes of this systematic review will provide directions for future research and be published in relevant scientific journals and presented at research conferences. Systematic review registrationThe protocol has been registered at the International Prospective Register of Systematic Reviews (PROSPERO; registration number: CRD42020182540).

BackgroundRecurrent course and disruption of circadian rhythms are among the core features of bipolar disorder (BD). Thus, ongoing symptom monitoring is an essential part of good clinical management. ObjectiveWe conducted a study to validate the English version of the ASERT (Aktibipo questionnaire), a tool for self-assessment of mood symptoms. We also analyzed the relationship of self-assessed symptoms with clinician ratings and actigraphy measures, and investigated the possibility of predicting depressive episodes using subjective and digital measures. MethodsThis was a longitudinal study of individuals with BD, followed for up to 11 months. The participants completed weekly mood self-assessments (ASERT) using a smartphone app and wore wrist actigraphs. During monthly appointments, the severity of their mood symptoms was rated by clinicians, and the participants completed questionnaires addressing overall functioning (FAST), and biological rhythms (BRIAN). ResultsThe study confirmed the validity and reliability of the ASERT as a measure of subjective mood. Additionally, we found significant associations between ASERT responses, clinical scales, and actigraphy data. In our analysis, a combination of self-assessment and actigraphy data detected depression relapse with 67% sensitivity, 90% specificity, and 81% balanced accuracy. Furthermore, we observed a strong correlation between the stability of daily routine and overall functioning, emphasizing the significance of circadian rhythm disruptions in BD. ConclusionThis study highlights the potential of digital tools, such as digitally administered self-assessments and actigraphy, to enhance the management of BD by providing valuable insights into mood states and detecting relapse. Further research is needed to refine and optimize these tools for widespread clinical application, such as informing personalized treatment plans.