Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects

Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group

The pattern of structural brain alterations associated with major depressive disorder (MDD) remains unresolved. This is in part due to small sample sizes of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex interactions between clinical characteristics and brain morphology. To address this, we meta-analyzed three-dimensional brain magnetic resonance imaging data from 1728 MDD patients and 7199 controls from 15 research samples worldwide, to identify subcortical brain volumes that robustly discriminate MDD patients from healthy controls. Relative to controls, patients had significantly lower hippocampal volumes (Cohen\u27s d=-0.14, % difference=-1.24). This effect was driven by patients with recurrent MDD (Cohen\u27s d=-0.17, % difference=-1.44), and we detected no differences between first episode patients and controls. Age of onset 21 was associated with a smaller hippocampus (Cohen\u27s d=-0.20, % difference=-1.85) and a trend toward smaller amygdala (Cohen\u27s d=-0.11, % difference=-1.23) and larger lateral ventricles (Cohen\u27s d=0.12, % difference=5.11). Symptom severity at study inclusion was not associated with any regional brain volumes. Sample characteristics such as mean age, proportion of antidepressant users and proportion of remitted patients, and methodological characteristics did not significantly moderate alterations in brain volumes in MDD. Samples with a higher proportion of antipsychotic medication users showed larger caudate volumes in MDD patients compared with controls. This currently largest worldwide effort to identify subcortical brain alterations showed robust smaller hippocampal volumes in MDD patients, moderated by age of onset and first episode versus recurrent episode status

Dissecting the Shared Genetic Architecture of Suicide Attempt, Psychiatric Disorders, and Known Risk Factors

BACKGROUND: Suicide is a leading cause of death worldwide, and nonfatal suicide attempts, which occur far more frequently, are a major source of disability and social and economic burden. Both have substantial genetic etiology, which is partially shared and partially distinct from that of related psychiatric disorders. METHODS: We conducted a genome-wide association study (GWAS) of 29,782 suicide attempt (SA) cases and 519,961 controls in the International Suicide Genetics Consortium (ISGC). The GWAS of SA was conditioned on psychiatric disorders using GWAS summary statistics via multitrait-based conditional and joint analysis, to remove genetic effects on SA mediated by psychiatric disorders. We investigated the shared and divergent genetic architectures of SA, psychiatric disorders, and other known risk factors. RESULTS: Two loci reached genome-wide significance for SA: the major histocompatibility complex and an intergenic locus on chromosome 7, the latter of which remained associated with SA after conditioning on psychiatric disorders and replicated in an independent cohort from the Million Veteran Program. This locus has been implicated in risk-taking behavior, smoking, and insomnia. SA showed strong genetic correlation with psychiatric disorders, particularly major depression, and also with smoking, pain, risk-taking behavior, sleep disturbances, lower educational attainment, reproductive traits, lower socioeconomic status, and poorer general health. After conditioning on psychiatric disorders, the genetic correlations between SA and psychiatric disorders decreased, whereas those with nonpsychiatric traits remained largely unchanged. CONCLUSIONS: Our results identify a risk locus that contributes more strongly to SA than other phenotypes and suggest a shared underlying biology between SA and known risk factors that is not mediated by psychiatric disorders

Major depressive disorder

Depression in its various forms is a commonly seen disorder in general practice. Indeed, over 90% of patients suffering from depression are seen, diagnosed and treated in primal) care. The most severe, chronic and complicated cases are referred on to a psychiatrist.peer-reviewe

A mega-Analysis of genome-wide association studies for major depressive disorder

Prior genome-wide association studies (GWAS) of major depressive disorder (MDD) have met with limited success. We sought to increase statistical power to detect disease loci by conducting a GWAS mega-analysis for MDD. In the MDD discovery phase, we analyzed more than 1.2 million autosomal and X chromosome single-nucleotide polymorphisms (SNPs) in 18 759 independent and unrelated subjects of recent European ancestry (9240 MDD cases and 9519 controls). In the MDD replication phase, we evaluated 554 SNPs in independent samples (6783 MDD cases and 50 695 controls). We also conducted a cross-disorder meta-analysis using 819 autosomal SNPs with P less than 0.0001 for either MDD or the Psychiatric GWAS Consortium bipolar disorder (BIP) mega-analysis (9238 MDD cases/8039 controls and 6998 BIP cases/7775 controls). No SNPs achieved genome-wide significance in the MDD discovery phase, the MDD replication phase or in pre-planned secondary analyses (by sex, recurrent MDD, recurrent early-onset MDD, age of onset, pre-pubertal onset MDD or typical-like MDD from a latent class analyses of the MDD criteria). In the MDD-bipolar cross-disorder analysis, 15 SNPs exceeded genome-wide significance (P less than 5 × 10−8), and all were in a 248 kb interval of high LD on 3p21.1 (chr3:52 425 083–53 822 102, minimum P=5.9 × 10−9 at rs2535629). Although this is the largest genome-wide analysis of MDD yet conducted, its high prevalence means that the sample is still underpowered to detect genetic effects typical for complex traits. Therefore, we were unable to identify robust and replicable findings. We discuss what this means for genetic research for MDD. The 3p21.1 MDD-BIP finding should be interpreted with caution as the most significant SNP did not replicate in MDD samples, and genotyping in independent samples will be needed to resolve its status

Tobacco smoking as a risk factor for major depressive disorder : a population-based study

Background : Smoking is disproportionately prevalent among people with psychiatric illness. Aims : To investigate smoking as a risk factor for major depressive disorder. Method : A population-based sample of women was studied using case–control and retrospective cohort study designs. Exposure to smoking was self-reported, and major depressive disorder diagnosed using the Structured Clinical Interview for DSM–IV–TR (SCID–I/NP). Results : Among 165 people with major depressive disorder and 806 controls, smoking was associated with increased odds for major depressive disorder (age-adjusted odds ratio (OR)=1.46, 95% CI 1.03–2.07). Compared with non-smokers, odds for major depressive disorder more than doubled for heavy smokers (>20 cigarettes/day). Among 671 women with no history of major depressive disorder at baseline, 13 of 87 smokers and 38 of 584 non-smokers developed de novo major depressive disorder during a decade of follow-up. Smoking increased major depressive disorder risk by 93% (hazard ratio (HR)=1.93, 95% CI 1.02–3.69); this was not explained by physical activity or alcohol consumption. Conclusions : Evidence from cross-sectional and longitudinal data suggests that smoking increases the risk of major depressive disorder in women

Prevalence and correlates of depressive disorders in people with Type 2 diabetes: results from the International Prevalence and Treatment of Diabetes and Depression (INTERPRET‐DD) study, a collaborative study carried out in 14 countries

Aims To assess the prevalence and management of depressive disorders in people with Type 2 diabetes in different countries. Methods People with diabetes aged 18–65 years and treated in outpatient settings were recruited in 14 countries and underwent a psychiatric interview. Participants completed the Patient Health Questionnaire and the Problem Areas in Diabetes scale. Demographic and medical record data were collected. Results A total of 2783 people with Type 2 diabetes (45.3% men, mean duration of diabetes 8.8 years) participated. Overall, 10.6% were diagnosed with current major depressive disorder and 17.0% reported moderate to severe levels of depressive symptomatology (Patient Health Questionnaire scores >9). Multivariable analyses showed that, after controlling for country, current major depressive disorder was significantly associated with gender (women) (PPPPP<0.0001). The proportion of those with either current major depressive disorder or moderate to severe levels of depressive symptomatology who had a diagnosis or any treatment for their depression recorded in their medical records was extremely low and non-existent in many countries (0–29.6%). Conclusions Our international study, the largest of this type ever undertaken, shows that people with diabetes frequently have depressive disorders and also significant levels of depressive symptoms. Our findings indicate that the identification and appropriate care for psychological and psychiatric problems is not the norm and suggest a lack of the comprehensive approach to diabetes management that is needed to improve clinical outcomes

Treating depressive disorders with the unified protocol: A preliminary randomized evaluation.

OBJECTIVES: This study aims to examine the efficacy of the Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) for individuals diagnosed with a depressive disorder. METHOD: Participants included 44 adults who met criteria for major depressive disorder, persistent depressive disorder, or another specified depressive disorder according to the Anxiety Disorder Interview Schedule (ADIS). These individuals represent a subset of patients from a larger clinical trial comparing the UP to single-disorder protocols (SDPs) for discrete anxiety disorders and a waitlist control (WLC) condition (Barlow et al., 2017); inclusion criteria for the parent study required participants to have a principal anxiety disorder. RESULTS: Significant reductions in depressive symptoms were observed within the UP condition across clinician-rated and self-report measures of depression from baseline to post-treatment, as well as to the 12-month follow-up assessment. Compared to the WLC group, individuals in the UP condition demonstrated significantly lower levels on our continuous, clinician-rated measure of depressive symptoms at post-treatment. There were no differences between the UP and SDP conditions on depressive symptoms at post-treatment or at the 12-month follow-up timepoint. CONCLUSIONS: In this exploratory set of analyses, the UP evidenced efficacy for reduction of depressive symptoms, adding to the growing support for its utility in treating depression.R01 MH090053 - NIMH NIH HHSAccepted manuscrip

Differential diagnosis of bipolar disorder and major depressive disorder

AbstractBackgroundPatients with bipolar disorder spend approximately half of their lives symptomatic and the majority of that time suffering from symptoms of depression, which complicates the accurate diagnosis of bipolar disorder.MethodsChallenges in the differential diagnosis of bipolar disorder and major depressive disorder are reviewed, and the clinical utility of several screening instruments is evaluated.ResultsThe estimated lifetime prevalence of major depressive disorder (i.e., unipolar depression) is over 3 and one-half times that of bipolar spectrum disorders. The clinical presentation of a major depressive episode in a bipolar disorder patient does not differ substantially from that of a patient with major depressive disorder (unipolar depression). Therefore, it is not surprising that without proper screening and comprehensive evaluation many patients with bipolar disorder may be misdiagnosed with major depressive disorder (unipolar depression). In general, antidepressants have demonstrated little or no efficacy for depressive episodes associated with bipolar disorder, and treatment guidelines recommend using antidepressants only as an adjunct to mood stabilizers for patients with bipolar disorder. Thus, correct identification of bipolar disorder among patients who present with depression is critical for providing appropriate treatment and improving patient outcomes.LimitationsClinical characteristics indicative of bipolar disorder versus major depressive disorder identified in this review are based on group differences and may not apply to each individual patient.ConclusionThe overview of demographic and clinical characteristics provided by this review may help medical professionals distinguish between major depressive disorder and bipolar disorder. Several validated, easily administered screening instruments are available and can greatly improve the recognition of bipolar disorder in patients with depression