Assessment measures for suicidal behavior range from depression scales to longer suicide-specific instruments. In this review, we systematically summarize and discuss the currently used instruments for assessing suicidal behavior in the context of ketamine and its enantiomers. We searched Medline/PubMed, Embase, and PsycINFO databases for ketamine (and its enantiomers) human studies exploring this drug's antisuicidal effects on major depressive disorder patients, published from February 2000 to June 2023. Forty-six studies were included, identifying 16 assessment tools, mostly explicit and clinician-rated measures. Prominent tools included the Montgomery-Åsberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HAM-D), and both the clinician and patient-rated Beck Scales for Suicide Ideation (SSI and BSS). With the exception of the Suicide Ideation and Behavior Assessment Tool (SIBAT), to the best of our knowledge, no other instrument that assesses suicidality seems to be specifically developed for measuring treatment response in rapid-acting antidepressants trials. Most scales have been validated in conventional antidepressant or psychotherapy contexts, though, for MADRS, as well as for SSI, BDI, and HAM-D, efforts have been made towards investigating their psychometric properties in the field of rapid-acting antidepressants. The heterogeneity of suicidal behavior assessment in ketamine studies may hinder adequate comparisons between them. Although there does not seem to be a universally preferable instrument for measuring suicidal behavior to date, the MADRS potentially emerges as an adequately recommended choice.

Abdominal pain is one of the most prominent symptoms in patients with chronic pancreatitis (CP) and can manifest intermittently or persistently. The mechanism of pain is not yet clear, and no effective treatment is currently available. This study aimed to explore the risk factors for pain in patients with CP, which may provide new insights for developing effective pain control modalities.

This clinical study was based on a single-centre research database that included 570 patients with CP. We compared the differences in baseline data, clinical characteristics, and psychophysiology traits between patients with and without pain. Subsequently, patients will be followed up to assess changes in their risk factors and explore their relationship with pain.

In the final risk factor model, young age (P = .031; odds ratio [OR] = 0.986 [0.973, 0.999]), prolonged disease duration (P < .001; OR = 1.307 [1.127, 1.516]), heavy smoking (P = .014; OR = 1.331 [1.060, 1.617]), alcohol consumption (P = .003; OR = 1.419 [1.127, 1.788]), low body mass index (P < .001; OR = 0.786 [0.703, 0.879]), pancreatic exocrine insufficiency (P = .040; OR = 1.683 [1.024, 2.767]), acute pancreatitis attacks (P = .027; OR = 1.759 [1.067, 2.902]), anxiety, and depression (P = .016; OR = 1.047 [1.009, 1.088]; P = .014; OR = 1.068 [1.013, 1.126]) were associated with CP pain. Reducing tobacco and alcohol intake (P = .001; OR = 2.367 [1.525, 4.637]; P = .024; OR = 2.011 [1.085, 3.199]), increasing the body mass index (P = .005; OR = 1.968 [1.265, 3.805]), and improving anxiety (P = .001; OR = 1.164 [1.081, 1.340]) were identified to be beneficial for pain relief. Compared to the effects on persistent pain, pancreatic enzyme supplementation (P = .004; OR = 1.794 [1.186, 2.502]) had a clear effect on intermittent pain in patients with CP.

We identified a multifactorial model of pain risk factors for CP and confirmed that modifying these risk factors could influence patient pain symptoms.

Seasonal Affective Disorder (SAD) is a frequent and severe disorder. The prevalence of SAD varies from 1 % to 10 %, influenced by latitude and assessment methods, and is higher in women and younger populations. SAD is also overrepresented in patients with bipolar disorders. In this context, screening for SAD appears crucial, particularly because specific treatments are available. We aimed to examine comprehensively and critically the existing scales and questionnaires for assessing and screening SAD.

A systematic literature review was performed using PRISMA guidelines and searching on PubMed, Cochrane Library, and PsycINFO databases up to April 2024.

Out of 791 articles screened, 28 met the inclusion criteria. Seven scales were identified, divided into those for screening and those for severity measurement. The Seasonal Pattern Assessment Questionnaire (SPAQ) is widely used and validated in multiple languages and disorders. Of note, the SPAQ tends to overestimate SAD prevalence. It has good internal validity but limited reliability for diagnosing seasonal depression alone. The SIGH-SAD allows detailed symptom evaluation, with good psychometric properties, although the score interpretation can be complex. The SHQ is more specific and sensitive than SPAQ but longer and more complex. The ISV offers a detailed assessment of seasonal variations and a good reliability but is more complex and less studied than SPAQ. The SBQ provides specific cognitive assessment related to SAD, with good sensitivity and specificity, though more validation is needed. The BDI-add includes atypical symptoms for SAD assessment but lacks comprehensive psychometric data. Finally, the HIGH-SAD is useful for distinguishing unipolar from bipolar disorder in SAD patients, with good reliability but requiring more validation.

The SPAQ remains the reference scale despite some limitations. The review highlights the need for ongoing validation and potentially new scales integrating seasonal and mood dimensions for more accurate SAD diagnosis.

Posttraumatic Stress Disorder (PTSD) is often comorbid with depression in both the general population and among veterans who have experienced combat trauma. However, there is a lack of studies inquiring into the comorbidity of prolonged PTSD and depression. The network paradigm offers a novel approach to studying this comorbidity via bridge symptom analysis. This study explores the bridge symptoms between depression and prolonged/chronic PTSD in patients diagnosed with both conditions, 10 years after trauma exposure, using network analysis. The sample consisted of 60 male, treatment-seeking veterans (aged 31 to 59) with diagnoses of both depression and PTSD. Bridge nodes detected in the present bridge symptom analysis include reduced sleep, inner tension, poor concentration/concentration difficulties, pessimistic and suicidal thoughts, distressing dreams, restricted range of affects, and agitation. The detected bridge nodes could partially be attributed to the characteristics of the sample, which consisted of patients diagnosed with PTSD comorbid with depression.

Clinical assessments rely primarily on patients' emotional expressions and scale scores. However, due to cognitive differences and the complexity of emotional expression among adolescents, existing assessment tools often present challenges in their selection and application. This study reviews and analyzes the literature related to 8 commonly used adolescent depression assessment scales, including the Hamilton Depression Scale (HAMD), the Beck Depression Inventory (BDI), the Center for Epidemiologic Studies Depression Scale (CES-D), the Reynolds Adolescent Depression Scales (RADS), the Children's Depression Inventory (CDI), the Kutcher Adolescent Depression Scale (KADS), the Patient Health Questionnaire (PHQ) and the Depression Screener for Teenagers (DesTeen). Through a comprehensive analysis of each scale's strengths, limitations and practical applications, this narrative review aims to guide healthcare practitioners and researchers in selecting optimal measurement tools for different clinical and research contexts.

Relevant studies on 8 frequently used or well-supported adolescent depression assessment scales (CDI, RADS, CES-D, BDI, PHQ, KADS, HAMD, DesTeen) were retrieved from PubMed, Web of Science, CNKI, and Wanfang databases. A total of 102 articles were ultimately selected for data extraction to determine the reliability and validity of these scales. Additionally, 13 original development studies of the included scales were further reviewed to extract and analyze information on their developmental background, structural dimensions, item composition, and applicability.

Recent studies on depression assessment scales have focused on the development of precise diagnosis and personalized evaluation. All 8 adolescent depression assessment scales generally exhibit good reliability and validity. Among them, the HAMD is used for detailed clinical evaluation of depressive symptoms but suffers from complexity due to its reliance on professional assessors. The BDI and the CES-D provide the most comprehensive dimensions. While the BDI is suitable for clinical assessments, it has the drawback of containing items that may be difficult to understand. The CES-D is well-suited for epidemiological research and large-scale screenings but has the limitation of unclear differentiation between emotional and somatic symptoms. The RADS is recognized for its comprehensive items and high reliability and validity, although its lengthy items may lead to respondent fatigue. The CDI allows multidimensional assessment of depressive symptoms but has been debated regarding its applicability across different age groups. The KADS, explicitly designed for adolescents, is a promising tool; however, its relatively recent development has resulted in limited validation studies. The PHQ is appropriate for rapid screening and tracking treatment effects but lacks sufficient emotional evaluation. The DesTeeen, designed for adolescents, features concise and clear item phrasing, but it's only available in the German language.

The 8 standard scales demonstrate high accuracy in screening adolescent depression, but challenges persist in selecting scales for different contexts and ensuring their cross-cultural validity.

Depression and anxiety are among the most common mental health conditions globally that impact the lifestyle of affected individuals. Mental conditions and chronic diseases are linked to each other bidirectionally. Depression and anxiety with comorbid chronic conditions are often neglected or under-screened and possess challenges in treatment. This study aimed to know the frequency and determinants of depression and anxiety along with the severity level among common chronic communicable and non-communicable diseases.

We enrolled 200 healthy controls and 800 cases with equal number (n = 400) of patients with communicable and non-communicable diseases. Depression and anxiety were screened through Hamilton's rating scale for depression and anxiety separately. We also measured the determinants of severe depression among patients with chronic diseases. Data was analyzed through SPSS version 23.

We found higher frequency of depression (31% vs 11%; p=<0.001) and anxiety (13.25% vs 6%; p = 0.021) among cases as compared to healthy controls respectively. We found higher levels of depression among participants with non-communicable diseases as compared to communicable diseases (37.25% vs 24.75%; p < 0.05) respectively. Moreover, there was a higher frequency of anxiety in participants with communicable diseases as compared to those with non-communicable diseases, but the difference was non-significant (14% vs 12.5% p = 0.081). Among non-communicable diseases the highest percentage was found among individuals with cancer (67%), followed by diabetes (38%), cardiovascular diseases (33%) and respiratory disorders (11%). Among participants with communicable diseases, the highest percentage of depression was found in patients with Tuberculosis (29%) followed by HIV/AIDS (28%), Long COVID-19 (25%) and Hepatitis B/C (17%).

There is a significantly higher percentage of depression and anxiety among participants with chronic diseases. It calls for a comprehensive approach to patient care that incorporates mental health as a fundamental aspect of the treatment and management of chronic diseases. Understanding the predictors of severe depression across different chronic conditions helps in stratifying patients who may benefit most from integrated psychiatric and psychological interventions.

Major depressive disorder (MDD) often co-occur with fibromyalgia (FM), and both conditions have been associated with impaired resting state functional connectivity (rs-FC). The present systematic review aims to summarize the evidence on rs-FC in individuals with MDD and FM compared with healthy controls and explore overlapping connectivity patterns and their relationships with clinical symptoms.

A systematic search of the EMBASE, PubMed, Scopus and ScienceDirect databases was conducted according to PRISMA guidelines. Studies were included that addressed rs-FC using seed-based analysis in MDD and FM patients compared to HC. Methodological quality and risk of bias were assessed using a 13-point checklist adapted from previous neuroimaging meta-analyzes.

A total of 33 articles were included in the analysis (17 with MDD and 16 with FM). The sample comprised 1,877 individuals, including 947 patients and 930 controls, with a mean age of 39.83 years. The seeds were categorized into six neural networks. Shared disruptions across MDD and FM studies have been identified in key circuits, including decreased connectivity between the insula and anterior cingulate cortex (ACC), middle frontal gyrus (MFG), superior frontal gyrus (SFG), and putamen. Increased FC was observed between the dorsolateral prefrontal cortex (DLPFC) and ACC, as well as between the thalamus and precuneus. Decreased insula-ACC connectivity correlated with greater pain intensity and catastrophizing in FM and with more severe depressive symptoms in MDD. Unique patterns of rs-FC were also observed: FM-specific changes involved the periaqueductal gray, hypothalamus, and thalamus, indicating impaired pain modulation and emotional processing. In contrast, MDD-specific changes were primarily observed in the reward, salience, and default mode networks, reflecting impaired emotional regulation. The studies showed considerable heterogeneity in the selection of seeds and study designs, which limits the feasibility of meta-analyses and underlines the need for standardized methods.

This study provides information about overlapping and distinct neural mechanisms in FM and MDD, suggesting potentially the presence of a potential neurosignature that reflects shared disruptions in pain and emotion regulation networks while highlighting unique pathways underlying their respective pathophysiology.

Background/Objectives: Depression is associated with an increased risk for the development and progression of cardiovascular disease. This research investigated the association between depressive symptoms and inflammation in the development of atherosclerotic coronary events. Methods: This retrospective observational study included 276 patients who were not previously diagnosed with atherosclerotic coronary artery disease at the beginning of the research. Participants were categorized using the Hamilton Depression Rating Scale (HDRS) and the Structured Clinical Interview for DSM-5 (SCID) into two groups: the depression group and the control group. Inflammatory biomarkers (C-reactive protein (CRP), fibrinogen, interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and cortisol) were measured at the beginning of the study, as well as at six months, one year, and two years. Results: Among patients with mild depression (17.3% vs. 4.2%) or moderate depression (15.4% vs. 6.7%), there were significantly more men than women, while among patients with very severe depression, there were significantly more women than men (21.7% vs. 11.5%). Participants with depression showed significantly higher increases at 2 years compared to baseline for all investigated parameters (p < 0.001). Depressed patients were significantly associated with an acute coronary syndrome (p = 0.038). Conclusions: This research highlights that individuals with depression face a greater risk of developing an acute coronary syndrome than those without depression.

Antidepressive treatment outcomes can be assessed using sum scores from measurement scales, but symptom clusters may better capture the multidimensional structure of depression. Little is known about the comparative effectiveness of different treatment modalities on these clusters. We sought to evaluate the effects of rTMS and antidepressant medication on four symptom clusters and the extent to which these differ between treatments. In addition, we assessed whether baseline cluster scores predicted (non)response.

Data were obtained from two clinical trials (DETECT: rTMS vs. medication; PITA: tricyclic antidepressants). Primary outcomes were symptom cluster scores: 'General Depression', 'Anxiety', 'Somatic Symptoms' and 'Insomnia'. In the primary analysis based on DETECT, a MANCOVA comparing rTMS with medication was performed. For validation, a MANCOVA was performed replacing medication data from DETECT with data from PITA. Baseline symptom cluster scores were compared between responders and non-responders, as well as treatment groups.

In both the primary and validation analyses, no difference was seen between rTMS and medication on the symptom clusters. Similar patterns of response were observed in all groups, with 'Insomnia' showing the greatest effect of treatment. Baseline cluster scores did not predict treatment response.

We did not find a differential effect of rTMS or antidepressant medication on depressive symptom clusters. Both treatments demonstrated comparable response patterns for all clusters, and baseline cluster scores did not differ between responders and non-responders. Future studies with larger samples or more homogeneous treatments may elucidate the role of symptom clusters as a tool for more individualized treatment.

PITA NCT03548675 DETECT The Netherlands Trial Register NL7628.

As multiple sophisticated techniques are used to evaluate psychometric scales, in theory reducing error and enhancing the measurement of patient-reported outcomes, we aimed to determine whether applying different psychometric analyses would demonstrate important differences in treatment effects.

We conducted a secondary analysis of individual participant data (IPD) from 20 antidepressant treatment trials obtained from Vivli.org (n = 6843). Pooled item-level data from the Hamilton Rating Scale for Depression (HRSD-17) were analyzed using confirmatory factory analysis (CFA), item response theory (IRT), and network analysis (NA). Multilevel models were used to analyze differences in trial effects at approximately 8 weeks (range 4-12 weeks) post-treatment commencement, with standardized mean differences calculated as Cohen's d. The effect size outcomes for the original total depression scores were compared with psychometrically informed outcomes based on abbreviated and weighted depression scores.

Several items performed poorly during psychometric analyses and were eliminated, resulting in different models being obtained for each approach. Treatment effects were modified as follows per psychometric approach: 10.4%-14.9% increase for CFA, 0%-2.9% increase for IRT, and 14.9%-16.4% reduction for NA.

Psychometric analyses differentially moderate effect size outcomes depending on the method used. In a 20-trial sample, factor analytic approaches increased treatment effect sizes relative to the original outcomes, NA decreased them, and IRT results reflected original trial outcomes.

This study aimed to determine if using advanced psychometrics methods would inform any clinically or statistically important differences in clinical trial outcomes when compared to original findings. We applied factor analysis (FA), item response theory (IRT), and network analysis (NA) to the most commonly used measure of depression in clinical settings - the Hamilton Rating Scale for Depression (HRSD) - to identify and remove nonperforming survey items and calculate weighted item scores. We found that the efficacy reported in trials increased when using FA to removed items, but decreased when using NA. There was almost no change in efficacy when using IRT. Using weighted scores based on respective models offered no additional utility in terms of increasing or decreasing efficacy outcomes.

The placebo response in clinical trials has four components: regression to the mean (RTM), measurement artifacts, natural tendency (NT) of the disease, and the genuine placebo effect. Our objective is to determine what contributes to the size of the placebo-effect in clinical trials by meta-regressions of randomized placebo-controlled clinical trials.

We identified five diseases where data on the rates of NT were available to search for a sample of n = 150 (5x30) randomized controlled trials. We extracted various study descriptors and performed meta-regressions to predict improvement in treatment and placebo groups.

We sampled 30 trials each from the following diagnoses: osteoarthritis of the knee, irritable bowel syndrome, depression, sleep disorders, migraine, and extracted relevant information. We estimated the effects due to RTM and NT and analyzed the improvement in placebo and treatment groups by fitting two regression models. Both models were highly significant, explaining 72% of the variance. Improvement in the placebo group can be significantly predicted by improvement in the treatment group (beta = .84), whether a study was analyzed according to intention to treat (beta = -.10) or was a multicenter study (beta = .12). Improvement in the treatment group can be explained by the improvement in the placebo group (beta = .83), whether a study was a multicenter trial (beta = -.16), and by RTM (beta = -.18). The treatment effect is smaller in sleep studies (beta = -.17).

The high correlation of r = .73 between placebo improvement and treatment improvement rates is genuine and not explainable by study or disease characteristics. We conclude from our data that the placebo-effect is the major driver of treatment effects in clinical trials that alone explains 69% of the variance. This leaves only limited space for effects due to pharmacological substances. Context effects are more important than pharmacological ones in the conditions studied by us.

This study aimed to evaluate the efficacy of a single infusion of ketamine in inducing rapid remission of severe suicidal ideation, compared to Midazolam, in a population with acute suicidal thoughts. In a double-blind randomized controlled trial conducted in Tehran, Iran, from January to July 2022 (IRCT20220118053756N1), 36 inpatients with acute severe suicidal ideation were enrolled. Participants were randomly assigned to receive either a single dose of ketamine (0.5 mg/kg) or Midazolam (0.02 mg/kg). Suicidality was assessed using the Beck Scale for Suicide Ideation (BSSI) and the Suicide-Visual Analog Scale (S-VAS) before the intervention and at 12 and 24 h post-administration.

At baseline, the Midazolam group exhibited significantly higher BSSI scores and a higher rate of borderline personality disorder than the Ketamine group. Mean BSSI and S-VAS scores at 12 and 24 h after the treatment decreased significantly compared to baseline in both groups. Despite these observations, no statistically significant differences were found between the groups in terms of BSSI and S-VAS scores.

The protocol for this RCT was registered at the Iranian Registry of Clinical Trials (IRCT). The trial registration details are as follows: IRCT registration number IRCT20220118053756N1, with the registration date being June 12, 2022 (1401/03/22). It is important to note that this trial was retrospectively registered.

To assess the beneficial and harmful effects of duloxetine versus 'active placebo', placebo or no intervention for adults with major depressive disorder.

Systematic review with meta-analysis and trial sequential analysis of randomised trials.

Cochrane Central Register of Controlled Trials, MEDLINE, Embase, PsycINFO and other relevant databases up until January 2023. We requested clinical study reports from 36 competent authorities.

All randomised clinical trials comparing duloxetine versus placebo, 'active placebo' or no intervention, irrespective of publication type, publication status, publication year and language for treatment of major depressive disorder in adults.

Five authors in pairs extracted data using a standardised data extraction sheet. A third review author was consulted for disagreements. Intervention effects were assessed by both random-effects and fixed-effect model meta-analyses, risk of bias assessments were performed by two independent review authors using Cochrane's risk of bias tool V.2 and the certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation.

We included 28 trials randomising a total of 7872 participants. All results were at high risk of bias. The trials' assessment time points were between 6 and 16 weeks after randomisation. Meta-analyses showed evidence of a beneficial effect of duloxetine on depressive symptoms (mean difference -1.81, Hamilton Depression Rating Scale (HDRS-17) points; 95% CI -2.34 to -1.28; heterogeneity I2=0.0%; 12 trials) and quality of life (mean difference -3.79 points, 95% CI -5.11 to -2.46; I2=0.0%; three trials), but the effect sizes were below our predefined minimal clinically important differences. Trial sequential analysis showed that we did not have enough information to assess the effects of duloxetine on serious adverse events (SAEs) (OR 0.67, 95% CI 0.44 to 1.02; I2=0.0%; 19 trials) or suicide or suicide attempts (OR 1.08, 95% CI 0.37 to 3.16; six trials). Duloxetine increased the risk of non-SAEs (risk ratio 1.27, 95% CI 1.22 to 1.32; I2=73.0%; 24 trials). The adverse events with the lowest number needed to harm (NNH) were nausea (NNH 6), dry mouth (NNH 13), somnolence (NNH 17), withdrawal syndrome (NNH 19), sweating (NNH 20), dizziness (NNH 21) and constipation (NNH 21).

Duloxetine appears to reduce depressive symptom scores and improve quality of life scores in the short term, but the effect sizes are minimal and of questionable patient importance. The short- and long-term effects of duloxetine on risks of SAEs and suicidality are uncertain. Duloxetine increases the risks of several short-term adverse events. Systematic assessments of benefits and harms over longer periods are required.

PROSPERO 2016 CRD42016053931.

This study aims to quantitatively evaluate the efficacy and safety of various treatment regimens for treatment-resistant depression (TRD) across oral, intravenous, and intranasal routes to inform clinical guidelines.

A systematic review identified randomized controlled trials on TRD, with efficacy measured by changes in the Montgomery-Åsberg Depression Rating Scale (MADRS). We developed pharmacodynamic and covariate models for different administration routes, using Monte Carlo simulations to estimate efficacy distribution. Dropout and adverse event-related dropout rates were analyzed via single-arm meta-analysis.

Involving 22 studies with 56 treatment arms and 3059 patients, our findings suggest combination therapies outperform monotherapy, achieving an additional 6.5% reduction in MADRS scores over 12 weeks. The most effective combinations were olanzapine with fluoxetine and quetiapine with selective serotonin reuptake inhibitors/ selective serotonin and norepinephrine reuptake inhibitors. Injectable treatments, particularly ayahuasca, produced rapid effects, with a 77% reduction in MADRS scores at 15 days. Intranasal treatments reached efficacy sooner than oral ones, with 28-day efficacy similar to the 12-week efficacy of the olanzapine-fluoxetine combination. Dropout rates due to adverse events were similar across methods (4.5%-5.2%), but total dropouts were highest for oral (17.9%) and lowest for intranasal routes (10.6%). Additionally, there was considerable variation in the incidence of headache, dizziness, and nausea across different administration routes.

The quantitative evaluation of 22 TRD treatments illuminates key pharmacodynamic parameters, bolstering the development of clinical guidelines and aiding the design of clinical trials and medical decision-making.

Neurotropin-3 (NT-3), a marker of neural plasticity, is reported to be altered in bipolar disorder (BD). This study was designed to evaluate NT-3 gene polymorphism (rs 6489630, rs 6332, and rs 11063714) in BD and its association with disease severity and cognition.

The study included 176 BD cases and 176 controls. All the participants were tested for NT-3 polymorphism and plasma NT-3. ACE-III scores were used to analyze cognition.

The NT-3 polymorphism (rs 6489630) was associated with cognitive impairment in BD (P = .010). The attention score was found to be decreased in the CT genotype (P = .028) when compared to the CC and TT genotypes of the rs6489630 variant. The visuospatial ability score was decreased in the GG genotype (P = .044) compared to the AG genotype of the rs11063714 variant. BD patients with the maniac episode showed a decrease in levels of Neurotrophin-3 in comparison to both the control group (P = .045) and the remission group (P = .017). Plasma NT-3 was associated with the YMRS (r = -0.221, P = .003), HDRS (r = 0.209, P = .005) and visuospatial ability score (r = 0.180, P = .017) in patients with BD.

Single nucleotide polymorphisms of NT-3 are associated with cognitive dysfunction in BD.

The resting-state functional connectivity (FC) patterns of the fronto-parietal network (FPN) and language network (LN) underlying bipolar disorder (BD) are obscure. This study aimed to uncover abnormal FC patterns of FPN and LN underlying BD and their evolution following treatment.

Imaging data at rest state and clinical variables were acquired from 82 patients with BD (with 43 finishing the follow-up) and 88 healthy controls (HCs). Seed-based FC analysis was performed, and correlations between FCs and clinical variables were investigated with whole-brain multiple regression analyses. Furthermore, a neuroimaging-transcription spatial association analysis was conducted.

At baseline, BD patients presented elevated FPN-LN and FPN-prefrontal gyrus FCs, and hyperconnectivity between the LN and bilateral thalamus, right angular gyrus (AG), and right cerebellum. Following 3 months of treatment intervention, there were decreased FCs between the FPN and left superior temporal gyrus (STG), left superior frontal gyrus (SFG), left insula, and bilateral middle temporal gyrus (MTG) (part of LN). Neuroimaging transcription analysis discovered genes correlated with FC alterations in BD.

Aberrant FC patterns of FPN and LN might be involved in the neural pathogenetic and therapeutic mechanisms of BD. We also provided potential genetic pathways underlying these functional impairments in BD.

Depression is frequently described to occur in migraine, and depression screening questionnaires are commonly used to evaluate depressive symptoms in patients with migraine. The present study aimed to investigate how the most common depression screening tools are used in migraine studies to determine whether they are applied and interpreted correctly.

PubMed was systematically searched, and we included any study using the Beck Depression Inventory (BDI), Patient Health Questionnaire-9 (PHQ-9), Hospital Anxiety Depression Scale (HADS) or Hamilton Depression Rating Scale (HAM-D). The study included adults diagnosed with migraine based on the International Classification of Headache Disorders (ICHD-2 or ICHD-3).

The literature search generated 78 studies. Thirty-five (45%) of the included studies used a depression screening tool as evidence of depression. This applied to 53, 46, 47 and 13% of studies using PHQ, BDI, HADS and HAM-D, respectively. Only one study out of 35 confirmed the diagnosis with a diagnostic interview. The data presentation and interpretation across the studies was highly heterogeneous.

Screening tools as evidence of depression in patients with migraine may lead to inaccurate estimates of depression among migraine patients. There is a need for guidelines on and validation of depression screening tools in patients with migraine.

Resting-state functional connectivity analysis has a potential to unearth the putative neuronal underpinnings of various disorders of the brain. Major depressive disorder (MDD) is regarded as a disorder arising from alterations in functional networks of the brain.

There is paucity of literature on resting-state functional magnetic resonance imaging (Rs-fMRI) in MDD, especially from the Indian subcontinent. The purpose of our study was to elucidate the differences in Rs-fMRI connectivity between MDD patients and age and gender matched healthy controls (HC).

In this prospective single institute-based study, the patients were recruited consecutively based on Hamilton depression rating scale (HAM-D). Age and gender matched HC were also recruited. Rs-fMRI and anatomical MRI images were acquired for all the subjects (MDD and HC group) and subsequent analysis was done using the CONN toolbox.

A total of 49 subjects were included in the final analysis (MDD = 28 patients, HC = 21). HAM-D score was noted to be 24.4 ± 4.8 in the MDD group. There was no significant difference between MDD and HC groups as far as age, gender, employment status, and level of education is concerned. Region-of-interest-based analysis of Rs-fMRI data showed a significantly lower connectivity between the left insula and left nucleus accumbens and between left paracingulate gyrus and bilateral posterior middle temporal gyri in MDD group as compared to HC group.

There is reduced connectivity between certain key regions of the brain in MDD patients, that is, between the left insular cortex and the left nucleus accumbens and between the left paracingulate gyrus and the bilateral posterior middle temporal gyrus. These findings could explain the basis of clinical features of MDD such as anhedonia, rumination of thoughts, reduced visuo-spatial comprehension, reduced language function, and response to external stimuli.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize 'expected value' by selecting actions that maximize potential reward while minimizing associated costs, including effort 'costs' and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity.

We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (reflecting perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N = 52, 43 in-episode) and comparisons (N = 27).

Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures was related to symptom severity, over and above effects of ability (i.e. performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia and behavioral apathy were associated with increased physical effort costs. Finally, greater overall depression was related to decreased patch leaving thresholds.

Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as worry. Decreased leaving threshold associations with symptom severity are consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear.

This study aimed to assess the effects of PD and paroxetine on brain functional hubs in drug-free patients and to identify neuroimaging biomarkers for diagnosing and predicting TR in patients with PD.

Imaging data from 54 medication-free PD patients and 54 matched healthy controls (HCs) underwent DC and functional connectivity (FC) analyses before and after a 4-week paroxetine treatment. Diagnosis and prediction of TR models for PD were constructed using support vector machine (SVM) and support vector regression (SVR), with DC as features.

Patients with PD showed aberrant DC and FC in the anterior cingulum, temporal, and occipital areas compared with HCs at baseline. After treatment, DC of the patients increased in the calcarine cortex, lingual gyrus, and cerebellum IV/V, along with improved clinical symptoms. Utilizing voxel-wise DC values at baseline, the SVM effectively distinguished patients with PD from HCs with an accuracy of 83.33%. In SVR, the predicted TR significantly correlated with the observed TR (correlation coefficient (r) = 0.893, Mean Squared Error = 0.009).

Patients with PD exhibited abnormal DC and FC, notably in the limbic network, temporal, and occipital regions. Paroxetine ameliorated patients' symptoms while altering their brain FC. SVM and SVR models, utilizing baseline DC, effectively distinguished the patients from HCs and accurately predicted TR.

Psychotic depression (PD) and schizophrenia (SCZ) share overlapping symptoms yet differ in etiology, progression, and treatment approaches. Differentiating these disorders through symptom-based diagnosis is challenging, emphasizing the need for a clearer understanding of their distinct cognitive and neural mechanisms.

This study aims to compare cognitive impairments and brain functional activities in PD and SCZ to pinpoint distinguishing characteristics of each disorder.

We evaluated cognitive function in 42 PD and 30 SCZ patients using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and resting-state functional magnetic resonance imaging (rs-fMRI). Regional homogeneity (ReHo) values were derived from rs-fMRI data, and group differences in RBANS scores were analyzed. Additionally, Pearson correlation analysis was performed to assess the relationship between cognitive domains and brain functional metrics.

(1) The SCZ group showed significantly lower RBANS scores than the PD group across all cognitive domains, particularly in visuospatial/constructional ability and delayed memory (p < 0.05); (2) The SCZ group exhibited a significantly higher ReHo value in the left precuneus compared to the PD group (p < 0.05); (3) A negative correlation was observed between visuospatial construction, delayed memory scores, and the ReHo value of the left precuneus.

Cognitive impairment is more pronounced in SCZ than in PD, with marked deficits in visuospatial and memory domains. Enhanced left precuneus activity further differentiates SCZ from PD and correlates with cognitive impairments in both disorders, providing neuroimaging-based evidence to aid differential diagnosis and insights into cognitive dysfunction mechanisms, while also paving a clearer path for psychiatric research.

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

In an influential network meta-analysis, the tricyclic antidepressant (TCA) amitriptyline was found to be the most efficacious of 21 antidepressants, hence outranking selective serotonin reuptake inhibitors (SSRIs) and serotonin and noradrenaline reuptake inhibitors (SNRIs). The alpha2/5HT2A/2C/3/H1 antagonist mirtazapine was ranked as the second most effective and appeared at least as effective as the SSRIs and SNRIs that followed next. Since the most common effect parameter in depression trials-the sum score of the Hamilton Depression Rating Scale (HDRS-17-sum)-includes three items measuring sleep and two measuring appetite and weight, this outcome could be the result of amitriptyline and mirtazapine being more sedative and orexigenic. The main aim of this study was to compare mirtazapine with SSRIs or amitriptyline with respect to impact on core depression symptoms.

Access to patient-level data from all company-sponsored, acute-phase, HDRS-based, and randomized trials of mirtazapine in adult major depression available to Merck was granted. Thirty-two studies compared mirtazapine to placebo and/or amitriptyline or an SSRI whereas five compared mirtazapine to another TCA or an SNRI, venlafaxine. Data were divided into subgroups for direct comparisons of mirtazapine vs placebo or different subgroups of antidepressants. Indirect comparisons of SSRIs vs amitriptyline were also undertaken. Mixed models for repeated measures were used to assess efficacy as reflected by i) HDRS-17-sum, ii) six core depression symptoms (HDRS-6-sum), and iii) all individual items.

The dataset consisted of 5974 participants. Mirtazapine (n = 1362) outperformed SSRIs (n = 1369) on HDRS-17-sum, but this was due to differences regarding items reflecting sleep, appetite, and gastrointestinal dysfunction-with respect to reducing depressed mood, suicidality, and psychic anxiety, SSRIs and/or venlafaxine were more effective. Amitriptyline (n = 622) was superior to mirtazapine (n = 606) in reducing depressed mood, and the combined group of all TCAs (n = 831) outperformed mirtazapine (n = 824) also with respect to other core depression symptoms. Since there were no head-to-head comparisons of amitriptyline vs SSRIs, no firm conclusion may be drawn with respect to relative efficacy of the two, but indirect comparisons support the notion that amitriptyline and other tricyclics may be superior also to SSRIs.

While the apparent superiority of mirtazapine over SSRIs is explained by its sedative and orexigenic properties, and by its absence of gastrointestinal side effects, amitriptyline appeared more effective in reducing core symptoms of depression than mirtazapine and possibly also than SSRIs; given the indirect nature of the latter comparison, this outcome should however be interpreted with caution. Lack of information regarding dosing was another important limitation. The study illustrates the value of item-based analyses when assessing the relative efficacy of antidepressants.

The Swedish Research Council, the Swedish Brain Foundation. The Gothenburg Society of Medicine, the Swedish Society of Medicine, Åke Wiberg's Foundation, Märta Lundqvist's Foundation, Fredrik and Ingrid Thuring's Foundation, Söderström-Königska Foundation and Frimurare-Barnhusdirektionen in Gothenburg.

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions and treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due to its non-invasive nature and cost-effectiveness. With the rise of computational psychiatry, the integration of EEG with artificial intelligence has yielded remarkable results in diagnosing depression. This review offers a comparative analysis of two predominant methodologies in research: traditional machine learning and deep learning methods. Furthermore, this review addresses key challenges in current research and suggests potential solutions. These insights aim to enhance diagnostic accuracy for depression and also foster further development in the area of computational psychiatry.

Depression is a leading cause of disability worldwide, and treatments could be more effective. Identifying methods to improve treatment success has the potential to reduce disease burden dramatically. Preparing or "priming" someone to respond more effectively to psychotherapy (e.g., cognitive behavioral therapy [CBT]) by preceding sessions with aerobic exercise, a powerful neurobiological activator, could enhance the success of the subsequently performed therapy. However, the success of this priming approach for increasing engagement of working mechanisms of psychotherapy (e.g., increased working alliance and behavioral activation) has yet to be formally tested.

The CBT + trial will be a parallel-arm randomized controlled trial that will recruit 40 adult participants with DSM-5 diagnosed depression (verified with clinical interview) via referrals, mass emails, local flyers, and social media posts. Participants will be randomized to an ActiveCBT or CalmCBT condition. The ActiveCBT group will receive an 8-week CBT intervention primed with 30 min of moderate-intensity aerobic exercise (cycling on a stationary bike at a 13 rating of perceived exertion). The CalmCBT group will receive the same 8-week CBT intervention while resting for 30 min before CBT (i.e., cycling vs no cycling is the only difference). The primary outcome measures will be mean working alliance (assessed with the client version of the Working Alliance Inventory-Short Revised) and mean behavioral activation (self-reported Behavioral Activation for Depression Scale) recorded at each of the 8 therapy sessions. Secondary outcomes include evaluation of state anhedonia and serum brain-derived neurotrophic factor before the active/calm conditions, between the condition and therapy, and after the therapy. Additional exploratory analyses will evaluate group differences in algorithm-generated ratings of therapist-participant interactions via the Lyssn platform.

The novel approach of priming CBT with moderate-intensity aerobic exercise evaluated in a randomized controlled trial (CBT + trial) has the potential to demonstrate the usefulness of exercise as an augmentation strategy that improves working mechanisms of therapy and overall treatment outcomes for adults with depression.

ClinicalTrials.gov NCT06001346 . Registered on August 21, 2023.

Using functional near-infrared spectroscopy (fNIRS) previous studies have found that activation differences in the dorsolateral prefrontal cortex (DLPFC) during an autobiographical memory task (AMT) under the condition of different emotional valences may be neurophysiological markers of depression and different depression subtypes. Additionally, compared with non-anxious depression, anxious depression presents abnormal hemodynamic activation in the DLPFC. This study aimed to use fNIRS to investigate hemodynamic activation in the DLPFC of depression patients with and without anxiety during AMT triggered by different emotional valence stimuli.

We recruited 194 patients with depression (91 with non-anxious depression, 103 with anxious depression) and 110 healthy controls from Chinese college students. A 53-channel fNIRS was used to detect cerebral hemodynamic differences in the three groups during AMT.

The results showed that: (1) the activation of oxy-Hb in the left DLPFC was significantly higher under positive emotional valence than under negative emotional valence for healthy controls and patients with non-anxious depression, while there was no significant difference between positive and negative emotional valence observed in response to anxious depression; and (2) Oxy-Hb activation under negative emotional valence was significantly higher in the anxious depression group than in the non-anxious depression group.

This study revealed that the hemodynamic hyperactivation of negative emotional valence in the left DLPFC may be due to the neurophysiological differences between anxious and non-anxious patients with depression.

Individuals with major depressive disorder (MDD) can experience reduced motivation and cognitive function, leading to challenges with goal-directed behavior. When selecting goals, people maximize 'expected value' by selecting actions that maximize potential reward while minimizing associated costs, including effort 'costs' and the opportunity cost of time. In MDD, differential weighing of costs and benefits are theorized mechanisms underlying changes in goal-directed cognition and may contribute to symptom heterogeneity.

We used the Effort Foraging Task to quantify cognitive and physical effort costs, and patch leaving thresholds in low effort conditions (reflecting perceived opportunity cost of time) and investigated their shared versus distinct relationships to clinical features in participants with MDD (N=52, 43 in-episode) and comparisons (N=27).

Contrary to our predictions, none of the decision-making measures differed with MDD diagnosis. However, each of the measures were related to symptom severity, over and above effects of ability (i.e., performance). Greater anxiety symptoms were selectively associated with lower cognitive effort cost (i.e. greater willingness to exert effort). Anhedonia and behavioral apathy were associated with increased physical effort costs. Finally, greater overall depression was related to decreased patch leaving thresholds.

Markers of effort-based decision-making may inform understanding of MDD heterogeneity. Increased willingness to exert cognitive effort may contribute to anxiety symptoms such as worry. Decreased leaving thresholds associations with symptom severity is consistent with reward rate-based accounts of reduced vigor in MDD. Future research should address subtypes of depression with or without anxiety, which may relate differentially to cognitive effort decisions.

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

Depression and anxiety negatively affect asthma-related quality of life (QoL). Yet, little is known regarding mood and asthma-related factors that best uniquely explain asthma-related QoL in children.

This cross-sectional study evaluated the unique variance explained by caregiver and child depressive and anxiety symptom severity in child asthma-related QoL, apart from that explained by demographics and asthma control.

Children aged 7 to 17 years with asthma (n = 205) and their caregivers with major depressive disorder were included. A 3-stage hierarchical linear regression analysis was conducted with the Pediatric Asthma Quality of Life Questionnaire total scores considered as the outcome. Predictors included demographic characteristics (stage 1); asthma control assessed by the Asthma Control Test (stage 2); and caregiver depression and anxiety (Hamilton Rating Scale for Depression and the Spielberger State/Trait Anxiety Scale) and child depression and anxiety (Children's Depression Inventory and the Screen for Child Anxiety-Related Disorders) (stage 3).

Demographic characteristics accounted for only 5.5% of the Pediatric Asthma Quality of Life Questionnaire scores. Asthma control significantly increased variance explained in QoL to 32.6%, whereas caregiver and child depression and anxiety symptoms significantly increased variance explained to 42.6%. Child anxiety was found to uniquely explain the largest proportion of variance in QoL (rs2 = 0.584).

After adjusting variance in QoL for demographic characteristics and asthma control, caregiver and child depression and anxiety measures significantly increased the proportion of variance explained in a child's asthma-related QoL. In addition to better asthma control, child and caregiver depression and anxiety should be addressed to increase child asthma-related QoL.

ClinicalTrials.gov Identifier: NCT02809677.

The relationship between chronic daily headache (CDH), depression symptoms, and brain volume remains unclear.

To investigate the effects of CDH on brain volume and the impact of depressive symptoms (DSs) as well as the effects of demography and medication overuse, PubMed, Embase, and Web of Science databases were systematically searched using appropriate keyword strings to retrieve observational studies from inception to May 2023.

Two distinct comparisons were made in CDH patients: (1) those with DSs versus their pain-free counterparts and (2) those without DSs versus pain-free controls. The first comprised nine studies enrolling 225 CDH patients with DSs and 234 controls. Beck depression inventory, Hamilton depression scale, and Hospital anxiety/depression scale were used to assess DSs, revealing significantly more DSs in CDH patients with DSs compared to their controls (all p < 0.05). Besides, the second analysed four studies involving 117 CDH patients without DSs and 155 comparators. Compared to CDH patients without DSs, those with DSs had a smaller brain volume than controls (p = 0.03). Furthermore, CDH patients with DSs who did not overuse medications showed a smaller right cerebral cortical volume than overusers (p = 0.003). A significant inverse correlation between female prevalence and brain volume (p = 0.02) was revealed using regression analysis.

Pain-induced persistent depressive symptoms not only incur structural alterations but also encompass affective-motivational changes, involving medication use and gender-specific health concerns.

This study highlighted the importance of an integrated CDH treatment, emphasizing psychological interventions for the affective-motivational component alongside pain management.

Low social support increases the risk of Major depressive disorder (MDD), yet its effects on brain function are unclear. Thirty-two MDD patients with low social support, 52 with high social support, and 54 healthy controls were recruited. We investigated regional brain activity in MDD patients with low social support using resting-state functional Magnetic Resonance Imaging, employing measures such as degree centrality (DC), regional homogeneity, amplitude of low-frequency fluctuations, and fractional amplitude of low-frequency fluctuations. Abnormal regions identified in these analyses were selected as regions of interest for functional connectivity (FC) analysis. We then explored relationships among social support, brain dysfunction, MDD severity, and insecurity using partial correlation and moderated mediation models. Our findings reveal that MDD patients with low social support show decreased DC in the right superior temporal pole and right medial geniculate nucleus, coupled with increased FC between the right superior temporal pole and right inferior temporal gyrus, and the right supramarginal gyrus compared to those with high social support. Furthermore, the DC of the right medial geniculate nucleus positively correlates with social support, while the FC between the right superior temporal pole and right supramarginal gyrus negatively correlates with both social support and subjective support. Additionally, a moderated mediation model demonstrates that the FC between the right superior temporal pole and right supramarginal gyrus mediates the relationship between social support and depression severity, with security moderating this mediation. These findings underscore the impact of low social support on brain function and depression severity in MDD patients.

The role of cerebellar-cerebral functional connectivity (CC-FC) in obsessive-compulsive disorder (OCD), its trajectory post-pharmacotherapy, and its potential as a prognostic biomarker and genetic mechanism remain uncertain. To address these gaps, this study included 37 drug-naive OCD patients and 37 healthy controls (HCs). Participants underwent baseline functional magnetic resonance imaging (fMRI), followed by four weeks of paroxetine treatment for patients with OCD, and another fMRI scan post-treatment. We examined seed-based CC-FC differences between the patients and HCs, and pre- and post-treatment patients. Support vector regression (SVR) based on CC-FC was performed to predict treatment response. Correlation analysis explored associations between CC-FC and clinical features, as well as gene profiles. Compared to HCs, drug-naive OCD patients exhibited reduced CC-FC in executive, affective-limbic, and sensorimotor networks, with specific genetic profiles associated with altered CC-FC. Gene enrichment analyses highlighted the involvement of these genes in various biological processes, molecular functions, and pathways. Post-treatment, the patients showed partial clinical improvement and partial restoration of the previously decreased CC-FC. Abnormal CC-FC at baseline correlated negatively with compulsions severity and social functional impairment, while changes in CC-FC correlated with cognitive function changes post-treatment. CC-FC emerged as a potential predictor of symptom severity in patients following paroxetine treatment. This longitudinal resting-state fMRI study underscores the crucial role of CC-FC in the neuropsychological mechanisms of OCD and its pharmacological treatment. Transcriptome-neuroimaging spatial correlation analyses provide insight into the neurobiological mechanisms underlying OCD pathology. Furthermore, SVR analyses hold promise for advancing precision medicine approaches in treating patients with OCD.

Objective: Although theorists and researchers have stressed the importance of rupture resolution episodes for successful treatment process and outcome, little is known about patients' retrospective reflections about rupture resolution. Aim: The overarching goal of the present study was to use a mixed-method approach to examine patients' retrospective reflections on the frequency, types, and consequences of rupture resolution episodes and the association between rupture resolution episodes and patients' attachment orientation and treatment outcome. Method: Thirty-eight patients diagnosed with major depressive disorder (MDD) were interviewed, on average three years after termination, about their experiences of ruptures in short-term dynamic psychotherapy. Results: Thirty patients reported having experienced at least one rupture, with patients who showed less improvement in depressive symptoms more likely to report having had a rupture. Ruptures were judged as having been successfully resolved for 13 of these patients; suggesting that patients with a high level of attachment anxiety were less likely to be judged as having had a successful resolution. Patients whose ruptures were successfully resolved with the therapist's help reported better treatment process and outcome than patients whose ruptures were not successfully resolved. Conclusion: Results highlight the importance of hearing patients' perspectives on ruptures, rupture resolution, and treatment outcome.

The 17-item Hamilton Rating Scale for Depression (HRSD-17) is the most popular depression measure in antidepressant clinical trials. Prior evidence indicates poor replicability and inconsistent factorial structure. This has not been studied in pooled randomised trial data, nor has a psychometrically optimal model been developed.

To examine the psychometric properties of the HRSD-17 for pre-treatment and post-treatment clinical trial data in a large pooled database of antidepressant randomised controlled trial participants, and to determine an optimal abbreviated version.

Data for 6843 participants were obtained from the data repository Vivli.org and randomly split into groups for exploratory (n = 3421) and confirmatory (n = 3422) factor analysis. Invariance methods were used to assess potential sex differences.

The HRSD-17 was psychometrically sub-optimal and non-invariant for all models. High item variances and low variance explained suggested redundancy in each model. EFA failed at baseline and produced four item models for outcome groups (five for placebo-outcome), which were metric but not scalar invariant.

In antidepressant trial data, the HRSD-17 was psychometrically inadequate and scores were not sex invariant. Neither full nor abbreviated HRSD models are suitable for use in clinical trial settings and the HRSD's status as the gold standard should be reconsidered.

Recent systematic reviews highlight great variability in defining and assessing treatment-resistant depression (TRD). A key problem is that definitions are consensus rather than data-led. This study seeks to offer a comprehensive socio-demographic and clinical description of a relevant sample.

As part of a pragmatic randomized controlled trial, patients (N = 129) were managed in primary care for persistent depression and diagnosed with TRD. Data included previous treatment attempts, characteristics of the depressive illness, functioning, quality of life, co-occurring problems including suicidality, psychiatric and personality disorders, physical health conditions, and adverse events.

Findings show a severe and chronic course of depression with a duration of illness of 25+ years. Overall, 82.9 % had at least one other psychiatric diagnosis and 82.2 % at least one personality disorder; 69.8 % had significant musculoskeletal, gastrointestinal, genitourinary, or cardiovascular and respiratory physical health problems. All but 14 had severe difficulties in social and occupational functioning and reported severely impaired quality of life. Suicidal ideation was high: 44.9 % had made at least one serious suicide attempt and several reported multiple attempts with 17.8 % reporting a suicide attempt during childhood or adolescence. Of the patients, 79.8 % reported at least one adverse childhood experience.

Potential for recall bias, not examining possible interactions, and absence of a control group.

Our findings reveal a complex and multifaceted condition and call for an urgent reconceptualization of TRD, which encompasses many interdependent variables and experiences. Individuals with TRD may be at a serious disadvantage in terms of receiving adequate treatment.

To mitigate limitations of self-reported mood assessments, we introduce a novel affective bias task. The task quantifies instantaneous emotional state by leveraging the phenomenon of affective bias, in which people interpret external emotional stimuli in a manner consistent with their current emotional state. This study establishes task stability in measuring and tracking depressive symptoms in clinical and nonclinical populations. Initial assessment in a large nonclinical sample established normative ratings. Depressive symptoms were measured and compared with task performance in a nonclinical sample, as well as in a clinical cohort of individuals who were undergoing surgical evaluation for severe epilepsy. In both cohorts, a stronger negative affective bias was associated with a higher Beck Depression Inventory-II score. The affective bias task exhibited high stability and interrater reliability as well as construct validity in predicting depression levels in both cohorts, suggesting that the task is a reliable proxy for mood and a diagnostic tool for detecting depressive symptoms.

Discrepancies in self-rated and observer-rated depression severity may underlie the basis for biological heterogeneity in depressive disorders and be an important predictor of outcomes and indicators to optimize intervention strategies. However, the neural mechanisms underlying this discrepancy have been understudied. This study aimed to examine the brain networks that represent the neural basis of the discrepancy between self-rated and observer-rated depression severity using resting-state functional MRI. To examine the discrepancy between self-rated and observer-rated depression severity, self- and observer-ratings discrepancy (SOD) was defined, and the higher and lower SOD groups were selected from depressed patients as participants showing extreme deviation. Resting-state functional MRI analysis was performed to examine regions with significant differences in functional connectivity in the two groups. The results showed that, in the higher SOD group compared to the lower SOD group, there was increased functional connectivity between the frontal pole and precuneus, both of which are subregions of the default mode network that have been reported to be associated with ruminative and self-referential thinking. These results provide insight into the association of brain circuitry with discrepancies between self- and observer-rated depression severity and may lead to more treatment-oriented diagnostic reclassification in the future.