Diagnosis Of Depressive Disorder Research Articles

Clinic-Based Characterization of Adolescents and Young Adults With Migraine: Psychological Functioning, Headache Days, and Disability.

This cross-sectional observational study retrospectively examined clinical data collected from adolescents and young adults (AYAs) seeking care in a specialty headache clinic. We characterized participants' headache characteristics and psychological functioning and examined the association between self-reported anxiety and depressive symptoms and headache frequency, severity, and disability. During their clinic visit, AYAs (M age = 18.36; range = 14-32, 79.5% female) completed an intake questionnaire and reported about their headache characteristics (i.e., frequency, severity, and duration of symptoms in months), mental health history (i.e., previous diagnosis of an anxiety or depressive disorder), and utilization of emergency department (ED) services for migraine. AYAs also completed psychometrically validated screening tools for anxiety and depressive symptoms (i.e., the GAD-7 and PHQ-9). We computed descriptive statistics and examined associations among scores on psychological measures and headache characteristics, including migraine-related disability. We also tested whether individuals with clinically elevated GAD-7 and PHQ-9 scores had higher levels of disability relative to those with fewer/subclinical levels of anxiety and depressive symptoms. Participants (N = 283) reported more than 19 headache days per month on average, with more than 90% describing their average headache intensity as moderate or severe. Nearly half of AYAs reported severe headache-related disability. Approximately one-quarter of AYAs reported a previous diagnosis anxiety or depressive disorder diagnosis, and more than one-third scored above clinical cutoffs on the PHQ-9 and GAD-7. Higher scores on both psychological screening instruments were associated with greater headache frequency. More than 10% of patients endorsed current suicidal ideation; this was not related to headache-related disability. Participants reported a high degree of ED utilization for headache; these rates were unrelated to endorsement of psychological comorbidities. In this sample of AYAs, headache characteristics were generally unrelated to scores on measures on psychological functioning. However, the observed rates of clinically elevated anxiety/depressive symptoms and suicidality in this sample of AYAs underscore the importance of screening for psychological comorbidities in neurology clinics that serve this age group, irrespective of self-reported disability. Results also emphasize the need to expand access to behavioral health services for AYAs with headache disorders and the importance of incorporating a biopsychosocial perspective to the transition of health care from pediatrics to adult neurology practice.

High-Order line graphs of fMRI data in major depressive disorder.

Resting-state functional magnetic resonance imaging (rs-fMRI) technology and the complex network theory can be used to elucidate the underlying mechanisms of brain diseases. The successful application of functional brain hypernetworks provides new perspectives for the diagnosis and evaluation of clinical brain diseases; however, many studies have not assessed the attribute information of hyperedges and could not retain the high-order topology of hypergraphs. In addition, the study of multi-scale and multi-layered organizational properties of the human brain can provide richer and more accurate data features for classification models of depression. This work aims to establish a more accurate classification framework for the diagnosis of major depressive disorder (MDD) using the high-order line graph algorithm. And accuracy, sensitivity, specificity, precision, F1 score are used to validate its classification performance. Based on rs-fMRI data from 38 MDD subjects and 28 controls, we constructed a human brain hypernetwork and introduced a line graph model, followed by the construction of a high-order line graph model. The topological properties under each order line graph were calculated to measure the classification performance of the model. Finally, intergroup features that showed significant differences under each order line graph model were fused, and a support vector machine classifier was constructed using multi-kernel learning. The Kolmogorov-Smirnov nonparametric permutation test was used as the feature selection method and the classification performance was measured with the leave-one-out cross-validation method. The high-order line graph achieved a better classification performance compared with other traditional hypernetworks (accuracy=92.42%, sensitivity=92.86%, specificity=92.11%, precision=89.66%, F1=91.23%). Furthermore, the brain regions found in the present study have been previously shown to be associated with the pathology of depression. This work validated the classification model based on the high-order line graph, which can better express the topological features of the hypernetwork by comprehensively considering the hyperedge information under different connection strengths, thereby significantly improving the classification accuracy of MDD. Therefore, this method has potential for use in the clinical diagnosis of MDD.

Effect of l-arginine compared to placebo on sexual function in women with major depressive disorder: a randomized controlled trial

BackgroundWhile some evidence suggests that l-arginine may improve sexual function and alleviate depression, it has not been investigated in women with depression to assess both its effects on the depression and sexual function concurrently.MethodsPatients who had received a diagnosis of major depressive disorder, as determined by predetermined inclusion and exclusion criteria, were enrolled in this triple-blind clinical trial. Patients were divided into two groups: group A, received L-arginine 1 gram twice daily, and group B, received a placebo for four weeks. They were evaluated at baseline, after four and eight weeks with the Hamilton Depression Rating Scale (HDRS), and Rosen’s questionnaire or Female Sexual Function Index (FSFI).ResultsA decrease in the severity of depression was observed in all patients, which was determined due to Hamilton’s questionnaire (P-value < 0.001). During the time in group A, FSFI increased. Based on the FSFI questionnaire, they had improvement in some domains, including the lubrication index and orgasm index, which significantly changed in the eighth week compared to the baseline (P-value < 0.05). However, these two indicators did not change statistically significantly compared to the placebo group.ConclusionL-arginine supplementation can improve sexual function, particularly lubrication and orgasm, and mood in women with depression, with minimal side effects observed. Additional research is necessary to validate these results by examining the effects of higher dosages, extended durations, and larger populations of depressed patients.Trial registrationIranian Registry of Clinical Trial: IRCT20100127003210N26.

Algorithm-based modular psychotherapy vs. cognitive-behavioral therapy for patients with depression, psychiatric comorbidities and early trauma: a proof-of-concept randomized controlled trial.

Effect sizes of psychotherapies currently stagnate at a low-to-moderate level. Personalizing psychotherapy by algorithm-based modular procedures promisesimproved outcomes, greater flexibility, and a better fit between research and practice. However, evidence for the feasibility and efficacy of modular-based psychotherapy, using a personalized treatment algorithm, is lacking. This proof-of-concept randomized controlled trial was conducted in 70 adult outpatients with a primary DSM-5 diagnosis of major depressive disorder, a score higher than 18 on the 24-item Hamilton Rating Scale for Depression (HRSD-24), at least one comorbid psychiatric diagnosis according to the Structured Clinical Interview for DSM-5 (SCID-5), a history of at least "moderate to severe" childhood maltreatment on at least one domain of the Childhood Trauma Questionnaire (CTQ), and exceeding the cut-off value on at least one of three measures of early trauma-related transdiagnostic mechanisms: the Rejection Sensitivity Questionnaire (RSQ), the Interpersonal Reactivity Index (IRI), and the Difficulties in Emotion Regulation Scale-16 (DERS-16). Patients were randomized to 20 sessions of either standard cognitive-behavioral therapy alone (CBT) or CBT plus transdiagnostic modules according to a mechanism-based treatment algorithm (MoBa), over 16 weeks. We aimed to assess the feasibility of MoBa, and to compare MoBa vs. CBT with respect to participants' and therapists' overall satisfaction and ratings of therapeutic alliance (using the Working Alliance Inventory - Short Revised, WAI-SR), efficacy, impact on early trauma-related transdiagnostic mechanisms, and safety. The primary outcome for efficacy was the HRSD-24 score at post-treatment. Secondary outcomes included, among others, the rate of response (defined as a reduction of the HRSD-24 score by at least 50% from baseline and a score <16 at post-treatment), the rate of remission (defined as a HRSD-24 score ≤8 at post-treatment), and improvements in early trauma-related mechanisms of social threat response, hyperarousal, and social processes/empathy. We found no difficulties in the selection of the transdiagnostic modules in the individual patients, applying the above-mentioned cut-offs, and in the implementation of MoBa. Both participants and therapists reported higher overall satisfaction and had higher WAI-SR ratings with MoBa than CBT. Both approaches led to major reductions of depressive symptoms at post-treatment, with a non-significant superiority of MoBa over CBT. Patients randomized to MoBa were nearly three times as likely to experience remission at the end of therapy (29.4% vs. 11.4%; odds ratio, OR = 3.2, 95% CI: 0.9-11.6). Among mechanism-based outcomes, MoBa patients showed a significantly higher post-treatment effect on social processes/empathy (p<0.05) compared to CBT patients, who presented an exacerbation on this domain at post-treatment. Substantially less adverse events were reported for MoBa compared to CBT. These results suggest the feasibility and acceptability of an algorithm-based modular psychotherapy complementing CBT in depressed patients with psychiatric comorbidities and early trauma. While initial evidence of efficacy was observed, potential clinical advantages and interindividual heterogeneity in treatment outcomes will have to be investigated in fully powered confirmation trials.

Perceived Need for Mental Health Care in a Cohort of Persons With Inflammatory Bowel Disease.

Psychiatric comorbidity is common in inflammatory bowel disease (IBD) and can negatively affect disease outcomes. We explored the perceived need for mental health care among persons with IBD. Persons with IBD completed self-report questionnaires, including the Hospital Anxiety and Depression Scale (HADS), and reported whether they wanted help with their mood. Each was also assessed using the Structured Clinical Interview for DSM-IV-TR Axis-I Disorders (SCIDs). We used logistic regression analyses to determine factors associated with the perceived need for mental health care. Of 245 participants, 28% met the criteria for a past diagnosis of depression or anxiety disorder by SCID, and nearly 23% met the criteria for a current diagnosis of depression or anxiety disorder. One-third (n = 74) reported a perceived need for mental health care. Among those meeting criteria for a current SCID diagnosis of depression or anxiety, only 58% reported needing mental health care. Need for mental health care was reported by 79% of persons currently treated for either depression or 71% treated for anxiety. Persons with a perceived need for mental health care had higher mean HADS for depression and HADS for anxiety scores and also higher IBD symptom activity scores. Of those reporting no perceived need for mental health care, 13% had a current diagnosis of depression or anxiety disorder by SCID; even fewer had symptoms of depression or anxiety. Symptoms of depression or anxiety are more important than a formal diagnosis of depression or anxiety in predicting which persons with IBD will perceive a need for mental health care.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach

BackgroundMajor depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability. MethodsThis study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application. ResultsPatients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress. ConclusionThe LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

The Maternal Psychic Impact of Infection by SARS-CoV-2 during Pregnancy: Results from a Preliminary Prospective Study.

Due to a higher risk of maternal complications during pregnancy, as well as pregnancy complications such as stillbirth, SARS-CoV-2 contamination during pregnancy is a putative stress factor that could increase the risk of perinatal maternal mental health issues. We included women older than 18 years, who delivered a living baby at the Geneva University Hospitals' maternity wards after 29 weeks of amenorrhea (w.a.) and excluded women who did not read or speak fluent French. We compared women who declared having had COVID-19, confirmed by a positive PCR test for SARS-CoV-2, during pregnancy with women who did not, both at delivery and at one month postpartum. We collected clinical data by auto-questionnaires between time of childbirth and the third day postpartum regarding the occurrence of perinatal depression, peritraumatic dissociation, and peritraumatic distress during childbirth, measured, respectively, by the EPDS (depression is score > 11), PDI (peritraumatic distress is score > 15), and PDEQ (scales). At one month postpartum, we compared the proportion of women with a diagnosis of postpartum depression (PPD) and birth-related posttraumatic stress disorder (CB-PTSD), using PCL-5 for CB-PTSD and using diagnosis criteria according DSM-5 for both PPD and CB-PTSD, in the context of a semi-structured interview, conducted by a clinician psychologist. Off the 257 women included, who delivered at the University Hospitals of Geneva between 25 January 2021 and 10 March 2022, 41 (16.1%) declared they had a positive PCR test for SARS-CoV-2 during their pregnancy. Regarding mental outcomes, except birth-related PTSD, all scores provided higher mean values in the group of women who declared having been infected by SARS-CoV-2, at delivery and at one month postpartum, without reaching any statistical significance: respectively, 7.8 (±5.2, 8:4-10.5) versus 6.5 (±4.7, 6:3-9), p = 0.139 ***, for continuous EPDS scores; 10 (25.0) versus 45 (21.1), p = 0.586 *, for dichotomous EPDS scores (≥11); 118 (55.7) versus 26 (63.4), p = 0.359 *, for continuous PDI scores; 18.3 (±6.8, 16:14-21) versus 21.1 (±10.7, 17:15-22), 0.231 ***, for dichotomous PDI scores (≥15); 14.7 (±5.9, 13:10-16) versus 15.7 (±7.1, 14:10-18), p = 0.636 ***, for continuous PDEQ scores; 64 (30.0) versus 17 (41.5), p = 0.151 *, for dichotomous PDEQ scores (≥15); and 2 (8.0) versus 5 (3.6), p = 0.289 *, for postpartum depression diagnosis, according DSM-5. We performed Chi-squared or Fisher's exact tests, depending on applicability for the comparison of categorical variables and Mann-Whitney nonparametric tests for continuous variables; p < 0.05 was considered as statistically significant. Surprisingly, we did not find more birth-related PTSD as noted by the PCL-5 score at one month postpartum in women who declared a positive PCR test for SARS-CoV-2:15 (10.6) versus no case of birth related PTSD in women who were infected during pregnancy (p = 0.131 *). Our study showed that mental outcomes were differently distributed between women who declared having been infected by SARS-CoV-2 compared to women who were not infected. However, our study was underpowered to explore all the factors associated with psychiatric issues during pregnancy, postpartum, depending on the exposure to SARS-CoV-2 infection during pregnancy. Future longitudinal studies on bigger samples and more diverse populations over a longer period are needed to explore the long-term psychic impact on women who had COVID-19 during pregnancy.

Effectiveness of Blended Internet-based Self-help and Face-to-face Intervention for Depression: A Pilot Study from India

Background: Depression is a common mental health disorder with a wide treatment gap despite the availability of a number of effective treatment options. Blended interventions adopt a novel approach in combining internet-based self-help with brief face-to-face sessions thereby combining the advantages of both approaches and offering scope to address the treatment gap. The present study examined the effectiveness of a blended intervention combining the use of guided internet-based self-help with four to six face-to-face sessions in the treatment of mild to moderate depression. Method: A single group, open-label design was adopted with baseline, postintervention, and three-month follow-up assessments. The sample comprised 60 individuals with a primary diagnosis of major depressive disorder, dysthymia, or recurrent depressive disorder of mild or moderate severity meeting eligibility criteria. Standardized measures (self-report and clinician-rated) were used to examine the effectiveness of the intervention in reducing depression, improving functioning, self-esteem, and psychological recovery. A total of 40 participants completed the intervention and follow-up assessments. Results: Significant improvements were noted from baseline to post-assessment for both completers’ and intent-to-treat samples, with medium to large effect size on most outcome measures and maintenance of gains at follow-up. Eighty-five percent of the completers showed clinically significant improvement. Subgroups of blended intervention participants with and without pharmacotherapy showed similar outcomes. Completers were higher on interest in learning self-help skills and on the Growth subscale of psychological recovery, compared to the dropouts at baseline. Conclusion: The blended intervention demonstrated effectiveness in reducing depression and improving self-esteem, and functional and psychological recovery.

Prospective prediction of anxiety onset in the Canadian longitudinal study on aging (CLSA): A machine learning study

BackgroundAnxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders. MethodsWe applied ML to the data from the Canadian Longitudinal Study on Aging (CLSA) to predict the onset of anxiety disorders approximately three years in the future. We used Shapley value-based methods to determine the top factor for prediction. We also investigated whether anxiety onset can be predicted by baseline depression-related predictors alone. ResultsOur model was able to predict anxiety onset accurately (Area under the Receiver Operating Characteristic Curve or AUC = 0.814 ± 0.016 (mean ± standard deviation), balanced accuracy = 0.741 ± 0.016, sensitivity = 0.743 ± 0.033, and specificity = 0.738 ± 0.010). The top predictive factors included prior depression or mood disorder diagnosis, high frailty, anxious personality, and low emotional stability. Depression and mood disorders are well known comorbidity of anxiety; however a prior depression or mood disorder diagnosis could not predict anxiety onset without other factors. LimitationWhile our findings underscore the importance of a prior depression diagnosis in predicting anxiety, they also highlight that it alone is inadequate, signifying the necessity to incorporate additional predictors for improved prediction accuracy. ConclusionOur study showcases promising prospects for using machine learning to develop personalized prediction models for anxiety onset in middle-aged and older adults using easy-to-access survey data.

The diversity of clinical features of depressive disorders and their diagnostic challenges

Depression disorder, a mental illness characterized by persistent low mood, has a variety of symptoms, including emotional, cognitive, physiological and behavioral manifestations. However, the non-specificity of these symptoms, the differences in subjective feelings of patients, the influence of sociocultural factors, and the concealment of patients all pose challenges for the accurate diagnosis of depressive disorders. This paper discusses the clinical characteristics of depressive disorder and the challenges faced in the diagnosis process, and puts forward coping strategies and suggestions, such as detailed medical history inquiry, comprehensive physical examination, the use of standardized rating scales, and the improvement of doctors' cognition and diagnostic ability, so as to improve the diagnostic accuracy of depressive disorder and provide patients with more accurate treatment plans.

Efficacy and safety of aticaprant, a kappa receptor antagonist, adjunctive to oral SSRI/SNRI antidepressant in major depressive disorder: results of a phase 2 randomized, double-blind, placebo-controlled study.

This was a double-blind, randomized, phase 2 study of adults (18-64 years) with DSM-5 diagnosis of major depressive disorder (MDD), with moderate-to-severe episode severity (Montgomery-Åsberg Depression Rating Scale [MADRS] ≥25) despite an adequate course with ongoing antidepressant for ≥6 weeks to ≤12 months. Following a double-blind placebo lead-in period (up to 3 weeks), participants were randomized to receive once daily aticaprant 10 mg or continue placebo, added to their ongoing treatment, for 6 weeks. Of 184 participants enrolled, 169 were included in safety analyses (aticaprant n = 85, placebo n = 84) and 166 in full intent-to-treat (fITT) efficacy analyses; 121 placebo lead-in non-responders (<30% reduction in MADRS total score) in fITT were included in enriched ITT (eITT) analyses. Improvement (least squares mean difference [upper limit 1-sided 80% CI] versus placebo) in MADRS total score at week 6 for aticaprant was significant versus placebo (eITT: -2.1 [-1.09], 1-sided p = 0.044; effect size (ES) 0.23; fITT -3.1 [2.21], 1-sided p = 0.002; ES 0.36). The between-group difference was larger among participants with Snaith-Hamilton Pleasure Scale (SHAPS) score greater/equal to versus less than baseline median SHAPS. The most common treatment-emergent adverse events reported for aticaprant (versus placebo) were headache (11.8% versus 7.1%), diarrhea (8.2% versus 2.4%), nasopharyngitis (5.9% versus 2.4%), and pruritus (5.9% versus 0%). One participant (1.2%) in each arm discontinued treatment due to an adverse event. In this study of participants with MDD and inadequate response to SSRI/SNRI, adjunctive treatment with aticaprant significantly reduced depressive symptoms versus placebo, without resulting in significant safety signals, supporting further investigation in larger trials.

Sociodemographic and clinical risk factors for suicidal ideation and suicide attempt in functional/dissociative seizures and epilepsy: a large cohort study.

People with functional/dissociative seizures (FDS) are at elevated suicidality risk. To identify risk factors for suicidality in FDS or epilepsy. Retrospective cohort study from the UK's largest tertiary mental healthcare provider, with linked national admission data from the Hospital Episode Statistics. Participants were 2383 people with a primary or secondary diagnosis of FDS or epilepsy attending between 01 January 2007 and 18 June 2021. Outcomes were a first report of suicidal ideation and a first hospital admission for suicide attempt (International Classification of Diseases, version 10: X60-X84). Demographic and clinical risk factors were assessed using multivariable bias-reduced binomial-response generalised linear models. In both groups, ethnic minorities had significantly reduced odds of hospitalisation following suicide attempt (OR: 0.45-0.49). Disorder-specific risk factors were gender, age and comorbidity profile. In FDS, both genders had similar suicidality risk; younger age was a risk factor for both outcomes (OR: 0.16-1.91). A diagnosis of depression or personality disorders was associated with higher odds of suicidal ideation (OR: 1.91-3.01). In epilepsy, females had higher odds of suicide attempt-related hospitalisation (OR: 1.64). Age had a quadratic association with both outcomes (OR: 0.88-1.06). A substance abuse disorder was associated with higher suicidal ideation (OR: 2.67). Developmental disorders lowered the risk (OR: 0.16-0.24). This is the first study systematically reporting risk factors for suicidality in people with FDS. Results for the large epilepsy cohort complement previous studies and will be useful in future meta-analyses. Risk factors identified will help identify higher-risk groups in clinical settings.

Potential of niacin skin flush response in adolescent depression identification and severity assessment: a case-control study

BackgroundThe diagnosis of adolescent Depressive Disorder (DD) lacks specific biomarkers, posing significant challenges. This study investigates the potential of Niacin Skin Flush Response (NSFR) as a biomarker for identifying and assessing the severity of adolescent Depressive Disorder, as well as distinguishing it from Behavioral and Emotional Disorders typically emerging in childhood and adolescence(BED).MethodsIn a case-control study involving 196 adolescents, including 128 Depressive Disorder, 32 Behavioral and Emotional Disorders, and 36 healthy controls (HCs), NSFR was assessed. Depressive symptoms were measured using the Patient Health Questionnaire-9 (PHQ-9) and anxious symptoms with the Generalized Anxiety Disorder 7-item scale (GAD-7). Pearson correlation analysis determined the relationships between NSFR and the severity of depression in DD patients. Receiver Operating Characteristic (ROC) was used to identify DD from BED integrating NSFR data with clinical symptom measures.ResultsThe adolescent Depressive Disorder group exhibited a higher rate of severe blunted NSFR (21.4%) compared to BED (12.5%) and HC ( 8.3%). Adolescent Depressive Disorder with psychotic symptoms showed a significant increase in blunted NSFR (p = 0.016). NSFR had negative correlations with depressive (r = -0.240, p = 0.006) and anxious (r = -0.2, p = 0.023) symptoms in adolescent Depressive Disorder. Integrating NSFR with three clinical scales improved the differentiation between adolescent Depressive Disorder and BED (AUC increased from 0.694 to 0.712).ConclusionThe NSFR demonstrates potential as an objective biomarker for adolescent Depressive Disorder, aiding in screening, assessing severity, and enhancing insights into its pathophysiology and diagnostic precision.

Prevalence of co-occurring conditions among youths receiving treatment with primary anxiety, ADHD, or depressive disorder diagnoses

IntroductionAnxiety disorders, depressive disorders, and attention-deficit/hyperactivity disorder (ADHD) are some of the most common conditions that youths (&amp;lt;18 years old) receive mental health treatment for. These conditions are associated with high-risk substance use or substance use disorders (SUDs). This study sought to identify the proportion of youths (&amp;lt;18 years old) with anxiety disorders, depressive disorders, or ADHD as a primary diagnosis in community mental health centers (CMHCs) having co-occurring high-risk substance use or a SUD.MethodsAnalysis included binary logistic regression models using the Mental Health Client-Level Data 2017–2019 datasets which contains annual cross-sectional administrative data from mental health treatment facilities. The final sample included n = 458,888 youths with an anxiety disorder as a primary diagnosis, n = 570,388 youths with a depressive disorder as a primary diagnosis, and n = 945,277 youths with ADHD as a primary diagnosis.ResultsIn the subsample with anxiety as a primary diagnosis, approximately 5% of youth had high-risk substance use or a SUD. Approximately 10% of youth with depression as a primary diagnosis had high-risk substance use or a SUD. Among youth with ADHD as a primary diagnosis, 5% had high-risk substance use or a SUD. Odds of having a co-occurring high-risk substance use or SUD differed based on the youth’s age, race and ethnicity, gender, and other mental health diagnoses.ConclusionsEffective care for this high-need youth population at CMHCs will require mental health clinicians to possess knowledge and skills related to substance use treatment.

Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions

Purpose This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions. Design/methodology/approach A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced. Findings However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor. Research limitations/implications The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments. Practical implications Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity. Social implications The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions. Originality/value Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.

Predictors of non-suicidal self-injury in adolescents with depressive disorder: the role of alexithymia, childhood trauma, and body investment.

This study analyzes the relationship of alexithymia, childhood trauma, and body investment to non-suicidal self-injury (NSSI) behaviors in adolescents with depressive disorder and whether they have predictive and diagnostic value for non-suicidal self-injury (NSSI) behaviors in adolescents with depressive disorder. A total of 225 patients with a diagnosis of adolescent depressive disorder were included in the study and were divided into two groups according to the DSM-5 criteria: 98 cases without NSSI and 127 cases with NSSI. Compare the demographic data, 24-item Hamilton Depression Scale (HAMD-24), 20-item Toronto Alexithymia Scale (TAS-20), Childhood Trauma Questionnaire-Short Form (CTQ-SF), and Body Investment Scale (BIS) scores between two groups. Binary logistic regression was used to analyze the independent risk factors contributing to NSSI behaviors in adolescents with depression, and establish four predictive models. Based on the models' predictive probability, the ROC curves were plotted to calculate the value of the predictive diagnostic effect. The group without NSSI had lower scores than the group with NSSI on HAMD-24 total score, TAS-20 total score, difficulty identifying feelings, difficulty describing feelings, and externally focused thinking, as well as lower scores on CTQ-SF total score, physical neglect, emotional neglect, physical abuse, and emotional abuse. In contrast, the BIS total score, body image feelings and attitudes, body care, and body protection factor scores were higher for the group without NSSI. The BIS body care factor score and the CTQ-SF emotional abuse factor score were significantly linked with adolescents diagnosed with depressive disorder who exhibited NSSI behaviors. These results provide a good diagnostic model for adolescents with depressive disorder. Low levels of body care and childhood emotional abuse may independently contribute to the implementation of NSSI in adolescents with depressive disorder. Body investment and childhood trauma are valuable in diagnosing and predicting NSSI behaviors and should be considered as potentially important factors in clinical treatment.

Disentangling the Diagnoses of Psychotic Depression, Catatonia, Neurocognitive Disorders, and Delirium: A Multidisciplinary Approach

An ambiguous clinical presentation can create diagnostic uncertainty, leading to delays in effective interventions and increasing the likelihood of adverse clinical outcomes. Unfortunately, many psychiatric conditions share overlapping clinical symptoms. Psychotic depression, catatonia, neurocognitive disorders, and delirium are all examples of distinct clinical conditions with overlapping symptomatology, and all require decidedly different therapeutic interventions. While much literature is devoted to the diagnostic criteria and management of these clinical conditions, little is dedicated to the challenge of disentangling these diagnoses when faced with a complex clinical presentation.We present here a complicated psychiatric inpatient hospitalization of a 77-year-old male who exemplified this diagnostic challenge. He primarily presented with severely depressed mood and decreased functionality, remaining in bed for much of the day with little attention to basic hygiene and oral intake. While records from a recent outside hospitalization carried a historical diagnosis of catatonia, his moderate level of engagement with providers and somatic delusion of disseminated prostate cancer suggested an alternate diagnosis of psychotic depression. However, before a diagnostic conclusion could be reached, other clinical characteristics surfaced. Following a singular administration of olanzapine shortly after his initial assessment, he developed Lilliputian hallucinations, prominent parkinsonism, and significantly altered mentation.Neuroleptic sensitivity raised clinical suspicion for an underlying neurocognitive disorder such as dementia with Lewy bodies, and a consult was placed to the neuropsychology service. While dementia could not be fully ruled out, they did recommend additional workup to rule out herpes simplex encephalitis, autoimmune limbic encephalitis, and paraneoplastic syndromes. After his MRI imaging revealed moderate microvascular ischemic changes, a true multidisciplinary approach to care was taken, as neurology was consulted to also help narrow the differential. An EEG was unremarkable and a serum encephalitis panel was also unremarkable. Olanzapine was discontinued with significant improvement in parkinsonism and cognition; however, severe neurovegetative symptoms and somatic and nihilistic delusions persisted. While antipsychotics remained clinically indicated for a suspected depression with psychotic features, caution was taken due to established neuroleptic sensitivity, subtle catatonic features, and the related risks of neuroleptic malignant syndrome. Ultimately, low-dose quetiapine was initiated with some benefit; however, he continued to remain acutely depressed and withdrawn with prominent negativism. Trials with benzodiazepines for these catatonic features were also approached with caution to avoid potential risks of delirium.Over time, a multidisciplinary approach to psychopharmacologic interventions and medication trials led the team to a principal diagnosis of major depressive disorder with psychotic features and subtle catatonia. The abrupt changes observed early in the hospital course were determined to be related to olanzapine-induced anticholinergic delirium. With greater diagnostic certainty, the team was able to counsel the patient and family about the importance of pursuing electroconvulsive therapy (ECT). The patient's cardiovascular risk factors and known microvascular changes were considered in the determination of the appropriateness for ECT. After starting ECT, he was immediately noted to have objective improvement, and following his fifth treatment, he was deemed appropriate for discharge after exhibiting significant improvement in depression and functionality and reporting near-resolution of somatic delusions. He was able to complete ECT on an outpatient basis and follow-up visits note durable improvements in mood, functionality, and quality of life, continuing to enjoy an active lifestyle of socializing with family and playing tennis. We use this case to further explore the clinical challenges that stem from the symptomatic similarity between psychotic depression, catatonia, neurocognitive disorders, and delirium and discuss optimal therapeutic approaches when the presentation appears complex and equivocal.

Racial And Ethnic Inequities In Postpartum Depressive Symptoms, Diagnosis, And Care In 7 US Jurisdictions.

Understanding whether racial and ethnic inequities exist along the postpartum mental health care continuum is vital because inequitable identification of depression can lead to inequitable referral to and receipt of care. We aimed to expand on existing cross-sectional and single-state data documenting potential racial and ethnic disparities in postpartum depression care. Using early (from two to six months) and late (from twelve to fourteen months) postpartum survey data from seven US jurisdictions, we documented patterns of early postpartum depressive symptoms, perinatal mood and anxiety disorder (PMAD) diagnosis, and receipt of postpartum mental health care overall and by racial and ethnic identity. Of 4,542 people who delivered live births in 2020, 11.8percent reported early postpartum depressive symptoms. Among the sample with these symptoms, only 25.4percent reported receiving a PMAD diagnosis, and 52.8percent reported receiving some form of postpartum mental health care. There were no significant differences in diagnosis by race and ethnicity. Respondents identifying as Asian; Native Hawaiian or Pacific Islander; Southwest Asian, Middle Eastern, or North African; Hispanic; and non-Hispanic Black were significantly less likely than non-Hispanic White respondents to receive mental health care, demonstrating stark inequities in the management of postpartum depressive symptoms. Policies mandating and reimbursing universal postpartum depression screening, facilitating connection to care, reducing insurance coverage gaps, and enhancing clinician training in culturally responsive care may promote equitable postpartum mental health care.

Clinical subtypes of postpsychotic depression in the first episode of schizophrenia: results of a cross-sectional observational study

BACKGROUND: Clinical diagnosis of depression in schizophrenia is difficult because of the dimensional overlap of a number of affective symptoms with negative manifestations of schizophrenia spectrum disorders. In the very first attack of schizophrenia, when the negative symptom complex is not yet clear enough, the difficulties of differential diagnosis become even more relevant, especially when depression occurs in the post-attack stage during the formation of remission and the manifestation of postpsychotic depression.&#x0D; AIM: To establish a diagnostic approach based on a combination of clinical-psychopathological and psychometric methods for the assessment of postpsychotic depression in patients with a first episode of schizophrenia.&#x0D; MATERIAL AND METHODS: We are examined 1112 patients who had suffered a first psychotic episode. Аfter introducing inclusion/exclusion criteria, two groups were formed in the final design: 243 patients with postpsychotic depression (PPD) and 119 patients without depression in partial remission. Clinical-psychopathological, psychometric (PANSS, CDSS, SCL-90-R scales), and statistical (descriptive, non-parametric statistics — significance level p 0.05) methods were used.&#x0D; RESULTS: Based on the concept of positive-negative affectivity, three types of PPD have been identified: with signs of positive affectivity — anxious, sensitive-psychasthenic, depersonalisation-hypochondriacal and melancholic-anergic subtypes; with signs of negative affectivity anhedonic, dysthymic-apathetic and dysphoric subtypes; formed due to psychopathological manifestations of the non-affective register — obsessive-ruminative, agoraphobic, paranoid-symbolic subtypes. Patients who were diagnosed with PPD with positive affectivity after an attack were predominat (p 0.05): 46.91% of the total number of depressed patients. PPD formed due to non-affective spectrum disorders is accompanied by the strongest subjective severity of suffering in patients, compared to patients diagnosed with PPD with positive affectivity. Subjectively, the easiest for patients is PPD with negative affectivity, which, however, does not mean that it can be considered as favourable from the standpoint of therapy and rehabilitation.&#x0D; CONCLUSION: PPD is detected in a significant number of patients with a first psychotic episode, even after excluding the group with pharmacogenic depression, both due to subjective complaints, nosogenic depressive experiences, and as a result of a psychometric assessment of the patients’ condition. The concept of “dissociated post-attack depression” is substantiated.