The effect of repetitive transcranial magnetic stimulation on the Hamilton Depression Rating Scale-17 criterion in patients with major depressive disorder without psychotic features: a systematic review and meta-analysis of intervention studies.

AIM: In line with the publication of clinical information related to the therapeutic process of repetitive transcranial magnetic stimulation (rTMS) and the updating of relevant treatment guidelines, the present meta-analysis study was designed and conducted to determine the effect of repetitive transcranial magnetic stimulation (rTMS) on the Hamilton Depression Rating Scale-17 (HDRS-17) criterion in patients with major depressive disorder (MDD) without psychotic features. METHODS: In this study, a systematic search was conducted in electronic databases such as PubMed [Medline], Scopus, Web of Science, Embase, Ovid, Cochrane Library, and ClinicalTrials. gov using relevant keywords. The search period in this study was from January 2000 to January 2022, which was updated until May 2023. Randomized controlled trials (RCTs) that determined the effect of repetitive transcranial magnetic stimulation (rTMS) on the Hamilton Depression Rating Scale-17 (HDRS-17) criterion in patients with major depressive disorder (MDD) without psychotic features were included in the analysis. The quality of the included RCTs was assessed using the Cochrane Risk of Bias checklist. Statistical analyses were performed using STATA (Version 16) and RevMan (Version 5). RESULTS: Following the combination of results from 16 clinical trial studies in the present meta-analysis, it was found that the mean Hamilton Depression Rating Scale-17 (HDRS-17) in patients with major depressive disorder (MDD) decreases by an average of 1.46 units (SMD: -1.46; % 95 CI: -1.65, -1.27, I square: 45.74%; P heterogeneity: 0.56). Subgroup analysis results indicated that the standardized mean difference of Hamilton Depression Rating Scale-17 (HDRS-17) varied based on the number of treatment sessions: patients receiving 10 or fewer repetitive transcranial magnetic stimulation (rTMS) sessions showed a mean Hamilton Depression Rating Scale-17 (HDRS-17) reduction of 2.60 units (SMD: -2.60; % 95 CI: -2.86, -2.33, I square: 55.12%; P heterogeneity: 0.55), while those receiving 11 to 20 sessions showed a mean Hamilton Depression Rating Scale-17 (HDRS-17) reduction of 0.28 units (SMD: -0.28; % 95 CI: -0.65, -0.09, I square: 39.91%; P heterogeneity: 0.89). CONCLUSION: In conclusion, our meta-analysis demonstrates the efficacy of repetitive transcranial magnetic stimulation (rTMS) in reducing depressive symptoms in major depressive disorder (MDD) patients. The complex results of subgroup analysis revealed insight on the possible benefits of a more focused strategy with fewer sessions, as well as the impact of treatment session frequency. These findings add to our understanding of repetitive transcranial magnetic stimulation (rTMS) as a therapeutic intervention for the treatment of major depressive illnesses.

Biomarker-Guided Tailored Therapy in Major Depression.

This chapter provides a comprehensive examination of a broad range of biomarkers used for the diagnosis and prediction of treatment outcomes in major depressive disorder (MDD). Genetic, epigenetic, serum, cerebrospinal fluid (CSF), and neuroimaging biomarkers are analyzed in depth, as well as the integration of new technologies such as digital phenotyping and machine learning. The intricate interplay between biological and psychological elements is emphasized as essential for tailoring MDD management strategies. In addition, the evolving link between psychotherapy and biomarkers is explored to uncover potential associations that shed light on treatment response. This analysis underscores the importance of individualized approaches in the treatment of MDD that integrate advanced biological insights into clinical practice to improve patient outcomes.

Electroencephalography-based endogenous phenotype of diagnostic transition from major depressive disorder to bipolar disorder.

The neuropathology of mood disorders, including the diagnostic transition from major depressive disorder (MDD) to bipolar disorder (BD), is poorly understood. This study investigated resting-state electroencephalography (EEG) activity in patients with MDD and those whose diagnosis changed from MDD to BD. Among sixty-eight enrolled patients with MDD, the diagnosis of 17 patients converted to BD during the study period. We applied machine learning techniques to differentiate the two groups using sensor- and source-level EEG features. At the sensor level, patients with BD showed higher theta band power at the AF3 channel and low-alpha band power at the FC5 channel compared to patients with MDD. At the source level, patients with BD showed higher theta band activity in the right anterior cingulate and low-alpha band activity in the left parahippocampal gyrus. These four EEG features were selected for discriminating between BD and MDD with the best classification performance showing an accuracy of 80.88%, a sensitivity of 76.47%, and a specificity of 82.35%. Our findings revealed distinct theta and low-alpha band activities in patients with BD and MDD. These differences could potentially serve as candidate neuromarkers for the diagnosis and diagnostic transition between the two distinct mood disorders.

Originalbeiträge (Originals). Patientencharakteristika an einer psychodynamischen Ausund Weiterbildungsambulanz.

Patient characteristics at a psychodynamic training institute Outpatient clinics affiliated with psychotherapeutic training institutions play a crucial role in ensuring the quality of future psychotherapists' training. OBJECTIVE: In the present study we examined the characteristics of patients in terms of symptomatology and psychodynamic dimensions. METHODS: The study utilized online questionnaires completed by n = 421 patients between September 2020 and March 2021. These questionnaires gathered information on sociodemographics, symptomatology (PHQ-D), relationship dynamics (IIP), personality functioning (OPD-SQS, IPO-16), and intrapsychic conflicts (OPD-KF). RESULTS: The sample consisted of 71.0 % females, with 65 % having prior experience with psychotherapeutic treatments. Notably, 74.9 % of the patients fulfilled the criteria of a depressive disorder in PHQ-D (with 34.8 % identified as Major Depressive Disorder, MDD). Additionally, 53.1 % of all patients surpassed the threshold for the diagnosis of a personality disorder based on the IPO-16. DISCUSSION: Findings suggest that patients seeking treatment at these clinics exhibit significant psychological distress and often have a history of prior psychotherapeutic interventions.

[Integration of basic and clinical researches to develop the biomarker of |depression].

Because of absence of the objective biomarker for major depressive disorder (MDD) or depressive state, psychiatrists depend on subjective examinations in order to properly diagnose their patients. We recently identified the candidates of the objective biomarker of depressive state of late-onset MDD by profiling gene expressions in white blood cells of patients and model mice. We also investigated whether gene expression profiling of white blood cells was useful to elucidate the biological alterations in the brain. Furthermore, we newly developed transgenic mice which will be useful for elucidating the neurological mechanisms of emotional abnormalities in psychiatric disorder. In this review, I introduce our recent research to help for understanding of translational approaches to develop the biomarker of depression.

Psychometric properties of International Trauma Questionnaire (ITQ) and symptom structure of complex post-traumatic stress disorder among Chinese patients with MDD.

AIMS: To assess the validity and internal reliability of the International Trauma Questionnaire (ITQ) among patients diagnosed with major depressive disorder (MDD) and to explore the network structure of Complex post-traumatic stress disorder (CPTSD) among MDD patients in China. METHODS: Eligible individuals were recruited from a large tertiary hospital in Guangdong Province. Trained researchers conducted in-person interviews and administered self-report questionnaires, including demographics, medical information, and psychological assessments. Confirmatory factor analyses (CFA) and network analysis were performed, with calculations of Average Variance Extracted (AVE), Cronbach's α, and composite reliability. RESULTS: A total of 113 patients with MDD participated in this study. The correlated six-factor one-order model was a good representation of the latent structure of ITQ (χ2= 60.114, df = 39, P = 0.017, SRMR = 0.070, RMSEA = 0.050, TLI = 0.952, CFI = 0.972, BIC = 175.508). All ITQ subscales possessed acceptable convergent validity and internal reliability, except for affective dysregulation and re-experiencing. The square root of AVE for affective dysregulation was lower than its correlations with other clusters. Network analysis revealed that node C4 ('I feel worthless'), as a core symptom, was significantly associated with the development of CPTSD. CONCLUSIONS: The clinical applicability of the ITQ was demonstrated by its overall validity and reliability among patients with MDD. However, the affective dysregulation and re-experiencing clusters still need to be revised and enhanced. Timely screening, recognition, and diagnosis are critical due to the worse clinical outcomes seen in comorbid patients.

Multilevel hybrid handcrafted feature extraction based depression recognition method using speech.

BACKGROUND AND PURPOSE: Diagnosis of depression is based on tests performed by psychiatrists and information provided by patients or their relatives. In the field of machine learning (ML), numerous models have been devised to detect depression automatically through the analysis of speech audio signals. While deep learning approaches often achieve superior classification accuracy, they are notably resource-intensive. This research introduces an innovative, multilevel hybrid feature extraction-based classification model, specifically designed for depression detection, which exhibits reduced time complexity. MATERIALS AND METHODS: MODMA dataset consisting of 29 healthy and 23 Major depressive disorder audio signals was used. The constructed model architecture integrates multilevel hybrid feature extraction, iterative feature selection, and classification processes. During the Hybrid Handcrafted Feature (HHF) generation stage, a combination of textural and statistical methods was employed to extract low-level features from speech audio signals. To enhance this process for high-level feature creation, a Multilevel Discrete Wavelet Transform (MDWT) was applied. This technique produced wavelet subbands, which were then input into the hybrid feature extractor, enabling the extraction of both high and low-level features. For the selection of the most pertinent features from these extracted vectors, Iterative Neighborhood Component Analysis (INCA) was utilized. Finally, in the classification phase, a one-dimensional nearest neighbor classifier, augmented with ten-fold cross-validation, was implemented to achieve detailed, results. RESULTS: The HHF-based speech audio signal classification model attained excellent performance, with the 94.63 % classification accuracy. CONCLUSIONS: The findings validate the remarkable proficiency of the introduced HHF-based model in depression classification, underscoring its computational efficiency.

Sensitivity and specificity of Aboriginal-developed items to supplement the adapted PHQ-9 screening measure for depression: results from the Getting it Right study.

OBJECTIVE: To determine the psychometric properties of an Aboriginal and Torres Strait Islander-developed depressive symptom screening scale. DESIGN: Prospective diagnostic accuracy study. SETTING: Ten primary health care services or residential alcohol and other drug rehabilitation services in Australia that predominantly serve Aboriginal and Torres Strait Islander peoples. PARTICIPANTS: 500 adults (18 years or older) who identified as Aboriginal and/or Torres Strait Islander and were able to communicate sufficiently to respond to questionnaire and interview questions. Recruitment occurred between 25 March 2015 and 2 November 2016. MAIN OUTCOME MEASURE: Criterion validity of seven Aboriginal and Torres Strait Islander-developed items, using the adapted Patient Health Questionnaire 9 (aPHQ-9) and depression module of the Mini International Neuropsychiatric Interview (MINI) 6.0.0 as the criterion standards. RESULTS: The seven-item scale had good internal consistency (α = 0.83) and correlated highly with the aPHQ-9 (ρ = 0.76). All items were significantly associated with diagnosis of a current major depressive episode. Discriminant function and decision tree analysis identified three items forming a summed scale that classified 85% of participants correctly. These three items showed equivalent sensitivity and specificity to the aPHQ-9 when compared with the MINI-identified diagnosis of a current major depressive episode. CONCLUSION: Three items developed by and for Aboriginal and Torres Strait Islander people may provide effective, efficient and culturally appropriate screening for depression in Aboriginal and Torres Strait Islander health care contexts.

The adjustment disorder is not a wastebasket diagnosis: a grounded theory study of psychiatrists' and psychologists' clinical reasoning.

Background: the aim of this study is to understand the diagnostic process undertaken by psychiatrists and psychologists regarding adjustment disorder (AD) in their clinical practice and how they differentiate it from major depressive episode (MDE).Methods: A hermeneutic study using grounded theory techniques was carried out. Semi-structured interviews were conducted with twelve psychiatrists and eight psychologists in Colombia, and transcribed verbatim. Initial line-by-line coding was performed, followed by focused and axial coding to construct categories explaining the professionals' reasoning process.Results: The clinical reasoning of professionals regarding AD was understood through four major categories. (1) Difficulty in addressing the experience of stressful events, as there is a risk of pathologizing and medicalizing them. (2) Mental health diagnoses are necessary but not apodictic. (3) The diagnostic category of AD allows for the description of a fluctuating depressive and anxious syndrome occurring in reaction to a stressful event, whose abnormality criteria are based on intersubjective knowledge of the patient's life history and consequential reasoning regarding the need for professional support. (4) The AD label could potentially protect against overdiagnosis of MDE and overuse of antidepressants. Many clinicians in their practice thus subordinate the diagnosis of MDE to ensuring it is not AD, contrary to what is outlined in diagnostic manuals.Conclusion: This study allowed us to understand the clinical reasoning of psychiatrists and psychologists about AD as a diagnosis that inherently indicates the need to work on coping and intervene in the stressor and should be considered as a diagnostic possibility in the same hierarchy as MDE in reactive syndromes, rather than a residual category.

Clinicians use consequential and intersubjective reasoning to diagnose Adjustment Disorder (AD).Systemic pressures lead to overdiagnosis of Major Depressive Episode (MDE) and excessive antidepressant use.AD should be recognized as a valid non-residual diagnostic category.

Helpful or harmful? The effect of a diagnostic label and its later retraction on person impressions.

Diagnostic labels for mental health conditions can inadvertently reinforce harmful stereotypes and exacerbate stigma. If a diagnosis is incorrect and a label is wrongly applied, this may negatively impact person impressions even if the inaccurate label is later corrected. This registered report examined this issue. Participants (N = 560) read a vignette about a hospital patient who was either diagnosed with schizophrenia, diagnosed with major depressive disorder, or not diagnosed with a mental health condition. The diagnostic labels were later retracted strongly, retracted weakly, or not retracted. Participants completed several stigma measures (desire for social distance, perceived dangerousness, and unpredictability), plus several inferential-reasoning measures that tested their reliance on the diagnostic label. As predicted, each mental health diagnosis elicited stigma, and influenced inferential reasoning. This effect was stronger for the schizophrenia diagnosis compared to the major depressive disorder diagnosis. Importantly, the diagnostic label continued to influence person judgments after a clear retraction (strong or weak), highlighting the limitations of corrections in reducing reliance on person-related misinformation and mental health stigma.

Predictors of psychosocial impairment in a transdiagnostic sample: Unique effects of repetitive negative thinking.

BACKGROUND: Repetitive negative thinking (RNT) is a transdiagnostic process involving perseverative, unproductive, and uncontrollable thoughts. Although RNT may impede adaptive psychosocial functioning by prolonging negative mood states, strengthening cognitive biases, and preventing effective problem-solving, the extent to which RNT is associated with risk for poor psychosocial outcomes is unclear. Given that this has clear transdiagnostic treatment implications, the present study aimed to isolate the unique relationship of RNT with social functioning and life satisfaction in a mixed clinical and non-clinical sample. METHODS: In 201 mid-to-later life adult participants (27 with primary diagnoses of bipolar disorder, 84 with major depressive disorder, and 90 healthy volunteers), we measured RNT, social functioning, life satisfaction, trait rumination, DSM-5 diagnoses, depressive symptoms, manic symptoms, cognitive control performance, and global cognitive functioning. RESULTS: Linear regression models revealed that RNT, but not rumination, was significantly associated with poorer social functioning (ß = 0.42 p < .001) and reduced life satisfaction (ß = -0.42, p < .001) after controlling for clinical and cognitive covariates. LIMITATIONS: Limited demographic diversity, cross-sectional design, self-reporting of outcomes. CONCLUSIONS: Results suggest that RNT may confer risk for key psychosocial outcomes during middle to later adulthood, over and above the effects of clinical and cognitive variables and independent of diagnostic status. Findings lend support to the notion of RNT as a transdiagnostic process and suggest that RNT may be an important therapeutic target for adults with poor social functioning and/or reduced life satisfaction.

Mobile sensing-based depression severity assessment in participants with heterogeneous mental health conditions.

Mobile sensing-based depression severity assessment could complement the subjective questionnaires-based assessment currently used in practice. However, previous studies on mobile sensing for depression severity assessment were conducted on homogeneous mental health condition participants; evaluation of possible generalization across heterogeneous groups has been limited. Similarly, previous studies have not investigated the potential of free-living audio data for depression severity assessment. Audio recordings from free-living could provide rich sociability features to characterize depressive states. We conducted a study with 11 healthy individuals, 13 individuals with major depressive disorder, and eight individuals with schizoaffective disorders. Communication logs and location data from the participants' smartphones and continuous audio recordings of free-living from a wearable audioband were obtained over a week for each participant. The depression severity prediction model trained using communication log and location data features had a root mean squared error (rmse) of 6.80. Audio-based sociability features further reduced the rmse to 6.07 (normalized rmse of 0.22). Audio-based sociability features also improved the F1 score in the five-class depression category classification model from 0.34 to 0.46. Thus, free-living audio-based sociability features complement the commonly used mobile sensing features to improve depression severity assessment. The prediction results obtained with mobile sensing-based features are better than the rmse of 9.83 (normalized rmse of 0.36) and the F1 score of 0.25 obtained with a baseline model. Additionally, the predicted depression severity had a significant correlation with reported depression severity (correlation coefficient of 0.76, p < 0.001). Thus, our work shows that mobile sensing could model depression severity across participants with heterogeneous mental health conditions, potentially offering a screening tool for depressive symptoms monitoring in the broader population.

Differentiation Across Bipolar Disorder and Major Depressive Disorder by Whole-Night Polysomnographic Findings.

Abstract.Background: There is growing evidence that understanding the role of sleep disturbance in bipolar disorder (BD) and major depressive disorder (MDD) is helpful when studying the high heterogeneity of patients across psychiatric disorders.Objective: The present study was designed to investigate the transdiagnostic role of sleep disturbance measured by polysomnography (PSG) in differentiating from MDD with BD.Methods: A total of 256 patients with MDD and 107 first-episode and never medicated patients with BD using the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, criteria were recruited. All patients completed 1 night of PSG recording, and the changes in objective sleep structure parameters were determined by PSG analysis.Results: We showed that patients with MDD had statistically longer rapid eye movement (REM) latency, a higher percentage of stage N2 sleep, and lower percentages of stage N3 sleep and REM sleep than those with BD after controlling for confounding factors (all P < .05). Moreover, using the logistic regression analysis, we identified that REM latency was associated with BD diagnosis among the PSG sleep features. The cutoff value for PSG characteristics to differentiate BD from MDD was 261 in REM latency (sensitivity: 41.4% and specificity: 84.1%).Conclusions: Our findings suggest that PSG-measured sleep abnormalities, such as reduced REM latency, may be a diagnostic differentiating factor between MDD and BD, indicating their roles in identifying homogeneous transdiagnostic subtypes across psychiatric disorders.

Characterizing human spontaneous thoughts and its application in major depressive disorder.

BACKGROUND: Spontaneous thought is a universal, complex, and heterogeneous cognitive activity that significantly impacts mental activity and strongly correlates with mental disorders. METHODS: Utilizing the think-aloud method, we captured spontaneous thoughts during rest from 38 diagnosed with depression, alongside 36 healthy controls and 137 healthy individuals. Through a comprehensive assessment of various dimensions of thought content, we compared thought content between individuals with depression and healthy controls, and between healthy women and men. Finally, we employed natural language processing (NLP) to develop regression models for multidimensional content assessment and a classification model to differentiate between individuals with and without depression. RESULTS: Compared to healthy controls, individuals with depression had more internally oriented and less externally oriented spontaneous thoughts. They focused more on themselves and negative things, and less on positive things, experiencing higher levels of negative emotions and lower levels of positive emotions. Besides, we found that compared to healthy men, healthy women's spontaneous thoughts focus more on interoception, the self, past events, and negative events, and they experience higher levels of negative emotions. Meanwhile, we identified the potential application of the think-aloud method to collect spontaneous thoughts and integrate NLP in the field of depression. CONCLUSIONS: This study offers direct insights into the stream of thought during individuals' resting state, revealing differences between individuals with depression and healthy controls, as well as sex differences in the content of spontaneous thoughts. It enhances our understanding of spontaneous thought and offers a new perspective for preventing, diagnosing, and treating depression.

Identifying neuroimaging biomarkers in major depressive disorder using machine learning algorithms and functional near-infrared spectroscopy (fNIRS) during verbal fluency task.

One of the most prevalent psychiatric disorders is major depressive disorder (MDD), which increases the probability of suicidal ideation or untimely demise. Abnormal frontal hemodynamic changes detected by functional near-infrared spectroscopy (fNIRS) during verbal fluency task (VFT) have the potential to be used as an objective indicator for assessing clinical symptoms. However, comprehensive quantitative and objective assessment instruments for individuals who exhibit symptoms suggestive of depression remain undeveloped. Drawing from a total of 467 samples in a large-scale dataset comprising 289 MDD patients and 178 healthy controls, fNIRS measurements were obtained throughout the VFT. To identify unique MDD biomarkers, this research introduced a data representation approach for extracting spatiotemporal features from fNIRS signals, which were subsequently utilized as potential predictors. Machine learning classifiers (e.g., Gradient Boosted Decision Trees (GBDT) and Multilayer Perceptron) were implemented to assess the ability to predict selected features. The mean and standard deviation of the cross-validation indicated that the GBDT model, when combined with the 180-feature pattern, distinguishes patients with MDD from healthy controls in the most effective manner. The accuracy of correct classification for the test set was 0.829 ± 0.053, with an AUC of 0.895 (95 % CI: 0.864-0.925) and a sensitivity of 0.914 ± 0.051. Channels that made the most important contribution to the identification of MDD were identified using Shapley Additive Explanations method, located in the frontopolar area and the dorsolateral prefrontal cortex, as well as pars triangularis Broca's area. Assessment of abnormal prefrontal activity during the VFT in MDD serves as an objectively measurable biomarker that could be utilized to evaluate cognitive deficits and facilitate early screening for MDD. The model suggested in this research could be applied to large-scale case-control fNIRS datasets to detect unique characteristics of MDD and offer clinicians an objective biomarker-based analytical instrument to assist in the evaluation of suspicious cases.

Childhood Trauma Questionnaire-based child maltreatment profiles to predict efficacy of the Cognitive Behavioral Analysis System of Psychotherapy versus non-specific psychotherapy in adults with early-onset chronic depression: cluster analysis of data from a randomised controlled trial.

BACKGROUND: Child maltreatment is a broadly confirmed risk factor for mental and physical illness. Some psychological treatments specifically target mental health conditions associated with child maltreatment. For example, the Cognitive Behavioral Analysis System of Psychotherapy (CBASP) focuses on maladaptive interpersonal behaviours in chronic depression. However, how the assessment of child maltreatment could inform personalised treatment is unclear. We used data from a previously published clinical trial to investigate whether a pre-established child maltreatment clustering approach predicts differential outcomes after CBASP versus non-specific supportive psychotherapy in patients with early-onset chronic depression. METHODS: We did a cluster analysis of data from a previous randomised controlled trial of unmedicated adult outpatients with early-onset chronic depression who were treated at eight university clinics and psychological institutes in Germany with 32 sessions of CBASP or non-specific supportive psychotherapy. Participants were eligible for the original trial if they were aged 18-65 years; had major depressive disorder (MDD) with an early onset and duration of at least 2 years, current MDD superimposed on a pre-existing dysthymic disorder, or recurrent MDD with incomplete remission between episodes as defined by DSM-IV; and had a score of at least 20 points on the 24-item Hamilton Rating Scale for Depression (HRSD-24). Participants were included in the current study if they had completed the short form of the Childhood Trauma Questionnaire (CTQ) at trial baseline. We used an agglomerative hierarchical clustering approach to derive child maltreatment clusters from individual patterns across the five domains of the CTQ. We used linear mixed models to investigate whether clustering could predict differential clinical outcomes (change in symptom severity on the HRSD-24) up to 2 years after treatment onset. People with lived experience were involved in the current study. FINDINGS: 253 patients (129 [51%] treated with CBASP and 124 [49%] with supportive psychotherapy) had complete CTQ records and were included in the analysis. 169 (67%) participants were women, 84 (33%) were men, and the mean age was 45·9 years (SD 11·7). We identified seven child maltreatment clusters and found significant differences in treatment effects of CBASP and supportive psychotherapy between the clusters (F(6,948·76)=2·47; p=0·023); differences were maintained over the 2-year follow-up. CBASP was superior in distinct clusters of co-occurring child maltreatment: predominant emotional neglect (change in ß -6·02 [95% CI -11·9 to -0·13]; Cohen's d=-0·98 [95% CI -1·94 to -0·02]; p=0·045), predominant emotional neglect and abuse (-6·39 [-10·22 to -2·56]; -1·04 [-1·67 to -0·42]; p=0·0011), and emotional neglect and emotional and physical abuse (-9·41 [-15·91 to -2·91]; -1·54 [-2·6 to -0·47]; p=0·0046). INTERPRETATION: CTQ-based cluster analysis can facilitate identification of patients with early-onset chronic depression who would specifically benefit from CBASP. Child maltreatment clusters could be implemented in clinical assessments and serve to develop and personalise trauma-informed care in mental health. FUNDING: The German Research Foundation and the German Federal Ministry of Education and Research.

Vortioxetine for depression in adults: A systematic review and dose-response meta-analysis of randomized controlled trials.

AIM: Major depressive disorder (MDD) is a prevalent psychiatric condition and vortioxetine offers promising antidepressant effects due to its unique pharmacological profile. However, the dose-response relationships of vortioxetine for MDD is not well established. We aimed to conduct dose-response meta-analyses to fill this gap. METHODS: We systematically searched multiple electronic databases for randomized controlled trials of vortioxetine for MDD, with the last search conducted on 08 February, 2024. The dose-response relationship was evaluated using a one-stage random-effects dose-response meta-analysis with restricted cubic spline model. The primary outcome was efficacy (mean change in depression scale score), with secondary outcomes including response, dropout for any reasons (acceptability), dropout for adverse events (tolerability), and any adverse events (safety). RESULTS: The dose-response meta-analysis comprised 16 studies, with 4,294 participants allocated to the vortioxetine group and 2,299 participants allocated to the placebo group. The estimated 50% effective dose was 4.37 mg/day, and the near-maximal effective dose (95% effective dose) was 17.93 mg/day. Visual inspection of the dose-efficacy curve suggests that a plateau possibly had not been reached yet at 20 mg/day. Acceptability, tolerability and safety decreased as the dose increased. Subgroup analysis indicated that no significant differences were observed in acceptability, tolerability and safety among the dosage groups. CONCLUSIONS: Vortioxetine may potentially provide additional therapeutic benefits when exceeding the current licensed dosage without significantly impacting safety. Conducting clinical trials exceeding the current approved dosage appears necessary to fully comprehend its efficacy and risk.

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center.

BACKGROUND: Depression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressive disorder (MDD) and matched controls. We also analyzed subgroups within our MDD sample, including late-life depression and recurrent depression. We further investigated whether machine learning (ML) algorithms could distinguish MDD and each subgroup from controls based on their CoD. METHODS: We conducted a comprehensive analysis of CoD in individuals who died from nontraumatic causes. The diagnosis of lifetime MDD was ascertained based on the DSM-5 criteria using information from a structured interview with a knowledgeable informant. Eleven established ML algorithms were used to differentiate MDD individuals from controls by simultaneously analyzing different disease category groups to account for multiple tests. The McNemar test was further used to compare paired nominal data. RESULTS: The initial dataset included records of 1,102 individuals, among whom 232 (21.1%) had a lifetime diagnosis of MDD. Each MDD individual was strictly paired with a control non-psychiatric counterpart. In the MDD group, the most common CoD were circulatory (67.2%), respiratory (13.4%), digestive (6.0%), and cancer (5.6%). Despite employing a range of ML models, we could not find distinctive CoD patterns that could reliably distinguish individuals with MDD from individuals in the control group (average accuracy: 50.6%; accuracy range: 39-59%). These findings were consistent even when considering factors within the MDD group, such as late-life or recurrent MDD. When comparing groups with paired nominal tests, no differences were found for circulatory (p=0.450), respiratory (p=0.790), digestive (p=1.000), or cancer (p=0.855) CoD. CONCLUSIONS: Our analysis revealed that autopsy-confirmed CoD exhibited remarkable similarity between individuals with depression and their matched controls, underscoring the existing heterogeneity in the literature. Future research should prioritize more severe manifestations of depression and larger sample sizes, particularly in the context of CoD related to cancer.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.