Heterogeneity Of Major Depressive Disorder Research Articles

Individual Variations in Nucleus Accumbens Responses Associated with Major Depressive Disorder Symptoms.

Abnormal reward-related responses in the nucleus accumbens (NAcc) have been reported for major depressive disorder (MDD) patients. However, variability exists in the reported results, which could be due to heterogeneity in neuropathology of depression. To parse the heterogeneity of MDD we investigated variation of NAcc responses to gain and loss anticipations using fMRI. We found NAcc responses to monetary gain and loss were significantly variable across subjects in both MDD and healthy control (HC) groups. The variations were seen as a hyperactive response subtype that showed elevated activation to the anticipation of both gain and loss, an intermediate response with greater activation to gain than loss, and a suppressed-activity with reduced activation to both gain and loss compared to a non-monetary condition. While these response variability were seen in both MDD and HC subjects, specific symptoms were significantly associated with the right NAcc variation in MDD. Both the hyper- and suppressed-activity subtypes of MDD patients had severe suicidal ideation and anhedonia symptoms. The intermediate subjects had less severity in these symptoms. These results suggest that differing propensities in reward responsiveness in the NAcc may affect the development of specific symptoms in MDD.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Subgenual Cingulate-Amygdala Functional Disconnection and Vulnerability to Melancholic Depression.

The syndromic heterogeneity of major depressive disorder (MDD) hinders understanding of the etiology of predisposing vulnerability traits and underscores the importance of identifying neurobiologically valid phenotypes. Distinctive fMRI biomarkers of vulnerability to MDD subtypes are currently lacking. This study investigated whether remitted melancholic MDD patients, who are at an elevated lifetime risk for depressive episodes, demonstrate distinctive patterns of resting-state connectivity with the subgenual cingulate cortex (SCC), known to be of core pathophysiological importance for severe and familial forms of MDD. We hypothesized that patterns of disrupted SCC connectivity would be a distinguishing feature of melancholia. A total of 63 medication-free remitted MDD (rMDD) patients (33 melancholic and 30 nonmelancholic) and 39 never-depressed healthy controls (HC) underwent resting-state fMRI scanning. SCC connectivity was investigated with closely connected bilateral a priori regions of interest (ROIs) relevant to MDD (anterior temporal, ventromedial prefrontal, dorsomedial prefrontal cortices, amygdala, hippocampus, septal region, and hypothalamus). Decreased (less positive) SCC connectivity with the right parahippocampal gyrus and left amygdala distinguished melancholic rMDD patients from the nonmelancholic rMDD and HC groups (cluster-based familywise error-corrected p⩽0.007 over individual a priori ROIs corresponding to approximate Bonferroni-corrected p⩽0.05 across all seven a priori ROIs). No areas demonstrating increased (more positive) connectivity were observed. Abnormally decreased connectivity of the SCC with the amygdala and parahippocampal gyrus distinguished melancholic from nonmelancholic rMDD. These results provide the first resting-state neural signature distinctive of melancholic rMDD and may reflect a subtype-specific primary vulnerability factor given a lack of association with the number of previous episodes.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. While efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity, and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1,056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared to observed scores assessed 10–12 years after baseline. ML model prediction accuracy was also compared to that of conventional logistic regression models. Area under the receiver operating characteristic curve (AUC) based on ML (.63 for high chronicity and .71–.76 for the other prospective outcomes) was consistently higher than for the logistic models (.62–.70) despite the latter models including more predictors. 34.6–38.1% of respondents with subsequent high persistence-chronicity and 40.8–55.8% with the severity indicators were in the top 20% of the baseline ML predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML predicted risk distribution. These results confirm that clinically useful MDD risk stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Leptin Dysregulation Is Specifically Associated With Major Depression With Atypical Features: Evidence for a Mechanism Connecting Obesity and Depression

Obesity-related dysregulation of leptin signaling (e.g., hyperleptinemia due to central functional resistance) may affect mood. However, evidence for leptin dysregulation in major depressive disorder (MDD) is conflicting. Inconclusive findings may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the relationship of leptin with MDD, its common subtypes (typical and atypical), and clinical features. The sample consisted of participants (aged 18 to 65 years) from the Netherlands Study of Depression and Anxiety with current (n = 1062) or remitted (n = 711) MDD and healthy control subjects (n = 497). Diagnoses of MDD and subtypes were based on DSM-IV symptoms. Additional symptoms were measured with the Inventory of Depressive Symptomatology. Blood levels of leptin and adiposity indexes (body mass index and waist circumference) were assessed. As compared to control subjects, higher leptin was associated with the atypical MDD subtype both for remitted (n = 144, odds ratio = 1.53, 95% confidence interval = 1.16-2.03, p = .003) and current (n = 270, odds ratio = 1.90, 95% confidence interval = 1.51-2.93, p = 5.3e-8) cases. This association was stronger for increasing adiposity levels (leptin by body mass index interaction, p < .02), strengthening the hypothesis of the involvement of leptin resistance. No association with leptin was found for overall MDD or the typical subtype. Among currently depressed patients, higher leptin was associated with key symptoms identifying the atypical subtype, such as hyperphagia, increased weight, and leaden paralysis. Leptin dysregulation (resistance) may represent an underlying mechanism connecting obesity and MDD with atypical features. Development of treatment effectively targeting leptin resistance may benefit patients with atypical depression characterized by obesity-related metabolic alterations.

Ketamine for depression: evidence, challenges and promise.

Major depressive disorder and bipolar depression are among the most prevalent and disabling mental disorders worldwide. Real-world effectiveness trials in major depressive disorder have underscored that most pharmacological options target monoamines, which are involved in a minority (15-20%) of synaptic contacts in the mammalian brain. Most synapses (∼50%) use the amino acid glutamate as their primary neurotransmitter, and preclinical models of depression have implicated aberrant glutamatergic neurotransmission for 25 years (1). More recently, the N-methyl-D-aspartate (NMDA) glutamate receptor antagonist ketamine was shown to produce rapid and robust antidepressant effects in patients with treatment-resistant major depressive disorder and bipolar depression (2–7).

Polygenic dissection of major depression clinical heterogeneity.

The molecular mechanisms underlying major depressive disorder (MDD) are largely unknown. Limited success of previous genetics studies may be attributable to heterogeneity of MDD, aggregating biologically different subtypes. We examined the polygenic features of MDD and two common clinical subtypes (typical and atypical) defined by symptom profiles in a large sample of adults with established diagnoses. Data were from 1530 patients of the Netherlands Study of Depression and Anxiety (NESDA) and 1700 controls mainly from the Netherlands Twin Register (NTR). Diagnoses of MDD and its subtypes were based on DSM-IV symptoms. Genetic overlap of MDD and subtypes with psychiatric (MDD, bipolar disorder, schizophrenia) and metabolic (body mass index (BMI), C-reactive protein, triglycerides) traits was evaluated via genomic profile risk scores (GPRS) generated from meta-analysis results of large international consortia. Single nucleotide polymorphism (SNP)-heritability of MDD and subtypes was also estimated. MDD was associated with psychiatric GPRS, while no association was found for GPRS of metabolic traits. MDD subtypes had differential polygenic signatures: typical was strongly associated with schizophrenia GPRS (odds ratio (OR)=1.54, P=7.8e-9), while atypical was additionally associated with BMI (OR=1.29, P=2.7e-4) and triglycerides (OR=1.21, P=0.006) GPRS. Similar results were found when only the highly discriminatory symptoms of appetite/weight were used to define subtypes. SNP-heritability was 32% for MDD, 38% and 43% for subtypes with, respectively, decreased (typical) and increased (atypical) appetite/weight. In conclusion, MDD subtypes are characterized by partially distinct polygenic liabilities and may represent more homogeneous phenotypes. Disentangling MDD heterogeneity may help the psychiatric field moving forward in the search for molecular roots of depression.

Neural Correlates of Three Promising Endophenotypes of Depression: Evidence from the EMBARC Study

Major depressive disorder (MDD) is clinically, and likely pathophysiologically, heterogeneous. A potentially fruitful approach to parsing this heterogeneity is to focus on promising endophenotypes. Guided by the NIMH Research Domain Criteria initiative, we used source localization of scalp-recorded EEG resting data to examine the neural correlates of three emerging endophenotypes of depression: neuroticism, blunted reward learning, and cognitive control deficits. Data were drawn from the ongoing multi-site EMBARC study. We estimated intracranial current density for standard EEG frequency bands in 82 unmedicated adults with MDD, using Low-Resolution Brain Electromagnetic Tomography. Region-of-interest and whole-brain analyses tested associations between resting state EEG current density and endophenotypes of interest. Neuroticism was associated with increased resting gamma (36.5-44 Hz) current density in the ventral (subgenual) anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC). In contrast, reduced cognitive control correlated with decreased gamma activity in the left dorsolateral prefrontal cortex (dlPFC), decreased theta (6.5-8 Hz) and alpha2 (10.5-12 Hz) activity in the dorsal ACC, and increased alpha2 activity in the right dlPFC. Finally, blunted reward learning correlated with lower OFC and left dlPFC gamma activity. Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC gamma activity was linked to reduced reward sensitivity. Three putative endophenotypes of depression were found to have partially dissociable resting intracranial EEG correlates, reflecting different underlying neural dysfunctions. Overall, these findings highlight the need to parse the heterogeneity of MDD by focusing on promising endophenotypes linked to specific pathophysiological abnormalities.

Biochemical markers subtyping major depressive disorder.

The pathophysiology of major depressive disorder (MDD) remains elusive, and there is no established biochemical marker used in the daily clinical setting. This situation may result in part from the heterogeneity of MDD, which might include heterogeneous subgroups with different biological mechanisms. In this review, we discuss three promising biological systems/markers to potentially subtype MDD: the dopamine system, the hypothalamic-pituitary-adrenal axis, and chronic inflammatory markers. Several lines of evidence suggest that a facet of MDD is a dopamine agonist-responsive subtype. Focusing on the hypothalamic-pituitary-adrenal axis, depressive spectrum disorders show hypercortisolism to hypocortisolism, which could be detected by hormonal challenge tests, such as the dexamethasone/corticotrophin-releasing hormone test. Finally, accumulating evidence suggests that at least some MDD patients show characteristics similar to those of chronic inflammatory diseases, including neuroinflammatory markers and reduced tryptophan due to the increased activation of the tryptophan-kynurenine pathway. Future studies should examine the inter-relations between these systems/markers to subtype and integrate the pathophysiology of MDD.

The prospective association between obesity and major depression in the general population: does single or recurrent episode matter?

BackgroundObesity and major depressive disorder (MDD) are important public health problems. MDD is a heterogeneous disorder and the direction of its association with obesity remains unclear. Evidence grows that recurrent MDD (MDD-R) differs in etiology and prognosis from single episode MDD (MDD-S), which could affect associations with obesity. However, evidence on this differential effect is lacking. The aim of this study was to examine the direction of the association between obesity and MDD, single or recurrent episode.MethodsA longitudinal study was performed in a cohort of 1094 participants of the PREVEND study, on whom data were collected at baseline and at an average 2-year follow-up. MDD-S and MDD-R were assessed by the Composite International Diagnostic Interview (CIDI 2.1). Obesity was defined as Body Mass Index ≥ 30 kg/m2. Binary logistic regression analyses were conducted to examine whether obesity predicts MDD-S/MDD-R or vice versa, adjusted for potential confounders.ResultsProspective analyses showed that BMI at baseline was associated with the onset of MDD-R (Odds ratio, OR = 1.32; 95% confidence interval, 95%CI: 1.11; 1.57) during 2-year follow-up, but not with the onset of MDD-S (OR = 0.98; 95%CI: 0.89; 1.07). Obesity at baseline was not associated with the onset of MDD-S during follow-up (OR = 0.75; 95%CI: 0.25; 2.30), but associated with the onset of MDD-R during follow-up (OR = 11.63; 95%CI: 1.05; 128.60). Neither MDD-S nor MDD-R were associated with the development of obesity during 2-year follow-up (OR = 1.67, 95%CI: 0.64; 4.29 and OR = 2.32, 95%CI: 0.82; 6.58, respectively).ConclusionsOur findings add to the available evidence that obesity might specifically be associated with the onset of multiple episodes of major depression (MDD-R). Although the reverse association was not found, MDD-R tends to be also associated with subsequent development of obesity, but larger studies are needed to fully assess this issue. The heterogeneity of MDD should be considered when examining the effect of obesity on MDD.

In pursuit of neuroimaging biomarkers to guide treatment selection in major depressive disorder: a review of the literature.

Over the last few decades, neuroimaging techniques have advanced the identification of structural, functional, and neurochemical brain abnormalities that are associated with the increased risk, clinical course, and treatment outcomes of major depressive disorder (MDD). This paper reviews specific neuroimaging abnormalities that, on the basis of early studies, may discriminate between MDD patients who do or do not respond to current therapeutic modalities, such as antidepressants, cognitive behavioral therapy, or novel therapies. Differences in gray matter volume, white matter coherence, brain activity via structural and functional magnetic resonance imaging techniques, and concentrations of specific brain metabolites (as measured with magnetic resonance spectroscopy), are potential biomarkers discussed in this review. Given the heterogeneity of MDD, larger, multisite studies with increased statistical power will be needed to identify more precise imaging biomarkers of treatment response in MDD.

Familiality and SNP heritability of age at onset and episodicity in major depressive disorder.

Strategies to dissect phenotypic and genetic heterogeneity of major depressive disorder (MDD) have mainly relied on subphenotypes, such as age at onset (AAO) and recurrence/episodicity. Yet, evidence on whether these subphenotypes are familial or heritable is scarce. The aims of this study are to investigate the familiality of AAO and episode frequency in MDD and to assess the proportion of their variance explained by common single nucleotide polymorphisms (SNP heritability). For investigating familiality, we used 691 families with 2-5 full siblings with recurrent MDD from the DeNt study. We fitted (square root) AAO and episode count in a linear and a negative binomial mixed model, respectively, with family as random effect and adjusting for sex, age and center. The strength of familiality was assessed with intraclass correlation coefficients (ICC). For estimating SNP heritabilities, we used 3468 unrelated MDD cases from the RADIANT and GSK Munich studies. After similarly adjusting for covariates, derived residuals were used with the GREML method in GCTA (genome-wide complex trait analysis) software. Significant familial clustering was found for both AAO (ICC=0.28) and episodicity (ICC=0.07). We calculated from respective ICC estimates the maximal additive heritability of AAO (0.56) and episodicity (0.15). SNP heritability of AAO was 0.17 (p=0.04); analysis was underpowered for calculating SNP heritability of episodicity. AAO and episodicity aggregate in families to a moderate and small degree, respectively. AAO is under stronger additive genetic control than episodicity. Larger samples are needed to calculate the SNP heritability of episodicity. The described statistical framework could be useful in future analyses.

Influence of age of onset on limbic and paralimbic structures in depression.

Major depressive disorder (MDD) onset during childhood/adolescence is associated with a greater illness burden and distinct clinical profile. However, limited research exists on the effect of age of MDD onset on volumetric abnormalities in para/limbic structures during adulthood. Subgenual anterior cingulate cortex (sgACC), hippocampus and caudate nucleus volumes were measured by manual tracing in depressed individuals (n = 45) and healthy controls (HC; n = 19). Volumetric comparisons were carried out between HC and MDD patients divided into those with pediatric (≤ 18 years; n = 17) and adult onset (≥ 19 years; n = 28). The adult MDD-onset group had smaller sgACC volumes than the pediatric-onset and HC groups (age, sex controlled). No differences in caudate and hippocampus volumes existed. sgACC and hippocampal volumes were inversely correlated with depression severity. Surprisingly, pediatric MDD-onset was not associated with more pronounced sgACC, hippocampus and caudate volume reductions. Nevertheless, age of illness onset appears to be a meaningful dimension of study in efforts to understand the neurobiological heterogeneity of MDD.

Evidence for a differential role of HPA-axis function, inflammation and metabolic syndrome in melancholic versus atypical depression

The hypothalamic-pituitary-adrenal (HPA) axis and the inflammatory response system have been suggested as pathophysiological mechanisms implicated in the etiology of major depressive disorder (MDD). Although meta-analyses do confirm associations between depression and these biological systems, effect sizes vary greatly among individual studies. A potentially important factor explaining variability is heterogeneity of MDD. Aim of this study was to evaluate the association between depressive subtypes (based on latent class analysis) and biological measures. Data from 776 persons from the Netherlands Study of Depression and Anxiety, including 111 chronic depressed persons with melancholic depression, 122 with atypical depression and 543 controls were analyzed. Inflammatory markers (C-reactive protein, interleukin-6, tumor necrosis factor-α), metabolic syndrome components, body mass index (BMI), saliva cortisol awakening curves (area under the curve with respect to the ground (AUCg) and with respect to the increase (AUCi)), and diurnal cortisol slope were compared among groups. Persons with melancholic depression had a higher AUCg and higher diurnal slope compared with persons with atypical depression and with controls. Persons with atypical depression had significantly higher levels of inflammatory markers, BMI, waist circumference and triglycerides, and lower high-density lipid cholesterol than persons with melancholic depression and controls. This study confirms that chronic forms of the two major subtypes of depression are associated with different biological correlates with inflammatory and metabolic dysregulation in atypical depression and HPA-axis hyperactivity in melancholic depression. The data provide further evidence that chronic forms of depressive subtypes differ not only in their symptom presentation, but also in their biological correlates. These findings have important implications for future research on pathophysiological pathways of depression and treatment.

Subthreshold bipolar disorder in a U.S. national representative sample: Prevalence, correlates and perspectives for psychiatric nosography

ObjectiveThere is growing clinical and epidemiological evidence that undiagnosed bipolar features are source of clinical heterogeneity in major depressive disorder (MDD). This study examined and compared the prevalence and correlates of lifetime major depressive episode plus subthreshold hypomania D(m) with pure MDD, bipolar II disorder, and bipolar I disorder. MethodData were drawn from the 2001–2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), a large cross-sectional survey (n=43,093) representative of the U.S. population. ResultsThe lifetime prevalences were 2.53% for D(m), 1.12% for bipolar II disorder, 2.19% for bipolar I disorder and 10.70% for pure MDD. All bipolar disorders (i.e., D(m), BP-II, and BP-I) were half as frequent as MDD.Lifetime and 12-month psychiatric comorbidity, course and clinical characteristics, symptoms, health status and treatment-seeking rates were significantly different in participants with lifetime D(m) when compared to participants with pure MDD, but not when compared to participants with bipolar II disorder. LimitationsSubthreshold hypomania diagnostic was based on the lifetime presence of at least one of the three screening questions for criterion A for hypomania, without a lifetime history of manic or hypomanic episode. This narrow definition, both in terms of the choice of hypomanic symptoms and their duration, could have led to an underestimation of the proportion of participants with a lifetime history of D(m). In addition, the cross-sectional nature of this study does not allow causal associations to be drawn. ConclusionsOur data confirm and extend evidence of the clinical significance and validity of a subthreshold bipolarity-specifier applied to individuals with DSM-IV MDD. Major depression with subthreshold hypomania could be more accurately incorporated into the bipolar II disorder diagnosis.

Stability and transitions of depressive subtypes over a 2-year follow-up.

Identifying depressive subtypes is an important tool in reducing the heterogeneity of major depressive disorder. However, few studies have examined the stability of putative subtypes of depression over time. The sample included 488 persons from the Netherlands Study of Depression and Anxiety (NESDA) who had major depressive disorder at baseline and at the 2-year follow-up assessment. A latent transition analysis (LTA) was applied to examine the stability of depressive subtypes across time-points. Differences in demographic, clinical, psychosocial and health correlates between subtypes were evaluated in a subsample of persons with stable subtypes. Three subtypes were identified at each time-point: a moderate subtype (prevalence T0 39%, T1 42%), a severe typical subtype (T0 30%, T1 25%), and a severe atypical subtype (T0 31%, T1 34%). The LTA showed 76% stability across the 2-year follow-up, with the greatest stability in the severe atypical class (79%). Analyses of correlates in the stable subtypes showed a predominance of women and more overweight and obesity in the severe atypical subtype, and a greater number of negative life events and higher neuroticism and functioning scores in the severe typical subtype. Subtypes of major depressive disorder were found to be stable across a 2-year follow-up and to have distinct determinants, supporting the notion that the identified subtypes are clinically meaningful.

The contribution of BDNF and 5-HTT polymorphisms and early life stress to the heterogeneity of major depressive disorder: A preliminary study

Two reported genetic polymorphisms related to the production of brain-derived neurotrophic factor (BNDF) and reuptake by the serotonin transporter (5-HTT) appear to contribute to depression in combination with stressful life events. The aim of the current study was to investigate the contribution of early life stress (ELS), BDNF (Val versus Met alleles) and 5-HTT polymorphisms (L versus S alleles) to melancholic (n = 65) and non-melancholic depression (n = 59). A mediation approach ((G × G) × E mediation model) was employed to confirm the indirect effects of ELS on the relationship between 5-HTTPLR × BDNF polymorphism combinations and depression subtype. A series of binary logistic regressions were then conducted to determine whether genotype, ELS, and their interaction were able to predict depression subtype. Key findings indicated that BDNF and 5-HTT polymorphisms in combination with ELS contributed to the development of non-melancholic depression. An interaction between BDNF and ELS increased the risk of non-melancholia by 3.327, whereas the interaction between 5-HTT and ELS increased risk by 2.406. The results support a role for genetic factors in the development of non-melancholia. The lack of findings in melancholia indicates that other mechanisms may underlie the subtype. Alternatively, null findings may reflect a Type II error associated with a small sample size. Future studies should consider further examination of differential gene-environment interactions for melancholia versus non-melancholia.

Resting state network alterations in major Depression

Major depressive disorder (MDD) is increasingly being conceptualized as a systems disorder mediated by a disturbance of multiple brain networks. The analysis of functional connectivity (fc) in the resting state has emerged as a powerful tool when applied to neuropsychiatric disorders. We employed resting state functional magnetic resonance imaging to examine fc in multiple resting state networks gained by means of probabilistic group Independent Component Analysis in 43 patients with acute MDD compared to 43 matched controls from a data-driven perspective. Individual network representations were estimated with the dual regression approach. Patients exhibited reduced fc in several networks. Contrary to our expectation that fc alterations would primarily be detected in affective and cognitive networks, effects were strongest in networks implicated in sensory processing, especially visual networks. Reduced fc was also detected in a basal ganglia network, consistent with a role of the basal ganglia in reward-related and affective processing. While imaging research in MDD has focused on networks directly implicated in mood regulation and cognition, these data suggest that network pathology in MDD is not confined to the primary mood-regulating circuit. Considering the heterogeneity of MDD, a systemic approach taking into account alterations in multiple networks may represent the most promising step for the definition of biologically relevant subgroups and potential biomarkers.

Sad benefit in face working memory: An emotional bias of melancholic depression

Emotion biases feature prominently in cognitive theories of depression and are a focus of psychological interventions. However, there is presently no stable neurocognitive marker of altered emotion–cognition interactions in depression. One reason may be the heterogeneity of major depressive disorder. Our aim in the present study was to find an emotional bias that differentiates patients with melancholic depression from controls, and patients with melancholic from those with non-melancholic depression. We used a working memory paradigm for emotional faces, where two faces with angry, happy, neutral, sad or fearful expression had to be retained over one second. Twenty patients with melancholic depression, 20 age-, education- and gender-matched control participants and 20 patients with non-melancholic depression participated in the study. We analysed performance on the working memory task using signal detection measures. We found an interaction between group and emotion on working memory performance that was driven by the higher performance for sad faces compared to other categories in the melancholic group. We computed a measure of “sad benefit”, which distinguished melancholic and non-melancholic patients with good sensitivity and specificity. However, replication studies and formal discriminant analysis will be needed in order to assess whether emotion bias in working memory may become a useful diagnostic tool to distinguish these two syndromes.

Sertoli cell therapy: A novel possible treatment strategy for treatment-resistant major depressive disorder

By the year 2020, depression will be the 2nd most common health problem in the world. Current medications to treat depression are effective in less than 50% of patients. There is the need for novel treatments for depression to address the high rates of resistance to current treatment and the chronic residual symptoms in many patients treated for depression. The heterogeneity of major depressive disorder suggests that multiple neurocircuits and neurochemicals are involved in its pathogenesis thus, finding an alternative to neurotransmitter agonist- or antagonist-based treatments offers an important new approach. Cellular therapy is an emerging treatment strategy for multiple diseases, including depression. Based upon their in vivo function as “nurse cells” within the testis and the documented viability, efficacy, and safety of Sertoli cells transplanted into multiple tissues, including brain, the potential for these cells to provide a neuroprotective, anti-inflammatory, and trophic environment for neurons should be considered. It is proposed that the combination of self-protective, immunoregulatory and trophic properties of Sertoli cells may confer a unique potential for depression treatment and avoid many of the risks and challenges associated with stem cell therapies. At the very least, studies of the effects of Sertoli cell transplantation will add substantially to our understanding of the cellular and molecular processes that underlie depression.