Diagnosis Of Major Depressive Disorder Research Articles

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC’s full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis

Depression stands as one of the most pernicious mental disorders in contemporary society, characterized by a highly intricate pathological mechanism. Specifically, individuals suffering from Major Depressive Disorder (MDD) exhibit heightened vulnerability to suicidal tendencies. Currently, healthcare practitioners often encounter challenges related to the misdiagnosis and underdiagnosis of depression during clinical assessments. Consequently, it is of paramount importance to develop highly accurate auxiliary diagnostic tools for depression. Unfortunately, traditional machine learning and deep learning methodologies frequently neglect the integration of multi-source data and disregard the intricate topological structure and high-order attributes of brain networks. In this study, a multi-scale feature fusion classification framework is proposed to distinguish between MDD patients and healthy controls. Within the proposed model, a novel method, the Cross-Level High-Order Interaction (CLHOI), is introduced and implemented on a low-order functional connectivity (LOFC) matrix to derive two distinct high-order functional connectivity (HOFC) matrices. Subsequently, a Multi-Channel Fusion Graph Convolutional Network (MFGCN) is trained by integrating high-order and low-order brain graph features along with phenotypic information. The results of 10-fold cross-validation experiments conducted on the publicly available REST-meta-MDD dataset indicate that the fusion of multi-scale features improves the average accuracy by approximately 3%, resulting in an accuracy rate of 77.6%. Simultaneously, the hypothesis asserting the existence of intricate information interactions at various levels within brain connectivity networks is validated. Moreover, our model exhibits strong explanatory capabilities, effectively identifying brain regions closely associated with MDD, including the Precentral gyrus, Superior frontal gyrus, Cuneus, Lingual gyrus, and Fusiform gyrus. In comparison to numerous advanced studies within the same domain, our approach has produced competitive results. Furthermore, our proposed method can be readily extended to facilitate the diagnosis of various neurological diseases.

The relationship between familial-genetic risk and pharmacological treatment in a Swedish national sample of patients with major depression, bipolar disorder, and schizophrenia.

Using Swedish registers, we examine whether the prescription of and the response to antidepressants (AD), mood stabilizers (MS), and antipsychotics (AP) in the treatment of, respectively, major depression (MD), bipolar disorder (BD), and schizophrenia (SZ), are influenced by familial-genetic risk. We examined individuals born in Sweden 1960-1995 with a first diagnosis of MD (n = 257,177), BD (n = 23,032), and SZ (n = 4248) from 2006 to 2018. Drug classes and Defined Daily Dose (DDD) were obtained from the Pharmacy register using the Anatomical Therapeutic Chemical system. We utilized the Familial Genetic Risk Scores (FGRS) calculated from morbidity risks in first- through fifth degree relatives. Treatment with antidepressants (AD) in MD, mood-stabilizers (MS) in BD, and antipsychotics (AP) in SZ were associated with significantly higher disorder-specific familial-genetic risks. Using dosage trajectory analysis of AD, MS, and AP treatment for MD, BD, and SZ, respectively, familial-genetic risk was positively associated with higher and/or increasing drug dosages over time. For MD and BD, examining cases started on the most common pharmacologic treatment class (SSRIs for MD and "other anti-epileptics" for BD), familial-genetic risks were significantly lower in those who did not versus did later receive treatment from other AD and MS classes, respectively. Higher familial-genetic risk for BD predicted switching AD medication in cases of MD. Among pharmacologically treated cases of BD, familial-genetic risk was significantly higher for those treated with lithium. In a large population-based patient cohort, we found evidence of a wide-spread association between higher familial-genetic risk and i) increased likelihood of receiving pharmacologic treatment but 2) responding more poorly to it-as indicated by a switching of medications -- and/or requiring higher doses. Further investigations into the clinical utility of genetic risk scores in the clinical managements of MD, BD, and SZ are warranted.

Differentiation Between Alzheimer̓s Disease and Old Age Depression: Overview and Conception of a Questionnaire for Relatives

Differential diagnosis of Alzheimer's disease and major depression in the elderly is difficult because of overlapping and similar symptoms. A questionnaire for relatives may facilitate the diagnostic burden in clinical practice. Based on a selection of clinical criteria, an expert survey, and statistical analysis of the expert survey, a questionnaire for relatives was designed. 18 out of 48 suggested clinical criteria were assessed by neuropsychologists as suitable for the differential diagnosis and were summarized in a questionnaire for relatives (ADAD). First results of the validation of the questionnaire gave an indication of the suitability of the clinical criteria. The ADAD questionnaire could be simply implemented in clinical practice to differentiate between symptoms of Alzheimer's disease and major depression, but it needs to be evaluated in a larger group of patients.

DepressionGraph: A Two-Channel Graph Neural Network for the Diagnosis of Major Depressive Disorders Using rs-fMRI

Major depressive disorder (MDD) is a prevalent psychiatric condition with a complex and unknown pathological mechanism. Resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a valuable non-invasive technology for MDD diagnosis. By utilizing rs-fMRI data, a dynamic brain functional connection network (FCN) can be constructed to represent the complex interacting relationships of multiple brain sub-regions. Graph neural network (GNN) models have been widely employed to extract disease-associated information. The simple averaging or summation graph readout functions of GNNs may lead to a loss of critical information. This study introduces a two-channel graph neural network (DepressionGraph) that effectively aggregates more comprehensive graph information from the two channels based on the node feature number and node number. Our proposed DepressionGraph model leverages the transformer–encoder architecture to extract the relevant information from the time-series FCN. The rs-fMRI data were obtained from a cohort of 533 subjects, and the experimental data show that DepressionGraph outperforms both traditional GNNs and simple graph readout functions for the MDD diagnosis task. The introduced DepressionGraph framework demonstrates efficacy in extracting complex patterns from rs-fMRI data and exhibits promising capabilities for the precise diagnosis of complex neurological disorders. The current study acknowledges a potential gender bias due to an imbalanced gender distribution in the dataset. Future research should prioritize the development and utilization of gender-balanced datasets to mitigate this limitation and enhance the generalizability of the findings.

Client Centered Therapy for Clients with a Diagnosis of Major Depression

This research aims to reduce symptoms of depression using client centered therapy (CCT) and after that a test is carried out to see its effectiveness. The definition of depression according to the World Health Organization is a mental health disorder characterized by intense feelings of sadness, reduced interest in things you usually enjoy, decreased energy, feelings of guilt, low self-esteem, insomnia, reduced appetite, fatigue, lack of concentration and lack of thinking. to suicide (WHO, 2023). The subject in this study was a 24-year-old man who was diagnosed with severe depression. The approach used is a quantitative approach with an experimental method using a Single Case Design or Single-Subject Design. The data obtained used observation methods, interviews, psychological tests consisting of cognitive tests (WAIS), personality (Grafis, Wartegg, SSCT) and inventory, namely the DASS scale (Depression Anxiety Stress Scale). The research results showed that client centered therapy (CCT) was effective in reducing depressive symptoms experienced by the subjects. After carrying out quantitative measurements, it showed a decrease in depression scores from the very severe category (26) to the normal category (1). Qualitatively, it shows a reduction in depressive symptoms in the subjects, which can be seen from cognitive changes that become more capable of positive thinking and changes in more productive behavior. As for further research, it is hoped that client centered therapy can be used to treat clients with other diagnoses to enrich research results related to client centered therapy.Penelitian ini bertujuan untuk menurunkan gejala depresi dengan menggunakan client centered therapy (CCT) dan setelah itu dilakukan uji untuk melihat efektivitasnya. Defini depresi mengacu pada World Health Organization merupakan gangguan kesehatan mental yang ditandai dengan perasaan sedih yang intens, berkurangnya minat terhadap sesuatu yang biasa disenangi, penurunan energi, adanya perasaan bersalah, rendah diri, insomnia, berkurangnya nafsu makan, kelelahan, kurang konsentrasi dan adanya pemikiran untuk bunuh diri (WHO, 2023). Subjek pada penelitian ini yaitu seorang laki-laki berumur 24 tahun yang terdiagnosa mengalami depresi berat. Pendekatan yang digunakan adalah pendekatan kuantitatif dengan metode eksperimen menggunakan rancangan Single Case Design atau Single-Subject Design. Data yang diperoleh menggunakan metode observasi, wawancara, tes psikologi yang terdiri dari tes kognitif (WAIS), kepribadian (Grafis, Wartegg, SSCT) dan inventori yaitu skala DASS (Depression Anxiety Stress Scale). Hasil penelitian menunjukkan bahwa client centered therapy (CCT) efektif untuk menurunkan gejala depresi yang dialami oleh subjek. Setelah dilakukan pengukuran secara kuantitatif menunjukkan penurunan skor depresi dari kategori sangat parah (26) menjadi kategori normal (1). Secara kualitatif menunjukkan berkurangnya gejala depresi pada subjek yaitu terlihat dari perubahan kognitif yang menjadi lebih mampu berpikir positif dan perubahan pada perilaku yang lebih produktif. Adapun untuk penelitian selanjutnya, diharapkan dapat menggunakan client centered therapy untuk menangani klien dengan diagnosis lain guna memperkaya hasil penelitian terkait client centered therapy.

Arthroscopic subacromial decompression improved outcomes in situationally depressed patients compared to clinically depressed or nondepressed patients

The purpose of this study is to evaluate patient reported outcomes after arthroscopic extensive débridement of the shoulder with subacromial decompression (SAD) for subacromial impingement using the Patient-Reported Outcomes Measurement Information System (PROMIS) system and evaluate if depression (Dep) (clinical or situational) impacts patients achieving a Minimal Clinically Important Difference (MCID). Preoperative PROMIS Physical function (PF), Mood, and Dep scores were obtained at the closest date prior to arthroscopic rotator cuff repair and postoperative scores were collected at every clinical visit thereafter. Final PROMIS score used for data analysis was determined by the patients final PROMIS value between 90 to 180 days. Clinical Dep was determined by patients having a formal diagnosis of "Depression or Major Depressive Disorder" at the time of their surgery. Situationally depressed patients, those without a formal diagnosis yet exhibited symptomatic depressive symptoms, were classified by having a PROMIS-Dep cutoff scores larger than 52.5. A total of 136 patients were included for final statistical analysis. 13 patients had a clinical but not situational diagnosis of Dep, 86 patients were identified who had no instance of clinical or situational Dep (nondepressed). 35 patients were situationally depressed. All three cohorts demonstrated a significant improvement in postoperative PROMIS Dep, PI, and PF score relative to their preoperative value (P=.001). Situationally depressed patients achieved greater delta PROMIS-Dep compared to patients with major depressive disorder. Depressed patients had a higher chance of achieving MCID for PROMIS-Dep compared to nondepressed patients (P=.01). Logistic regression analysis demonstrated that underlying Dep did not alter the odds of obtaining MCID compared to nondepressed patients. Nonsmoking patients had significantly greater odds of achieving MCID for PF (P=.02). Patients improved after undergoing SAD regardless of underlying Dep or depressive symptoms. Depressed patients exhibited greater change in PROMIS scores compared to nondepressed patients. Smoking remains a risk factor for postoperative outcomes in patients undergoing SAD for subacromial impingement. Identifying and counseling patients with underlying depressive symptoms without a formal major depressive disorder diagnosis may lead to improved outcomes. These findings may help guide clinicians in deciding who would benefit the most from this procedure.

Real-world clinical outcomes and treatment patterns in patients with MDD treated with vortioxetine: a retrospective study

BackgroundThis study included evaluation of the effectiveness of vortioxetine, a treatment for adults with major depressive disorder (MDD), using patient-reported outcome measures (PROMs) in a real-world setting.MethodsThis retrospective chart review analyzed the care experiences of adult patients with a diagnosis of MDD from Parkview Physicians Group – Mind-Body Medicine, Midwestern United States. Patients with a prescription for vortioxetine, an initial baseline visit, and ≥ 2 follow-up visits within 16 weeks from September 2014 to December 2018 were included. The primary outcome measure was effectiveness of vortioxetine on depression severity as assessed by change in Patient Health Questionnaire-9 (PHQ-9) scores ~ 12 weeks after initiation of vortioxetine. Secondary outcomes included changes in depression-related symptoms (i.e., sexual dysfunction, sleep disturbance, cognitive function, work/social function), clinical characteristics, response, remission, and medication persistence. Clinical narrative notes were also analyzed to examine sleep disturbance, sexual dysfunction, appetite, absenteeism, and presenteeism. All outcomes were examined at index (start of vortioxetine) and at ~ 12 weeks, and mean differences were analyzed using pairwise t tests.ResultsA total of 1242 patients with MDD met inclusion criteria, and 63.9% of these patients had ≥ 3 psychiatric diagnoses and 65.9% were taking ≥ 3 medications. PHQ-9 mean scores decreased significantly from baseline to week 12 (14.15 ± 5.8 to 9.62 ± 6.03, respectively; p < 0.001). At week 12, the response and remission rates in all patients were 31.0% and 23.1%, respectively, and 67% continued vortioxetine treatment. Overall, results also showed significant improvements by week 12 in anxiety (p < 0.001), sexual dysfunction (p < 0.01), sleep disturbance (p < 0.01), cognitive function (p < 0.001), work/social functioning (p = 0.021), and appetite (p < 0.001). A significant decrease in presenteeism was observed at week 12 (p < 0.001); however, no significant change was observed in absenteeism (p = 0.466).ConclusionsUsing PROMs, our study results suggest that adults with MDD prescribed vortioxetine showed improvement in depressive symptoms in the context of a real-world clinical practice setting. These patients had multiple comorbid psychiatric and physical diagnoses and multiple previous antidepressant treatments had failed.

Finding a balance: resilience in older adults after depression in later life

Abstract Older adults who have had a major depressive disorder (MDD) have a high risk of relapse. Although risk factors for depression have been researched extensively, less is known about protective factors, and what experiences might strengthen subsequent resilience and help to prevent relapse. Therefore, this qualitative study explored factors of resilience in older adults who recovered from MDD and did not relapse across at least six years. Twenty-five semi-structured interviews were held with older adults aged 73–85 years who participated in the Netherlands Study of Depression in Older Persons from 2008 to 2014 and were re-interviewed for the present study in 2020–2021. Participants were defined as resilient based on having an MDD diagnosis at baseline but not on two- and six-year follow-up. We used grounded theory coding techniques and thematic analysis to identify factors contributing to resilience. Factors contributing to resilience included: taking agency; receiving social support and engaging in social activities; doing activities individually; and managing thought processes. Resilience after late-life depression appeared to be a dynamic process involving internal and external factors, including finding a balance between rest and activity, between taking initiative and receiving support by others, and between accepting negative emotions and ignoring negative thoughts. Additionally, the ability to learn from depression shows that resilience is not only about avoiding psychopathology, but also about the recovery process and preventing relapse. These findings highlight the need for research and interventions to focus on understanding and influencing the dynamics underlying resilience.

A Study of Sexual Dysfunction and Quality of Life in Female Patients on Selective Serotonin Reuptake Inhibitor (SSRI)

Context: Sexual dysfunction is one of the most common and disconcerting side effect during treatment with Selective serotonin reuptake inhibitors (SSRIs). Sexual side effects from these medications have significant impact on self esteem, quality of life and can lead to non compliance and relapse in long term treatment. Aims: To study prevalence, severity of sexual dysfunction and its possible association with quality of life in female patients on treatment with SSRI for depression and anxiety. Materials and Methods: This was an observational, cross sectional, single-centre study. Hundred consecutive female patients who were sexually active, on SSRIs for six weeks or more were recruited. Diagnosis of Major depressive disorder (MDD), Anxiety disorders and female sexual dysfunction was done by clinician administered interview as per Diagnostic and Statistical Manual of mental disorders (DSM 5). The patients were further assessed for demographic details, CSFQ-F-C, HAM-D, HAM-A and WHOQOL-BREF scales for sexual dysfunction, depression, anxiety and quality of life respectively. p value of &lt;0.05 was considered to be statistically significant. Results: Prevalence of sexual dysfunction was 84%. Patients with sexual dysfunction scored significantly lower value in social relationships (p= 0.0002) and environment (p= 0.033) domains of quality of life. Frequency of sexual dysfunction among patients on fluoxetine was 86.20%, on escitalopram was 86.76% and on sertraline was 66.6%. Frequency of sexual dysfunction was more with higher dose of SSRIs. Conclusions: Patients on various SSRIs experienced sexual dysfunction and had poor quality of life. Frequency of sexual dysfunction was more with higher doses of SSRIs. Keywords: Sexual dysfunction, Selective serotonin reuptake inhibitors, Depression, Anxiety, quality of life.

Plasma circRNA HIPK2 as a putative biomarker for the diagnosis and prediction of therapeutic effects in major depressive disorder

BackgroundCircular RNAs (circRNAs) are a prevalent type of non-coding RNAs exhibiting extensive expression in mammalian cells. Owing to their involvement in diverse pathophysiological mechanisms of major depressive disorder (MDD) and their inherent stability in peripheral blood, circRNAs have emerged as potential biomarkers of considerable significance. This study aimed to identify and validate circular RNA HIPK2 (circHIPK2) in MDD patients and to investigate its potential as a biomarker for the diagnosis and prognosis of MDD. MethodsPatients with MDD (n = 81) and healthy controls (HCs) (n = 48) were recruited for our study (October 2022 to June 2023). The expression of circHIPK2 in plasma was assessed using absolute quantitative polymerase chain reaction (qPCR). ResultsThe expression of circHIPK2 in plasma of patients with MDD exhibited a significant increase compared to HCs. The circHIPK2 levels showed an area under the curve (AUC) of 0.796, corresponding to a specificity of 97.9% and a sensitivity of 60.4% in diagnosing MDD. Additionally, the rate of change in circHIPK2 over a 14-day period exhibited an AUC curve of 0.819, indicating its predictive value for antidepressive effects. ConclusionsCircHIPK2 could serve as a potential biomarker for diagnosing MDD and predicting therapeutic effects of MDD.

US Medicaid program: An analysis of the spending and utilization patterns for antidepressants from 2017 to 2021

BackgroundMajor depressive disorder (MDD) is a serious mental health condition that contributes to health complications, financial burden and death. In 2020, about one in five US adults had a lifetime diagnosis of MDD. With Major Depressive Disorder (MDD) being a common mental health concern, it is important to understand treatment patterns within public health systems like Medicaid, as they play a crucial role in providing care to diverse populations. ObjectiveThe study investigated antidepressant usage and market distribution in the Medicaid Program. By doing so, the study aimed to provide insights into how these trends reflect broader changes in mental health treatment practices and policy implications within the Medicaid system during the study period. MethodsPublic Medicaid data from 2017 to 2021 were analyzed, focusing on 30 FDA-approved antidepressants. Spending and prescription data were aggregated using Excel and Python. ResultsThe total US Medicaid expenditure on antidepressants increased from about $1 billion dollars in 2017 to $1.12 billion dollars in 2021, an increase of about 10%. Consistently, SSRIs were the class of antidepressants that Medicaid spent the most on. The highest Medicaid spending on a single antidepressant in 2017 and 2018 was bupropion. During the remaining years of the study (2019, 2020, 2021) Medicaid appropriated most funds toward Vortioxetine. The total number of antidepressant prescriptions increased from 52 million scripts to 59 million scripts (an increase of about 14%). ConclusionsThe increase in Medicaid spending on antidepressants during the study period can be explained by an increase in utilization (a 14% increase in antidepressant prescriptions from 2017 to 2021), and a shift toward prescribing newer more costly antidepressants (like SSRIs and others) and away from prescribing older, less costly antidepressants like monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs).”

P175: Cohort differences in depressive burden in old age: role of psychosocial, behavioral, and functional factors

Objective:Rapid societal changes occurred during the course of the 20th century. Previous literature has found an increase in depression over time for younger and middle- aged populations. Among older adults, the prevalence of major depression has been found to be stable over time, while for the milder forms, the findings are limited by the largely non-representative nature of analyzed samples. Given the dramatic secular changes in several factors linked to old-age depression, a careful examination of depressive symptom burden and prevalence of depression in representative cohorts of similarly-aged older adults separated in time is required.Methods:We will analyze data on 2,041 older adults from the Swedish National Study on Aging and Care in Kungsholmen. Separate individuals, aged 60 and 81 years were assessed with a Comprehensive Psychopathological Rating Scale (CPRS) during detailed clinical examinations, separated in time by 15 years (2001 vs. 2016). Information on 21 depressive symptoms, is subsequently combined into diagnoses of major depression (presence of at least one core symptom [low mood and/or loss of interest], and at least five out of the nine symptoms); minor depression (presence of at least one core symptom, and two to four symptoms in total), and subsyndromal depression (presence of at least two symptoms in the absence of any other depression diagnoses). Psychosocial (loneliness, bereavement), behavioral (alcohol consumption, smoking), and functional factors (impairments in activities of daily living) are used as potential explanatory factors for any observed cohort differences in symptom burden or prevalence of depression.Results:For the 60-year old age-group, comparison of symptom burden and diagnostic status will be done across 739 participants assessed in 2001 and 677 people assessed in 2013. For the 81-year old age- group, comparisons will involve 236 people assessed in 2001, 194 people assessed in 2010, and 195 people assessed in 2016.Conclusion:Preliminary results are expected by March, once data entry and cleaning are completed. We hypothesize that the burden of depressive symptoms and the prevalence of depression will be lower in later born cohorts and that explanatory factors may account for some of the cohort effect.

Enhancing Provider Mental Health Screening in Primary Care: A Quality Improvement Project.

Background: Depression and anxiety occur in 7.8% and 19.1% of the US population, respectively. About half of those patients are diagnosed in primary care. Objective: The purpose of this quality improvement project was to improve the screening and diagnosing of anxiety and/or depression among adult patients at a primary care clinic by implementing an evidence-based mental health screening interview technique. Methods: The team implemented a mental health screening interview technique that incorporates background, affect, trouble, handling, and empathy and motivational interviewing techniques in addition to self-report surveys. Pre- and post-intervention surveys were conducted to assess providers' perceptions of the new interview technique. ICD-10 code data were gathered to assess the effectiveness of the new mental health screening interview technique. Results: The number of documented diagnoses of single-episode major depressive disorder decreased by 18%, recurrent major depressive disorder increased by 34%, and anxiety disorders increased by 3%. There were more favorable provider perceptions of the new screening interview technique versus the traditional screening method. Conclusions: The results show an improvement in provider comfort and providers preferred the new mental health screening when using an evidence-based mental health screening interview technique. Implications for nursing: The results show the potential benefits of using a structured mental health interview with self-report screening tools when diagnosing anxiety and depression in primary care.

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

ObjectiveThe 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. MethodAgreement 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). ResultsThe 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. ConclusionsMINI 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.

Moral injury and its mental health consequences among protesters: findings from Israel’s civil protest against the government's judicial reform

ABSTRACT Background: Government actions and participating in protracted-duration protests against it affect protesters' mental health, leading to high distress levels, such as posttraumatic and depressive symptoms. Aside from exposure to violence and other issues, protest participation can pose unique challenges to the protesters as they may be exposed to potentially morally injurious events (PMIEs), such as the betrayal of leaders they once trusted. This study's primary objective was to examine the extent of psychological difficulties among civilians participating in long-duration protests in Israel. More specifically, the study aimed to understand the contribution of exposure to protest-related PMIEs to psychological difficulties such as posttraumatic and depressive symptoms. Method: Participants comprised 4036 Israelis who were actively involved in the unfolding civil protest movement against the government-led judicial overhaul between January 2023 and August 2023. The protesters completed validated self-report questionnaires that included measures of PMIE exposure, PTSD and depressive symptoms. Results: About half (44.3%) of the sample met the criteria for self-report diagnosis of major depression and 10.6% for PTSD. Most of the protesters indicated their exposure to at least one moral injury event, with 63.9% reporting exposure to PMIE-Betrayal. Protesters exposed to PMIEs reported significantly higher levels of both PTSD and depression than non-PMIE-exposed protesters. Hierarchical regression analyses revealed that, beyond demographics and protest-related characteristics such as exposure to violence, PMIE dimensions significantly contributed to both PTSD and depression levels. Conclusions: The findings highlight the mental burden of protesters during the civil protests against the judicial overhaul in Israel. More central to the present research, the findings highlight the critical contribution of PMIEs exposure to this burden. Clinicians treating protesters coping with depression and PTSD following the civil actions should attend to their exposure to PMIEs, which may relate to the deleterious psychological effects among protesters.

Relevance of pharmacogenetic analyses and therapeutic drug monitoring of antidepressants for an individualized treatment of peripartum psychopathology.

Psychiatric disorders burden the peripartum period, often requiring psychopharmacological treatment, including antidepressants. Efficacy and tolerability of antidepressants are influenced by the physiological changes of the peripartum and individual metabolic profiles, which in turn can be modified by pregnancy. The objective of this study is to assess the relationship between antidepressants' pharmacokinetic profiles during pregnancy and individual metabolic profiles, along with the efficacy of the treatment. In total 87 outpatients with diagnoses of bipolar disorder, major depression, anxiety, obsessive-compulsive disorder and post-traumatic stress disorder who required antidepressant treatment during pregnancy were recruited. Genotyping analysis of hepatic cytochrome P450 (CYPs) individual isoforms was performed. Antidepressants' blood concentrations and psychometric assessments were collected at five time points. Antidepressants' cord blood concentrations were assessed at birth. Sertraline showed greater stability in plasma concentrations and a lower placental penetrance index. Most of the antidepressants' concentrations below the therapeutic range were found in women with an extensive/ultrarapid metabolic profile. Antidepressants mainly metabolized by CYP2C19 were less frequently below the therapeutic range compared with antidepressants metabolized by CYP2D6. Pregnancy modulates cytochrome activity and drugs' pharmacokinetics. Genotyping analysis of CYPs isoforms and therapeutic drug monitoring might be used to guide clinicians in a well-tolerated treatment of psychiatric symptoms in pregnant women.

Premorbid intelligence quotient and school failure as risk markers for bipolar disorder and major depressive disorder

Mood disorders significantly impact global health, with MDD ranking as the second leading cause of disability in the United States and BD ranking 18th. Despite their prevalence and impact, the relationship between premorbid intelligence and the subsequent development of BD and MDD remains inconclusive.This study investigates the potential of premorbid Intelligence Quotient (IQ) and school failure frequency as risk factors for Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in a birth cohort setting. We analyze data from the Pelotas population-based birth cohort study, comprising 3580 participants aged 22, who had no prior mood disorder diagnoses. Utilizing regression models and accounting for potential confounders, we assess the impact of IQ and school failure, measured at age 18, on the emergence of BD and MDD diagnoses at age 22, using individuals without mood disorders as comparators. Results reveal that lower IQ (below 70) at 18 is associated with an increased risk of BD (Adjusted Odds Ratio [AOR] 1.75, 95%CI: 1.00–3.09, p < 0.05), while higher IQ (above 120) is linked to MDD (AOR 2.16, 95%CI: 1.24–3.75, p < 0.001). Moreover, an elevated number of school failures is associated with increased BD risk (AOR 1.23, 95%CI: 1.11–1.41, p < 0.001), particularly for BD type 1 (AOR 1.36, 95% CI: 1.17–1.58, p < 0.001). These findings offer insights into the distinct premorbid intellectual characteristics of BD and MDD and contribute to a deeper understanding of their developmental trajectories, potentially informing the development of risk assessment tools for mood disorders.

Response to ketamine treatment for major depressive disorder not associated with number of psychiatric comorbidities

BackgroundKetamine has demonstrated efficacy in treating depression unresponsive to traditional interventions and is increasingly utilized in psychiatric practice. However, limited information is available regarding effectiveness in patients with psychiatric comorbidity. The aim of this study is to compare the response of ketamine treatment for depression in patients in a community sample with and without one or more coexisting psychiatric diagnoses. MethodsA retrospective review of medical records from a community ketamine clinic was done. Statistical analyses compared response to intravenous ketamine treatment, as defined by a 50% reduction in Patient Health Questionnaire (PHQ-9) score or final score of <10, in patients with an isolated diagnosis of major depressive disorder (MDD) to those with comorbid generalized anxiety disorder (GAD), obsessive-compulsive disorder (OCD), post-traumatic stress disorder (PTSD) and/or attention-deficit/hyperactivity disorder (ADHD). ResultsData from 129 patient were analyzed (55.8 % female). It was found that the number of psychiatric comorbidities was not associated with ketamine treatment response rate nor mean change in PHQ-9 scores. LimitationsLevel of previous treatment resistance was not assessed systematically, and psychiatric interview and some elements of the patients’ care were not standardized, such as treatment schedule and dosing. ConclusionsThese findings suggest ketamine may be an effective treatment for depression in appropriate patients with psychiatric comorbidities including GAD, OCD, PTSD and/or ADHD. Additional investigation is warranted to evaluate various patient-level and other factors that may impact ketamine's ability to attenuate depressive symptoms.

Automatic Diagnosis of Major Depressive Disorder Using a High- and Low-Frequency Feature Fusion Framework.

Major Depressive Disorder (MDD) is a common mental illness resulting in immune disorders and even thoughts of suicidal behavior. Neuroimaging techniques serve as a quantitative tool for the assessment of MDD diagnosis. In the domain of computer-aided magnetic resonance imaging diagnosis, current research predominantly focuses on isolated local or global information, often neglecting the synergistic integration of multiple data sources, thus potentially overlooking valuable details. To address this issue, we proposed a diagnostic model for MDD that integrates high-frequency and low-frequency information using data from diffusion tensor imaging (DTI), structural magnetic resonance imaging (sMRI), and functional magnetic resonance imaging (fMRI). First, we designed a meta-low-frequency encoder (MLFE) and a meta-high-frequency encoder (MHFE) to extract the low-frequency and high-frequency feature information from DTI and sMRI, respectively. Then, we utilized a multilayer perceptron (MLP) to extract features from fMRI data. Following the feature cross-fusion, we designed the ensemble learning threshold voting method to determine the ultimate diagnosis for MDD. The model achieved accuracy, precision, specificity, F1-score, MCC, and AUC values of 0.724, 0.750, 0.882, 0.600, 0.421, and 0.667, respectively. This approach provides new research ideas for the diagnosis of MDD.