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Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.

Suicidal behavior (SB) can be impulsive or methodical; violent or not; follow a stressor or no obvious precipitant. This study tested whether childhood trauma, affective lability, and aggressive and impulsive traits predicted greater SI variability. We also assessed whether affective lability, aggressive or impulsive traits explain childhood trauma’s effects on SI variability and whether those with highly variable SI respond to stressful events with increases in SI. Finally, we assessed variable SI’s trajectory over 2 years. Depressed participants (n = 51) had ecological momentary assessments (EMA) over 7 days at baseline, 3, 6, 12, 18, and 24 months. SI variability was assessed using the square Root of the Mean Square of Successive Deviations. Mixed Effects Models were fit as appropriate. Childhood trauma was associated with subsequent aggression. Physical abuse predicted both aggression and affective lability as well as SI variability, but not impulsivity. In two-predictor models, physical abuse’s effect on SI variability was no longer significant, when controlling for the effect of higher aggression and impulsivity. Those with high SI variability exhibited greater increases in SI after stressors compared with those with less variability. We did not find that SI variability changed over time, suggesting it might be trait-like, at least over 2 years. Variable SI predisposes to marked SI increases after stressful events and may be a trait increasing risk for impulsive SB, at least over 2 years.

Serotonergic dysfunction is implicated in major depressive disorder (MDD), but the mechanisms of this relationship remain elusive. Serotonin 1A (5-HT1A) autoreceptors regulate brain-wide serotonin neuron firing and are positioned to assert large-scale effects on negative emotion. Here we investigated the relationship between raphe 5-HT1A binding and brain-wide network dynamics of negative emotion. 22 healthy-volunteers (HV) and 27 medication-free participants with MDD underwent positron emission tomography (PET) using [11C]CUMI-101 (CUMI) to quantify 5-HT1A binding in midbrain raphe nuclei and functional magnetic resonance imaging (fMRI) scanning during emotionally negative picture viewing. Causal connectivity across regions responsive to negative emotion was estimated in the fMRI data using a multivariate dynamical systems model. During negative picture viewing, MDD subjects demonstrated significant hippocampal inhibition of amygdala, basal-ganglia, thalamus, orbital frontal cortex, inferior frontal gyrus and dorsomedial prefrontal cortex (IFG, dmPFC). MDD-related connectivity was not associated with raphe 5-HT1A binding. However, greater hippocampal inhibition of amygdala, thalamus, IFG and dmPFC correlated with hippocampal 5-HT1A binding. Correlation between hippocampal 5-HT1A binding and the hippocampal inhibition network was specific to MDD but not HV. MDD and HV groups also differed with respect to the correlation between raphe and hippocampal 5-HT1A binding which was more pronounced in HV. These findings suggest that increased hippocampal network inhibition in MDD is linked to hippocampal serotonergic dysfunction which may in turn arise from disrupted linkage in raphe to hippocampus serotonergic circuitry.

Prior research suggests that sleep disturbances are associated with increased risk of suicide. However, sleep disturbances are associated with a wide range of psychiatric disorders, and it is unknown whether this association is independent of psychopathology. In a large nationally representative prospective survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), we used structural equation modeling to examine the shared and specific effects of three sleep complaints (i.e., trouble falling asleep, early morning awakening, and hypersomnia) on the 3-year occurrence of attempting suicide. Because psychiatric disorders increase the risk of suicide attempt almost exclusively through a general psychopathology factor representing their shared effect, covariates included that factor, prior history of suicide attempt, and a wide range of sociodemographic and clinical characteristics. The 3-year prevalence rate of suicide attempt was 0.6% (n = 241). Compared with participants who did not attempt suicide between the two waves, those who did reported significantly more frequently having trouble falling asleep (44.6% vs. 16.6%), early morning awakening (38.9% vs. 12.7%), and hypersomnia (35.0% vs. 10.7%). Following adjustments, effects of sleep complaints on this risk were significant and exerted almost exclusively through a general sleep complaints factor representing the shared effect across all sleep complaints. There were no residual associations of any individual sleep complaint with attempting suicide above that association. Sleep complaints are associated with an increased risk of attempting suicide independently of psychopathology, and should be included in suicide risk assessments as these symptoms may provide targets for reducing the risks of suicidal behaviors.

Major depressive disorder (MDD) is a serious, heterogeneous disorder accompanied by brain-related changes, many of which are still to be discovered or refined. Arterial spin labeling (ASL) is a neuroimaging technique used to measure cerebral blood flow (CBF; perfusion) to understand brain function and detect differences among groups. CBF differences have been detected in MDD, and may reveal biosignatures of disease-state. The current work aimed to discover and replicate differences in CBF between MDD participants and healthy controls (HC) as part of the EMBARC study. Participants underwent neuroimaging at baseline, prior to starting study medication, to investigate biosignatures in MDD. Relative CBF (rCBF) was calculated and compared between 106 MDD and 36 HC EMBARC participants (whole-brain Discovery); and 58 MDD EMBARC participants and 58 HC from the DLBS study (region-of-interest Replication). Both analyses revealed reduced rCBF in the right parahippocampus, thalamus, fusiform and middle temporal gyri, as well as the left and right insula, for those with MDD relative to HC. Both samples also revealed increased rCBF in MDD relative to HC in both the left and right inferior parietal lobule, including the supramarginal and angular gyri. Cingulate and prefrontal regions did not fully replicate. Lastly, significant associations were detected between rCBF in replicated regions and clinical measures of MDD chronicity. These results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.

Medications to treat major depressive disorder (MDD) are not equally effective across patients. Given that neural response to rewards is altered in MDD and given that reward-related circuitry is modulated by dopamine and serotonin, we examined, for the first time, whether reward-related neural activity moderated response to sertraline, an antidepressant medication that targets these neurotransmitters. 222 unmedicated adults with MDD randomized to receive sertraline (n=110) or placebo (n=112) in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study completed demographic and clinical assessments, and pre-treatment functional magnetic resonance imaging while performing a reward task. We tested whether an index of reward system function in the ventral striatum (VS), a key reward circuitry region, moderated differential response to sertraline versus placebo, assessed with the Hamilton Rating Scale for Depression over 8 weeks. We observed a significant moderation effect of the reward index, reflecting the temporal dynamics of VS activity, on Week-8 depression levels (Fs≥9.67,ps≤0.002). Specifically, VS responses that were abnormal with respect to predictions from reinforcement learning theory were associated with lower Week-8 depression symptoms in the sertraline versus placebo arms. Thus, a more abnormal pattern of pre-treatment VS dynamic response to reward expectancy (expected outcome value) and prediction error (difference between expected and actual outcome), likely reflecting serotonergic and dopaminergic deficits, was associated with better response to sertraline than placebo. Pre-treatment measures of reward-related VS activity may serve as objective neural markers to advance efforts to personalize interventions by guiding individual-level choice of antidepressant treatment. Trial Registration: NCT01407094; http://clinicaltrials.gov/show/NCT01407094

Abnormalities in brain structural measures, such as cortical thickness and subcortical volumes, are observed in patients with major depressive disorder (MDD) who also often show heterogeneous clinical features. This study seeks to identify the multivariate associations between structural phenotypes and specific clinical symptoms, a novel area of investigation. T1-weighted magnetic resonance imaging measures were obtained using 3 T scanners for 178 unmedicated depressed patients at four academic medical centres. Cortical thickness and subcortical volumes were determined for the depressed patients and patients' clinical presentation was characterized by 213 item-level clinical measures, which were grouped into several large, homogeneous categories by K-means clustering. The multivariate correlations between structural and cluster-level clinical-feature measures were examined using canonical correlation analysis (CCA) and confirmed with both 5-fold and leave-one-site-out cross-validation. Four broad types of clinical measures were detected based on clustering: an anxious misery composite (composed of item-level depression, anxiety, anhedonia, neuroticism and suicidality scores); positive personality traits (extraversion, openness, agreeableness and conscientiousness); reported history of physical/emotional trauma; and a reported history of sexual abuse. Responses on the item-level anxious misery measures were negatively associated with cortical thickness/subcortical volumes in the limbic system and frontal lobe; reported childhood history of physical/emotional trauma and sexual abuse measures were negatively correlated with entorhinal thickness and left hippocampal volume, respectively. In contrast, the positive traits measures were positively associated with hippocampal and amygdala volumes and cortical thickness of the highly-connected precuneus and cingulate cortex. Our findings suggest that structural brain measures may reflect neurobiological mechanisms underlying MDD features.

Prior research suggests that sleep disturbances are associated with increased risk of suicide. However, sleep disturbances are associated with a wide range of psychiatric disorders, and it is unknown whether this association is independent of psychopathology. In a large nationally representative prospective survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), we used structural equation modeling to examine the shared and specific effects of three sleep complaints (i.e., trouble falling asleep, early morning awakening, and hypersomnia) on the 3-year occurrence of attempting suicide. Because psychiatric disorders increase the risk of suicide attempt almost exclusively through a general psychopathology factor representing their shared effect, covariates included that factor, prior history of suicide attempt, and a wide range of sociodemographic and clinical characteristics. The 3-year prevalence rate of suicide attempt was 0.6% (n = 241). Compared with participants who did not attempt suicide between the two waves, those who did reported significantly more frequently having trouble falling asleep (44.6% vs. 16.6%), early morning awakening (38.9% vs. 12.7%), and hypersomnia (35.0% vs. 10.7%). Following adjustments, effects of sleep complaints on this risk were significant and exerted almost exclusively through a general sleep complaints factor representing the shared effect across all sleep complaints. There were no residual associations of any individual sleep complaint with attempting suicide above that association. Sleep complaints are associated with an increased risk of attempting suicide independently of psychopathology, and should be included in suicide risk assessments as these symptoms may provide targets for reducing the risks of suicidal behaviors.

Major depressive disorder (MDD) is a serious, heterogeneous disorder accompanied by brain-related changes, many of which are still to be discovered or refined. Arterial spin labeling (ASL) is a neuroimaging technique used to measure cerebral blood flow (CBF; perfusion) to understand brain function and detect differences among groups. CBF differences have been detected in MDD, and may reveal biosignatures of disease-state. The current work aimed to discover and replicate differences in CBF between MDD participants and healthy controls (HC) as part of the EMBARC study. Participants underwent neuroimaging at baseline, prior to starting study medication, to investigate biosignatures in MDD. Relative CBF (rCBF) was calculated and compared between 106 MDD and 36 HC EMBARC participants (whole-brain Discovery); and 58 MDD EMBARC participants and 58 HC from the DLBS study (region-of-interest Replication). Both analyses revealed reduced rCBF in the right parahippocampus, thalamus, fusiform and middle temporal gyri, as well as the left and right insula, for those with MDD relative to HC. Both samples also revealed increased rCBF in MDD relative to HC in both the left and right inferior parietal lobule, including the supramarginal and angular gyri. Cingulate and prefrontal regions did not fully replicate. Lastly, significant associations were detected between rCBF in replicated regions and clinical measures of MDD chronicity. These results (1) provide reliable evidence for ASL in detecting differences in perfusion for multiple brain regions thought to be important in MDD, and (2) highlight the potential role of using perfusion as a biosignature of MDD.

Suicidal behavior is often conceptualized as a response to overwhelming stress. Our model posits that given a propensity for acting on suicidal urges, stressors such as life events or major depressive episodes (MDEs) determine the timing of suicidal acts. Depressed patients (n=415) were assessed prospectively for suicide attempts and suicide, life events and MDE over 2 years. Longitudinal data were divided into 1-month intervals characterized by MDE (yes/no), suicidal behavior (yes/no) and life event scores. Marginal logistic regression models were fit, with suicidal behavior as the response variable and MDE and life event score in either the same or previous month, respectively, as time-varying covariates. Among 7843 person-months, 33% had MDE and 73% had life events. MDE increased the risk for suicidal behavior (odds ratio (OR)=4.83, P⩽0.0001). Life event scores were unrelated to the timing of suicidal behavior (OR=1.06 per 100 point increase, P=0.32), even during a MDE (OR=1.12, P=0.15). However, among those without borderline personality disorder (BPD), both health- and work-related life events were key precipitants, as was recurrent MDE, with a 13-fold effect. The relationship of life events to suicidal behavior among those with BPD was more complex. Recurrent MDE was a robust precipitant for suicidal behavior, regardless of BPD comorbidity. The specific nature of life events is key to understanding the timing of suicidal behavior. Given unanticipated results regarding the role of BPD and study limitations, these findings require replication. Of note, that MDE, a treatable risk factor, strongly predicts suicidal behaviors is cause for hope.

Strategies for generating knowledge in medicine have included observation of associations in clinical or research settings and more recently, development of pathophysiological models based on molecular biology. Although critically important, they limit hypothesis generation to an incremental pace. Machine learning and data mining are alternative approaches to identifying new vistas to pursue, as is already evident in the literature. In concert with these analytic strategies, novel approaches to data collection can enhance the hypothesis pipeline as well. In data farming, data are obtained in an 'organic' way, in the sense that it is entered by patients themselves and available for harvesting. In contrast, in evidence farming (EF), it is the provider who enters medical data about individual patients. EF differs from regular electronic medical record systems because frontline providers can use it to learn from their own past experience. In addition to the possibility of generating large databases with farming approaches, it is likely that we can further harness the power of large data sets collected using either farming or more standard techniques through implementation of data-mining and machine-learning strategies. Exploiting large databases to develop new hypotheses regarding neurobiological and genetic underpinnings of psychiatric illness is useful in itself, but also affords the opportunity to identify novel mechanisms to be targeted in drug discovery and development.

Despite advances in the assessment and management of suicidal behavior (SB), suicide remains a leading cause of morbidity and mortality in the United States, and suicide rates have increased dramatically over the past 30 years.1 There is ample evidence that suicidal thoughts and behaviors are transdiagnostic phenomena that can arise even in the absence of other diagnosable mental health disorders.2 While attempts to elucidate a unified model of SB have identified genetic, neurobiological, and psychological factors that are associated with increased risk, these risk factors, or their combination, have only modest statistical and limited clinical utility.3 Thus, predicting who will engage in SB remains challenging. We assert that, rather than being a unified construct, SB likely represents a final common pathway of multiple separate pathological processes. Here, we hypothesize that the pattern of suicidal thinking helps distinguish at least two of these suicidal subtypes.

In spite of considerable efforts, no genes of major effect have been found across an entire diagnostic category in psychiatry. Possible reasons for this may include difficulties in defining the phenotype, the complex relationship between genotype and gene expression and population stratification. This last problem has often been managed by restricting genetic sampling to only one ethnic group. An unintended consequence of using this strategy is that the major repositories of genetic material for the study of psychiatric conditions in the United States suffer from a paucity of genetic samples from non-Caucasian groups. Thus, these groups are being relatively understudied in terms of the genetic antecedents to psychiatric disease. The authors provide solutions including the need to augment the representation of African-American, Latino and Asian-Americans among research participants; a more nuanced approach to identify ancestry; and the development of analytic and genetic strategies to handle the issue of ethnic heterogeneity in samples.

The aim of the study is to compare the prevalence of suicidal ideation and attempts in the United States in 1991-1992 and 2001-2002, and identify sociodemographic groups at increased risk for suicidal ideation and attempts. Data were drawn from the National Institute on Alcohol Abuse and Alcoholism 1991-1992 National Longitudinal Alcohol Epidemiologic Survey (n=42,862) and the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (n=43,093), two nationally representative household surveys of non-institutionalized civilians aged 18 years and older, residing in the United States. The lifetime prevalence of suicide attempts remained unchanged in the United States between 1991-1992 and 2001-2002. Specific groups, namely 18- to 24-year-old white and black women, 25- to 44-year-old white women and 45- to 64-year-old Native American men were identified as being at high risk for suicide attempts. Despite prevention and treatment efforts, the lifetime prevalence of suicide attempts remains unchanged. Given the morbidity and mortality associated with suicide attempts, urgent action is needed to decrease the prevalence of suicide attempts in the United States.

Most mental disorders, when examined independently, are associated with an elevated risk for suicide attempt. However, mental disorders often co-occur, and that co-occurrence is well explained by models where specific mental disorders are understood as manifestations of latent dimensions of psychopathology. To date, it remains unclear whether the risk of suicide attempt is due to specific mental disorders, to specific dimensions of psychopathology (that is, internalizing and externalizing dimensions), to a general psychopathology factor or to a combination of these explanations. In a large nationally representative prospective survey, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC), we used structural equation modeling to examine the shared and specific effects of Axis I and Axis II disorders on the occurrence of suicide attempts in the general population and among individuals with a lifetime history of suicidal ideation. Effects of mental disorders on the risk of suicide attempt were exerted almost exclusively through a general psychopathology factor representing the shared effect across all mental disorders. Effects of remitted psychiatric disorders on the risk of suicide attempt were fully mediated by current mental disorders. Similar patterns of associations were found in individuals with suicidal ideation. These results held when using different approaches to modeling psychiatric comorbidity. Our findings underscore the importance of adopting dimensional approaches to comorbidity in the study of suicidal behavior. Because mental disorders increase the risk of suicide attempt through a general psychopathology liability, this dimension should be considered as an important therapeutic target to substantially advance suicide prevention.

The NMDA receptor antagonist ketamine can improve major depressive disorder (MDD) within hours. To evaluate the putative role of glutamatergic and GABAergic systems in ketamine’s antidepressant action, medial prefrontal cortical (mPFC) levels of glutamate + glutamine (Glx) and γ-aminobutyric acid (GABA) were measured before, during, and after ketamine administration using proton magnetic resonance spectroscopy. Ketamine (0.5 mg/kg i.v.) was administered to eleven depressed patients with MDD. Glx and GABA mPFC responses were measured as ratios relative to unsuppressed voxel tissue water (W) successfully in 8/11 patients. Ten of 11 patients remitted (50% reduction in 24-item Hamilton Depression Rating Scale and total ≤ 10) within 230 minutes of commencing ketamine. mPFC Glx/W and GABA/W peaked at 37.8%±7.5% and 38.0%±9.1% above baseline in ~26 minutes. Mean areas under the curve (AUC) for Glx/W (p = 0.025) and GABA/W (p = 0.005) increased and correlated (r = 0.796; p=0.018). Clinical improvement correlated with 90-minute norketamine concentration (df=6, r=−0.78, p=0.023), but no other measures. Rapid increases in Glx and GABA in MDD following ketamine administration support the postulated antidepressant role of glutamate and for the first time raises the question of GABA’s role in the antidepressant action of ketamine. These data support the hypothesis1 that ketamine administration may cause an initial increase in glutamate that potentially activates mammalian target of rapamycin (mTOR) pathway via AMPA receptors, since ketamine blocks NMDA receptors. The role of the contemporaneous surge in GABA remains to be determined.2