Comprehensive phenotyping of depression disease trajectory and risk: Rationale and design of Texas Resilience Against Depression study (T-RAD)

Clinical and sociodemographic features of the Texas resilience against depression (T-RAD) study: Findings from the initial cohort

OBJECTIVE

The burden of major depressive disorder is compounded by a limited understanding of its risk factors, the limited efficacy of treatments, and the lack of precision approaches to guide treatment selection. The Texas Resilience Against Depression (T-RAD) study was designed to explore the etiology of depression by collecting comprehensive socio-demographic, clinical, behavioral, neurophysiological/neuroimaging, and biological data from depressed individuals (D2K) and youth at risk for depression (RAD).

METHODS

This report details the baseline sociodemographic, clinical, and functional features from the initial cohort (D2K N = 1040, RAD N = 365).

RESULTS

Of the total T-RAD sample, n = 1078 (76.73 %) attended ≥2 in-person visits, and n = 845 (60.14 %) attended ≥4 in-person visits. Most D2K (84.82 %) had a primary diagnosis of any depressive disorder, with a bipolar disorder diagnosis being prevalent (13.49 %). RAD participants (75.89 %) did not have a psychiatric diagnosis, but other non-depressive diagnoses were present. D2K participants had 9-item Patient Health Questionnaire scores at or near the moderate range (10.58 ± 6.42 > 24 yrs.; 9.73 ± 6.12 10-24 yrs). RAD participants were in the non-depressed range (2.19 ± 2.65). While the age ranges in D2K and RAD differ, the potential to conduct analyses that compare at-risk and depressed youth is a strength of the study. The opportunity to examine the trajectory of depressive symptoms in the D2K cohort over the lifespan is unique.

LIMITATIONS

As a longitudinal study, missing data were common.

CONCLUSION

T-RAD will allow data to be collected from multiple modalities on a clinically well-characterized sample. These data will drive important discoveries on diagnosis, treatment, and prevention of depression.

Active suicidal ideation associated with dysfunction in default mode network using resting-state EEG and functional MRI - Findings from the T-RAD Study

Suicide in youth and young adults is a serious public health problem. However, the biological mechanisms of suicidal ideation (SI) remain poorly understood. The primary goal of these analyses was to identify the connectome profile of suicidal ideation using resting state electroencephalography (EEG). We evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years, n = 111) with current or past diagnoses of either a depressive disorder or bipolar disorder who were enrolled in the Texas Resilience Against Depression Study (T-RAD). Neurocircuitry was analyzed using orthogonalized power envelope connectivity computed from resting state EEG. Suicidal ideation was assessed with the 3-item Suicidal Thoughts factor of the Concise Health Risk Tracking self-report scale. The statistical pipeline involved dimension reduction using principal component analysis, and the association of neuroimaging data with SI using regularized canonical correlation analysis. From the original 111 participants and the correlation matrix of 4950 EEG connectivity pairs in each band (alpha, beta, theta), dimension reduction generated 1305 EEG connectivity pairs in the theta band, 2337 EEG pairs in the alpha band, and 914 EEG connectivity pairs in the beta band. Overall, SI was consistently involved with dysfunction of the default mode network (DMN). This report provides preliminary evidence of DMN dysfunction associated with active suicidal ideation in adolescents. Using EEG using power envelopes to compute connectivity moves us closer to using neurocircuit dysfunction in the clinical setting to identify suicidal ideation.

Immune Dysregulation in Treatment-Resistant Depression: Precision Approaches to Treatment Selection and Development of Novel Treatments

Owing to the link between immune dysfunction and treatment-resistant depression (TRD) and the overwhelming evidence that the immune dysregulation and major depressive disorder (MDD) are associated with each other, using immune profiles to identify the biological distinct subgroup may be the step forward to understanding MDD and TRD. This report aims to briefly review the role of inflammation in the pathophysiology of depression (and TRD in particular), the role of immune dysfunction to guide precision medicine, tools used to understand immune function, and novel statistical techniques.

Leveraging the microbiome to understand clinical heterogeneity in depression: findings from the T-RAD study

Alterations in the gut microbiome have been linked to a variety of mental illnesses including anxiety and depression. This study utilized advanced bioinformatics tools that integrated both the compositional and community nature of gut microbiota to investigate how gut microbiota influence clinical symptoms in a sample of participants with depression. Gut microbiota of 179 participants with major depressive disorder (MDD) in the Texas Resilience Against Depression (T-RAD) study were analyzed by 16S rRNA gene sequencing of stool samples. Severity of anxiety, depression, and anhedonia symptoms were assessed with General Anxiety Disorder - 7 item scale, Patient Health 9-item Questionnaire, and Dimensional Anhedonia Rating Scale, respectively. Using weighted correlation network analysis, a data-driven approach, three co-occurrence networks of bacterial taxa were identified. One of these co-occurrence networks was significantly associated with clinical features including depression and anxiety. The hub taxa associated with this co-occurrence module -one Ruminococcaceae family taxon, one Clostridiales vadinBB60 group family taxon, and one Christencenellaceae family taxon- were connected to several additional butyrate-producing bacteria suggesting that deficits in butyrate production may contribute to clinical symptoms. Therefore, by considering the community nature of the gut microbiome in a real world clinical sample, this study identified a gut microbial co-occurrence network that was significantly associated with clinical anxiety in a cohort of depressed individuals.

Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study

BACKGROUND

Major depressive disorder (MDD) may be associated with accelerated brain aging (higher brain age than chronological age). This report evaluated whether brain age is a clinically useful biomarker by checking its test-retest reliability using magnetic resonance imaging scans acquired 1 week apart and by evaluating the association of accelerated brain aging with symptom severity and antidepressant treatment outcomes.

METHODS

Brain age was estimated in participants of the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study using T1-weighted structural magnetic resonance imaging (MDD n = 290; female n = 192; healthy control participants n = 39; female n = 24). Intraclass correlation coefficient was used for baseline-to-week-1 test-retest reliability. Association of baseline Δ brain age (brain age minus chronological age) with Hamilton Depression Rating Scale-17 and Concise Health Risk Tracking Self-Report domains (impulsivity, suicide propensity [measures: pessimism, helplessness, perceived lack of social support, and despair], and suicidal thoughts) were assessed at baseline (linear regression) and during 8-week-long treatment with either sertraline or placebo (repeated-measures mixed models).

RESULTS

Mean ± SD baseline chronological age, brain age, and Δ brain age were 37.1 ± 13.3, 40.6 ± 13.1, and 3.1 ± 6.1 years in MDD and 37.1 ± 14.7, 38.4 ± 12.9, and 0.6 ± 5.5 years in healthy control groups, respectively. Test-retest reliability was high (intraclass correlation coefficient = 0.98-1.00). Higher baseline Δ brain age in the MDD group was associated with higher baseline impulsivity and suicide propensity and predicted smaller baseline-to-week-8 reductions in Hamilton Depression Rating Scale-17, impulsivity, and suicide propensity with sertraline but not with placebo.

CONCLUSIONS

Brain age is a reliable and potentially clinically useful biomarker that can prognosticate antidepressant treatment outcomes.

Characterizing inflammatory profiles of suicidal behavior in adolescents: Rationale and design

BACKGROUND

The suicide rate in youth and young adults continues to climb - we do not understand why this increase is occurring, nor do we have adequate tools to predict or prevent it. Increased efforts to treat underlying depression and other disorders that are highly associated with suicide have had limited impact, despite considerable financial investments in developing and disseminating available methods. Thus, there is a tremendous need to identify potential markers of suicide behavior for youth during this high-risk period.

METHODS

Funded by the American Foundation for Suicide Prevention (AFSP), this study aims to map immune dysfunction to suicidal behavior and establish a reliable immune signature of suicide risk that can 1) guide future research into fundamental pathophysiology and 2) identify targets for drug development. The study design is an observational study where blood samples and a comprehensive array of clinical measures are collected from three groups of adolescents (n = 75 each) (1) with suicidal behavior [recent (within 3 months) suicide attempt or suicidal ideation warranting urgent evaluation,] (2) at risk for mood disorders, and (3) who are healthy (no psychiatric history). Participants will complete self-report and clinical assessments, along with a blood draw, at baseline, 3 months, 6 months and 12 months, and online self-report assessments once a month.

RESULTS

The recruitment for this study is ongoing.

LIMITATIONS

Observational, variability in treatment regimens.

CONCLUSIONS

This study will help elucidate immune mechanisms that may play a causal role in suicide and serve as targets for future therapeutic development.

Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study

BACKGROUND

To address the clinical heterogeneity of Major Depressive Disorder (MDD), this investigation determined whether resting state functional magnetic resonance imaging (fMRI) could be deployed to identify circuit based homogeneous subgroups, and whether subgroups identified show differential treatment outcomes.

METHODS

Pretreatment resting state fMRIs obtained from 278 outpatients with nonpsychotic MDD from Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression Study were used to create data-driven subgroups using CLICK clustering. These subgroups were then compared using baseline clinical data, as well as baseline-to-week 8 changes in depression severity measured using the 17-item Hamilton Rating Scale for Depression (HAMD17) and response/remission rates by treatment group.

RESULTS

Three subgroups were identified. Cluster-1 was characterized by overallhyperconnectivity coupled with profound hypoconnectivity between the supramarginal gyrus (executive control network; ECN) and the superior frontal cortex (dorsal attention network; DAN). Cluster-2 was characterized by overall hypoconnectivity coupled with hyperconnectivity between supramarginal gyrus (ECN) and superior frontal cortex (DAN). Cluster-3 showed hypoconnectivity, especially profound between the angular cortex (default mode network; DMN) and middle frontal cortex (ECN). While baseline clinical measures did not differentiate the three clusters, Cluster-3 had the remission rate (51.6%) compared to Cluster-1 and Cluster-2 (32.7% and 31.9%) when treated with sertraline.

LIMITATIONS

Due to the exploratory nature of these analyses, there were no adjustments for multiple comparisons.

CONCLUSIONS

Baseline functional connectivity can be used to subgroup patients with MDD that differ in acute phase treatment outcomes. Measures of connectivity may address the heterogeneity of MDD.

A prospective cohort study of depression (PROUD) in China: rationale and design

Background

Major depressive disorder (MDD) imposes a heavy global disease burden. However, current etiology, diagnosis and treatment remain unsatisfactory and no previous study has resolved this problem. Building on the strengths and limitations of previous cohort studies of MDD, the prospective cohort study of depression (PROUD) is a 3-year large-scale cohort study designed to collect multidimensional data with a flexible follow-up schedule and strategy. The goal is to establish a nationally representative, high-quality, standardized depression cohort to support precise diagnosis and treatment of MDD and address the gap in current research.

Methods

PROUD is a patient-based, nationally representative multicenter prospective cohort study with baseline and 3-year follow-up assessments. It will be carried out from January 2022 to December 2026 in 52 qualified tertiary hospitals in China. A total of 14,000 patients diagnosed with MDD, according to the DSM-5 criteria, and aged ≥ 16 years, will be recruited to PROUD. Participants aged 18-65 years who have not received any treatment during a depressive episode will be included in the precision medicine cohort (PMC) of PROUD (n=4,000). Patients who meet the general eligibility criteria but not the PMC criteria will be included in the naturalistic observation cohort (NOC) of PROUD (n=10,000). A multiple follow-up strategy, including scheduled, remote, telephone, external visits and patient self-reports, will be implemented to collect comprehensive sociodemographic, clinical information, biospecimens, neuroimaging, cognitive function and electrophysiology data and digital phenotypes according to strict standard operating procedures implemented across centers. Trial registration: ChiCTR2200059053, registered on 23 April 2022, http://www.chictr.org.cn/showproj.aspx?proj=165790.

Conclusions

PROUD is a prospective cohort study of MDD patients in China. It will provide a comprehensive database facilitating further analyses and aiding the development of homeostatic and precision medicine in China.

Social and environmental variables as predictors of mania: a review of longitudinal research findings

Considerable evidence suggests that psychosocial variables can shape the course of bipolar disorder. Here, though, we focus on the more specific idea that the social environment can predict the course of mania. We systematically review evidence from longitudinal studies concerning how social support, family interactions, traumatic life events, and recent life events relate to the age of onset, the frequency of episode recurrence, and the severity of manic symptoms. Although we find some evidence that the course of mania can be worsened by social environmental factors, the links are specific. Among social variables, some studies indicate that conflict and hostility are predictive, but more general social relationship qualities have not been found to predict mania. Some research indicates that childhood trauma, and recent life events involving goal attainment or sleep disruption can predict mania. Taken together, the profile of variables involving recent exposure that are most predictive include those that are activating, reward-related, or sleep-disrupting, which fits with general psychological hypotheses of behavioral activation and sleep disruption as important for mania. We discuss gaps in the literature, and we note future directions for research, including the need for more integrative, longitudinal research on a fuller range of social and biological risk variables.

Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) in patients with major depressive disorder: rationale and design of a prospective multicentre cohort study

INTRODUCTION

Major depressive disorder (MDD) represents a worldwide burden on healthcare and the response to antidepressants remains limited. Systems biology approaches have been used to explore the precision therapy. However, no reliable biomarker clinically exists for prognostic prediction at present. The objectives of the Integrated Module of Multidimensional Omics for Peripheral Biomarkers (iMORE) study are to predict the efficacy of antidepressants by integrating multidimensional omics and performing validation in a real-world setting. As secondary aims, a series of potential biomarkers are explored for biological subtypes.

METHODS AND ANALYSIS

iMore is an observational cohort study in patients with MDD with a multistage design in China. The study is performed by three mental health centres comprising an observation phase and a validation phase. A total of 200 patients with MDD and 100 healthy controls were enrolled. The protocol-specified antidepressants are selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors. Clinical visits (baseline, 4 and 8 weeks) include psychiatric rating scales for symptom assessment and biospecimen collection for multiomics analysis. Participants are divided into responders and non-responders based on treatment response (>50% reduction in Montgomery-Asberg Depression Rating Scale). Antidepressants' responses are predicted and biomarkers are explored using supervised learning approach by integration of metabolites, cytokines, gut microbiomes and immunophenotypic cells. The accuracy of the prediction models constructed is verified in an independent validation phase.

ETHICS AND DISSEMINATION

The study was approved by the ethics committee of Shanghai Mental Health Center (approval number 2020-87). All participants need to sign a written consent for the study entry. Study findings will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04518592.

Immune characterization of suicidal behavior in female adolescents

Background

To address the need to identify potential markers of suicide behavior for adolescents (ages 12-18 years), mass cytometry was used to explore the cellular mechanisms that may underpin immune dysregulation in adolescents with recent suicidal behavior.

Methods

Peripheral blood mononuclear cell (PBMC) samples from 10 female adolescents with a recent suicide attempt and 4 healthy female adolescents were used. A panel of 30 antibodies was analyzed using mass cytometry. We used two complementary approaches to 1) identify the cell types that significantly differed between the two groups, and 2) explore differences in the expression profile of markers on the surface of these cells. Mass cytometry data were investigated using (Center for Disease Control, 2021) Opt-SNE for dimension reduced (Curtin and Heron, 2019), FlowSOM for clustering, and (Bridge et al., 2006) EgdeR and SAM for statistical analyses.

Results

Opt-SNE (a data driven clustering analysis) identified 15 clusters of distinct cell types. From these 15 clusters, cluster 5 (classical monocytes) had statistically lower abundance in suicidal adolescents as compared to healthy controls, whereas cluster 7 (gamma-delta T cells) had statistically higher abundance in suicidal adolescents compared to healthy control. Furthermore, across the 15 cell types, chemokine receptors, CXCR3 (cluster 5) and CXCR5 (clusters 4, 5, 7, and 9), had an elevated expression profile in those with a recent suicide attempt versus healthy controls.

Conclusion

This report demonstrates the utility of high dimensional cell phenotyping in psychiatric disorders and provides preliminary evidence for distinct immune dysfunctions in adolescents with recent suicide attempts as compared to healthy controls.

Does body mass index predict response to intravenous ketamine treatment in adults with major depressive and bipolar disorder? Results from the Canadian Rapid Treatment Center of Excellence

BACKGROUND

Higher body mass index (BMI) has been found to predict greater antidepressant response to intravenous (IV) ketamine treatment. We evaluated the association between BMI and response to repeat-dose IV ketamine in patients with treatment-resistant depression (TRD).

METHODS

Adults (N = 230) with TRD received four infusions of IV ketamine at a community-based clinic. Changes in symptoms of depression (ie, Quick Inventory for Depressive Symptomatology-Self-Report 16; QIDS-SR16), suicidal ideation (SI; ie, QIDS-SR16 SI item), anxiety (ie, Generalized Anxiety Disorder-7 Scale), anhedonic severity (ie, Snaith-Hamilton Pleasure Scale), and functioning (ie, Sheehan Disability Scale) following infusions were evaluated. Participants were stratified by BMI as normal (18.0-24.9 kg/m2; n = 72), overweight (25-29.9 kg/m2; n = 76), obese I (30-34.9 kg/m2; n = 47), or obese II (≥35.0 kg/m2; n = 35).

RESULTS

Similar antidepressant effects with repeat-dose ketamine were reported between BMI groups (P = .261). In addition, categorical partial response (P = .149), response (P = .526), and remission (P = .232) rates were similar between the four BMI groups.

CONCLUSIONS

The findings are limited by the observational, open-label design of this retrospective analysis. Pretreatment BMI did not predict response to IV ketamine, which was effective regardless of BMI.

Dysregulation of mitochondrial dynamics, mitophagy and apoptosis in major depressive disorder: Does inflammation play a role?

Recent studies have suggested that mitochondrial dysfunction and dysregulated neuroinflammatory pathways are involved in the pathophysiology of major depressive disorder (MDD). Here, we aimed to assess the differences in markers of mitochondrial dynamics, mitophagy, general autophagy, and apoptosis in peripheral blood mononuclear cells (PBMCs) of MDD patients (n = 77) and healthy controls (HCs, n = 24). Moreover, we studied inflammation engagement as a moderator of mitochondria dysfunctions on the severity of depressive symptoms. We found increased levels of Mfn-2 (p < 0.001), short Opa-1 (S-Opa-1) (p < 0.001) and Fis-1 (p < 0.001) in MDD patients, suggesting an increase in the mitochondrial fragmentation. We also found that MDD patients had higher levels of Pink-1 (p < 0.001), p62/SQSTM1 (p < 0.001), LC3B (p = 0.002), and caspase-3 active (p = 0.001), and lower levels of parkin (p < 0.001) compared with HCs. Moreover, we showed that that MDD patients with higher CRP levels had higher levels of Mfn-2 (p = 0.001) and LC3B (p = 0.002) when compared with MDD patients with low CRP. Another notable finding was that the severity of depressive symptoms in MDD is associated with changes in protein levels in pathways related to mitochondrial dynamics and mitophagy, and can be dependent on the inflammatory status. Overall, our study demonstrated that a disruption in the mitochondrial dynamics network could initiate a cascade of abnormal changes relevant to the critical pathological changes during the course of MDD and lead to poor outcomes.

Impact of the COVID-19 pandemic on adults with current and prior depression: initial findings from the longitudinal Texas RAD study

BACKGROUND

Emerging work has suggested worsening mental health in the general population during the COVID-19 pandemic, but there is minimal data on individuals with a prior history of depression.

METHODS

Data regarding depression, anxiety and quality of life in adult participants with a history of a depressive disorder (n = 308) were collected before and during the COVID-19 pandemic. Mixed effects regression models were fit for these outcomes over the period May - August 2020, controlling for pre-pandemic depressive groups (none, mild, moderate-to-severe), demographic characteristics, and early COVID-19 related experiences (such as disruptions in routines, mental health treatment, and social supports).

RESULTS

In pre-to-early pandemic comparisons, the 3 pre-pandemic depressive categories varied significantly in anxiety (F_df=2,197 = 7.93, p < 0.0005) and psychological QOL (F_df=2,196 = 8.57, p = 0.0003). The mildly depressed group (F_df=1,201 = 6.01, p = 0.02) and moderate-to-severely depressed group (F_df=1,201 = 38.51, p < 0.0001) had a significant reduction in anxiety. There were no changes among the groups in any outcome from May to August 2020. However, early impact on mental health care access and disruption in routines predicted worse outcomes during this time.

LIMITATIONS

Follow-up data were self-reported. Furthermore, the duration was a relatively short span into the pandemic.

CONCLUSIONS

Symptoms of depression, anxiety, and quality of life were generally stable from 2019 throughout August 2020 in adults with a history of depression. Disruption in mental health care access and routines in May 2020 predicted worse symptom outcomes through August 2020.

Dysfunction of default mode network is associated with active suicidal ideation in youths and young adults with depression: Findings from the T-RAD study

Deaths due to suicide are one of the leading causes of mortality among youths and young adults. Active suicidal ideation (SI) is considered one of the strongest risk factors for suicide. Here, we evaluated the neurocircuitry of SI in a sample of youths and young adults (aged 10-26 years) with current or past diagnosis of either major depression or bipolar disorder who were enrolled in Texas Resilience Against Depression Study (T-RAD), and had neuroimaging and SI (assessed with the 3-item Suicidal Thoughts factor of Concise Health Risk Tracking self-report scale) data available (n = 72, 53 females). Resting-state functional connectivity (FC) was computed amongst 121 cortical and subcortical regions of interest resulting in 7260 FC pairs. Mean (SD) age and SI levels of participants were 19.6 years (4.01) and 1.48 (2.36) respectively. In univariate analyses, 34 out of the 7260 FC pairs were correlated with SI (p < .005). Stronger connectivity of default mode network (DMN) with striatum was associated with higher SI. Conversely, higher SI was associated with weaker connectivity of limbic network with hippocampus, DMN, dorsal attention network, and executive control network. In multivariate analyses, these 34 FC pairs together had an average correlation of 0.54 after five-fold cross-validation. In conclusion, SI was associated with distinct patterns of resting-state functional connectivity among youths and young adults with regions in DMN and the ventral striatum as key nodes.

Differential response to SSRI versus Placebo and distinct neural signatures among data-driven subgroups of patients with major depressive disorder

OBJECTIVE

To identify data-driven subgroups in Major Depressive Disorder (MDD) in order to elucidate underlying neural correlates and determine if these subgroups have utility in predicting response to antidepressant versus placebo.

METHODS

Using 27 clinical measures at baseline of Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC) study, participants with MDD (n=244) were sub grouped using principal component (PC) analysis. Baseline-to-week-8 changes in depression severity with sertraline versus placebo were compared in these subgroups. Resting-state functional connectivity of these subgroups were compared to those of healthy controls (n=38).

RESULTS

Eight subgroups were identified from four PCs: (PC1) severity of depression-associated symptoms, (PC2) sub-threshold mania and anhedonia, (PC3) childhood trauma, medical comorbidities, and sexual dysfunction, and (PC4) personality traits of openness and agreeableness. Participants with high childhood trauma experienced greater improvement with sertraline (Cohen's d=0.87), whereas those with either higher levels of subthreshold hypomanic symptoms (Cohen's d=0.67) or with lower levels of agreeableness and openness experienced greater improvement with placebo (Cohen's d=0.71). Participants with high childhood trauma had greater connectivity between salience and dorsal attention networks, whereas those with higher levels of subthreshold hypomanic symptoms and lower levels of agreeableness and openness had greater connectivity within limbic network and that of visual network with hippocampus and dorsal attention network.

CONCLUSION

Assessing history of childhood trauma, presence of subthreshold hypomanic symptoms and personality traits may help to identify subgroups of patients with MDD who respond differentially to sertraline or placebo and have distinct neural signatures.

Dorsolateral Prefrontal Cortex and Subcallosal Cingulate Connectivity Show Preferential Antidepressant Response in Major Depressive Disorder

BACKGROUND

Major depressive disorder is associated with abnormal connectivity across emotion and reward circuits as well as other established circuits that may negatively impact treatment response. The goal of this study was to perform an exploratory reanalysis of archival data from a clinical trial to identify moderators of treatment outcome of sertraline over placebo.

METHODS

EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study participants completed magnetic resonance imaging before randomization to either sertraline or placebo for 8 weeks (n = 279). Seed-based functional connectivity was computed using 4 bilateral seeds (2 spheres defined bilaterally): amygdala, dorsolateral prefrontal cortex (DLPFC), subcallosal cingulate cortex, and ventral striatum. Functional connectivity maps were generated, principal component analysis was performed, linear mixed effects models were used to determine moderators of treatment outcome, and post hoc analyses were used to determine level of connectivity (low and high, -1 and +1 SD from the mean) that was most sensitive to improved depression severity (baseline to week 8) based on treatment.

RESULTS

Greater mean reduction in the 17-item Hamilton Rating Scale for Depression score by 8 weeks occurred with sertraline relative to placebo when connectivity in the DLPFC was low (3-way interaction test, p = .05). Conditional on low connectivity in the DLPFC and subcallosal cingulate cortex and high connectivity in the ventral striatum and amygdala, there was on average a 4.8-point greater reduction in the 17-item Hamilton Rating Scale for Depression score with sertraline relative to placebo (p = .003).

CONCLUSIONS

The level of functional connectivity seeded in both the DLPFC and the subcallosal cingulate cortex networks may play an important role in identifying a favorable response to sertraline over placebo.

An electroencephalographic signature predicts antidepressant response in major depression

Antidepressants are widely prescribed, but their efficacy relative to placebo is modest, in part because the clinical diagnosis of major depression encompasses biologically heterogeneous conditions. Here, we sought to identify a neurobiological signature of response to antidepressant treatment as compared to placebo. We designed a latent-space machine-learning algorithm tailored for resting-state electroencephalography (EEG) and applied it to data from the largest imaging-coupled, placebo-controlled antidepressant study (n = 309). Symptom improvement was robustly predicted in a manner both specific for the antidepressant sertraline (versus placebo) and generalizable across different study sites and EEG equipment. This sertraline-predictive EEG signature generalized to two depression samples, wherein it reflected general antidepressant medication responsivity and related differentially to a repetitive transcranial magnetic stimulation treatment outcome. Furthermore, we found that the sertraline resting-state EEG signature indexed prefrontal neural responsivity, as measured by concurrent transcranial magnetic stimulation and EEG. Our findings advance the neurobiological understanding of antidepressant treatment through an EEG-tailored computational model and provide a clinical avenue for personalized treatment of depression.

Higher S100B Levels Predict Persistently Elevated Anhedonia with Escitalopram Monotherapy Versus Antidepressant Combinations: Findings from CO-MED Trial

BACKGROUND

Elevated S100 calcium binding protein B (S100B) levels in systemic circulation may induce neuroinflammation and reflect greater blood-brain barrier (BBB) dysfunction. Neuroinflammation in patients with major depressive disorder (MDD), in turn, may reduce likelihood of improvement with serotonergic antidepressants.

METHODS

Levels of S100B were measured in plasma samples obtained prior to initiation of treatment with bupropion-plus-escitalopram, escitalopram-plus-placebo, or venlafaxine-plus-mirtazapine in participants of Combining Medications to Enhance Depression Outcomes trial (n = 153). Depression severity was measured with 16-item Quick Inventory of Depressive Symptomatology Self-Report and anhedonia was measured with 3 items of 30-item Inventory of Depressive Symptomatology. Differential changes in depression severity and anhedonia over acute-phase (baseline, weeks 1, 2, 4, 6, 8, 10, and 12) in the three treatment arms were tested with logS100B-by-treatment-arm interaction in mixed model analyses after controlling for age, gender, and body mass index.

RESULTS

There was a significant logS100B-by-treatment-arm interaction for anhedonia (F = 3.21; df = 2, 142; p = 0.04) but not for overall depression severity (F = 1.99; df = 2, 142; p = 0.14). Higher logS100B levels were associated with smaller reductions in anhedonia (effect size = 0.67, p = 0.047) in escitalopram monotherapy but not in the other two arms. Correlation coefficients of anhedonia severity averaged over acute-phase (including baseline) with baseline S100B levels were 0.57, -0.19, and 0.22 for escitalopram monotherapy, bupropion-plus-escitalopram and venlafaxine-plus-mirtazapine arms respectively.

CONCLUSION

Higher baseline S100B levels in depressed patients resulted in poorer response to escitalopram monotherapy. Addition of bupropion, a dopaminergic antidepressant, partially mitigated this effect.