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

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

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

BACKGROUND: Major 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. METHODS: This 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. RESULTS: Patients 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. CONCLUSION: The LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

The clinical utility and relevance in clinical practice of DSM-5 specifiers for major depressive disorder: A Delphi expert consensus study.

Major depressive disorder (MDD) is a heterogeneous syndrome, associated with different levels of severity and impairment on the personal functioning for each patient. Classification systems in psychiatry, including ICD-11 and DSM-5, are used by clinicians in order to simplify the complexity of clinical manifestations. In particular, the DSM-5 introduced specifiers, subtypes, severity ratings, and cross-cutting symptom assessments allowing clinicians to better describe the specific clinical features of each patient. However, the use of DSM-5 specifiers for major depressive disorder in ordinary clinical practice is quite heterogeneous. The present study, using a Delphi method, aims to evaluate the consensus of a representative group of expert psychiatrists on a series of statements regarding the clinical utility and relevance of DSM-5 specifiers for major depressive disorder in ordinary clinical practice. Experts reached an almost perfect agreement on statements related to the use and clinical utility of DSM-5 specifiers in ordinary clinical practice. In particular, a complete consensus was found regarding the clinical utility for ordinary clinical practice of using DSM-5 specifiers. The use of specifiers is considered a first step toward a "dimensional" approach to the diagnosis of mental disorders.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

BPI-GNN: Interpretable brain network-based psychiatric diagnosis and subtyping.

Converging evidence increasingly suggests that psychiatric disorders, such as major depressive disorder (MDD) and autism spectrum disorder (ASD), are not unitary diseases, but rather heterogeneous syndromes that involve diverse, co-occurring symptoms and divergent responses to treatment. This clinical heterogeneity has hindered the progress of precision diagnosis and treatment effectiveness in psychiatric disorders. In this study, we propose BPI-GNN, a new interpretable graph neural network (GNN) framework for analyzing functional magnetic resonance images (fMRI), by leveraging the famed prototype learning. In addition, we introduce a novel generation process of prototype subgraph to discover essential edges of distinct prototypes and employ total correlation (TC) to ensure the independence of distinct prototype subgraph patterns. BPI-GNN can effectively discriminate psychiatric patients and healthy controls (HC), and identify biological meaningful subtypes of psychiatric disorders. We evaluate the performance of BPI-GNN against 11 popular brain network classification methods on three psychiatric datasets and observe that our BPI-GNN always achieves the highest diagnosis accuracy. More importantly, we examine differences in clinical symptom profiles and gene expression profiles among identified subtypes and observe that our identified brain-based subtypes have the clinical relevance. It also discovers the subtype biomarkers that align with current neuro-scientific knowledge.

Intelligent classification of major depressive disorder using rs-fMRI of the posterior cingulate cortex.

Major Depressive Disorder (MDD) is a widespread psychiatric condition that affects a significant portion of the global population. The classification and diagnosis of MDD is crucial for effective treatment. Traditional methods, based on clinical assessment, are subjective and rely on healthcare professionals' expertise. Recently, there's growing interest in using Resting-State Functional Magnetic Resonance Imaging (rs-fMRI) to objectively understand MDD's neurobiology, complementing traditional diagnostics. The posterior cingulate cortex (PCC) is a pivotal brain region implicated in MDD which could be used to identify MDD from healthy controls. Thus, this study presents an intelligent approach based on rs-fMRI data to enhance the classification of MDD. Original rs-fMRI data were collected from a cohort of 430 participants, comprising 197 patients and 233 healthy controls. Subsequently, the data underwent preprocessing using DPARSF, and the amplitudes of low-frequency fluctuation values were computed to reduce data dimensionality and feature count. Then data associated with the PCC were extracted. After eliminating redundant features, various types of Support Vector Machines (SVMs) were employed as classifiers for intelligent categorization. Ultimately, we compared the performance of each algorithm, along with its respective optimal classifier, based on classification accuracy, true positive rate, and the area under the receiver operating characteristic curve (AUC-ROC). Upon analyzing the comparison results, we determined that the Random Forest (RF) algorithm, in conjunction with a sophisticated Gaussian SVM classifier, demonstrated the highest performance. Remarkably, this combination achieved a classification accuracy of 81.9 % and a true positive rate of 92.9 %. In conclusion, our study improves the classification of MDD by supplementing traditional methods with rs-fMRI and machine learning techniques, offering deeper neurobiological insights and aiding accuracy, while emphasizing its role as an adjunct to clinical assessment.

Depressive disorder subtypes, depressive symptom clusters, and risk of obesity and diabetes: A systematic review.

BACKGROUND: Overlapping but divided literatures suggest certain depression facets may pose greater obesity and diabetes risk than others. Our objectives were to integrate the major depressive disorder (MDD) subtype and depressive symptom cluster literatures and to clarify which facets are associated with the greatest cardiometabolic disease risk. METHODS: We conducted a systematic review of published studies examining associations of ≥2 MDD subtypes or symptom clusters with obesity or diabetes risk outcomes. We report which facets the literature is "in favor" of (i.e., having the strongest or most consistent results). RESULTS: Forty-five articles were included. Of the MDD subtype-obesity risk studies, 14 were in favor of atypical MDD, and 8 showed similar or null associations across subtypes. Of the symptom cluster-obesity risk studies, 5 were in favor of the somatic cluster, 1 was in favor of other clusters, and 5 were similar or null. Of the MDD subtype-diabetes risk studies, 7 were in favor of atypical MDD, 3 were in favor of other subtypes, and 5 were similar or null. Of the symptom cluster-diabetes risk studies, 7 were in favor of the somatic cluster, and 5 were similar or null. LIMITATIONS: Limitations in study design, sample selection, variable measurement, and analytic approach in these literatures apply to this review. CONCLUSIONS: Atypical MDD and the somatic cluster are most consistently associated with obesity and diabetes risk. Future research is needed to establish directionality and causality. Identifying the depression facets conferring the greatest risk could improve cardiometabolic disease risk stratification and prevention programs.

Emotional subtypes in patients with depression: A cluster analysis.

Major depressive disorder (MDD) is associated with deficits in emotion experience, expression and regulation. Whilst emotion regulation deficits prolong MDD, emotion expression influences symptomatic presentations, and anticipatory pleasure deficits predict recurrence risk. Profiling MDD patients from an emotion componential perspective can characterize subtypes with different clinical and functional outcomes. This study aimed to investigate emotional subtypes of MDD. A two-stage cluster analysis applied to 150 MDD patients. Clustering variables included emotion experience measured by Temporal Experience of Pleasure Scale, emotion expression measured by Toronto Alexithymia Scale, and emotion regulation measured by Emotion Regulation Questionnaire. We validated the resultant clusters by comparing their symptoms and functioning with that of 50 controls. Cluster 1 (n = 50) exhibited intact emotion experience and expression yet adopted reappraisal rather than suppression strategy, whereas Cluster 2 (n = 66) exhibited generalized emotional deficits. Cluster 3 (n = 34) exhibited emotion expression deficits and adopted both reappraisal and suppression strategies. On validation, Cluster 2 exhibited the worst, but Cluster 1 exhibited the least symptoms and social functioning impairments. Cluster 3 was intermediate among the two other subtypes. Our findings support the existence of different emotional subtypes in MDD patients, and have clinical and theoretical implications for developing future specific treatments for MDD.

Genetic evidence suggests posttraumatic stress disorder as a subtype of major depressive disorder.

BACKGROUNDMajor depressive disorder (MDD) and posttraumatic stress disorder (PTSD) are highly comorbid and exhibit strong correlations with one another. We aimed to investigate mechanisms of underlying relationships between PTSD and 3 kinds of depressive phenotypes, namely, MDD, depressed affect (DAF), and depression (DEP, including both MDD and the broad definition of depression).METHODSGenetic correlations between PTSD and the depressive phenotypes were tested using linkage disequilibrium score regression. Polygenic overlap analysis was used to estimate shared and trait-specific causal variants across a pair of traits. Causal relationships between PTSD and the depressive phenotypes were investigated using Mendelian randomization. Shared genomic loci between PTSD and MDD were identified using cross-trait meta-analysis.RESULTSGenetic correlations of PTSD with the depressive phenotypes were in the range of 0.71-0.80. The estimated numbers of causal variants were 14,565, 12,965, 10,565, and 4,986 for MDD, DEP, DAF, and PTSD, respectively. In each case, causal variants contributing to PTSD were completely or largely covered by causal variants defining each of the depressive phenotypes. Mendelian randomization analysis indicated that the genetically determined depressive phenotypes confer a causal effect on PTSD (b = 0.21-0.31). Notably, genetically determined PTSD confers a causal effect on DEP (b = 0.14) and DAF (b = 0.15), but not MDD. Cross-trait meta-analysis of MDD and PTSD identified 47 genomic loci, including 29 loci shared between PTSD and MDD.CONCLUSIONEvidence from shared genetics suggests that PTSD is a subtype of MDD. This study provides support to the efforts in reducing diagnostic heterogeneity in psychiatric nosology.FUNDINGThe National Key Research and Development Program of China and the National Natural Science Foundation of China.

Brain structural connectivity, anhedonia, and phenotypes of major depressive disorder: A structural equation model approach.

Aberrant brain structural connectivity in major depressive disorder (MDD) has been repeatedly reported, yet many previous studies lack integration of different features of MDD with structural connectivity in multivariate modeling approaches. In n = 595 MDD patients, we used structural equation modeling (SEM) to test the intercorrelations between anhedonia, anxiety, neuroticism, and cognitive control in one comprehensive model. We then separately analyzed diffusion tensor imaging (DTI) connectivity measures in association with those clinical variables, and finally integrated brain connectivity associations, clinical/cognitive variables into a multivariate SEM. We first confirmed our clinical/cognitive SEM. DTI analyses (FWE-corrected) showed a positive correlation of anhedonia with fractional anisotropy (FA) in the right anterior thalamic radiation (ATR) and forceps minor/corpus callosum, while neuroticism was negatively correlated with axial diffusivity (AD) in the left uncinate fasciculus (UF) and inferior fronto-occipital fasciculus (IFOF). An extended SEM confirmed the associations of ATR FA with anhedonia and UF/IFOF AD with neuroticism impacting on cognitive control. Our findings provide evidence for a differential impact of state and trait variables of MDD on brain connectivity and cognition. The multivariate approach shows feasibility of explaining heterogeneity within MDD and tracks this to specific brain circuits, thus adding to better understanding of heterogeneity on the biological level.

Spatial Expression Pattern of ZNF391 Gene in the Brains of Patients With Schizophrenia, Bipolar Disorders or Major Depressive Disorder Identifies New Cross-Disorder Biotypes: A Trans-Diagnostic, Top-Down Approach.

The results generated from large psychiatric genomic consortia show us some new vantage points to understand the pathophysiology of psychiatric disorders. We explored the potential of integrating the transcription output of the core gene underlying the commonality of psychiatric disorders with a clustering algorithm to redefine psychiatric disorders. Our results showed that an extended MHC region was associated with the common factor of schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) at the level of genomic significance, with rs7746199 (P = 4.905e-08), a cis-eQTL to the gene ZNF391, pinpointed as a potential causal variant driving the signals in the region. Gene expression pattern of ZNF391 in the brain led to the emergence of 3 biotypes, independent of disorder. The 3 biotypes performed significantly differently in working memory and demonstrated different gray matter volumes in the right inferior frontal orbital gyrus (RIFOG), with a partial causal pathway arising from ZNF391 to RIFOG to working memory. Our study illustrates the potential of a trans-diagnostic, top-down approach in understanding the commonality of psychiatric disorders.

Predicting Long-Term Outcomes in First-Admission Psychosis: Does the Hierarchical Taxonomy of Psychopathology Aid DSM in Prognostication?

The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical, dimensional model of psychological symptoms and functioning. Its goals are to augment the use and address the limitations of traditional diagnoses, such as arbitrary thresholds of severity, within-disorder heterogeneity, and low reliability. HiTOP has made inroads to addressing these problems, but its prognostic validity is uncertain. The present study sought to test the prediction of long-term outcomes in psychotic disorders was improved when the HiTOP dimensional approach was considered along with traditional (ie, DSM) diagnoses. We analyzed data from the Suffolk County Mental Health Project (N = 316), an epidemiologic study of a first-admission psychosis cohort followed for 20 years. We compared 5 diagnostic groups (schizophrenia/schizoaffective, bipolar disorder with psychosis, major depressive disorder with psychosis, substance-induced psychosis, and other psychoses) and 5 dimensions derived from the HiTOP thought disorder spectrum (reality distortion, disorganization, inexpressivity, avolition, and functional impairment). Both nosologies predicted a significant amount of variance in most outcomes. However, except for cognitive functioning, HiTOP showed consistently greater predictive power across outcomes-it explained 1.7-fold more variance than diagnoses in psychiatric and physical health outcomes, 2.1-fold more variance in community functioning, and 3.4-fold more variance in neural responses. Even when controlling for diagnosis, HiTOP dimensions incrementally predicted almost all outcomes. These findings support a shift away from the exclusive use of categorical diagnoses and toward the incorporation of HiTOP dimensions for better prognostication and linkage with neurobiology.

Major depressive disorder subtypes and depression symptoms in multiple sclerosis: What is different compared to the general population?

OBJECTIVE: To compare and characterize major depressive disorder (MDD) subtypes (i.e., pure atypical, pure melancholic and mixed atypical-melancholic) and depression symptoms in persons with multiple sclerosis (PwMS) with persons without MS (Pw/oMS) fulfilling the DSM-5 criteria for a past 12-month MDD. METHODS: MDD in PwMS (n = 92) from the Swiss Multiple Sclerosis Registry was compared with Pw/oMS (n = 277) from a Swiss community-based study. Epidemiological MDD diagnoses were based on the Mini-SPIKE (shortened form of the Structured Psychopathological Interview and Rating of the Social Consequences for Epidemiology). Logistic and multinomial regression analyses (adjusted for sex, age, civil status, depression and severity) were computed for comparisons and characterization. Latent class analysis (LCA) was conducted to empirically identify depression subtypes in PwMS. RESULTS: PwMS had a higher risk for the mixed atypical-melancholic MDD subtype (OR = 2.22, 95% CI = 1.03-4.80) compared to Pw/oMS. MDD in PwMS was specifically characterized by a higher risk of the two somatic atypical depression symptoms 'weight gain' (OR = 6.91, 95% CI = 2.20-21.70) and 'leaden paralysis' (OR = 3.03, 95% CI = 1.35-6.82) and the symptom 'irritable/angry' (OR = 3.18, 95% CI = 1.08-9.39). CONCLUSIONS: MDD in PwMS was characterized by a higher risk for specific somatic atypical depression symptoms and the mixed atypical-melancholic MDD subtype. The pure atypical MDD subtype, however, did not differentiate between PwMS and Pw/oMS. Given the high phenomenological overlap with MS symptoms, the mixed atypical-melancholic MDD subtype represents a particular diagnostic challenge.

Melancholic features in major depression - a European multicenter study.

There is still a debate, if melancholic symptoms can be seen rather as a more severe subtype of major depressive disorder (MDD) or as a separate diagnostic entity. The present European multicenter study comprising altogether 1410 MDD in- and outpatients sought to investigate the influence of the presence of melancholic features in MDD patients. Analyses of covariance, chi-squared tests, and binary logistic regression analyses were accomplished to determine differences in socio-demographic and clinical variables between MDD patients with and without melancholia. We found a prevalence rate of 60.71% for melancholic features in MDD. Compared to non-melancholic MDD patients, they were characterized by a significantly higher likelihood for higher weight, unemployment, psychotic features, suicide risk, inpatient treatment, severe depressive symptoms, receiving add-on medication strategies in general, and adjunctive treatment with antidepressants, antipsychotics, benzodiazepine (BZD)/BZD-like drugs, low-potency antipsychotics, and pregabalin in particular. With regard to the antidepressant pharmacotherapy, we found a less frequent prescription of selective serotonin reuptake inhibitors (SSRIs) in melancholic MDD. No significant between-group differences were found for treatment response, non-response, and resistance. In summary, we explored primarily variables to be associated with melancholia which can be regarded as parameters for the presence of severe/difficult-to treat MDD conditions. Even if there is no evidence to realize any specific treatment strategy in melancholic MDD patients, their prescribed medication strategies were different from those for patients without melancholia.

The DSM-5 Clinical and Public Health Committee (CPHC): operations, mechanics, controversies and recommendations.

BACKGROUND: For DSM - 5, the American Psychiatric Association Board of Trustees established a robust vetting and review process that included two review committees that did not exist in the development of prior DSMs, the Scientific Review Committee (SRC) and the Clinical and Public Health Committee (CPHC). The CPHC was created as a body that could independently review the clinical and public health merits of various proposals that would fall outside of the strictly defined scientific process. METHODS: This article describes the principles and issues which led to the creation of the CPHC, the composition and vetting of the committee, and the processes developed by the committee - including the use of external reviewers. RESULTS: Outcomes of some of the more involved CPHC deliberations, specifically, decisions concerning elements of diagnoses for major depressive disorder, autism spectrum disorder, catatonia, and substance use disorders, are described. The Committee's extensive reviews and its recommendations regarding Personality Disorders are also discussed. CONCLUSIONS: On the basis of our experiences, the CPHC membership unanimously believes that external review processes to evaluate and respond to Work Group proposals is essential for future DSM efforts. The Committee also recommends that separate SRC and CPHC committees be appointed to assess proposals for scientific merit and for clinical and public health utility and impact.

A study on classification features of depressive symptoms in adolescents.

Although extensive literature has addressed depression among adolescents, few studies have emphasized the classification features of depressive symptoms in adolescents. To gain insight into the hierarchy and heterogeneity of depression in adolescents based on symptoms, 5086 adolescents completed the Chinese version of the Center for Epidemiological Studies Depression Scale (CES-D). Using Latent Class Analysis (LCA), we identified different subgroups of adolescents based on depressive symptoms. Multivariate logistic regression analysis was implemented to examine the relations between latent classes and demographic covariates. Four latent classes of individuals with depressive symptoms displaying a pattern of hierarchical organization were identified. The four classes were ordered by the degree of severity, ranging from the students reporting the highest number of depressive symptoms to the lowest number: "probable clinical depression", "subthreshold depression", "mild depression" and "low depression", accounting for 8.2%, 19.2%, 41.8% and 30.8% of total sample respectively. Further analyses revealed that compared to the "mild depression" class, the rest of three classes differed significantly across age groups and only child (vs. sibling) status. In conclusion, classifying the groups of adolescents based on features of depressive symptoms is potentially useful for understanding risk factors and developing tailored prevention and intervention programs for this age group.

Fatigue and brain arousal in patients with major depressive disorder.

Fatigue is considered a key symptom of major depressive disorder (MDD), yet the term lacks specificity. It can denote a state of increased sleepiness and lack of drive (i.e., downregulated arousal) as well as a state of high inner tension and inhibition of drive with long sleep onset latencies (i.e., upregulated arousal), the latter typically found in depression. It has been proposed to differentiate fatigue along the dimension of brain arousal. We investigated whether such stratification within a group of MDD patients would reveal a subgroup with distinct clinical features. Using an automatic classification of EEG vigilance stages, an arousal stability score was calculated for 15-min resting EEGs of 102 MDD patients with fatigue. 23.5% of the patients showed signs of hypoarousal with EEG patterns indicating drowsiness or sleep; this hypoaroused subgroup was compared with remaining patients (non-hypoaroused subgroup) concerning self-rated measures of depressive symptoms, sleepiness, and sleep. The hypoaroused subgroup scored higher on the Beck Depression Inventory items "loss of energy" (Z = - 2.13, p = 0.033; ɳ2 = 0.044, 90% CI 0.003-0.128) and "concentration difficulty" (Z = - 2.40, p = 0.017; ɳ2 = 0.056, 90% CI 0.009-0.139), and reported higher trait and state sleepiness (p < 0.05) as compared to the non-hypoaroused group. The non-hypoaroused subgroup, in contrast, reported more frequently the presence of suicidal ideation (Chi2 = 3.81, p = 0.051; ɳ2 = 0.037, 90% CI 0.0008-0.126). In this study, we found some evidence that stratifying fatigued MDD patients by arousal may lead to subgroups that are pathophysiologically and clinically more homogeneous. Brain arousal may be a worth while target in clinical research for better understanding the mechanisms underlying suicidal tendencies and to improve treatment response.

Depression prevalence using the HADS-D compared to SCID major depression classification: An individual participant data meta-analysis.

OBJECTIVES: Validated diagnostic interviews are required to classify depression status and estimate prevalence of disorder, but screening tools are often used instead. We used individual participant data meta-analysis to compare prevalence based on standard Hospital Anxiety and Depression Scale - depression subscale (HADS-D) cutoffs of ≥8 and ≥11 versus Structured Clinical Interview for DSM (SCID) major depression and determined if an alternative HADS-D cutoff could more accurately estimate prevalence. METHODS: We searched Medline, Medline In-Process & Other Non-Indexed Citations via Ovid, PsycINFO, and Web of Science (inception-July 11, 2016) for studies comparing HADS-D scores to SCID major depression status. Pooled prevalence and pooled differences in prevalence for HADS-D cutoffs versus SCID major depression were estimated. RESULTS: 6005 participants (689 SCID major depression cases) from 41 primary studies were included. Pooled prevalence was 24.5% (95% Confidence Interval (CI): 20.5%, 29.0%) for HADS-D ≥8, 10.7% (95% CI: 8.3%, 13.8%) for HADS-D ≥11, and 11.6% (95% CI: 9.2%, 14.6%) for SCID major depression. HADS-D ≥11 was closest to SCID major depression prevalence, but the 95% prediction interval for the difference that could be expected for HADS-D ≥11 versus SCID in a new study was -21.1% to 19.5%. CONCLUSIONS: HADS-D ≥8 substantially overestimates depression prevalence. Of all possible cutoff thresholds, HADS-D ≥11 was closest to the SCID, but there was substantial heterogeneity in the difference between HADS-D ≥11 and SCID-based estimates. HADS-D should not be used as a substitute for a validated diagnostic interview.

Depression development: From lifestyle changes to motivational deficits.

Until now, depression research has taken a surprisingly narrow approach to modelling the disease, mainly focusing on some form of psychomotor retardation within a mechanistic framework of depression etiology. However, depression has many symptoms and each is associated with a vast number of substrates. Thus, to deepen our insights, this SI ("Depression Symptoms") reviewed the behavioral and neurobiological sequelae of individual symptoms, specifically, psychomotor retardation, sadness, low motivation, fatigue, sleep/circadian disruption, weight/appetite changes, and cognitive affective biases. This manuscript aims to integrate the most central information provided by the individual reviews. As a result, a dynamic model of depression development is proposed, which views depression as a cumulative process, where different symptoms develop at different stages, referred to as early, intermediate, and advanced, that require treatment with different pharmaceutical agents, that is, selective serotonin reuptake inhibitors early on and dopamine-based antidepressants at the advanced stage. Furthermore, the model views hypothalamic disruption as the source of early symptoms and site of early intervention. Longitudinal animal models that are capable of modelling the different stages of depression, including transitions between the stages, may be helpful to uncover novel biomarkers and treatment approaches.