Reward related ventral striatal activity and differential response to sertraline versus placebo in depressed individuals

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.

Neuroimaging for precision medicine in psychiatry

Although the lifetime burden due to mental disorders is increasing, we lack tools for more precise diagnosing and treating prevalent and disabling disorders such as major depressive disorder. We lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry, focusing on major depressive and anxiety disorders. We begin by outlining evidence for the use of functional neuroimaging to stratify the heterogeneity of these disorders, based on underlying circuit dysfunction. We then review the current landscape of how functional neuroimaging-derived circuit predictors can predict treatment outcomes and clinical trajectories in depression and anxiety. Future directions for advancing clinically appliable neuroimaging measures are considered. We conclude by considering the opportunities and challenges of translating neuroimaging measures into practice. As an illustration, we highlight one approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

A distinctive subcortical functional connectivity pattern linking negative affect and treatment outcome in major depressive disorder

Major depressive disorder (MDD) is associated with functional disturbances in subcortical regions. In this naturalistic prospective study (NCT03294525), we aimed to investigate relationships among subcortical functional connectivity (FC), mood symptom profiles and treatment outcome in MDD using multivariate methods. Medication-free participants with MDD (n = 135) underwent a functional magnetic resonance imaging scan at baseline and completed posttreatment clinical assessment after 8 weeks of antidepressant monotherapy. We used partial least squares (PLS) correlation analysis to explore the association between subcortical FC and mood symptom profiles. FC score, reflecting the weighted representation of each individual in this association, was computed. Replication analysis was undertaken in an independent sample (n = 74). We also investigated the relationship between FC score and treatment outcome in the main sample. A distinctive subcortical connectivity pattern was found to be associated with negative affect. In general, higher FC between the caudate, putamen and thalamus was associated with greater negative affect. This association was partly replicated in the independent sample (similarity between the two samples: r = 0.66 for subcortical connectivity, r = 0.75 for mood symptom profile). Lower FC score predicted both remission and response to treatment after 8 weeks of antidepressant monotherapy. The emphasis here on the role of dorsal striatum and thalamus consolidates prior work of subcortical connectivity in MDD. The findings provide insight into the pathogenesis of MDD, linking subcortical FC with negative affect. However, while the FC score significantly predicted treatment outcome, the low odds ratio suggests that finding predictive biomarkers for depression remains an aspiration.

Striatal correlates of Bayesian beliefs in self-efficacy in adolescents and their relation to mood and autonomy: a pilot study

Major depressive disorder often originates in adolescence and is associated with long-term functional impairment. Mechanistically characterizing this heterogeneous illness could provide important leads for optimizing treatment. Importantly, reward learning is known to be disrupted in depression. In this pilot fMRI study of 21 adolescents (16-20 years), we assessed how reward network disruption impacts specifically on Bayesian belief representations of self-efficacy (SE-B) and their associated uncertainty (SE-U), using a modified instrumental learning task probing activation induced by the opportunity to choose, and an optimal Hierarchical Gaussian Filter computational model. SE-U engaged caudate, nucleus accumbens (NAcc), precuneus, posterior parietal and dorsolateral prefrontal cortex (PFWE < 0.005). Sparse partial least squares analysis identified SE-U striatal activation as associating with one's sense of perceived choice and depressive symptoms, particularly anhedonia and negative feelings about oneself. As Bayesian uncertainty modulates belief flexibility and their capacity to steer future actions, this suggests that these striatal signals may be informative developmentally, longitudinally and in assessing response to treatment.

Selection of the optimal dose of sertraline for depression: A dose-response meta-analysis of randomized controlled trials

Sertraline has been shown to be effective in the treatment of depression. However, the relationship between the dosage of sertraline and its efficacy and safety are unclear. We identified RCTs that compared sertraline with placebo for the treatment of depression, conducted conventional meta-analyses on the efficacy and safety of sertraline, and assessed the nonlinear dose-response relationship between sertraline dosage and the changes in HAM-D and CGI-S scores, dropout from care for any reason or due to adverse effects, and the rate of adverse effects, using a 1-stage restricted cubic spline regression model. Twenty-one RCTs involving 4,235 patients were included. The pooled mean differences (MD) in the change in HAM-D total score [MD=-2.34, 95% confidence interval (CI) -2.93, -1.76], CGI-S score and MADRS score, but also the dropout rate for adverse effects, and rate of adverse effects were higher in sertraline group. The therapeutic response of sertraline for treating depression increased with the dosage. Meanwhile, the risk of total adverse reactions slightly decreased between 50 and 150 mg, and increased at doses above 150 mg. The dose-dependence of both efficacy and safety need to be considered when choosing the optimal dosage of sertraline.

Cingulate prediction of response to antidepressant and cognitive behavioral therapies for depression: Meta-analysis and empirical application

We sought to identify baseline (pre-treatment) neural markers associated with treatment response in major depressive disorder (MDD), specific to treatment type, Cognitive Behavioral Therapy (CBT) or pharmacotherapy (selective serotonin reuptake inhibitors; SSRI). We conducted a meta-analysis of functional magnetic resonance imaging (fMRI) studies to identify neural prognostic indicators of response to CBT or SSRI. To verify the regions derived from literature, the meta-analytic regions were used to predict clinical change in a verification sample of participants with MDD who received either CBT (n = 60) or an SSRI (n = 19) as part of prior clinical trials. The meta-analysis consisted of 21 fMRI studies that used emotion-related tasks. It yielded prognostic regions of the perigenual (meta pgACC) and subgenual anterior cingulate cortex (meta sgACC), associated with SSRI and CBT response, respectively. When applying the meta-analytic regions to predict treatment response in the verification sample, reactivity of the meta pgACC was prognostic for SSRI response, yet the effect direction was opposite of most prior studies. Meta sgACC reactivity failed to be prognostic for CBT response. Results confirm the prognostic potential of neural reactivity of ACC subregions in MDD but further research is necessary for clinical translation.

The facets of psychopathology in patients with cancer: Cross-sectional and longitudinal network analyses

OBJECTIVE

Cancer patients display heterogeneous psychopathology, comprising depressive, anxiety, hostility, and somatic symptoms. Often, clinical pictures evolve over time deteriorating the individual functioning and prognosis. Network models can reveal the relationships between symptoms, thus providing clinical insights.

METHOD

This study examined data of the Brief Symptom Inventory and the Distress Thermometer, from 1108 cancer outpatients. Gaussian Graphical Models were estimated using regularized and non-regularized Bayesian methods. In addition, we used community detection methods to identify the most relevant symptom groupings, and longitudinal network analyses on 515 participants to examine the connections between symptoms over three months.

RESULTS

The network models derived from baseline data suggested symptoms clustered into three main complexes (depression/anxiety, hostility, and somatic symptoms). Symptoms related to depression and hostility were highly connected with suicidal and death thoughts. Faintness, weakness, chest pain, and dyspnoea, among somatic symptoms, were more strongly connected with psychopathological features. Longitudinal analyses revealed that sadness, irritability, nervousness, and tension predicted each other. Panic and death thoughts predicted fearfulness and faintness.

CONCLUSIONS

Somatic symptoms, sadness, irritability, chronic and acute anxiety interact between each other, shaping the heterogeneous clinical picture of distress in cancer. This study, strengthened by robust methods, is the first to employ longitudinal network analyses in cancer patients. Further studies should evaluate whether targeting specific symptoms might prevent the onset of chronic distress and improve clinical outcomes in cancer patients.

Biomarkers in Psychiatric Drug Development: From Precision Medicine to Novel Therapeutics

Burden of psychiatric disorders is compounded by their wide prevalence as well as the limited efficacy of currently available treatments and the current approaches for prescribing these treatments. The selection of treatments continues to be subjective and often results in a trial-and-error approach. Emerging research suggests that biological markers (or biomarkers) can be used to develop precision medicine approaches for psychiatric disorders. Furthermore, the biomarkers also promise to elucidate the underlying pathophysiological mechanisms which in turn can be used to develop novel therapeutic treatments. In this chapter we have focused on mood disorders and reviewed studies on electroencephalography (EEG), magnetic resonance imaging (MRI), and blood-based biomarkers that can guide selection of one treatment versus another (treatment-selection biomarker) as well as biomarkers that can guide the development of novel therapeutics. These studies suggest that the use of objective physiological data is poised to alter the landscape of psychiatric diagnosis and treatment. However, practical and economic barriers remain as major hurdles. The key to finding such translational diagnostic and therapeutic biomarkers is a better understanding of the underlying pathophysiology, and despite the tremendous advances in neuroscience, it is clear there remains much left to be elucidated.

MRI predictors of pharmacotherapy response in major depressive disorder

Major depressive disorder is among the most prevalent psychiatric disorders, exacting a substantial personal, social, and economic toll. Antidepressant treatment typically involves an individualized trial and error approach with an inconsistent success rate. Despite a pressing need, no reliable biomarkers for predicting treatment outcome have yet been discovered. Brain MRI measures hold promise in this regard, though clinical translation remains elusive. In this review, we summarize structural MRI and functional MRI (fMRI) measures that have been investigated as predictors of treatment outcome. We broadly divide these into five categories including three structural measures: volumetric, white matter burden, and white matter integrity; and two functional measures: resting state fMRI and task fMRI. Currently, larger hippocampal volume is the most widely replicated predictor of successful treatment. Lower white matter hyperintensity burden has shown robustness in late life depression. However, both have modest discriminative power. Higher fractional anisotropy of the cingulum bundle and frontal white matter, amygdala hypoactivation and anterior cingulate cortex hyperactivation in response to negative emotional stimuli, and hyperconnectivity within the default mode network (DMN) and between the DMN and executive control network also show promise as predictors of successful treatment. Such network-focused measures may ultimately provide a higher-dimensional measure of treatment response with closer ties to the underlying neurobiology.

Associations between cannabis use, cannabis use disorder, and mood disorders: longitudinal, genetic, and neurocognitive evidence

RATIONALE

Cannabis use among people with mood disorders increased in recent years. While comorbidity between cannabis use, cannabis use disorder (CUD), and mood disorders is high, the underlying mechanisms remain unclear.

OBJECTIVES

We aimed to evaluate (1) the epidemiological evidence for an association between cannabis use, CUD, and mood disorders; (2) prospective longitudinal, genetic, and neurocognitive evidence of underlying mechanisms; and (3) prognosis and treatment options for individuals with CUD and mood disorders.

METHODS

Narrative review of existing literature is identified through PubMed searches, reviews, and meta-analyses. Evidence was reviewed separately for depression, bipolar disorder, and suicide.

RESULTS

Current evidence is limited and mixed but suggestive of a bidirectional relationship between cannabis use, CUD, and the onset of depression. The evidence more consistently points to cannabis use preceding onset of bipolar disorder. Shared neurocognitive mechanisms and underlying genetic and environmental risk factors appear to explain part of the association. However, cannabis use itself may also influence the development of mood disorders, while others may initiate cannabis use to self-medicate symptoms. Comorbid cannabis use and CUD are associated with worse prognosis for depression and bipolar disorder including increased suicidal behaviors. Evidence for targeted treatments is limited.

CONCLUSIONS

The current evidence base is limited by the lack of well-controlled prospective longitudinal studies and clinical studies including comorbid individuals. Future studies in humans examining the causal pathways and potential mechanisms of the association between cannabis use, CUD, and mood disorder comorbidity are crucial for optimizing harm reduction and treatment strategies.

Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial

BACKGROUND

The lack of biomarkers to inform antidepressant selection is a key challenge in personalized depression treatment. This work identifies candidate biomarkers by building deep learning predictors of individual treatment outcomes using reward processing measures from functional magnetic resonance imaging, clinical assessments, and demographics.

METHODS

Participants in the EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care) study (n = 222) underwent reward processing task-based functional magnetic resonance imaging at baseline and were randomized to 8 weeks of sertraline (n = 106) or placebo (n = 116). Subsequently, sertraline nonresponders (n = 37) switched to 8 weeks of bupropion. The change in Hamilton Depression Rating Scale was measured after treatment. Reward processing, clinical measurements, and demographics were used to train treatment-specific deep learning models.

RESULTS

The predictive model for sertraline achieved R² of 48% (95% CI, 33%-61%; p < 10^-3) in predicting the change in Hamilton Depression Rating Scale and number-needed-to-treat (NNT) of 4.86 participants in predicting response. The placebo model achieved R² of 28% (95% CI, 15%-42%; p < 10^-3) and NNT of 2.95 in predicting response. The bupropion model achieved R² of 34% (95% CI, 10%-59%, p < 10^-3) and NNT of 1.68 in predicting response. Brain regions where reward processing activity was predictive included the prefrontal cortex and cerebellar crus 1 for sertraline and the cingulate cortex, caudate, orbitofrontal cortex, and crus 1 for bupropion.

CONCLUSIONS

These findings demonstrate the utility of reward processing measurements and deep learning to predict antidepressant outcomes and to form multimodal treatment biomarkers.

Effect and neural mechanisms of the transcutaneous vagus nerve stimulation for relapse prevention in patients with remitted major depressive disorder: protocol for a longitudinal study

INTRODUCTION

After the first episode, patients with remitted major depressive disorder (MDD) have a 60% chance of experiencing a second episode. There are currently no accepted, effective methods to prevent the recurrence of MDD in remission. Transcutaneous vagus nerve stimulation (taVNS) is a non-invasive, safe and economical approach based on the efficacy of VNS in improving clinical depression symptoms. This clinical trial will study the efficacy of taVNS in preventing MDD relapse and investigate the underlying mechanisms of this.

METHODS AND ANALYSIS

We will conduct a multicentre, randomised, patient-blinded and evaluators double-blinded trial. We will randomise 90 eligible participants with recurrent MDD in remission in a 1:1 ratio into a real or sham taVNS group. All participants will be given six biopsychosocial assessments: proinflammatory cytokines, serum monoamine neurotransmitters, cognition, affective neuropsychology, multimodal neuroimaging and endocrinology. After the baseline measurements, all participants will be given corresponding interference for 6 months and then complete a 1-year follow-up. The assessments will be performed three times: at baseline, post-treatment and at the end of 1-year follow-up (except for multimodal MRI scanning, which will be conducted at the first two assessments only). Change in 17-item Hamilton Depression Rating Scale scores for MDD is the primary outcome parameter.

ETHICS AND DISSEMINATION

The study protocol was approved by the Medical Ethical Committee of Beijing Hospital of Traditional Chinese Medicine on 18 January 2019 (2018BL-076). The trial results will be published in peer-reviewed journals and at conferences.

TRIAL REGISTRATION NUMBER

ChiCTR1900022618.

Functional Neuroimaging Correlates of Placebo Response in Patients With Depressive or Anxiety Disorders: A Systematic Review

BACKGROUND

The mechanisms underlying placebo effects of psychotropic drugs remain poorly understood. We carried out the first, to our knowledge, systematic review of functional neuroimaging correlates of placebo response in adults with anxiety/depressive disorders.

METHODS

We systematically searched a large set of databases up to February 2021 based on a pre-registered protocol (PROSPERO CRD42019156911). We extracted neuroimaging data related to clinical improvement following placebo or related to placebo mechanisms. We did not perform a meta-analysis due to the small number of included studies and significant heterogeneity in study design and outcome measures.

RESULTS

We found 12 relevant studies for depressive disorders and 4 for anxiety disorders. Activity in the ventral striatum, rostral anterior cingulate cortex and other default mode network regions, orbitofrontal cortex, and dorsolateral prefrontal cortex correlated with placebo antidepressant responses. Activity in regions of the default mode network, including posterior cingulate cortex, was associated with placebo anxiolysis. There was also evidence for possible involvement of the endogenous opioid, dopamine, and serotonin systems in placebo antidepressant and anxiolytic effects.

CONCLUSIONS

Several brain regions and molecular systems may be involved in these placebo effects. Further adequately powered studies exploring causality and controlling for confounders are required.

Fronto-limbic neuroimaging biomarkers for diagnosis and prediction of treatment responses in major depressive disorder

The neuroimaging is an important tool for understanding the biomarkers and predicting treatment responses in major depressive disorder (MDD). The potential biomarkers and prediction of treatment response in MDD will be addressed in the review article. The brain regions of cognitive control and emotion regulation, such as the frontal and limbic regions, might represent the potential targets for MDD biomarkers. The potential targets of frontal lobes might include anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC). For the limbic system, hippocampus and amygdala might be the potentially promising targets for MDD. The potential targets of fronto-limbic regions have been found in the studies of several major neuroimaging modalities, such as the magnetic resonance imaging, near-infrared spectroscopy, electroencephalography, positron emission tomography, and single-photon emission computed tomography. Additional regions, such as brainstem and midbrain, might also play a part in the MDD biomarkers. For the prediction of treatment response, the gray matter volumes, white matter tracts, functional representations and receptor bindings of ACC, DLPFC, OFC, amygdala, and hippocampus might play a role in the prediction of antidepressant responses in MDD. For the response prediction of psychotherapies, the fronto-limbic, reward regions, and insula will be the potential targets. For the repetitive transcranial magnetic stimulation, the DLPFC, ACC, limbic, and visuospatial regions might represent the predictive targets for treatment. The neuroimaging targets of MDD might be focused in the fronto-limbic regions. However, the neuroimaging targets for the prediction of treatment responses might be inconclusive and beyond the fronto-limbic regions.

Efficacy of anticonvulsant ethosuximide for major depressive disorder: a randomized, placebo-control clinical trial

Results of a preclinical study suggested that the anticonvulsant drug ethosuximide may elicit ketamine-like rapid-acting antidepressant actions. We evaluated the antidepressant efficacy of ethosuximide versus placebo in non-medicated adult patients with major depressive disorder (MDD). This randomized, double-blind, placebo-controlled trial included patients at three mental health centers in China. Eighty eligible adults (aged 18-65 years) met the DSM-5 criteria for MDD. Patients in the acute single study received three doses (500, 1000, or 1500 mg) of ethosuximide or placebo. Patients in the repeated study received ethosuximide (1500 mg/day) or placebo for 2 weeks. The Hamilton Depression Rating Scale (HAM-D), the Montgomery-Åsberg Depression Rating Scale (MADRS), and the Hamilton Anxiety Rating Scale were used to assess antidepressant and antianxiety responses to ethosuximide. No significant reductions in depression and anxiety rating scale scores were observed after a single oral administration of ethosuximide, in comparison with placebo. Furthermore, patients receiving ethosuximide for 2 weeks did not show reductions in depression and anxiety rating scale scores. There were no serious adverse events. Responses to the study's primary and secondary outcome measures, the clinician-rated HAM-D and MADRS, showed no change from baseline to the end of treatment, with either ethosuximide or placebo. These results suggest that ethosuximide does not produce ketamine-like robust antidepressant actions in adult patients with MDD.

Intrinsic reward circuit connectivity profiles underlying symptom and quality of life outcomes following antidepressant medication: a report from the iSPOT-D trial

There is a critical need to better understand the neural basis of antidepressant medication (ADM) response with respect to both symptom alleviation and quality of life (QoL) in major depressive disorder (MDD). Reward neurocircuitry has been implicated in QoL, the neural basis of MDD, and the mechanisms of ADM response. Yet, we do not know whether change in reward neurocircuitry as a function of ADM is associated with change in symptoms and QoL. To address this gap in knowledge, we analyzed data from 128 patients with MDD who participated in the iSPOT-D trial and were assessed with functional neuroimaging pre- and post-ADM treatment (randomized to sertraline, venlafaxine-XR, or escitalopram). 58 matched healthy controls were scanned at the same time points. We quantified functional connectivity (FC) of reward neurocircuitry using nucleus accumbens (NAc) seed regions of interest, and then characterized how changes in FC relate to symptom response (primary outcome) and QoL response (secondary outcome). Symptom responders showed an increase in NAc-dorsal anterior cingulate cortex (ACC) FC relative to non-responders (p < 0.001) which was associated with improvement in physical QoL (p < 0.0003), and a decrease in NAc-inferior parietal lobule FC relative to controls (p < 0.001). QoL response was characterized by increases in FC between NAc-ventral ACC for environmental, NAc-thalamus for physical, and NAc-paracingulate gyrus for social domains (p < 0.001). Symptom responders to sertraline were distinguished by a decrease in NAc-insula FC (p < 0.001) and to venlafaxine-XR by an increase in NAc-inferior temporal gyrus FC (p < 0.005). Findings suggest that change in reward neurocircuitry may underlie differential ADM response profiles with respect to symptoms and QoL in depression.

Reward processing in major depressive disorder and prediction of treatment response - Neuropharm study

Major depressive disorder (MDD) is a prevalent brain disorder for which anhedonia is a core symptom, indicating aberrations in the neural processing of reward. The striatum, medial prefrontal cortex (mPFC) and anterior insula (AI) are core reward processing regions. Here we used a reward-related, card-guessing functional magnetic resonance imaging (fMRI) paradigm to assay brain responses to reward in 90 MDD individuals and 58 healthy controls. We evaluated group differences in task-responsive, reward-related striatal, mPFC, and AI reactivity and whether baseline reactivity predicted an eight-week escitalopram antidepressant treatment response in MDD individuals. Thirty-eight MDD individuals also completed the reward paradigm after treatment and we evaluated antidepressant effects on reward reactivity estimates. Multivariate statistical analysis of task-responsive striatum, mPFC and AI brain responses did not reveal statistically significant differences between MDD and HC individuals (p_uncorrected>0.23). Logistic regression models (five-fold cross-validation, statistical significance assessed with permutation testing) also did not support that baseline reward-related brain responses significantly predicted antidepressant treatment response (p_uncorrected>0.39). Finally, reward-related brain responses were not statistically significantly changed over the course of treatment (p_uncorrected>0.27). Our findings in a comparatively large MDD cohort do not support that these reward-related fMRI brain responses are informative biomarkers of MDD or antidepressant treatment response to escitalopram.

Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

Functional neuroimaging biomarkers of resilience in major depressive disorder

PURPOSE OF REVIEW

In this review we provide an overview of definitions and determinants of resilience in the context of neuroimaging research in major depressive disorder (MDD). We summarize emerging literature on functional neuroimaging biomarkers of resilience in MDD and discuss their clinical relevance and implications for future research.

RECENT FINDINGS

Resilience in MDD is characterized by dissociable profiles of activation and functional connectivity within brain networks involved in cognitive control, emotion regulation, and reward processing. Increased activation of frontal cortical brain regions implicated in cognitive appraisal and emotion regulation is a common characteristic of resilient individuals at high risk for MDD and of individuals with MDD with a favorable illness course. Furthermore, significant associations between fronto-striato-limbic functional connectivity and both positively interpreted stressful life events in resilient high-risk individuals and a favorable response to first-line treatments in depressed individuals suggest that neuro-compensatory changes and experience-dependent plasticity underlie resilience in MDD.

SUMMARY

Emerging research has identified functional neuroimaging biomarkers of resilience in MDD. A continued focus on identifying neurobiological underpinnings of resilience, in the context of dynamic environmental and developmental influences, will advance our understanding of resilience and improve approaches to prevention and treatment of MDD.

Mechanisms Underlying Unconscious Processing and Their Alterations in Post-traumatic Stress Disorder: Neuroimaging of Zero Monetary Outcomes Contextually Framed as "No Losses" vs. "No Gains"

Although unconscious processing is a key element of mental operation, its neural correlates have not been established. Also, clinical observations suggest that unconscious processing may be involved in the pathophysiology of post-traumatic stress disorder (PTSD), but the neurobiological mechanisms underlying such impairments remain unknown. The purpose of the present study was to examine putative mechanisms underlying unconscious processing by healthy participants and to determine whether these mechanisms may be altered in PTSD patients. Twenty patients with PTSD and 27 healthy individuals were administered a validated wheel of fortune-type gambling task during functional magnetic resonance imaging (fMRI). Unconscious processing was elicited using unconscious contextual framing of the zero monetary outcomes as "no loss," "no gain" or as "neutral." Brief passive visual processing of the "no loss" vs. "no gain" contrast by healthy participants yielded bilateral frontal-, temporal- and insular cortices and striatal activations. Between-group comparison revealed smaller activity in the left anterior prefrontal-, left dorsolateral prefrontal-, right temporal- and right insular cortices and in bilateral striatum in PTSD patients with the left dorsolateral prefrontal cortex activity been more pronounced in those with greater PTSD severity. These observations implicate frontal-, temporal-, and insular cortices along with the striatum in the putative mechanisms underlying unconscious processing of the monetary outcomes. Additionally, our results support the hypothesis that PTSD is associated with primary cortical and subcortical alterations involved in the above processes and that these alterations may be related to some aspects of PTSD symptomatology.

Pretreatment Reward Sensitivity and Frontostriatal Resting-State Functional Connectivity Are Associated With Response to Bupropion After Sertraline Nonresponse

BACKGROUND

Standard guidelines recommend selective serotonin reuptake inhibitors as first-line antidepressants for adults with major depressive disorder, but success is limited and patients who fail to benefit are often switched to non-selective serotonin reuptake inhibitor agents. This study investigated whether brain- and behavior-based markers of reward processing might be associated with response to bupropion after sertraline nonresponse.

METHODS

In a two-stage, double-blinded clinical trial, 296 participants were randomized to receive 8 weeks of sertraline or placebo in stage 1. Individuals who responded continued on another 8-week course of the same intervention in stage 2, while sertraline and placebo nonresponders crossed over to bupropion and sertraline, respectively. Data from 241 participants were analyzed. The stage 2 sample comprised 87 patients with major depressive disorder who switched medication and 38 healthy control subjects. A total of 116 participants with major depressive disorder treated with sertraline in stage 1 served as an independent replication sample. The probabilistic reward task and resting-state functional magnetic resonance imaging were administered at baseline.

RESULTS

Greater pretreatment reward sensitivity and higher resting-state functional connectivity between bilateral nucleus accumbens and rostral anterior cingulate cortex were associated with positive response to bupropion but not sertraline. Null findings for sertraline were replicated in the stage 1 sample.

CONCLUSIONS

Pretreatment reward sensitivity and frontostriatal connectivity may identify patients likely to benefit from bupropion following selective serotonin reuptake inhibitor failures. Results call for a prospective replication based on these biomarkers to advance clinical care.

Neuroimaging Biomarkers for Predicting Treatment Response and Recurrence of Major Depressive Disorder

The acute treatment duration for major depressive disorder (MDD) is 8 weeks or more. Treatment of patients with MDD without predictors of treatment response and future recurrence presents challenges and clinical problems to patients and physicians. Recently, many neuroimaging studies have been published on biomarkers for treatment response and recurrence of MDD using various methods such as brain volumetric magnetic resonance imaging (MRI), functional MRI (resting-state and affective tasks), diffusion tensor imaging, magnetic resonance spectroscopy, near-infrared spectroscopy, and molecular imaging (i.e., positron emission tomography and single photon emission computed tomography). The results have been inconsistent, and we hypothesize that this could be due to small sample size; different study design, including eligibility criteria; and differences in the imaging and analysis techniques. In the future, we suggest a more sophisticated research design, larger sample size, and a more comprehensive integration including genetics to establish biomarkers for the prediction of treatment response and recurrence of MDD.

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

Depression has a chronic and recurrent course often with early onset and is the leading cause of disability worldwide. In contrast to diagnoses for other conditions which rely on precise medical tests, the diagnosis of depression still focuses exclusively on symptom reports. As a result, heterogeneous patient groups are included under broad categories. Furthermore, in the absence of companion diagnostic tests, choosing specific treatments for patients remains imprecise with only one-third of patients entering remission with initial treatment, with others requiring multiple intervention steps to achieve remission. In addition to improving treatment outcomes, disease prevention is essential to reduce overall disease burden. Adolescence is a critical window where complex emotional, social, familial, and biological shifts may predispose to lifelong depression. Thus, personalized medicine, integrating individual variability in genes, brain function, and clinical phenotypes, can offer a comprehensive approach to provide precise diagnosis, novel drug development, optimal treatment assignment, and prevention of illness and its associated burden. Texas Resilience Against Depression study (T-RAD) encompasses two natural history, longitudinal (10 + years), prospective studies (D2K and RAD), each enrolling 2500 participants. The D2K study follows participants (ages 10 years and older) who have a current or past diagnosis of depression or bipolar disorder. The RAD study follows participants aged 10-24 years who are at risk for depression but not yet suffering from the disease. The T-RAD study will help to uncover the socio-demographic, lifestyle, clinical, psychological, and neurobiological factors that contribute to mood disorder onset, recurrence, progression, and differential treatment response.

Neuroimaging as a Tool for Individualized Treatment Choice in Depression: the Past, the Present and the Future

Purpose of Review

This paper aims to review the findings on neuroimaging as a tool for facilitating individualized treatment choice in depression.

Recent Findings

Neuroimaging has allowed the exploration of neural candidates for response biomarkers. In less than two decades, the field has expanded from small single drug studies to large multisite initiatives testing multiple interventions; from simple analytical methods to employing artificial intelligence, with an aim of establishing models based on a variety of data, such as neuroimaging, biological, psychological and clinical measures.

Summary

Neural biomarkers of response may play an important role in treatment response prediction. It seems likely that they will need to be considered together with other types of data in complex models in order to achieve the high accuracy and generalizability of results necessary for clinical use.