Diagnosis Of Major Depressive Disorder Research Articles

Resting-state functional magnetic resonance imaging and support vector machines for the diagnosis of major depressive disorder in adolescents

BACKGROUND Research has found that the amygdala plays a significant role in underlying pathology of major depressive disorder (MDD). However, few studies have explored machine learning-assisted diagnostic biomarkers based on amygdala functional connectivity (FC). AIM To investigate the analysis of neuroimaging biomarkers as a streamlined approach for the diagnosis of MDD in adolescents. METHODS Forty-four adolescents diagnosed with MDD and 43 healthy controls were enrolled in the study. Using resting-state functional magnetic resonance imaging, the FC was compared between the adolescents with MDD and the healthy controls, with the bilateral amygdala serving as the seed point, followed by statistical analysis of the results. The support vector machine (SVM) method was then applied to classify functional connections in various brain regions and to evaluate the neurophysiological characteristics associated with MDD. RESULTS Compared to the controls and using the bilateral amygdala as the region of interest, patients with MDD showed significantly lower FC values in the left inferior temporal gyrus, bilateral calcarine, right lingual gyrus, and left superior occipital gyrus. However, there was an increase in the FC value in Vermis-10. The SVM analysis revealed that the reduction in the FC value in the right lingual gyrus could effectively differentiate patients with MDD from healthy controls, achieving a diagnostic accuracy of 83.91%, sensitivity of 79.55%, specificity of 88.37%, and an area under the curve of 67.65%. CONCLUSION The results showed that an abnormal FC value in the right lingual gyrus was effective as a neuroimaging biomarker to distinguish patients with MDD from healthy controls.

An historical overview of artificial intelligence for diagnosis of major depressive disorder

The Artificial Intelligence (AI) technology holds immense potential in the realm of automated diagnosis for Major Depressive Disorder (MDD), yet it is not without potential shortcomings. This paper systematically reviews the research progresses of integrating AI technology with depression diagnosis and provides a comprehensive analysis of existing research findings. In this context, we observe that the knowledge-driven first-generation of depression diagnosis methods could only address deterministic issues in structured information, with the selection of depression-related features directly influencing identification outcomes. The data-driven second-generation of depression diagnosis methods achieved automatic learning of features but required substantial high-quality clinical data, and the results were often obtained solely from the black-box models which lack sufficient explainability. In an effort to overcome the limitations of the preceding approaches, the third-generation of depression diagnosis methods combined the strengths of knowledge-driven and data-driven approaches. Through the fusion of information, the diagnostic accuracy is greatly enhanced, but the interpretability remains relatively weak. In order to enhance interpretability and introduce diagnostic criteria, this paper offers a new approach using Large Language Models (LLMs) as AI agents for assisting the depression diagnosis. Finally, we also discuss the potential advantages and challenges associated with this approach. This newly proposed innovative approach has the potential to offer new perspectives and solutions in the diagnosis of depression.

Differential association between childhood trauma subtypes and neurocognitive performance in adults with major depression

BackgroundNeurocognitive impairment is one of the prominent manifestations of major depressive disorder (MDD). Childhood trauma enhances vulnerability to developing MDD and contributes to neurocognitive dysfunctions. However, the distinct impacts of different types of childhood trauma on neurocognitive processes in MDD remain unclear.MethodsThis study comprised 186 individuals diagnosed with MDD and 268 healthy controls. Childhood trauma was evaluated using the 28-item Childhood Trauma Questionnaire-Short Form. Neurocognitive abilities, encompassing sustained attention, vigilance, visual memory, and executive functioning, were measured by the Cambridge Neuropsychological Testing Automated Battery.ResultsMultivariable linear regressions revealed that childhood trauma and MDD diagnosis were independently associated with neurocognitive impairment. Physical neglect was associated with impaired visual memory and working memory. MDD diagnosis is associated with working memory and planning. Interactive analysis revealed that physical/sexual abuse was associated with a high level of vigilance and that emotional neglect was linked with better performance on cognitive flexibility in MDD patients. Furthermore, childhood emotional abuse, physical abuse, and emotional neglect were revealed to be risk factors for developing early-onset, chronic depressive episodes.ConclusionThus, specific associations between various childhood traumas and cognitive development in depression are complex phenomena that need further study.

The gut microbiota-brain connection: insights into major depressive disorder and bipolar disorder

Major depressive disorder (MDD) and bipolar disorder (BD) are two of the most prevalent mood disorders that seriously jeopardize both physical and mental health. The current diagnosis of MDD and BD relies primarily on clinical symptoms. However, correctly differentiating between MDD and BD during depressive episode states remains a substantial clinical challenge. The human gut hosts a large and diverse microbiota, which plays a pivotal role in various physiological processes. Emerging evidence suggests that the gut microbiota (GM) exerts beneficial effects on mental health disorders, including MDD, BD, and schizophrenia, through the microbe-gut-brain axis (MGBA). In recent years, the relationship between GM and mood disorders has garnered considerable attention, leading to intensive research in this area. The MGBA is a bidirectional communication system between the gut and the brain. Growing evidence indicates that the brain can influence the GM, which in turn may modulate the brain through this axis. This review aims to explore the changes in the GM of patients with MDD and BD and evaluate the effects of different treatments on their GM, including medication, probiotic, prebiotic and synbiotic interventions, and fecal microbiota transplantation (FMT). By doing so, we seek to identify potential disease-specific biomarkers, improve differential diagnosis, and offer novel therapeutic avenues for these disorders.

Peripheral Endothelial Dysfunction Is Associated With Incident Major Depressive Disorder.

A subset of individuals with major depressive disorder (MDD) have a high burden of cardiovascular risk factors and cerebral small-vessel disease, implicating vascular disease in the development of depression. Cross-sectional studies demonstrate a link between endothelial dysfunction and MDD, but the prospective association between peripheral endothelial dysfunction (PED) and an incident diagnosis of MDD is unknown. Patients undergoing a baseline assessment of cardiovascular risk were evaluated for PED using reactive hyperemia-peripheral arterial tonometry (≤1.8 consistent with PED). Patient medical records were reviewed to identify those who underwent a formal clinical evaluation of MDD after the index PED evaluation. The frequency of PED was compared in those with and without MDD. Logistic regression analyses were performed to assess the association between baseline PED and incident MDD. Between January 2006 and December 2020, 1614 patients underwent testing for PED. Four hundred eighty-four (30.1%) patients underwent a formal evaluation for MDD after (0-15 years) the index procedure (mean±SD age, 52.8±13.8 years; 65.2% women). Of these, 157 (32.4%) had PED and 108 (31.0%) were diagnosed with MDD. Individuals with MDD had a higher frequency of PED (40.2% versus 30.2%; P=0.034) compared with those without MDD. In multivariable analyses, PED was significantly associated with MDD (odds ratio, 2.3 [95% CI, 1.4-3.8]; P<0.001). PED is significantly associated with incident MDD. Thus, PED may be a useful marker to identify individuals at increased risk of depression who may benefit from more frequent and earlier management strategies.

Health-care utilisation and associated economic burden in Major Depressive Disorder and Treatment-Resistant Depression in a longitudinal population-based cohort study of 110.000 patients

Major depressive disorder (MDD), and particularly treatment-resistant depression (TRD), lead to high levels of health-care use and disease burden. The aim of this study was to evaluate and compare the health-care resource utilisation and associated costs in these patient groups. It was a population-wide, retrospective analysis of NHS health-care electronic records in northwest London using the Discover-NOW platform, one of the largest interlinked datasets in Europe hosted by Imperial College Health Partners, which contains coded emergency and routine, primary and secondary, physical and mental health data, covering a population of more than 2.5 million. Eligible patients were adults with a diagnosis of MDD who had been prescribed at least one antidepressant between 2015 and 2020. A total of 110,406 patients were included, 101,333 [92%] with MDD and 9,073 [8%] with TRD. Mean duration of depression was 52.8 (SD 41.7) months for MDD and 70.8 (SD 37.8) months for TRD (p<0.0001). Patients with TRD had significantly higher risks of both psychiatric and somatic comorbidities such as anxiety, asthma, and alcohol misuse (all p<0.0001). They also demonstrated increased primary care and emergency attendance rates, more frequent and longer hospitalizations, and on average 1.5 times greater total healthcare costs compared to MDD patients. Primary care visits represented the largest proportion overall (TRD mean 162 [SD 96] vs. MDD 108 [90] visits per patient, p<0.0001; cost per patient £17,348 [SD £33,040] vs. £12,011 [£25,588], p<0.0001) during the study period. In secondary care, accident and emergency visits accounted for the highest use (TRD mean 5.5 [10.6] vs. MDD 3.54 [SD 6.0] visits per patient, p<0.0001) while non-elective hospitalisations incurred the highest costs (mean £2,518 [£8,064] vs. £1,909 [SD £6,807], p<0.0001). Demand increased with the duration of depression and the number of lines of treatment. The study highlights the substantial clinical and economic burden associated with MDD and especially TRD across different care settings.

Sexual Trauma, Polygenic Scores, and Mental Health Diagnoses and Outcomes

Leveraging real-world clinical biobanks to investigate the associations between genetic and environmental risk factors for mental illness may help direct clinical screening efforts and evaluate the portability of polygenic scores across environmental contexts. To examine the associations between sexual trauma, polygenic liability to mental health outcomes, and clinical diagnoses of schizophrenia, bipolar disorder, and major depressive disorder in a clinical biobank setting. This genetic association study was conducted using clinical and genotyping data from 96 002 participants across hospital-linked biobanks located at Vanderbilt University Medical Center (VUMC), Nashville, Tennessee (including 58 262 individuals with high genetic similarity to the 1000 Genomes Project [1KG] Northern European from Utah reference population [1KG-EU-clustered] and 11 047 with high genetic similarity to the 1KG African-ancestry reference population of Yoruba in Ibadan, Nigeria [1KG-YRI-clustered]), and Mass General Brigham (MGB), Boston, Massachusetts (26 693 individuals with high genetic similarity to the combined European-ancestry superpopulation [1KG-EU-clustered]). Clinical data analyzed included diagnostic billing codes and clinical notes spanning from 1976 to 2023. Data analysis was performed from 2022 to 2024. Clinically documented sexual trauma disclosures and polygenic scores for schizophrenia, bipolar disorder, and major depressive disorder. Diagnoses of schizophrenia, bipolar disorder, and major depressive disorder, determined by aggregating related diagnostic billing codes, were the dependent variables in logistic regression models including sexual trauma disclosure status, polygenic scores, and their interactions as the independent variables. Across the VUMC and MGB biobanks, 96 002 individuals were included in analyses (VUMC 1KG-EU-clustered: 33 011 [56.7%] female; median [range] age, 56.8 [10.0 to >89] years; MGB 1KG-EU-clustered: 14 647 [54.9%] female; median [range] age, 58.0 [10.0 to >89] years; VUMC 1KG-YRI-clustered: 6961 [63.0%] female; median [range] age, 44.6 [10.1 to >89] years). Sexual trauma history was associated with all mental health conditions across institutions (ORs ranged from 8.83 [95% CI, 5.50-14.18] for schizophrenia in the VUMC 1KG-YRI-clustered cohort to 17.65 [95% CI, 12.77-24.40] for schizophrenia in the VUMC 1KG-EU-clustered cohort). Sexual trauma history and polygenic scores jointly explained 3.8% to 8.8% of mental health phenotypic variance. Schizophrenia and bipolar disorder polygenic scores had greater associations with mental health outcomes in individuals with no documented disclosures of sexual trauma (schizophrenia interaction: OR, 0.70 [95% CI, 0.56-0.88]; bipolar disorder interaction: OR, 0.83 [95% CI, 0.74-0.94]). Sexual trauma and mental health polygenic scores, while correlated with one another, were independent and joint risk factors for severe mental illness in a large, diverse hospital biobank population. Furthermore, associations of schizophrenia and bipolar disorder polygenic scores with respective diagnoses were greater in those without disclosures, suggesting that genetic predisposition to mental illness as measured by polygenic scores may be less impactful in the presence of this severe environmental risk factor.

Electroconvulsive therapy (ECT) and Psychiatric rehospitalization rates: a retrospective study

BackgroundElectroconvulsive therapy (ECT) induces a generalized seizure under anesthesia with an electrical current for treatment-resistant patients and may be underutilized. To our knowledge, no large-scale, American, nationwide hospital system retrospective study has examined how ECT affects psychiatric rehospitalization chances.MethodsWe analyzed initial inpatient encounters for adults aged > 18 years at HCA Healthcare Behavioral Health Units from 2016 to 2021 with diagnoses of major depressive disorder with/without psychosis, bipolar disorder with/without psychosis, schizoaffective disorder, and schizophrenia. Excluding pregnancy and incarceration cases, we compared psychiatric rehospitalization rates within 365 days for patients receiving ECT versus those with the same diagnoses not receiving ECT. Subgroup analyses were conducted by diagnosis, length of stay, sex, and race. Detailed statistical analyses included bivariate analyses with Fisher’s Exact Test and Wilcoxon Rank Sum Test.ResultsWe analyzed 38,109 distinct patients, 637 of which received ECT. The rehospitalization rate was 37.52% for ECT recipients versus 20.71% for non-ECT patients (p < 0.0001). ECT was associated with higher rehospitalization rates, particularly for severe diagnoses like psychosis (87.28%), schizoaffective disorder (3.77%), and schizophrenia (6.59%). ECT patients had significantly longer rehospitalization lengths of stay (mean 14.53 days vs. 6.54 days for non-ECT, p < 0.0001). White patients received ECT more frequently. Females received ECT more often, while males had higher rehospitalization rates.ConclusionsECT was associated with higher psychiatric rehospitalization rates and longer lengths of stay. This suggests ECT is more commonly used for complex, severe cases which may contribute to higher rehospitalization. Stronger social support and hospital and geographic ties among ECT patients may also play a role.

Characteristic activation pattern and network connectivity of prefrontal cortex in patients with type 2 diabetes mellitus and major depressive disorder during a verbal fluency task: A functional near-infrared spectroscopy study based on network-based statistic prediction.

Type 2 diabetes (T2DM) and major depressive disorder (MDD) together occur frequently among the elderly population. However, the inconsistency in assessments and limited medical resources in the community make it challenging to identify depression in patients with T2DM. This cross-sectional study aimed to investigate the activation pattern and network connectivity of prefrontal cortex (PFC) during a verbal fluency task (VFT) in patients with T2DM and MDD using functional near-infrared spectroscopy (fNIRS). Three parallel groups (T2DM with MDD group, T2DM group, and healthy group) with 100 participants in each group were included in the study. Recruitment took place from August 1, 2020, to December 31, 2023. Due to the close association between the PFC and depressive emotions, fNIRS was used to monitor brain activation and network connectivity of PFC in all participants during a task of Chinese-language phonological VFT. Network-based statistic prediction (NBS-predict) was adopted as data analysis method. Patients in the T2DM with MDD group showed characteristic activation pattern and network connectivity in contrast with patients with T2MD and healthy controls, including decreased activation in PFC, and decreased network connectivity of right dorsolateral prefrontal cortex (DLPFC). Furthermore, the network connectivity of the right DLPFC in patients with T2DM and MDD was negatively correlated with scores of Hamilton Depression Scale-24 (HAMD-24). There was a distinctive activation pattern and network connectivity of the prefrontal cortex in patients with T2DM and MDD. The right DLPFC could serve as a potential target for the diagnosis and intervention of MDD in patients with T2DM.

Use of passively collected actigraphy data to detect individual depressive symptoms in a clinical subpopulation and a general population.

The presentation of major depressive disorder (MDD) can vary widely due to its heterogeneity, including inter- and intraindividual symptom variability, making MDD difficult to diagnose with standard measures in clinical settings. Prior work has demonstrated that passively collected actigraphy can be used to detect MDD at a disorder level; however, given the heterogeneous nature of MDD, comprising multiple distinct symptoms, it is important to measure the degree to which various MDD symptoms may be captured by such passive data. The current study investigated whether individual depressive symptoms could be detected from passively collected actigraphy data in a (a) clinical subpopulation (i.e., moderate depressive symptoms or greater) and (b) general population. Using data from the National Health and Nutrition Examination Survey, a large nationally representative sample (N = 8,378), we employed a convolutional neural network to determine which depressive symptoms in each population could be detected by wrist-worn, minute-level actigraphy data. Findings indicated a small-moderate correspondence between the predictions and observed outcomes for mood, psychomotor, and suicide items (area under the receiver operating characteristic curve [AUCs] = 0.58-0.61); a moderate-large correspondence for anhedonia (AUC = 0.64); and a large correspondence for fatigue (AUC = 0.74) in the clinical subpopulation (n = 766); and a small-moderate correspondence for sleep, appetite, psychomotor, and suicide items (AUCs = 0.56-0.60) in the general population (n = 8,378). Thus, individual depressive symptoms can be detected in individuals who likely meet the criteria for MDD, suggesting that wrist-worn actigraphy may be suitable for passively assessing these symptoms, providing important clinical implications for the diagnosis and treatment of MDD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Polygenic liabilities and treatment trajectories in early-onset depression: a Danish register-based study.

The clinical course of major depressive disorder (MDD) is heterogeneous, and early-onset MDD often has a more severe and complex clinical course. Our goal was to determine whether polygenic scores (PGSs) for psychiatric disorders are associated with treatment trajectories in early-onset MDD treated in secondary care. Data were drawn from the iPSYCH2015 sample, which includes all individuals born in Denmark between 1981 and 2008 who were treated in secondary care for depression between 1995 and 2015. We selected unrelated individuals of European ancestry with an MDD diagnosis between ages 10-25 (N = 10577). Seven-year trajectories of hospital contacts for depression were modeled using Latent Class Growth Analysis. Associations between PGS for MDD, bipolar disorder, schizophrenia, ADHD, and anorexia and trajectories of MDD contacts were modeled using multinomial logistic regressions. We identified four trajectory patterns: brief contact (65%), prolonged initial contact (20%), later re-entry (8%), and persistent contact (7%). Relative to the brief contact trajectory, higher PGS for ADHD was associated with a decreased odds of membership in the prolonged initial contact (odds ratio = 1.06, 95% confidence interval = 1.01-1.11) and persistent contact (1.12, 1.03-1.21) trajectories, while PGS-AN was associated with increased odds of membership in the persistent contact trajectory (1.12, 1.03-1.21). We found significant associations between polygenic liabilities for psychiatric disorders and treatment trajectories in patients with secondary-treated early-onset MDD. These findings help elucidate the relationship between a patient's genetics and their clinical course; however, the effect sizes are small and therefore unlikely to have predictive value in clinical settings.

12270 Adolescent PCOS With Unexpected, Sudden-onset GAD And MDD: A Call For Vigilance In Longitudinal PCOS Care

Abstract Disclosure: K.I. Sarkisian: None. T. Aliabadi: None. F.Z. Stanczyk: None. Background: Polycystic Ovary Syndrome (PCOS) is a multifaceted endocrine disorder often associated with psychiatric disorders, yet the integration of psychiatric monitoring and multidisciplinary care following diagnosis remains understated as a guideline of clinical management of PCOS post-diagnosis. Historically, psychiatric comorbidities in PCOS have been attributed to social and personal factors such as self-image and self-esteem. However, the present patient’s denial of thoughts of disordered eating and lack of self-esteem related to acne or hirsutism challenges these assumptions.Clinical Case: A 13-year-old girl presented with classic symptoms of PCOS, including hirsutism, irregular and painful periods, and premature adrenarche. Laboratory investigations revealed an elevated total testosterone level (58 ng/dL, normal&amp;lt;38 ng/dL), consistent with PCOS. Previously, the patient had not expressed any concerns about mood. However psychological symptoms emerged alongside the physical symptoms. A psychological assessment performed by a licensed therapist revealed moderate-to-severe generalized anxiety disorder (GAD), which had rapidly worsened in a matter of weeks. Both the patient and the patient’s parents denied prior symptoms of moderate-to-severe anxiety before the onset of PCOS symptoms. Over the course of follow-up spanning 47 months, the patient documented symptoms, tracking both mood and PCOS fluctuations. During the follow-up period, the patient’s mood worsened, leading to the formal diagnosis of Major Depressive Disorder (MDD), despite a low assessed risk, no family history, and no symptoms prior to her PCOS development. The diagnoses of GAD and MDD were later confirmed through neuropsychiatric testing by a licensed psychologist.Clinical Lessons: While this presentation may not be considered novel in the context of PCOS, its overwhelming prevalence underscores the need for heightened awareness and research on the topic. The patient, previously considered low-risk for psychiatric conditions, experienced an unexpected onset of GAD and MDD concurrently with the diagnosis of PCOS. It is imperative to recognize that PCOS patients defy the conventional categorization of ‘healthy’ individuals concerning psychiatric care. This notion holds significance not only in clinical practice but also in research endeavors. Merely attributing psychiatric comorbidities in PCOS patients to social factors overlooks the unforeseen presentations of psychiatric disorders, as demonstrated in this patient. These manifestations cannot be solely attributed to social aspects of identity-insecurity, emphasizing the need for a more nuanced, proactive approach to psychiatric care in PCOS. This is crucial for achieving the best long-term prognosis and quality of life in affected individuals. Presentation: 6/1/2024

Emotional competence self-help app versus cognitive behavioural self-help app versus self-monitoring app to prevent depression in young adults with elevated risk (ECoWeB PREVENT): an international, multicentre, parallel, open-label, randomised controlled trial.

Effective, scalable interventions are needed to prevent poor mental health in young people. Although mental health apps can provide scalable prevention, few have been rigorously tested in high-powered trials built on models of healthy emotional functioning or tailored to individual profiles. We aimed to test a personalised emotional competence app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to prevent an increase in depression symptoms in young people. This multicentre, parallel, open-label, randomised controlled trial, within a cohort multiple randomised trial (including a parallel trial of wellbeing promotion) was done at four university trial sites in the UK, Germany, Spain, and Belgium. Participants were recruited from schools, universities, and social media from the four respective countries. Eligible participants were aged 16-22 years with increased vulnerability indexed by baseline emotional competence profile, without current or past diagnosis of major depression. Participants were randomly assigned (1:1:1) to usual practice plus either the personalised emotional competence self-help app, the generic CBT self-help app, or the self-monitoring app by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. Outcome assessors were masked to group allocation. The primary outcome was depression symptoms (according to Patient Health Questionnaire-9 [PHQ-9]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed. Between Oct 15, 2020, and Aug 3, 2021, 1262 participants were enrolled, including 417 to the emotional competence app, 423 to the CBT app, and 422 to the self-monitoring app. Mean age was 18·8 years (SD 2·0). Of 1262 participants self-reporting gender, 984 (78·0%) were female, 253 (20·0%) were male, 15 (1·2%) were neither, and ten (0·8%) were both. 178 participants in the emotional competence app group, 191 in the CBT app group, and 199 in the self-monitoring app group completed the follow-up assessment at 3 months. At 3 months, depression symptoms were lower with the CBT app than the self-monitoring app (mean difference in PHQ-9 -1·18 [95% CI -2·01 to -0·34]; p=0·006), but depression symptoms did not differ between the emotional competence app and the CBT app (0·63 [-0·22 to 1·49]; p=0·15) or the self-monitoring app and emotional competence app (-0·54 [-1·39 to 0·31]; p=0·21). 31 of the 541 participants who completed any of the follow-up assessments received treatment in hospital or were admitted to hospital for mental health-related reasons considered unrelated to interventions (eight in the emotional competence app group, 15 in the CBT app group, and eight in the self-monitoring app group). No deaths occurred. The CBT app delayed increases in depression symptoms in at-risk young people relative to the self-monitoring app, although this benefit faded by 12 months. Against hypotheses, the emotional competence app was not more effective at reducing depression symptoms than the self-monitoring app. CBT self-help apps might be valuable public mental health interventions for young people given their scalability, non-consumable nature, and affordability. European Commission.

Emotional competence self-help mobile phone app versus cognitive behavioural self-help app versus self-monitoring app to promote mental wellbeing in healthy young adults (ECoWeB PROMOTE): an international, multicentre, parallel, open-label, randomised controlled trial.

Based on evidence that mental health is more than an absence of mental disorders, there have been calls to find ways to promote flourishing at a population level, especially in young people, which requires effective and scalable interventions. Despite their potential for scalability, few mental wellbeing apps have been rigorously tested in high-powered trials, derived from models of healthy emotional functioning, or tailored to individual profiles. We aimed to test a personalised emotional competence self-help app versus a cognitive behavioural therapy (CBT) self-help app versus a self-monitoring app to promote mental wellbeing in healthy young people. This international, multicentre, parallel, open-label, randomised controlled trial within a cohort multiple randomised trial (including a parallel trial of depression prevention) was done at four university trial sites in four countries (the UK, Germany, Spain, and Belgium). Participants were recruited from schools and universities and via social media from the four respective countries. Eligible participants were aged 16-22 years with well adjusted emotional competence profiles and no current or past diagnosis of major depression. Participants were randomised (1:1:1) to usual practice plus either the emotional competence app, the CBT app or the self-monitoring app, by an independent computerised system, minimised by country, age, and self-reported gender, and followed up for 12 months post-randomisation. The primary outcome was mental wellbeing (indexed by the Warwick-Edinburgh Mental Well Being Scale [WEMWBS]) at 3-month follow-up, analysed in participants who completed the 3-month follow-up assessment. Outcome assessors were masked to group allocation. The study is registered with ClinicalTrials.gov, NCT04148508, and is closed. Between Oct 15, 2020, and Aug 3, 2021, 2532 participants were enrolled, and 847 were randomly assigned to the emotional competence app, 841 to the CBT app, and 844 to the self-monitoring app. Mean age was 19·2 years (SD 1·8). Of 2532 participants self-reporting gender, 1896 (74·9%) were female, 613 (24·2%) were male, 16 (0·6%) were neither, and seven (0·3%) were both. 425 participants in the emotional competence app group, 443 in the CT app group, and 447 in the self-monitoring app group completed the follow-up assessment at 3 months. There was no difference in mental wellbeing between the groups at 3 months (global p=0·47). The emotional competence app did not differ from the CBT app (mean difference in WEMWBS -0·21 [95% CI -1·08 to 0·66]) or the self-monitoring app (0·32 [-0·54 to 1·19]) and the CBT app did not differ from the self-monitoring app (0·53 [-0·33 to 1·39]). 14 of 1315 participants were admitted to or treated in hospital (or both) for mental health-related reasons, which were considered unrelated to the interventions (five participants in the emotional competence app group, eight in the CBT app group, and one in the self-monitoring app group). No deaths occurred. The emotional competence app and the CBT app provided limited benefit in promoting mental wellbeing in healthy young people. This finding might reflect the low intensity of these interventions and the difficulty improving mental wellbeing via universal digital interventions implemented in low-risk populations. European Commission.

A randomized controlled trial of a self-guided mobile app targeting repetitive negative thought to prevent depression in university students: study protocol of the Nurture-U Reducing Worry prevention trial

BackgroundTackling poor mental health in university students has been identified as a priority in higher education. However, there are few evidence-based prevention initiatives designed for students. Repetitive Negative Thought (RNT, e.g. worry, rumination) is elevated in university students and is a well-established vulnerability factor for anxiety and depression. Furthermore, there are now evidence-based cognitive-behavioural interventions to tackle RNT. A mobile self-help cognitive-behavioural app targeting RNT, adapted for students may therefore be an effective, scalable, and acceptable way to improve prevention in students.MethodsAn online single blind, two-arm parallel-group Randomised Controlled Trial (RCT) to examine the incidence of major depression and symptoms of anxiety and depression across 12 months in university students aged over 16 who screen into the study with self-reported high levels of worry and/or rumination and no current diagnosis of major depression. Eligible participants will be randomised to the active intervention arm (usual practice plus using a self-guided mobile app targeting RNT) or to the control arm (usual practice). In total, 648 participants aged over 16, with no current major depression, bipolar disorder or psychosis will be recruited from UK universities. Assessments will take place at baseline (pre-randomisation), 3 months and 12 months post- randomisation. Primary endpoint and outcome is incidence of major depression as determined by self-reported diagnostic criteria at 12-month follow-up. Depressive symptoms, anxiety, well-being, health-related quality of life, functioning and academic outcomes are secondary outcomes. Compliance, adverse events, and potentially mediating variables will be carefully monitored.DiscussionThe trial aims to provide a better understanding of the causal role of tackling RNT (worry, rumination) using a self-help mobile app with respect to preventing depression in university students. This knowledge will be used to develop and disseminate innovative evidence-based, feasible, and effective mobile-health public health strategies for preventing common mental health problems.Trial registrationhttps://www.isrctn.com/ISRCTN86795807 Date of registration: 27 October 2022

A Comparative Study on the Efficacy of Group Positive Psychotherapy and Group Cognitive Behavioral Therapy on Flourishing, Happiness and Satisfaction with Life: A Randomized Controlled Trial

The present study examines the efficacy of Positive Psychotherapy (PPT) on psychological well-being, happiness, and satisfaction with life. We investigated people suffering from different kinds of depressive disorders, comparing it to Cognitive Behavioral Therapy (CBT). PPT is an empirically validated intervention targeting positive resources and personal strengths. It aims at amplifying well-being while also ameliorating the impact of symptoms. In a controlled two-center-study, we randomly assigned 92 out-patient participants with a DSM-IV diagnosis of major depressive disorder and/or Dysthymia to 14 sessions of manualized PPT or CBT group therapy. We assessed outcomes related to well-being using the Flourishing Scale, the Satisfaction with Life Scale, and the Positive Psychotherapy Inventory (PPTI). The PPTI represents the five components of Seligman’s PERMA-model of flourishing, encompassing positive emotions, engagement, positive relationships, meaning, and accomplishment. We took measurements before and after the intervention, and at a 6-months follow-up. Moreover, we considered a list of moderators that may impact the efficacy of PPT. PPT resulted in significant improvements in all measures, and these improvements remained stable for up to 6 months. Using 2 × 2 mixed-effects models (T1 vs. T2), we found significant interactions for all three main scales and two out of the five PPTI subscales. These interactions depicted significantly better outcomes for PPT at post-treatment. Regarding the follow-up (T1 vs. T3), we did not find significant interactions, and thus, there were no differences between the two therapies. No demographics or characteristics of the treatments were significant moderators. Summarized, these findings provide support for the effectiveness of PPT in increasing happiness, well-being, and quality of life. We can confirm that PPT is an additional tool for the enhancement of positive mental health.

Effect of childhood trauma on cognitive function in individuals with major depressive disorder and healthy controls

BackgroundIndividuals with major depressive disorder (MDD) exhibit cognitive impairment, while childhood trauma (CT) is associated with an elevated risk of both MDD and cognitive dysfunction. The effect of CT on cognitive function in MDD patients and healthy controls (HCs) is unclear. MethodsMDD patients and HCs were enrolled between December 2013 and December 2016. The Childhood Trauma Questionnaire (CTQ) was used to assess CT. Depressive symptoms and cognitive function were assessed at baseline and after 8-week acute-phase treatment with selective serotonin reuptake inhibitors (SSRIs) in MDD patients. ResultsA total of 909 people were included in the analysis. The interaction between MDD and CT had a main effect on Digit Symbol-Coding Test (DSCT), Stroop Color Test (SCT), and Stroop Color-Word Test (SCWT) scores. The effect of CT on cognitive function disappeared after adjusting for MDD diagnosis and years of education. Neglect could predict poor performance on SCT and SCWT in the HC group. After acute-phase treatment with SSRIs, CT did not significantly predict changes in cognitive function or depressive symptoms. LimitationsThe CTQ assessment might cause recall bias, and the cross-sectional design could not establish the causal link between CT and cognitive function. ConclusionThe effect of CT on cognitive function was modulated by MDD diagnosis and years of education. CT did not predict changes in depressive symptoms or cognitive function after acute-phase treatment with SSRIs. The direct influence of CT on cognitive function in MDD patients may be over-estimated. Trial registrationClinicalTrials.gov: NCT02023567; registration date: December 2013.

Differential prevalence of psychiatric disorders and mental health characteristics associated with lifetime suicide attempts in the Asian American and Pacific Islander adult population

BackgroundAsian American and Pacific Islander (AAPI) adults are an understudied group in terms of their mental health and mental healthcare needs. This has been complicated by the difficulty of recruiting adequate national samples of AAPI adults for research. ObjectiveThis study aimed to analyze national data to examine the lifetime prevalence of major psychiatric disorders among AAPI adults relative to non-AAPI adults, as well as to identify and compare sociodemographic and clinical characteristics associated with lifetime suicide attempts among AAPI and non-AAPI adults. MethodsCross-sectional data on 36,109 adults, including 1801 AAPI adults, from the National Epidemiological Survey of Alcohol and Related Conditions-III (NESARC-III), were analyzed with a series of chi-square and logistic regression analyses. ResultsWe found a significantly lower lifetime prevalence of mental health disorders in the AAPI versus the non-AAPI population nationally, including 2% of AAPI adults reporting lifetime suicide attempts as compared to about 5% of non-AAPI adults. Female sex and a history of major depressive disorder diagnosis were associated with lifetime suicide attempts in the AAPI and non-AAPI populations. Several unique factors were associated with having a history of suicide attempts in only the AAPI population, including a military service history and a diagnosis of panic disorder. DiscussionOur analysis demonstrated the significant sectors of the AAPI population that merit research, support, and intervention, including the AAPI veteran population. This study identifies several characteristics among AAPI adults that may make them particularly vulnerable to psychiatric problems and suicide risk, which may inform targeted prevention and efforts to provide culturally competent care to this population.

Multi-modal risk factors differentiate suicide attempters from ideators in military veterans with major depressive disorder

BackgroundThe suicide rate for United States military veterans is 1.5× higher than that of non-veterans. To meaningfully advance suicide prevention efforts, research is needed to delineate factors that differentiate veterans with suicide attempt/s, particularly in high-risk groups, e.g., major depressive disorder (MDD), from those with suicidal ideation (no history of attempt/s). The current study aimed to identify clinical, neurocognitive, and neuroimaging variables that differentiate suicide-severity groups in veterans with MDD. MethodsSixty-eight veterans with a DSM-5 diagnosis of MDD, including those with no ideation or suicide attempt (N = 21; MDD-SI/SA), ideation-only (N = 17; MDD + SI), and one-or-more suicide attempts (N = 30; MDD + SA; aborted, interrupted, actual attempts), participated in this study. Participants underwent a structured diagnostic interview, neurocognitive assessment, and 3 T-structural/diffusion tensor magnetic-resonance-imaging (MRI). Multinomial logistic regression models were conducted to identify variables that differentiated groups with respect to the severity of suicidal behavior. ResultsRelative to veterans with MDD-SI/SA, those with MDD + SA had significantly higher left cingulum fractional anisotropy, decreased attentional control on emotional-Stroop, and faster response time with intact accuracy on Go/No-Go. Relative to MDD + SI, MDD + SA had higher left cingulum fractional anisotropy and faster response time with intact accuracy on Go/No-Go. LimitationsFindings are based on retrospective, cross-sectional data and cannot identify causal relationships. Also, a healthy control group was not included given the study's focus on differentiating suicide profiles in MDD. ConclusionsThis study suggests that MRI and neurocognition differentiate veterans with MDD along the suicide-risk spectrum and could inform suicide-risk stratification and prevention efforts in veterans and other vulnerable populations.

Diagnosis of Major Depressive Disorder Based on Individualized Brain Functional and Structural Connectivity.

Traditional neuroimaging studies have primarily emphasized analysis at the group level, often neglecting the specificity at the individual level. Recently, there has been a growing interest in individual differences in brain connectivity. Investigating individual-specific connectivity is important for understanding the mechanisms of major depressive disorder (MDD) and the variations among individuals. To integrate individualized functional connectivity and structural connectivity with machine learning techniques to distinguish people with MDD and healthy controls (HCs). Prospective. A total of 182 patients with MDD and 157 HCs and a verification cohort including 54 patients and 46 HCs. 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and diffusion tensor imaging with single-shot spin echo. Functional and structural brain networks from rs-fMRI and DTI data were constructed, respectively. Based on these networks, individualized functional connectivity (IFC) and individualized structural connectivity (ISC) were extracted using common orthogonal basis extraction (COBE). Subsequently, multimodal canonical correlation analysis combined with joint independent component analysis (mCCA + jICA) was conducted to fusion analysis to identify the joint and unique independent components (ICs) across multiple modes. These ICs were utilized to generate features, and a support vector machine (SVM) model was implemented for the classification of MDD. The differences in individualized connectivity between patients and controls were compared using two-sample t test, with a significance threshold set at P < 0.05. The established model was tested and evaluated using the receiver operating characteristic (ROC) curve. The classification performance of the constructed individualized connectivity feature model after multisequence fusion increased from 72.2% to 90.3%. Furthermore, the prediction model showed significant predictive power for assessing the severity of depression in patients with MDD (r = 0.544). The integration of IFC and ISC through multisequence fusion enhances our capacity to identify MDD, highlighting the advantages of the individualized approach and underscoring its significance in MDD research. 1 TECHNICAL EFFICACY: Stage 2.