Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number.

Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician's office. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen's κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of-distribution samples.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study.

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.

Developing and Implementing a Web-Based Branching Logic Survey to Support Psychiatric Crisis Evaluations of Individuals With Developmental Disabilities: Qualitative Study and Evaluation of Validity.

BACKGROUND: Individuals with developmental disabilities (DD) experience increased rates of emotional and behavioral crises that necessitate assessment and intervention. Psychiatric disorders can contribute to crises; however, screening measures developed for the general population are inadequate for those with DD. Medical conditions can exacerbate crises and merit evaluation. Screening tools using checklist formats, even when designed for DD, are too limited in depth and scope for crisis assessments. The Sources of Distress survey implements a web-based branching logic format to screen for common psychiatric and medical conditions experienced by individuals with DD by querying caregiver knowledge and observations. OBJECTIVE: This paper aims to (1) describe the initial survey development, (2) report on focus group and expert review processes and findings, and (3) present results from the survey's clinical implementation and evaluation of validity. METHODS: Sources of Distress was reviewed by focus groups and clinical experts; this feedback informed survey revisions. The survey was subsequently implemented in clinical settings to augment providers' psychiatric and medical history taking. Informal and formal consults followed the completion of Sources of Distress for a subset of individuals. A records review was performed to identify working diagnoses established during these consults. RESULTS: Focus group members (n=17) expressed positive feedback overall about the survey's content and provided specific recommendations to add categories and items. The survey was completed for 231 individuals with DD in the clinical setting (n=161, 69.7% men and boys; mean age 17.7, SD 10.3; range 2-65 years). Consults were performed for 149 individuals (n=102, 68.5% men and boys; mean age 18.9, SD 10.9 years), generating working diagnoses to compare survey screening results. Sources of Distress accuracy rates were 91% (95% CI 85%-95%) for posttraumatic stress disorder, 87% (95% CI 81%-92%) for anxiety, 87% (95% CI 81%-92%) for episodic expansive mood and bipolar disorder, 82% (95% CI 75%-87%) for psychotic disorder, 79% (95% CI 71%-85%) for unipolar depression, and 76% (95% CI 69%-82%) for attention-deficit/hyperactivity disorder. While no specific survey items or screening algorithm existed for unspecified mood disorder and disruptive mood dysregulation disorder, these conditions were caregiver-reported and working diagnoses for 11.7% (27/231) and 16.8% (25/149) of individuals, respectively. CONCLUSIONS: Caregivers described Sources of Distress as an acceptable tool for sharing their knowledge and insights about individuals with DD who present in crisis. As a screening tool, this survey demonstrates good accuracy. However, better differentiation among mood disorders is needed, including the addition of items and screening algorithm for unspecified mood disorder and disruptive mood dysregulation disorder. Additional validation efforts are necessary to include a more geographically diverse population and reevaluate mood disorder differentiation. Future study is merited to investigate the survey's impact on the psychiatric and medical management of distress in individuals with DD.

Validation of the structured interview section of the penn computerized adaptive test for neurocognitive and clinical psychopathology assessment (CAT GOASSESS).

Large-scale studies and burdened clinical settings require precise, efficient measures that assess multiple domains of psychopathology. Computerized adaptive tests (CATs) can reduce administration time without compromising data quality. We examined feasibility and validity of an adaptive psychopathology measure, GOASSESS, in a clinical community-based sample (N = 315; ages 18-35) comprising three groups: healthy controls, psychosis, mood/anxiety disorders. Assessment duration was compared between the Full and CAT GOASSESS. External validity was tested by comparing how the CAT and Full versions related to demographic variables, study group, and socioeconomic status. The relationships between scale scores and criteria were statistically compared within a mixed-model framework to account for dependency between relationships. Convergent validity was assessed by comparing scores of the CAT and the Full GOASSESS using Pearson correlations. The CAT GOASSESS reduced interview duration by more than 90 % across study groups and preserved relationships to external criteria and demographic variables as the Full GOASSESS. All CAT GOASSESS scales could replace those of the Full instrument. Overall, the CAT GOASSESS showed acceptable psychometric properties and demonstrated feasibility by markedly reducing assessment time compared to the Full GOASSESS. The adaptive version could be used in large-scale studies or clinical settings for intake screening.

Neural Responses to Intranasal Oxytocin in Youths With Severe Irritability.

OBJECTIVE: The authors investigated the neural impact of intranasal oxytocin on emotion processing areas in youths with severe irritability in the context of disruptive mood and behavior disorders. METHODS: Fifty-two participants with severe irritability, as measured by a score ≥4 on the Affective Reactivity Index (ARI), with diagnoses of disruptive behavior disorders (DBDs) and/or disruptive mood dysregulation disorder (DMDD) were randomly assigned to treatment with intranasal oxytocin or placebo daily for 3 weeks. Assessments were conducted at baseline and at the end of the trial; the primary outcomes were measures of irritability on the ARI and ratings on the Clinical Global Impressions severity scale (CGI-S) focusing on DBD and DMDD symptoms, and secondary outcomes included the CGI improvement scale (CGI-I) and ratings of proactive and reactive aggressive behavior on the Reactive-Proactive Aggression Questionnaire. Forty-three participants (22 in the oxytocin group and 21 in the placebo group) completed pre- and posttreatment functional MRI (fMRI) scans with the affective Stroop task. RESULTS: Youths who received oxytocin showed significant improvement in CGI-S and CGI-I ratings compared with those who received placebo. In the fMRI data, blood-oxygen-level-dependent (BOLD) responses to emotional stimuli in the dorsomedial prefrontal cortex and posterior cingulate cortex were significantly reduced after oxytocin compared with placebo. These BOLD response changes were correlated with improvement in clinical severity. CONCLUSIONS: This study provides initial and preliminary evidence that intranasal oxytocin may induce neural-level changes in emotion processing in youths with irritability in the context of DBDs and DMDD. This may lead to symptom and severity changes in irritability.

Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety, Depression, and Mood Disorders at a Large Integrated Health System.

INTRODUCTION: Standardized screening, objective evaluation, and management of behavioral health conditions are major challenges in primary care. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and Mood Disorder Questionnaire (MDQ) provide standardized screening and symptom management tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), and Mood Disorders (MD), respectively. This study explores family physicians' knowledge, attitudes, and practices regarding the utilization of GAD-7, PHQ-9, and MDQ in outpatient primary care offices. METHODS: The study method was a cross-sectional electronic and paper survey utilizing a self-administered questionnaire that assessed primary care physicians' demographics, knowledge, attitudes, and practices in rural and urban outpatient clinical settings regarding GAD-7, PHQ-9, and MDQ. Statistical software SAS 9.4 was used for descriptive and Chi-Square statistics. RESULTS: Out of 320 total participants,145 responded (45.3%). Responding family physicians demonstrated a high level of familiarity with the GAD-7 (97.9%), PHQ-9 (97.9%), and MDQ (81.3%) assessment tools. However, the reported utilization rates were relatively lower than knowledge, with 62.7%, 73.1%, and 31.9% extremely likely or likely to utilize the GAD-7, PHQ-9, and MDQ as screening and monitoring tools, respectively. Less than a quarter of the total respondents use the objective score for the future management of GAD, with significantly more residents utilizing the score for GAD-7 compared to attendings (P < .05). There was no statistical significance difference between residents and attendings for the objective evaluation of Major Depressive Disorder (P = .26) and Mood Disorders (P = .05). CONCLUSIONS: Despite being knowledgeable of the utility of GAD-7, PHQ-9, and MDQ, the primary care physicians in a large integrated health system in Central Pennsylvania and Northern Maryland report inconsistent utilization in their practice. Further studies are needed to determine the underlying factors contributing to the suboptimal usage of these screening tools and ways to increase it.

The Child Behavior Check List Usefulness in Screening for Severe Psychopathology in Youth: A Narrative Literature Review.

OBJECTIVE: This article will review the use of the CBCL to diagnose youth with psychopathological disorders focusing on: ADHD, Mood Disorders, Autism Spectrum disorders, and Disruptive Disorders. METHOD: Using a narrative review approach, we investigate the usefulness of the CBCL as a screening tool to detect childhood onset psychopathology across different diagnostic syndromes. RESULTS: The available literature supports the use of the CBCL for ADHD screening and as a measure of ADHD severity. While some studies support a specific profile linked with childhood bipolar disorder, replication studies for this profile found mixed results. The CBCL was also found to be useful in screening for patients presenting with Autism Spectrum Disorders, Conduct Disorder, and Childhood Bipolar Disorder all of which presents with more severely impaired scores. CONCLUSION: The CBCL holds promise as a screening tool for childhood psychopathology.

Study protocol for a randomized controlled trial testing the efficacy of Emotion Detectives In-Out: a blended version of the unified protocol for transdiagnostic treatment of emotional disorders in Portuguese children.

BACKGROUND: Childhood emotional disorders (EDs; i.e., anxiety and depressive disorders) are currently a public health concern. Their high prevalence, long-term effects, and profound influence on the lives of children and families highlight the need to identify and treat these disorders as early and effectively as possible. This clinical trial will examine the efficacy of a blended version (i.e., combining face-to-face and online sessions into one treatment protocol) of the Unified Protocol for Children (the "Emotion Detectives In-Out" program). This program is a manualized cognitive-behavioral therapy for the transdiagnostic treatment of EDs in children aged 7 to 12 years that aims to reduce the intensity and frequency of strong and aversive emotional experiences by helping children learn how to confront those emotions and respond to them in more adaptive ways. METHODS: This study is designed as a multicenter equivalence randomized controlled parallel-group two-arm trial comparing the Emotion Detectives In-Out program with an evidenced-based group intervention for children with anxiety disorders (the Coping Cat program). Participants will be children aged between 7 and 12 years with an anxiety disorder or with clinically significant anxiety symptoms as well as one of their parents or a legal representative. A minimum sample size of 138 children (69 per group) is needed to test whether the efficacy of the proposed intervention is equivalent to that of the well-established Coping Cat intervention. DISCUSSION: We expect Emotion Detectives In-Out to be a feasible and efficacious alternative intervention for treating children's EDs by allowing for a greater increase in children's access to care. A blended format is expected to overcome common barriers to treatment (e.g., parents´ lack of time to attend regular sessions) and make the intervention more accessible to families. TRIAL REGISTRATION: The clinical trial is registered at ClinicalTrials.gov (Identifier: NCT05747131, date assigned February 28, 2023).

Electroencephalography findings in menstrually-related mood disorders: A critical review.

The female reproductive years are characterized by fluctuations in ovarian hormones across the menstrual cycle, which have the potential to modulate neurophysiological and behavioral dynamics. Menstrually-related mood disorders (MRMDs) comprise cognitive-affective or somatic symptoms that are thought to be triggered by the rapid fluctuations in ovarian hormones in the luteal phase of the menstrual cycle. MRMDs include premenstrual syndrome (PMS), premenstrual dysphoric disorder (PMDD), and premenstrual exacerbation (PME) of other psychiatric disorders. Electroencephalography (EEG) non-invasively records in vivo synchronous activity from populations of neurons with high temporal resolution. The present overview sought to systematically review the current state of task-related and resting-state EEG investigations on MRMDs. Preliminary evidence indicates lower alpha asymmetry at rest being associated with MRMDs, while one study points to the effect being luteal-phase specific. Moreover, higher luteal spontaneous frontal brain activity (slow/fast wave ratio as measured by the delta/beta power ratio) has been observed in persons with MRMDs, while sleep architecture results point to potential circadian rhythm disturbances. In this review, we discuss the quality of study designs as well as future perspectives and challenges of supplementing the diagnostic and scientific toolbox for MRMDs with EEG.

Identification of Predictors of Mood Disorder Misdiagnosis and Subsequent Help-Seeking Behavior in Individuals With Depressive Symptoms: Gradient-Boosted Tree Machine Learning Approach.

BACKGROUND: Misdiagnosis and delayed help-seeking cause significant burden for individuals with mood disorders such as major depressive disorder and bipolar disorder. Misdiagnosis can lead to inappropriate treatment, while delayed help-seeking can result in more severe symptoms, functional impairment, and poor treatment response. Such challenges are common in individuals with major depressive disorder and bipolar disorder due to the overlap of symptoms with other mental and physical health conditions, as well as, stigma and insufficient understanding of these disorders. OBJECTIVE: In this study, we aimed to identify factors that may contribute to mood disorder misdiagnosis and delayed help-seeking. METHODS: Participants with current depressive symptoms were recruited online and data were collected using an extensive digital mental health questionnaire, with the World Health Organization World Mental Health Composite International Diagnostic Interview delivered via telephone. A series of predictive gradient-boosted tree algorithms were trained and validated to identify the most important predictors of misdiagnosis and subsequent help-seeking in misdiagnosed individuals. RESULTS: The analysis included data from 924 symptomatic individuals for predicting misdiagnosis and from a subset of 379 misdiagnosed participants who provided follow-up information when predicting help-seeking. Models achieved good predictive power, with area under the receiver operating characteristic curve of 0.75 and 0.71 for misdiagnosis and help-seeking, respectively. The most predictive features with respect to misdiagnosis were high severity of depressed mood, instability of self-image, the involvement of a psychiatrist in diagnosing depression, higher age at depression diagnosis, and reckless spending. Regarding help-seeking behavior, the strongest predictors included shorter time elapsed since last speaking to a general practitioner about mental health, sleep problems disrupting daily tasks, taking antidepressant medication, and being diagnosed with depression at younger ages. CONCLUSIONS: This study provides a novel, machine learning-based approach to understand the interplay of factors that may contribute to the misdiagnosis and subsequent help-seeking in patients experiencing low mood. The present findings can inform the development of targeted interventions to improve early detection and appropriate treatment of individuals with mood disorders.

An expert opinion on the pharmacological interventions for Disruptive Mood Dysregulation Disorder (DMDD).

INTRODUCTION: Disruptive Mood Dysregulation Disorder (DMDD) was officially introduced as a new diagnostic entity in the Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5), under the category of depressive disorders. AREAS COVERED: A comprehensive overview and a critical commentary on the currently investigated psychopharmacological approaches for the treatment of DMDD have been here provided. EXPERT OPINION: Behavioral and psychosocial interventions should be considered as first-line treatment strategies. When ineffective or partially effective, psychopharmacological strategy is recommended. Overall, pharmacological strategy should be preferred in those individuals with psychiatric comorbidities (e.g. ADHD). Indeed, so far published studies on pharmacological strategies in DMDD are scant and heterogeneous (i.e. age, assessment tools, symptomatology profile, comorbidity, and so forth). Therefore, DMDD psychopharmacological guidelines are needed, particularly to guide clinicians toward the patient's typical symptom profile who could benefit from psychopharmacological strategy.

Mood Disorder Severity and Subtype Classification Using Multimodal Deep Neural Network Models.

The subtype diagnosis and severity classification of mood disorder have been made through the judgment of verified assistance tools and psychiatrists. Recently, however, many studies have been conducted using biomarker data collected from subjects to assist in diagnosis, and most studies use heart rate variability (HRV) data collected to understand the balance of the autonomic nervous system on statistical analysis methods to perform classification through statistical analysis. In this research, three mood disorder severity or subtype classification algorithms are presented through multimodal analysis of data on the collected heart-related data variables and hidden features from the variables of time and frequency domain of HRV. Comparing the classification performance of the statistical analysis widely used in existing major depressive disorder (MDD), anxiety disorder (AD), and bipolar disorder (BD) classification studies and the multimodality deep neural network analysis newly proposed in this study, it was confirmed that the severity or subtype classification accuracy performance of each disease improved by 0.118, 0.231, and 0.125 on average. Through the study, it was confirmed that deep learning analysis of biomarker data such as HRV can be applied as a primary identification and diagnosis aid for mental diseases, and that it can help to objectively diagnose psychiatrists in that it can confirm not only the diagnosed disease but also the current mood status.

Screening for affective dysregulation in school-aged children: relationship with comprehensive measures of affective dysregulation and related mental disorders.

Affective dysregulation (AD) is characterized by irritability, severe temper outbursts, anger, and unpredictable mood swings, and is typically classified as a transdiagnostic entity. A reliable and valid measure is needed to adequately identify children at risk of AD. This study sought to validate a parent-rated screening questionnaire, which is part of the comprehensive Diagnostic Tool for Affective Dysregulation in Children (DADYS-Screen), by analyzing relationships with comprehensive measures of AD and related mental disorders in a community sample of children with and without AD. The sample comprised 1114 children aged 8-12 years and their parents. We used clinical, parent, and child ratings for our analyses. Across all raters, the DADYS-Screen showed large correlations with comprehensive measures of AD. As expected, correlations were stronger for measures of externalizing symptoms than for measures of internalizing symptoms. Moreover, we found negative associations with emotion regulation strategies and health-related quality of life. In receiver operating characteristic (ROC) analyses, the DADYS-Screen adequately identified children with AD and provided an optimal cut-off. We conclude that the DADYS-Screen appears to be a reliable and valid measure to identify school-aged children at risk of AD.

Negative Mood Dysregulation Loads Strongly Onto Common Factors With Many Forms of Psychopathology: Considerations for Assessing Nonspecific Symptoms.

There have been proposals to expand definitions for categorical disorders and dimensionally conceptualized syndromes (e.g., psychopathy) to include negative mood lability and dysregulation (NMD). Factor analytic results are often presented in support of these proposals, and we provide factor analytic demonstrations across clinically oriented samples showing that NMD indicators load strongly onto factors with a range of psychopathology. This is unsurprising from a transdiagnostic perspective but shows that factor analysis could potentially be used to justify expanding definitions for specific constructs even though NMD indicators show strong, nonspecific loadings on psychopathology factors ranging widely in nature. Expanding construct definitions and assessment approaches to emphasize NMD also may negatively impact discriminant validity. We agree that targeting NMD is essential for comprehensive assessment, but our demonstrative analyses highlight a need for using factor analysis and other statistical methods in a careful, theoretically driven manner when evaluating psychopathology structure and developing measures.

Patterns of emotion-network dynamics are orthogonal to mood disorder status: An experience sampling investigation.

Individuals differ markedly in how they experience the ebb and flow of emotions. In this study, we used daily experience sampling to examine whether these differences reflect the nature and presence of mood disorders or whether they can better be characterized as distinct dynamic emotion profiles that cut-across diagnostic boundaries. We followed 105 individuals in 2019-2020 with diagnoses of major depression, remitted major depression, bipolar disorder, or no history of disorder, over 14 days (n = 6,543 experience-sampling assessments). We applied group iterative multiple model estimation, using both diagnosis-based and data-driven methods to investigate similarities in unfolding within-person emotion-network time-courses. Results did not support diagnosis-based subgroupings but rather revealed two significant data-driven subgroups based on dynamic emotion patterns. These data-driven subgroups did not significantly differ in terms of clinical features or demographics, but did differ on key emotion metrics-instability, granularity, and inertia. These data-driven subgroupings, agnostic to diagnostic status, provide insights into the nature of idiographic emotion-network dynamics that cut-across clinical diagnostic divisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Neurocognitive risk phenotyping to predict mood symptoms in adolescence.

Predicting mood disorders in adolescence is a challenge that motivates research to identify neurocognitive predictors of symptom expression and clinical profiles. This study used machine learning to test whether neurocognitive variables predicted future manic or anhedonic symptoms in two adolescent samples risk-enriched for lifetime mood disorders (Sample 1, n = 73, ages = 13-25, M [SD] = 19.22 [2.49] years, 68% lifetime mood disorder) or familial mood disorders (Sample 2, n = 154, ages = 13-21, M [SD] = 16.46 [1.95] years, 62% first-degree family history of mood disorder). Participants completed cognitive testing and functional magnetic resonance imaging at baseline, for behavioral and neural measures of reward processing and executive functioning. Next, participants completed a daily diary procedure for 8-16 weeks. Penalized mixed-effects models identified neurocognitive predictors of future mood symptoms and stress-reactive changes in mood symptoms. Results included the following. In both samples, adolescents showing ventral corticostriatal reward hyposensitivity and lower reward performance reported more severe stress-reactive anhedonia. Poorer executive functioning behavior was associated with heightened anhedonia overall in Sample 1, but lower stress-reactive anhedonia in both samples. In Sample 1, adolescents showing ventral corticostriatal reward hypersensitivity and poorer executive functioning reported more severe stress-reactive manic symptoms. Clustering analyses identified, and replicated, five neurocognitive subgroups. Adolescents characterized by neural or behavioral reward hyposensitivities together with average-to-poor executive functioning reported unipolar symptom profiles. Adolescents showing neural reward hypersensitivity together with poor behavioral executive functioning reported a bipolar symptom profile (Sample 1 only). Together, neurocognitive phenotypes may hold value for predicting symptom expression and profiles of mood pathology. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Metabolomic Biomarker Signatures for Bipolar and Unipolar Depression.

Importance: Bipolar disorder (BD) is frequently misdiagnosed as major depressive disorder (MDD) because of overlapping symptoms and the lack of objective diagnostic tools. Objective: To identify a reproducible metabolomic biomarker signature in patient dried blood spots (DBSs) that differentiates BD from MDD during depressive episodes and assess its added value when combined with self-reported patient information. Design, Setting, and Participants: This diagnostic analysis used samples and data from the Delta study, conducted in the UK between April 27, 2018, and February 6, 2020. The primary objective was to identify BD in patients with a recent (within the past 5 years) diagnosis of MDD and current depressive symptoms (Patient Health Questionnaire-9 score of 5 or more). Participants were recruited online through voluntary response sampling. The analysis was carried out between February 2022 and July 2023. Main Outcomes and Measures: Patient data were collected using a purpose-built online questionnaire (n = 635 questions). DBS metabolites (n = 630) were analyzed using a targeted mass spectrometry-based platform. Mood disorder diagnoses were established using the Composite International Diagnostic Interview. Results: Of 241 patients in the discovery cohort, 170 (70.5%) were female; 67 (27.8%) were subsequently diagnosed with BD and 174 (72.2%) were confirmed as having MDD; and the mean (SD) age was 28.1 (7.1) years. Of 30 participants in the validation cohort, 16 (53%) were female; 9 (30%) were diagnosed with BD and 21 (70%) with MDD; and the mean (SD) age was 25.4 (6.3) years. DBS metabolite levels were assessed in 241 patients with depressive symptoms with a recent diagnosis of MDD, of whom 67 were subsequently diagnosed with BD by the Composite International Diagnostic Interview and 174 were confirmed as having MDD. The identified 17-biomarker panel provided a mean (SD) cross-validated area under the receiver operating characteristic curve (AUROC) of 0.71 (SD, 0.12; P < .001), with ceramide d18:0/24:1 emerging as the strongest biomarker. Combining biomarker data with patient-reported information significantly enhanced diagnostic performance of models based on extensive demographic data, PHQ-9 scores, and the outcomes from the Mood Disorder Questionnaire. The identified biomarkers were correlated primarily with lifetime manic symptoms and were validated in a separate group of patients who received a new clinical diagnosis of MDD (n = 21) or BD (n = 9) during the study's 1-year follow-up period, with a mean (SD) AUROC of 0.73 (0.06; P < .001). Conclusions and Relevance: This study provides a proof of concept for developing an accessible biomarker test to facilitate the differential diagnosis of BD and MDD and highlights the potential involvement of ceramides in the pathophysiological mechanisms of mood disorders.

Executive function in children with disruptive mood dysregulation disorder compared to attention-deficit/hyperactivity disorder and oppositional defiant disorder, and in children with different irritability levels.

Addressing current challenges in research on disruptive mood dysregulation disorder (DMDD), this study aims to compare executive function in children with DMDD, children with attention-deficit/hyperactivity disorder (ADHD), and children with oppositional defiant disorder (ODD). We also explore associations between irritability, a key DMDD characteristic, and executive function in a clinical sample regardless of diagnosis. Our sample include children (6-12 years) referred to child psychiatric clinics. Measures of daily-life (parent-reported questionnaire) and performance-based (neuropsychological tasks) executive function were applied. Identifying diagnoses, clinicians administered a standardized semi-structured diagnostic interview with parents. Irritability was assessed by parent-report. First, we compared executive function in DMDD (without ADHD/ODD), ADHD (without DMDD/ODD), ODD (without DMDD/ADHD) and DMDD + ADHD (without ODD). Second, we analyzed associations between executive function and irritability using the total sample. In daily life, children with DMDD showed clinically elevated and significantly worse emotion control scores compared to children with ADHD, and clinically elevated scores on cognitive flexibility compared to norm scores. Children with DMDD had significantly less working memory problems than those with ADHD. No differences were found between DMDD and ODD. Increased irritability was positively associated with emotional dyscontrol and cognitive inflexibility. For performance-based executive function, no diagnostic differences or associations with irritability were observed. We discuss how, in daily life, children with high irritability-levels get overwhelmed by feelings without accompanying regulatory capacities.

Diagnosis of bipolar and major depressive disorders: The appropriateness of MINI compared to the clinical interview in a sample of patients with mood disorders in tertiary mental health care.

OBJECTIVE: The Mini International Neuropsychiatric Interview (MINI) is one of the most used instruments for the assessment of Mental Disorders, playing an essential role in psychiatric research and in clinical and hospital practice. Despite this, the accuracy of the MINI, when used by a psychiatrist, is poorly studied, particularly in relation to Bipolar Disorder (BD). The early diagnosis of BD and Major Depressive Disorder (MDD) is extremely important, as it provides an opportunity for intervention that can reduce the impact on the patient's daily life and functionality. As such, this study assesses the suitability of MINI for diagnosing BD or MDD in a sample of patients with mood disorders. METHOD: Agreement between the MINI and the clinical interview was assessed in a sample of 347 outpatients by calculating Cohen's kappa, sensitivity, specificity, positive predictive value, negative predictive value, and the area under the curve (AUC). RESULTS: The sample consisted of 347 patients with mood disorders. 279 were women (80.40%), 105 (30.3%) were diagnosed with MDD and 242 (69.7%) with BD from the assessment performed in the clinical interview. In the MINI assessment, 97 individuals (28%) were classified with a diagnosis of MDD and 250 (72%) with BD. We found a sensitivity of 87.2% and specificity of 62.8% for the MINI in the diagnosis of BD and a Cohen's kappa between the MINI and the clinical interview of 0.51. The AUC was 0.75. CONCLUSIONS: MINI has greater sensitivity (87.2%) for the diagnosis of BD and greater specificity (87.2%) for the diagnosis of MDD. In addition, the moderate Cohen kappa (0.51) and AUC (0.75) values between the MINI and the clinical interview are acceptable when considering most available psychiatric diagnostic tools.