Melancholic and atypical depression as predictor and moderator of outcome in cognitive behavior therapy and pharmacotherapy for adult depression

Increased brain nucleus accumbens functional connectivity in melancholic depression

BACKGROUND

Melancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression.

METHODS

Fifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients.

RESULTS

Relative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients.

CONCLUSION

Melancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

Methods for quantifying the heterogeneity of psychopathology

OBJECTIVES

Specifiers for a major depressive disorder (MDE) are supposed to reduce diagnostic heterogeneity. However, recent literature challenges the idea that the atypical and melancholic specifiers identify more homogenous or coherent subgroups. We introduce the usage of distance metrics to characterize symptom heterogeneity. We attempt to replicate prior findings and explore whether symptom heterogeneity is reduced using specifier subgroups.

METHODS

We used data derived from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC Wave I; N = 5,749) and the Sequenced Treatment Alternatives to Relieve Depression study (STAR*D; N = 2,498). We computed Hamming and Manhattan distances from study participants' unique symptom profiles. Distances were standardized from 0-1 and compared by their within- and between-group similarities to their non-specifier counterparts for the melancholic and atypical specifiers.

RESULTS

There was no evidence of statistically significant differences in heterogeneity for specifier (i.e., melancholic or atypical) vs. non-specifier designations (i.e., non-melancholic vs. non-atypical).

CONCLUSION

Replicating prior work, melancholic and atypical depression specifiers appear to have limited utility in reducing heterogeneity. The current study does not support the claim that specifiers create more coherent subgroups as operationalized by similarity in the number of symptoms and their severity. Distance metrics are useful for quantifying symptom heterogeneity.

Psychological treatment of depression: A systematic overview of a 'Meta-Analytic Research Domain'

BACKGROUND

Over the past 16 years, we have developed a 'Meta-analytic Research Domain' (MARD) of all randomized trials of psychological treatments of depression. A MARD is a living systematic review of a research field, that cannot be otherwise covered by one (network) meta-analysis and includes multiple PICOs. In this paper we give an overview of the findings of this MARD.

METHODS

A narrative review of the results of the 118 meta-analyses on psychotherapies for depression that were published within our MARD.

RESULTS

Most research has been conducted on cognitive-behavioral therapy (CBT), but several other psychotherapies are also effective, with few differences between therapies. They can be effectively delivered in individual, group, telephone and guided self-help format and are effective in many different target groups and across different age groups, although the effects are significantly smaller in children and adolescents. Psychotherapies have comparable effects as pharmacotherapy at the short term but are probably more effective at the longer term. Combined treatment is more effective than either psychotherapy or pharmacotherapy alone at the short, but also at the longer term.

LIMITATIONS

We did not summarize all published meta-analyses (protocols, methodological studies) and have not compared our results to those found in other meta-analyses on comparable subjects.

CONCLUSION

Psychotherapies can contribute considerably to a reduction of the disease burden of depression. MARDs are an important next step in the aggregation of knowledge from randomized controlled trials in psychological treatments of depression as well as in other healthcare sectors.

A systematic meta-review of patient-level predictors of psychological therapy outcome in major depressive disorder

BACKGROUND

Psychological therapies are effective for treating major depressive disorder, but current clinical guidelines do not provide guidance on the personalization of treatment choice. Established predictors of psychotherapy treatment response could help inform machine learning models aimed at predicting individual patient responses to different therapy options. Here we sought to comprehensively identify known predictors.

METHODS

EMBASE, Medline, PubMed, PsycINFO were searched for systematic reviews with or without meta-analysis published until June 2020 to identify individual patient-level predictors of response to psychological treatments. 3113 abstracts were identified and 300 articles assessed. We qualitatively synthesized our findings by predictor category (sociodemographic; symptom profile; social support; personality features; affective, cognitive, and behavioural; comorbidities; neuroimaging; genetics) and treatment type. We used the AMSTAR 2 to evaluate the quality of included reviews.

RESULTS

Following screening and full-text assessment, 27 systematic reviews including 12 meta-analyses were eligible for inclusion. 74 predictors emerged for various psychological treatments, primarily cognitive behavioural therapy, interpersonal therapy, and mindfulness-based cognitive therapy.

LIMITATIONS

A paucity of studies examining predictors of psychological treatment outcome, as well as methodological heterogeneities and publication biases limit the strength of the identified predictors.

CONCLUSIONS

The synthesized predictors could be used to supplement clinical decision-making in selecting psychological therapies based on individual patient characteristics. These predictors could also be used as a priori input features for machine learning models aimed at predicting a given patient's likelihood of response to different treatment options for depression, and may contribute toward the development of patient-specific treatment recommendations in clinical guidelines.

Overlapping and segregated changes of functional hubs in melancholic depression and non-melancholic depression

BACKGROUND

Previous research found associations between neuropsychiatric disorders and patterns of highly connected "hub" nodes, which are crucial in coordinating brain functions. Melancholic depression is considered a relatively distinct and homogenous subtype of major depressive disorder (MDD), which responds better to pharmacological treatments than placebos or psychotherapies. Accordingly, melancholic depression probably has distinct neuropathological underpinnings. This study aims to examine the overlapping and segregated changes of functional hubs in melancholic and non-melancholic MDD.

METHODS

Thirty-one melancholic patients, 28 non-melancholic patients, and 32 healthy controls were included. Resting-state functional imaging data were analyzed using global functional connectivity.

RESULTS

Both melancholic and non-melancholic patients had increased GFC in the bilateral insula and decreased GFC in the PCC/precuneus compared to HCs. The distinction was that melancholic patients showed increased GFC in the bilateral thalamus, right inferior parietal lobule (IPL), and left cerebellum Crus I and decreased GFC in the left temporal lobe, whereas non-melancholic patients showed increased GFC in the left superior parietal lobe. Additionally, compared with non-melancholic patients, melancholic individuals displayed significant increases of GFC in the left IPL and cerebellum.

CONCLUSION

Increased GFC of the insula and decreased GFC of the PCC and precuneus are the common abnormalities of melancholic and non-melancholic MDD. Hyperconnectivity of the IPL and cerebellum might be distinctive neuropathological features of melancholic MDD.

Heterogeneity in major depression and its melancholic and atypical specifiers: a secondary analysis of STAR*D

OBJECTIVES

The melancholic and atypical specifiers for a major depressive episode (MDE) are supposed to reduce heterogeneity in symptom presentation by requiring additional, specific features. Fried et al. (2020) recently showed that the melancholic specifier may increase the potential heterogeneity in presenting symptoms. In a large sample of outpatients with depression, our objective was to explore whether the melancholic and atypical specifiers reduced observed heterogeneity in symptoms.

METHODS

We used baseline data from the Inventory of Depression Symptoms (IDS), which was available for 3,717 patients, from the Sequenced Alternatives to Relieve Depression (STAR*D) trial. A subsample met criteria for MDE on the IDS ("IDS-MDE"; N =2,496). For patients with IDS-MDE, we differentiated between those with melancholic, non-melancholic, non-melancholic, atypical, and non-atypical depression. We quantified the observed heterogeneity between groups by counting the number of unique symptom combinations pertaining to their given diagnostic group (e.g., counting the melancholic symptoms for melancholic and non-melancholic groups), as well as the profiles of DSM-MDE symptoms (i.e., ignoring the specifier symptoms).

RESULTS

When considering the specifier and depressive symptoms, there was more observed heterogeneity within the melancholic and atypical subgroups than in the IDS-MDE sample (i.e., ignoring the specifier subgroups). The differences in number of profiles between the melancholic and non-melancholic groups were not statistically significant, irrespective of whether focusing on the specifier symptoms or only the DSM-MDE symptoms. The differences between the atypical and non-atypical subgroups were smaller than what would be expected by chance. We found no evidence that the specifier groups reduce heterogeneity, as can be quantified by unique symptom profiles. Most symptom profiles, even in the specifier subgroups, had five or fewer individuals.

CONCLUSION

We found no evidence that the atypical and melancholic specifiers create more symptomatically homogeneous groups. Indeed, the melancholic and atypical specifiers introduce heterogeneity by adding symptoms to the DSM diagnosis of MDE.

Combining psychopharmacotherapy and psychotherapy is not associated with better treatment outcome in major depressive disorder - evidence from the European Group for the Study of Resistant Depression

Despite plenty of effective antidepressant (AD) treatments, the outcome of major depressive disorder (MDD) is often unsatisfactory, probably due to improvable exploitation of available therapies. This European, cross-sectional, naturalistic multicenter study investigated the frequency of additional psychotherapy in terms of a manual-driven psychotherapy (MDP) in 1410 adult in- and outpatients with MDD, who were primarily treated with AD psychopharmacotherapy. Socio-demographic and clinical patterns were compared between patients receiving both treatments and those lacking concomitant MDP. In a total of 1279 MDD patients (90.7%) with known status of additional MDP, those undergoing a psychopharmacotherapy-MDP combination (31.2%) were younger, higher educated, more often employed and less severely ill with lower odds for suicidality as compared to patients receiving exclusively psychopharmacotherapy (68.8%). They experienced an earlier mean age of MDD onset, melancholic features, comorbid asthma and migraine and received lower daily doses of their first-line ADs. While agomelatine was more often established in these patients, MDD patients without MDP received selective serotonin reuptake inhibitors more frequently. These two patient groups did not differ in terms of response, non-response and treatment resistant depression (TRD). Accordingly, the employment of additional MDP could not be related to better treatment outcomes in MDD. The fact that MDP was applied in a minority of patients with rather beneficial socio-demographic and clinical characteristics might reflect inferior accessibility of these psychotherapeutic techniques for socially and economically disadvantaged populations.

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.

Circadian depression: A mood disorder phenotype

Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.

The clinical characterization of the adult patient with depression aimed at personalization of management

Depression is widely acknowledged to be a heterogeneous entity, and the need to further characterize the individual patient who has received this diagnosis in order to personalize the management plan has been repeatedly emphasized. However, the research evidence that should guide this personalization is at present fragmentary, and the selection of treatment is usually based on the clinician's and/or the patient's preference and on safety issues, in a trial-and-error fashion, paying little attention to the particular features of the specific case. This may be one of the reasons why the majority of patients with a diagnosis of depression do not achieve remission with the first treatment they receive. The predominant pessimism about the actual feasibility of the personalization of treatment of depression in routine clinical practice has recently been tempered by some secondary analyses of databases from clinical trials, using approaches such as individual patient data meta-analysis and machine learning, which indicate that some variables may indeed contribute to the identification of patients who are likely to respond differently to various antidepressant drugs or to antidepressant medication vs. specific psychotherapies. The need to develop decision support tools guiding the personalization of treatment of depression has been recently reaffirmed, and the point made that these tools should be developed through large observational studies using a comprehensive battery of self-report and clinical measures. The present paper aims to describe systematically the salient domains that should be considered in this effort to personalize depression treatment. For each domain, the available research evidence is summarized, and the relevant assessment instruments are reviewed, with special attention to their suitability for use in routine clinical practice, also in view of their possible inclusion in the above-mentioned comprehensive battery of measures. The main unmet needs that research should address in this area are emphasized. Where the available evidence allows providing the clinician with specific advice that can already be used today to make the management of depression more personalized, this advice is highlighted. Indeed, some sections of the paper, such as those on neurocognition and on physical comorbidities, indicate that the modern management of depression is becoming increasingly complex, with several components other than simply the choice of an antidepressant and/or a psychotherapy, some of which can already be reliably personalized.

Depression Heterogeneity and Its Biological Underpinnings: Toward Immunometabolic Depression

Epidemiological evidence indicates the presence of dysregulated homeostatic biological pathways in depressed patients, such as increased inflammation and disrupted energy-regulating neuroendocrine signaling (e.g., leptin, insulin). Alterations in these biological pathways may explain the considerable comorbidity between depression and cardiometabolic conditions (e.g., obesity, metabolic syndrome, diabetes) and represent a promising target for intervention. This review describes how immunometabolic dysregulations vary as a function of depression heterogeneity by illustrating that such biological dysregulations map more consistently to atypical behavioral symptoms reflecting altered energy intake/expenditure balance (hyperphagia, weight gain, hypersomnia, fatigue, and leaden paralysis) and may moderate the antidepressant effects of standard or novel (e.g., anti-inflammatory) therapeutic approaches. These lines of evidence are integrated in a conceptual model of immunometabolic depression emerging from the clustering of immunometabolic biological dysregulations and specific behavioral symptoms. The review finally elicits questions to be answered by future research and describes how the immunometabolic depression dimension could be used to dissect the heterogeneity of depression and potentially to match subgroups of patients to specific treatments with higher likelihood of clinical success.

Predicting treatment effects in unipolar depression: A meta-review

There is increasing interest in clinical prediction models in psychiatry, which focus on developing multivariate algorithms to guide personalized diagnostic or management decisions. The main target of these models is the prediction of treatment response to different antidepressant therapies. This is because the ability to predict response based on patients' personal data may allow clinicians to make improved treatment decisions, and to provide more efficacious or more tolerable medications to the right patient. We searched the literature for systematic reviews about treatment prediction in the context of existing treatment modalities for adult unipolar depression, until July 2019. Treatment effect is defined broadly to include efficacy, safety, tolerability and acceptability outcomes. We first focused on the identification of individual predictor variables that might predict treatment response, and second, we considered multivariate clinical prediction models. Our meta-review included a total of 10 systematic reviews; seven (from 2014 to 2018) focusing on individual predictor variables and three focusing on clinical prediction models. These identified a number of sociodemographic, phenomenological, clinical, neuroimaging, remote monitoring, genetic and serum marker variables as possible predictor variables for treatment response, alongside statistical and machine-learning approaches to clinical prediction model development. Effect sizes for individual predictor variables were generally small and clinical prediction models had generally not been validated in external populations. There is a need for rigorous model validation in large external data-sets to prove the clinical utility of models. We also discuss potential future avenues in the field of personalized psychiatry, particularly the combination of multiple sources of data and the emerging field of artificial intelligence and digital mental health to identify new individual predictor variables.

Guidelines for the pharmacological acute treatment of major depression: conflicts with current evidence as demonstrated with the German S3-guidelines

Several international guidelines for the acute treatment of moderate to severe unipolar depression recommend a first-line treatment with antidepressants (AD). This is based on the assumption that AD obviously outperform placebo, at least in the case of severe depression. The efficacy of AD for severe depression can only be definitely clarified with individual patient data, but corresponding studies have only been available recently. In this paper, we point out discrepancies between the content of guidelines and the scientific evidence by taking a closer look at the German S3-guidelines for the treatment of depression. Based on recent studies and a systematic review of studies using individual patient data, it turns out that AD are marginally superior to placebo in both moderate and severe depression. The clinical significance of this small drug-placebo-difference is questionable, even in the most severe forms of depression. In addition, the modest efficacy is likely an overestimation of the true efficacy due to systematic method biases. There is no related discussion in the S3-guidelines, despite substantial empirical evidence confirming these biases. In light of recent data and with their underlying biases, the recommendations in the S3-guidelines are in contradiction with the current evidence. The risk-benefit ratio of AD for severe depression may be similar to the one estimated for mild depression and thus could be unfavorable. Downgrading of the related grade of recommendation would be a logical consequence.

A Propensity Score Analysis of Homework Adherence-Outcome Relations in Cognitive Behavioral Therapy for Depression

Little is known about whether or not a consistently high level of homework adherence over the course of therapy benefits patients. This question was examined in two samples of patients who were receiving individual Cognitive Behavioral Therapy (CBT) for depression (Ns = 128 [Sequenced Treatment Alternatives to Relieve Depression: STAR-D] and 183 [Continuation Phase Cognitive Therapy Relapse Prevention: C-CT-RP]). Logistic and linear regression and propensity score models were used to identify whether or not clinician assessments of homework adherence differentiated symptom reduction and remission, as assessed by the Hamilton Depression Rating Scale-17 (HDRS-17), the Quick Inventory of Depressive Symptomatology-Self-Reported Scale (QIDS-SR), and the QIDS-Clinician Scale (QIDS-C). CBT-related response and remission were equally likely between both high and low homework adherers in both studies and in all models. But in propensity adjusted models that adjusted for session attendance, for both the STAR-D and C-CT-RP samples, greater homework adherence was significantly associated with greater response and remission from depression in the first and last 8 sessions of CBT. Our results suggest that homework adherence can account for response and remission early and late in treatment, with adequate session attendence.

Impact, Diagnosis, Phenomenology, and Biology

This section provides summaries of the epidemiology, phenomenology, nosology, and the suspected biological substrates of the depressive disorders. It particularly emphasizes the historical evolution of the pertinent diagnostic constructs and the prognostic import both of the various diagnostic groupings and of the individual symptoms and symptom clusters.

The potential of predictive analytics to provide clinical decision support in depression treatment planning

PURPOSE OF REVIEW

To review progress developing clinical decision support tools for personalized treatment of major depressive disorder (MDD).

RECENT FINDINGS

Over the years, a variety of individual indicators ranging from biomarkers to clinical observations and self-report scales have been used to predict various aspects of differential MDD treatment response. Most of this work focused on predicting remission either with antidepressant medications versus psychotherapy, some antidepressant medications versus others, some psychotherapies versus others, and combination therapies versus monotherapies. However, to date, none of the individual predictors in these studies has been strong enough to guide optimal treatment selection for most patients. Interest consequently turned to decision support tools made up of multiple predictors, but the development of such tools has been hampered by small study sample sizes. Design recommendations are made here for future studies to address this problem.

SUMMARY

Recommendations include using large prospective observational studies followed by pragmatic trials rather than smaller, expensive controlled treatment trials for preliminary development of decision support tools; basing these tools on comprehensive batteries of inexpensive self-report and clinical predictors (e.g., self-administered performance-based neurocognitive tests) versus expensive biomarkers; and reserving biomarker assessments for targeted studies of patients not well classified by inexpensive predictor batteries.

Functional Connectivity of the Subcallosal Cingulate Cortex And Differential Outcomes to Treatment With Cognitive-Behavioral Therapy or Antidepressant Medication for Major Depressive Disorder

OBJECTIVE

The purpose of this article was to inform the first-line treatment choice between cognitive-behavioral therapy (CBT) or an antidepressant medication for treatment-naive adults with major depressive disorder by defining a neuroimaging biomarker that differentially identifies the outcomes of remission and treatment failure to these interventions.

METHOD

Functional MRI resting-state functional connectivity analyses using a bilateral subcallosal cingulate cortex (SCC) seed was applied to 122 patients from the Prediction of Remission to Individual and Combined Treatments (PReDICT) study who completed 12 weeks of randomized treatment with CBT or antidepressant medication. Of the 122 participants, 58 achieved remission (Hamilton Depression Rating Scale [HAM-D] score ≤7 at weeks 10 and 12), and 24 had treatment failure (<30% decrease from baseline in HAM-D score). A 2×2 analysis of variance using voxel-wise subsampling permutation tests compared the interaction of treatment and outcome. Receiver operating characteristic curves constructed using brain connectivity measures were used to determine possible classification rates for differential treatment outcomes.

RESULTS

The resting-state functional connectivity of the following three regions with the SCC was differentially associated with outcomes of remission and treatment failure to CBT and antidepressant medication and survived application of the subsample permutation tests: the left anterior ventrolateral prefrontal cortex/insula, the dorsal midbrain, and the left ventromedial prefrontal cortex. Using the summed SCC functional connectivity scores for these three regions, overall classification rates of 72%-78% for remission and 75%-89% for treatment failure was demonstrated. Positive summed functional connectivity was associated with remission with CBT and treatment failure with medication, whereas negative summed functional connectivity scores were associated with remission to medication and treatment failure with CBT.

CONCLUSIONS

Imaging-based depression subtypes defined using resting-state functional connectivity differentially identified an individual's probability of remission or treatment failure with first-line treatment options for major depression. This biomarker should be explored in future research through prospective testing and as a component of multivariate treatment prediction models.

Atypical depression: current perspectives

The history and present status of the definition, prevalence, neurobiology, and treatment of atypical depression (AD) is presented. The concept of AD has evolved through the years, and currently, in Diagnostic and Statistical Manual of Mental Disorders (DSM), Fifth Edition, the specifier of depressive episode with atypical feature is present for both diagnostic groups, that is, depressive disorders and bipolar and related disorders. This specifier includes mood reactivity, hyperphagia, hypersomnia, leaden paralysis, and interpersonal rejection sensitivity. Prevalence rates of AD are variable, depending on the criteria, methodology, and settings. The results of epidemiological studies using DSM criteria suggest that 15%-29% of depressed patients have AD, and the results of clinical studies point to a prevalence of 18%-36%. A relationship of AD with bipolar depression, seasonal depression, and obesity has also been postulated. Pathogenic research has been mostly focused on distinguishing AD from melancholic depression. The differences have been found in biochemical studies in the areas of hypothalamic-pituitary-adrenal axis, inflammatory markers, and the leptin system, although the results obtained are frequently controversial. A number of findings concerning such differences have also been obtained using neuroimaging and neurophysiological and neuropsychological methods. An initial concept of AD as a preferentially monoamine oxidase inhibitor-responsive depression, although confirmed in some further studies, is of limited use nowadays. Currently, despite numerous drug trials, there are no comprehensive treatment guidelines for AD. We finalize the article by describing the future research perspectives for the definition, neurobiology, and treatment. A better specification of diagnostic criteria and description of clinical picture, a genome-wide association study of AD, and establishing updated treatment recommendations for this clinical phenomenon should be the priorities for the coming years.

Evidence-Based Applications of Combination Psychotherapy and Pharmacotherapy for Depression

Combination treatment with psychotherapy and antidepressant medication can be provided from the initiation of treatment, sequentially after nonremission with a single-modality treatment or sequentially after remission to buttress the patient's recovery to prevent recurrence. Combination treatment from the initiation of care is best reserved for patients with high depression severity. Sequential addition of treatments, particularly psychotherapy after nonremission to antidepressant medication, is the best supported method of combination, improving remission rates and reducing relapse and recurrence in the long term. However, uncertainty persists around the optimal form of psychotherapy to combine with antidepressant medication for maximizing long-term gains. Better outcomes from combination treatment have been strongest in clinical trials that limited pharmacotherapy to a single antidepressant; benefits of combination treatment have been substantially smaller in trials that allowed flexible use of multiple antidepressant classes. Patients with recurrent major depressive disorder who benefit from combination treatment have better long-term outcomes if an active treatment component is maintained during recovery.