Salience Network Functional Connectivity Predicts Placebo Effects in Major Depression

Functional connectivity subtypes during a positive mood induction: Predicting clinical response in a randomized controlled trial of ketamine for treatment-resistant depression

Ketamine has shown promise in rapidly improving symptoms of depression and most notably treatment-resistant depression (TRD). However, given the heterogeneity of TRD, biobehavioral markers of treatment response are necessary for the personalized prescription of intravenous ketamine. Heterogeneity in depression can be manifested in discrete patterns of functional connectivity (FC) in default mode, ventral affective, and cognitive control networks. This study employed a data-driven approach to parse FC during positive mood processing to characterize subgroups of patients with TRD prior to infusion and determine whether these connectivity-based subgroups could predict subsequent antidepressant response to ketamine compared to saline infusion. 152 adult patients with TRD completed a baseline assessment of FC during positive mood processing and were randomly assigned to either ketamine or saline infusion. The assessment utilized Subgroup-Group Iterative Multiple Model Estimation to recover directed connectivity maps and applied Walktrap algorithm to determine data-driven subgroups. Depression severity was assessed pre- and 24-hr postinfusion. Two connectivity-based subgroups were identified: Subgroup A (n = 110) and Subgroup B (n = 42). We observed that treatment response was moderated by an infusion type by subgroup interaction (p = .040). For patients receiving ketamine, subgroup did not predict treatment response (β = -.326, p = .499). However, subgroup predicted response for saline patients. Subgroup B individuals, relative to A, were more likely to be saline responders at 24-hr postinfusion (β = -2.146, p = .007). Thus, while ketamine improved depressive symptoms uniformly across both subgroups, this heterogeneity was a predictor of placebo response. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Over-integration of visual network in major depressive disorder and its association with gene expression profiles

Major depressive disorder (MDD) is a common psychiatric condition associated with aberrant functional connectivity in large-scale brain networks. However, it is unclear how the network dysfunction is characterized by imbalance or derangement of network modular interaction in MDD patients and whether this disruption is associated with gene expression profiles. We included 262 MDD patients and 297 healthy controls, embarking on a comprehensive analysis of intrinsic brain activity using resting-state functional magnetic resonance imaging (R-fMRI). We assessed brain network integration by calculating the Participation Coefficient (PC) and conducted an analysis of intra- and inter-modular connections to reveal the dysconnectivity patterns underlying abnormal PC manifestations. Besides, we explored the potential relationship between the above graph theory measures and clinical symptoms severity in MDD. Finally, we sought to uncover the association between aberrant graph theory measures and postmortem gene expression data sourced from the Allen Human Brain Atlas (AHBA). Relative to the controls, alterations in systemic functional connectivity were observed in MDD patients. Specifically, increased PC within the bilateral visual network (VIS) was found, accompanied by elevated functional connectivities (FCs) between VIS and both higher-order networks and Limbic network (Limbic), contrasted by diminished FCs within the VIS and between the VIS and the sensorimotor network (SMN). The clinical correlations indicated positive associations between inter-VIS FCs and depression symptom, whereas negative correlations were noted between intra-VIS FCs with depression symptom and cognitive disfunction. The transcriptional profiles explained 21-23.5% variance of the altered brain network system dysconnectivity pattern, with the most correlated genes enriched in trans-synaptic signaling and ion transport regulation. These results highlight the modular connectome dysfunctions characteristic of MDD and its linkage with gene expression profiles and clinical symptomatology, providing insight into the neurobiological underpinnings and holding potential implications for clinical management and therapeutic interventions in MDD.

Lack of association between pretreatment glutamate/GABA and major depressive disorder treatment response

Studies have shown gamma-amino-butyric acid (GABA) and Glx (a combination of glutamate and glutamine) to be altered in major depressive disorder (MDD). Using proton Magnetic Resonance Spectroscopy (1H-MRS), this study aimed to determine whether lower pretreatment GABA and Glx levels in the medial frontal cortex, a region implicated in MDD pathophysiology, are associated with better antidepressant treatment response. Participants with MDD (N = 74) were antidepressant naïve or medication-free for at least three weeks before imaging. Two MEGA-PRESS 1H-MRS acquisitions were collected, interleaved with a water unsuppressed reference scan. GABA and Glx concentrations were quantified from an average difference spectrum, with preprocessing using Gannet and spectral fitting using TARQUIN. Following imaging, participants were randomized to escitalopram or placebo for 8 weeks in a double-blind design. Multivariable logistic regression models were applied with treatment type and age as covariates. Bayes Factor hypothesis testing was used to interpret the strength of the evidence. No significant association was found between pretreatment Glx, GABA, or Glx/GABA and depression remission status or the continuous outcome, percent change in symptom severity. In an exploratory analysis, no significant correlation was found between pretreatment Glx, GABA or Glx/GABA and days to response. Bayes factor analysis showed strong evidence towards the null hypotheses in all cases. To date, there are no replicated biomarkers in psychiatry. To address this, well-powered, placebo-controlled trials need to be undertaken and reported. The present analysis suggests pretreatment GABA, Glx, or their ratio cannot predict antidepressant treatment response. Future direction including examining glutamate and glutamine separately or examining biological subtypes of MDD separately.Trial Name: Advancing Personalized Antidepressant Treatment Using PET/MRI.Registration Number: NCT02623205 URL: https://clinicaltrials.gov/ct2/show/NCT02623205.

Artificial Intelligence and Predictive Modeling in the Management and Treatment of Episodic Migraine

PURPOSE OF REVIEW

Artificial intelligence (AI) has impacted different aspects of headache medicine, from history taking and diagnosis to drug development. AI has been shown to have predictive modeling in helping diagnose migraine and assist with patient care. Additionally, this technology has been adapted to help non-headache specialists with headache management. Similar practices have expanded to help diagnose cluster headache. AI has also been used to help streamline patient visits, and identify new drug targets.

RECENT FINDINGS

Various forms of AI models have been implemented in headache medicine; these have ranged from diagnosis engines to models helping track headache triggers. Additionally, AI has been used to assist in clinical trials and to help predict placebo responses to different medications. There are still several limitations with AI in setting of headache medicine. AI and diagnosis models have a role to play in headache medicine. However, technology is still in its infancy and limitations do exist.

Thalamocortical functional connectivity and rapid antidepressant and antisuicidal effects of low-dose ketamine infusion among patients with treatment-resistant depression

Previous studies have shown an association between the thalamocortical dysconnectivity and treatment-resistant depression (TRD). Whether a single subanesthetic dose of ketamine may change thalamocortical connectivity among patients with TRD is unclear. Whether these changes in thalamocortical connectivity is associated with the antidepressant and antisuicidal effects of ketamine treatment is also unclear. Two resting-state functional MRIs were collected in two clinical trials of 48 patients with TRD (clinical trial 1; 32 receiving ketamine, 16 receiving a normal saline placebo) and 48 patients with TRD and strong suicidal ideation (clinical trial 2; 24 receiving ketamine, 24 receiving midazolam), respectively. All participants underwent rs-fMRI before and 3 days after infusion. Seed-based functional connectivity (FC) was analyzed in the left/right thalamus. FCs between the bilateral thalamus and right middle frontal cortex (BA46) and between the left thalamus and left anterior paracingulate gyrus (BA8) increased among patients in the ketamine group in clinical trials 1 and 2, respectively. FCs between the right thalamus and bilateral frontal pole (BA9) and between the right thalamus and left rostral paracingulate gyrus (BA10) decreased among patients in the ketamine group in clinical trials 1 and 2, respectively. However, the associations between those FC changes and clinical symptom changes did not survive statistical significance after multiple comparison corrections. Whether ketamine-related changes in thalamocortical connectivity may be associated with ketamine's antidepressant and antisuicidal effects would need further investigation. Clinical trials registration: UMIN Clinical Trials Registry (UMIN-CTR): Registration number: UMIN000016985 and UMIN000033916.

Functional connectivity profiles in remitted depression and their relation to ruminative thinking

The triple network model suggests that dysfunction in three major brain networks - the default mode network (DMN), central executive network (CEN), and salience network (SN) - might contribute to cognitive impairments in various psychiatric disorders, including major depressive disorder (MDD). While hyperconnectivity in the DMN, hypoconnectivity in the CEN, and abnormal SN connectivity have been observed in acutely depressed patients, evidence for network alterations during remission is limited. Further, there are few studies examining connectivity in people in remission from MDD (rMDD) during emotional processing tasks, including during affective cognition (i.e., tasks that encompass affective processing in the context of cognitive processes, such as inhibition). To address these literature gaps, this study compared functional connectivity (FC) between resting and task conditions, specifically during the emotional Stroop (eStroop) task, as well as between rMDD and healthy volunteers (HVs), within and between nodes of the three networks. We also explored how FC relates to rumination in the rMDD group, given that rumination tends to persist in rMDD and involves affective and cognitive networks. We unexpectedly found greater FC during the task vs. rest condition within the DMN, and decreased FC during the task vs. rest conditions within the CEN and SN across the groups. Greater FC during the task vs. rest condition between DMN and SN nodes, as well as between CEN and SN nodes were also observed. These effects were more pronounced in the rMDD group as per our exploratory analyses. Additionally, the rMDD vs. HV group showed higher FC between DMN-CEN nodes, regardless of condition. Higher hopeless rumination scores were associated with decreased resting FC within the DMN, while higher active problem-solving scores were associated with increased task FC within the DMN in the rMDD group. These findings suggest that tasks engaging affective cognition processes influence FC within and among the three networks, with this effect more pronounced in the rMDD group. This might indicate potential protective and compensatory mechanisms in rMDD and expands our understanding of large-scale intrinsic network connectivity alterations during remission from depression. However, given the limited sample and the exploratory nature of some of our analyses, replication is necessary.

Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward

Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.

Internalizing Symptoms and Adverse Childhood Experiences Associated With Functional Connectivity in a Middle Childhood Sample

BACKGROUND

Research has found overlapping associations in adults of resting-state functional connectivity (RSFC) to both internalizing disorders (e.g., depression, anxiety) and a history of traumatic events. The present study aimed to extend this previous research to a younger sample by examining RSFC associations with both internalizing symptoms and adverse childhood experiences (ACEs) in middle childhood.

METHODS

We used generalized linear mixed models to examine associations between a priori within- and between-network RSFC with child-reported internalizing symptoms and ACEs using the Adolescent Brain Cognitive Development dataset (N = 10,168, mean age = 9.95 years, SD = 0.627).

RESULTS

We found that internalizing symptoms and ACEs were associated with both multiple overlapping and unique RSFC network patterns. Both ACEs and internalizing symptoms were associated with a reduced anticorrelation between the default mode network and the dorsal attention network. However, internalizing symptoms were uniquely associated with lower within-network default mode network connectivity, while ACEs were uniquely associated with both lower between-network connectivity of the auditory network and cingulo-opercular network, and higher within-network frontoparietal network connectivity.

CONCLUSIONS

The present study points to overlap in the RSFC associations with internalizing symptoms and ACEs, as well as important areas of specificity in RSFC associations. Many of the RSFC associations found have been previously implicated in attentional control functions, including modulation of attention to sensory stimuli. This may have critical importance in understanding internalizing symptoms and outcomes of ACEs.

The effects of esketamine and treatment expectation in acute major depressive disorder (Expect): study protocol for a pharmacological fMRI study using a balanced placebo design

BACKGROUND

Major depressive disorder (MDD) is a highly prevalent (8-15%), severely disabling disorder and is associated with enormous socioeconomic impact. Antidepressant medication for the treatment of MDD has proven effective in RCTs; however, placebo response is also substantial. Given the potential benefits of modulating the placebo response in patient care and pharmacological research, understanding the mechanisms underlying placebo response is of high clinical relevance. The placebo response is mediated by treatment expectation, i.e. an individual's belief about whether and how much they will improve as a consequence of their treatment. The mechanisms and moderators of treatment expectation effects in MDD are poorly understood. Initial brain imaging studies on placebo responses in MDD point towards the relevance of the lateral prefrontal cortex and the rostral anterior cingulate cortex (rACC). In this project, we will investigate the neural mechanisms underlying the antidepressant effects of treatment expectation associated with the fast-acting antidepressant esketamine in patients with MDD. Esketamine is an NMDA receptor antagonist inducing antidepressant effects within hours.

METHODS

We will employ a fully balanced placebo design with the factors "treatment" (i.v. esketamine / placebo) and verbally induced "expectation" (high / low) combined with fMRI (resting state, emotion and reward processing paradigms) to investigate the psychological and neural mechanisms underlying the antidepressant effects of expectation, and how these interact with the pharmacological effects of esketamine.

DISCUSSION

The insights gained by this project promise fundamental implications for clinical treatment and future drug trials. Unraveling the mechanisms underlying expectation effects on antidepressant treatment may inform (1) strategies to modulate these effects and thus improve assay sensitivity in RCTs and (2) novel treatment regiments aiming to maximize the synergistic effects of expectation and pharmacological treatment in the clinical care of patients with MDD.

TRIAL REGISTRATION

This trial has been prospectively registered with the EU Clinical Trials Register: EudraCT-No.: 2020-000784-23 (November 17, 2020).

Methodological and Quality Flaws in the Use of Artificial Intelligence in Mental Health Research: Systematic Review

BACKGROUND

Artificial intelligence (AI) is giving rise to a revolution in medicine and health care. Mental health conditions are highly prevalent in many countries, and the COVID-19 pandemic has increased the risk of further erosion of the mental well-being in the population. Therefore, it is relevant to assess the current status of the application of AI toward mental health research to inform about trends, gaps, opportunities, and challenges.

OBJECTIVE

This study aims to perform a systematic overview of AI applications in mental health in terms of methodologies, data, outcomes, performance, and quality.

METHODS

A systematic search in PubMed, Scopus, IEEE Xplore, and Cochrane databases was conducted to collect records of use cases of AI for mental health disorder studies from January 2016 to November 2021. Records were screened for eligibility if they were a practical implementation of AI in clinical trials involving mental health conditions. Records of AI study cases were evaluated and categorized by the International Classification of Diseases 11th Revision (ICD-11). Data related to trial settings, collection methodology, features, outcomes, and model development and evaluation were extracted following the CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies) guideline. Further, evaluation of risk of bias is provided.

RESULTS

A total of 429 nonduplicated records were retrieved from the databases and 129 were included for a full assessment-18 of which were manually added. The distribution of AI applications in mental health was found unbalanced between ICD-11 mental health categories. Predominant categories were Depressive disorders (n=70) and Schizophrenia or other primary psychotic disorders (n=26). Most interventions were based on randomized controlled trials (n=62), followed by prospective cohorts (n=24) among observational studies. AI was typically applied to evaluate quality of treatments (n=44) or stratify patients into subgroups and clusters (n=31). Models usually applied a combination of questionnaires and scales to assess symptom severity using electronic health records (n=49) as well as medical images (n=33). Quality assessment revealed important flaws in the process of AI application and data preprocessing pipelines. One-third of the studies (n=56) did not report any preprocessing or data preparation. One-fifth of the models were developed by comparing several methods (n=35) without assessing their suitability in advance and a small proportion reported external validation (n=21). Only 1 paper reported a second assessment of a previous AI model. Risk of bias and transparent reporting yielded low scores due to a poor reporting of the strategy for adjusting hyperparameters, coefficients, and the explainability of the models. International collaboration was anecdotal (n=17) and data and developed models mostly remained private (n=126).

CONCLUSIONS

These significant shortcomings, alongside the lack of information to ensure reproducibility and transparency, are indicative of the challenges that AI in mental health needs to face before contributing to a solid base for knowledge generation and for being a support tool in mental health management.

A pilot randomized sham controlled trial of bilateral iTBS for depression and executive function in older adults

INTRODUCTION

Executive function deficits (EFD) in late life depression (LLD) are associated with poor outcomes. Dysfunction of the cognitive control network (CCN) has been posited in the pathophysiology of LLD with EFD.

METHODS

Seventeen older adults with depression and EFD were randomized to iTBS or sham for 6 weeks. Intervention was delivered bilaterally using a recognized connectivity target.

RESULTS

A total of 89% (17/19) participants completed all study procedures. No serious adverse events occurred. Pre to post-intervention change in mean Montgomery-Asberg-depression scores was not different between iTBS or sham, p = 0.33. No significant group-by-time interaction for Montgomery-Asberg Depression rating scale scores (F _{3, 44}  = 0.51; p = 0.67) was found. No significant differences were seen in the effects of time between the two groups on executive measures: Flanker scores (F _{1, 14}  = 0.02, p = 0.88), Dimensional-change-card-sort scores F _{1, 14}  = 0.25, p = 0.63, and working memory scores (F _{1, 14}  = 0.98, p = 0.34). The Group-by-time interaction effect for functional connectivity (FC) within the Fronto-parietal-network was not significant (F _{1, 14}  = 0.36, p = 0.56). No significant difference in the effect-of-time between the two groups was found on FC within the Cingulo-opercular-network (F _{1, 14}  = 0, p = 0.98).

CONCLUSION

Bilateral iTBS is feasible in LLD. Preliminary results are unsupportive of efficacy on depression, executive function or target engagement of the CCN. A future Randomized clinical trial requires a larger sample size with stratification of cognitive and executive variables and refinement in the target engagement.

Computational approaches to treatment response prediction in major depression using brain activity and behavioral data: A systematic review

Major depressive disorder is a heterogeneous diagnostic category with multiple available treatments. With the goal of optimizing treatment selection, researchers are developing computational models that attempt to predict treatment response based on various pretreatment measures. In this paper, we review studies that use brain activity data to predict treatment response. Our aim is to highlight and clarify important methodological differences between various studies that relate to the incorporation of domain knowledge, specifically within two approaches delineated as data-driven and theory-driven. We argue that theory-driven generative modeling, which explicitly models information processing in the brain and thus can capture disease mechanisms, is a promising emerging approach that is only beginning to be utilized in treatment response prediction. The predictors extracted via such models could improve interpretability, which is critical for clinical decision-making. We also identify several methodological limitations across the reviewed studies and provide suggestions for addressing them. Namely, we consider problems with dichotomizing treatment outcomes, the importance of investigating more than one treatment in a given study for differential treatment response predictions, the need for a patient-centered approach for defining treatment outcomes, and finally, the use of internal and external validation methods for improving model generalizability.

Modulation of the functional connectome in major depressive disorder by ketamine therapy

BACKGROUND

Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized.

METHODS

Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status.

RESULTS

Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern.

CONCLUSION

Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

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.

Rostral anterior cingulate network effective connectivity in depressed adolescents and associations with treatment response in a randomized controlled trial

The rostral anterior cingulate cortex (rACC) is consistently implicated in the neurobiology of depression. While the functional connectivity of the rACC has been previously associated with treatment response, there is a paucity of work investigating the specific directional interactions underpinning these associations. We compared the fMRI resting-state effective connectivity of 94 young people with major depressive disorder and 91 healthy controls. Following the fMRI scan, patients were randomized to receive cognitive behavioral therapy for 12 weeks, plus either fluoxetine or a placebo. Using spectral dynamic causal modelling, we examined the effective connectivity of the rACC with eight other regions implicated in depression: the left and right anterior insular cortex (AIC), amygdalae, and dorsolateral prefrontal cortex (dlPFC); and in the midline, the subgenual (sgACC) and dorsal anterior cingulate cortex (dACC). Parametric empirical Bayes was used to compare baseline differences between controls and patients and responders and non-responders to treatment. Depressed patients demonstrated greater inhibitory connectivity from the rACC to the dlPFC, AIC, dACC and left amygdala. Moreover, treatment responders illustrated greater inhibitory connectivity from the rACC to dACC, greater excitatory connectivity from the dACC to sgACC and reduced inhibitory connectivity from the sgACC to amygdalae at baseline. The inhibitory hyperconnectivity of the rACC in depressed patients aligns with hypotheses concerning the dominance of the default mode network over other intrinsic brain networks. Surprisingly, treatment responders did not demonstrate connectivity which was more similar to healthy controls, but rather distinct alterations that may have predicated their enhanced treatment response.

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates. The objective of this review was to explore the use of techniques such as AI and the sub-discipline of machine learning (ML) to address placebo response in practical ways that can positively impact drug development. This examination focused on the critical factors that should be considered in applying AI and ML to the placebo response issue, examples of how these techniques can be used, and the regulatory considerations for integrating these approaches into clinical trials.

Accelerated transcranial magnetic stimulation (aTMS) to treat depression with treatment switching: study protocol of a pilot, randomized, delayed-start trial

Background

Repetitive transcranial magnetic stimulation (rTMS) is a technique for stimulating brain activity using a transient magnetic field to induce an electrical current in the brain producing depolarization of focal groups of brain cells. TMS is a protocol approved by the U.S. Food and Drug Administration in routine clinical practice as a treatment for depression. A major limitation of rTMS is the large amount of time taken for a standard protocol (38 min a day for 20–30 working days). The optimal type and duration of TMS are still uncertain, as is the optimal strategy for continuing or changing the type of rTMS if there is a poor initial response.

Objectives

The trial aims to assess whether a 1-week compressed course of left dorsolateral prefrontal (L DLPFC) 5 Hz accelerated rTMS (aTMS) treatment is as effective as an established 4-week course of non-accelerated rTMS and if additional 5 Hz L DLPFC aTMS treatments will be efficacious in non-responders as compared to 1 Hz right DLPFC aTMS treatment.

Methods

A randomized, single-blind, delayed-start trial was planned to commence in Jan 2020. A total of 60 patients will be enrolled from the Institute of Mental Health Singapore within a 2-year period and randomized into the early or delayed-start phase of the trial. The primary outcome of the trial is the improvement of Montgomery-Asberg Depression Rating scale at the end of the active treatment phase.

Discussion

If this study protocol proves to be effective, the findings of this trial will be updated to the College of Psychiatrists, Academy of Medicine Singapore, as well as published in a peer-reviewed journal to enhance local and international TMS treatment guidelines.

Trial registration

ClinicalTrials.gov ID: NCT03941106

Understanding the Neurocomputational Mechanisms of Antidepressant Placebo Effects

Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological mechanisms implicated in antidepressant placebo effects. However, findings from these studies have not yet elucidated a model-based theory that would explain the mechanism through which antidepressant expectancies evolve to induce persistent mood changes. Emerging evidence suggests that antidepressant placebo effects may be informed by models of reinforcement learning (RL). Such that an individual's expectation of improvement is updated with the arrival of new sensory evidence, by incorporating a reward prediction error (RPE), which signals the mismatch between the expected (expected value) and perceived improvement. Consistent with this framework, neuroimaging studies of antidepressant placebo effects have demonstrated placebo-induced μ-opioid activation and increased blood-oxygen-level dependent (BOLD) responses in regions tracking expected values (e.g., ventromedial prefrontal cortex (vmPFC)) and RPEs (e.g., ventral striatum (VS)). In this study, we will demonstrate the causal contribution of reward learning signals (expected values and RPEs) to antidepressant placebo effects by experimentally manipulating expected values using transcranial magnetic stimulation (TMS) targeting the vmPFC and μ-opioid striatal RPE signal using pharmacological approaches. We hypothesized that antidepressant placebo expectancies are represented in the vmPFC (expected value) and updated by means of μ-opioid-modulated striatal learning signal. In a 3 × 3 factorial double-blind design, we will randomize 120 antidepressant-free individuals with depressive symptoms to one of three between-subject opioid conditions: the μ-opioid agonist buprenorphine, the μ-opioid antagonist naltrexone, or an inert pill. Within each arm, individuals will be assigned to receive three within-subject counterbalanced forms of TMS targeting the vmPFC-intermittent Theta Burst Stimulation (TBS) expected to potentiate the vmPFC, continuous TBS expected to de-potentiate the vmPFC, or sham TBS. These experimental manipulations will be used to modulate trial-by-trial reward learning signals and related brain activity during the Antidepressant Placebo functional MRI (fMRI) Task to address the following aims: (1) investigate the relationship between reward learning signals within the vmPFC-VS circuit and antidepressant placebo effects; (2) examine the causal contribution of vmPFC expected value computations to antidepressant placebo effects; and (3) investigate the causal contribution of μ-opioid-modulated striatal RPEs to antidepressant placebo effects. The proposed study will be the first to investigate the causal contribution of μ-opioid-modulated vmPFC-VS learning signals to antidepressant placebo responses, paving the way for developing novel treatments modulating learning processes and objective means of quantifying and potentially reducing placebo effects during drug development.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT04276259.

The relationship between resting-state functional connectivity, antidepressant discontinuation and depression relapse

The risk of relapsing into depression after stopping antidepressants is high, but no established predictors exist. Resting-state functional magnetic resonance imaging (rsfMRI) measures may help predict relapse and identify the mechanisms by which relapses occur. rsfMRI data were acquired from healthy controls and from patients with remitted major depressive disorder on antidepressants. Patients were assessed a second time either before or after discontinuation of the antidepressant, and followed up for six months to assess relapse. A seed-based functional connectivity analysis was conducted focusing on the left subgenual anterior cingulate cortex and left posterior cingulate cortex. Seeds in the amygdala and dorsolateral prefrontal cortex were explored. 44 healthy controls (age: 33.8 (10.5), 73% female) and 84 patients (age: 34.23 (10.8), 80% female) were included in the analysis. 29 patients went on to relapse and 38 remained well. The seed-based analysis showed that discontinuation resulted in an increased functional connectivity between the right dorsolateral prefrontal cortex and the parietal cortex in non-relapsers. In an exploratory analysis, this functional connectivity predicted relapse risk with a balanced accuracy of 0.86. Further seed-based analyses, however, failed to reveal differences in functional connectivity between patients and controls, between relapsers and non-relapsers before discontinuation and changes due to discontinuation independent of relapse. In conclusion, changes in the connectivity between the dorsolateral prefrontal cortex and the posterior default mode network were associated with and predictive of relapse after open-label antidepressant discontinuation. This finding requires replication in a larger dataset.

Research Review: Brain network connectivity and the heterogeneity of depression in adolescence - a precision mental health perspective

BACKGROUND

Adolescence is a period of high risk for the onset of depression, characterized by variability in symptoms, severity, and course. During adolescence, the neurocircuitry implicated in depression continues to mature, suggesting that it is an important period for intervention. Reflecting the recent emergence of 'precision mental health' - a person-centered approach to identifying, preventing, and treating psychopathology - researchers have begun to document associations between heterogeneity in features of depression and individual differences in brain circuitry, most frequently in resting-state functional connectivity (RSFC).

METHODS

In this review, we present emerging work examining pre- and post-treatment measures of network connectivity in depressed adolescents; these studies reveal potential intervention-specific neural markers of treatment efficacy. We also review findings from studies examining associations between network connectivity and both types of depressive symptoms and response to treatment in adults, and indicate how this work can be extended to depressed adolescents. Finally, we offer recommendations for research that we believe will advance the science of precision mental health of adolescence.

RESULTS

Nascent studies suggest that linking RSFC-based pathophysiological variation with effects of different types of treatment and changes in mood following specific interventions will strengthen predictions of prognosis and treatment response. Studies with larger sample sizes and direct comparisons of treatments are required to determine whether RSFC patterns are reliable neuromarkers of treatment response for depressed adolescents. Although we are not yet at the point of using RSFC to guide clinical decision-making, findings from research examining the stability and reliability of RSFC point to a favorable future for network-based clinical phenotyping.

CONCLUSIONS

Delineating the correspondence between specific clinical characteristics of depression (e.g., symptoms, severity, and treatment response) and patterns of network-based connectivity will facilitate the development of more tailored and effective approaches to the assessment, prevention, and treatment of depression in adolescents.

Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45-0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.

The Functional Role of Large-scale Brain Network Coordination in Placebo-induced Anxiolysis

Anxiety reduction through mere expectation of anxiolytic treatment effects (placebo anxiolysis) has enormous clinical importance. Recent behavioral and electrophysiological data suggest that placebo anxiolysis involves reduced vigilance and enhanced internalization of attention; however, the underlying neurobiological mechanisms are not yet clear. Given the fundamental function of intrinsic connectivity networks (ICNs) in basic cognitive processes, we investigated ICN activity patterns associated with externally and internally directed mental states under the influence of an anxiolytic placebo medication. Based on recent findings, we specifically analyzed the functional role of the rostral anterior cingulate cortex (rACC) in coordinating placebo-dependent cue-related (phasic) and cue-unrelated (sustained) network activity. Under placebo, we observed a down-regulation of the entire salience network (SN), particularly in response to threatening cues. The rACC exhibited enhanced cue-unrelated functional connectivity (FC) with the SN, which correlated with reductions in tonic arousal and anxiety. Hence, apart from the frequently reported modulation of aversive cue responses, the rACC appears to be crucially involved in exerting a tonically dampening control over salience-responsive structures. In line with a more internally directed mental state, we also found enhanced FC within the default mode network (DMN), again predicting reductions in anxiety under placebo.

Subacute effects of the psychedelic ayahuasca on the salience and default mode networks

BACKGROUND

Neuroimaging studies have just begun to explore the acute effects of psychedelics on large-scale brain networks' functional organization. Even less is known about the neural correlates of subacute effects taking place days after the psychedelic experience. This study explores the subacute changes of primary sensory brain networks and networks supporting higher-order affective and self-referential functions 24 hours after a single session with the psychedelic ayahuasca.

METHODS

We leveraged task-free functional magnetic resonance imaging data 1 day before and 1 day after a randomized placebo-controlled trial exploring the effects of ayahuasca in naïve healthy participants (21 placebo/22 ayahuasca). We derived intra- and inter-network functional connectivity of the salience, default mode, visual, and sensorimotor networks, and assessed post-session connectivity changes between the ayahuasca and placebo groups. Connectivity changes were associated with Hallucinogen Rating Scale scores assessed during the acute effects.

RESULTS

Our findings revealed increased anterior cingulate cortex connectivity within the salience network, decreased posterior cingulate cortex connectivity within the default mode network, and increased connectivity between the salience and default mode networks 1 day after the session in the ayahuasca group compared to placebo. Connectivity of primary sensory networks did not differ between groups. Salience network connectivity increases correlated with altered somesthesia scores, decreased default mode network connectivity correlated with altered volition scores, and increased salience default mode network connectivity correlated with altered affect scores.

CONCLUSION

These findings provide preliminary evidence for subacute functional changes induced by the psychedelic ayahuasca on higher-order cognitive brain networks that support interoceptive, affective, and self-referential functions.

Cortical Connectivity Moderators of Antidepressant vs Placebo Treatment Response in Major Depressive Disorder: Secondary Analysis of a Randomized Clinical Trial

IMPORTANCE

Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.

OBJECTIVE

To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.

DESIGN, SETTING, AND PARTICIPANTS

In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.

INTERVENTIONS

Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.

MAIN OUTCOMES AND MEASURES

Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.

RESULTS

Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.

CONCLUSIONS AND RELEVANCE

These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01407094.

Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{R}}}^{{\bf{2}}}{\boldsymbol{=}}{\bf{0.59}}\,{\bf{and}}\,{\bf{p}}{\boldsymbol{=}}{\bf{1.52}}{\bf{e}}{\boldsymbol{ \mbox{-} }}{\bf{05}}$$\end{document}R2=0.59andp=1.52e‐05). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Effect of Intrinsic Patterns of Functional Brain Connectivity in Moderating Antidepressant Treatment Response in Major Depression

OBJECTIVE

Major depressive disorder is associated with aberrant resting-state functional connectivity across multiple brain networks supporting emotion processing, executive function, and reward processing. The purpose of this study was to determine whether patterns of resting-state connectivity between brain regions predict differential outcome to antidepressant medication (sertraline) compared with placebo.

METHODS

Participants in the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study underwent structural and resting-state functional MRI at baseline. Participants were then randomly assigned to receive either sertraline or placebo treatment for 8 weeks (N=279). A region of interest-based approach was utilized to compute functional connectivity between brain regions. Linear mixed-model intent-to-treat analyses were used to identify brain regions that moderated (i.e., differentially predicted) outcomes between the sertraline and placebo arms.

RESULTS

Prediction of response to sertraline involved several within- and between-network connectivity patterns. In general, higher connectivity within the default mode network predicted better outcomes specifically for sertraline, as did greater between-network connectivity of the default mode and executive control networks. In contrast, both placebo and sertraline outcomes were predicted (in opposite directions) by between-network hippocampal connectivity.

CONCLUSIONS

This study identified specific functional network-based moderators of treatment outcome involving brain networks known to be affected by major depression. Specifically, functional connectivity patterns of brain regions between and within networks appear to play an important role in identifying a favorable response for a drug treatment for major depressive disorder.

Intrinsic connectomes are a predictive biomarker of remission in major depressive disorder

Although major depressive disorder (MDD) is associated with altered functional coupling between disparate neural networks, the degree to which such measures are ameliorated by antidepressant treatment is unclear. It is also unclear whether functional connectivity can be used as a predictive biomarker of treatment response. Here, we used whole-brain functional connectivity analysis to identify neural signatures of remission following antidepressant treatment, and to identify connectomic predictors of treatment response. 163 MDD and 62 healthy individuals underwent functional MRI during pre-treatment baseline and 8-week follow-up sessions. Patients were randomized to escitalopram, sertraline or venlafaxine-XR antidepressants and assessed at follow-up for remission. Baseline measures of intrinsic functional connectivity between each pair of 333 regions were analyzed to identify pre-treatment connectomic features that distinguish remitters from non-remitters. We then interrogated these connectomic differences to determine if they changed post-treatment, distinguished patients from controls, and were modulated by medication type. Irrespective of medication type, remitters were distinguished from non-remitters by greater connectivity within the default mode network (DMN); specifically, between the DMN, fronto-parietal and somatomotor networks, the DMN and visual, limbic, auditory and ventral attention networks, and between the fronto-parietal and somatomotor networks with cingulo-opercular and dorsal attention networks. This baseline hypo-connectivity for non-remitters also distinguished them from controls and increased following treatment. In contrast, connectivity for remitters was higher than controls at baseline and also following remission, suggesting a trait-like connectomic characteristic. Increased functional connectivity within and between large-scale intrinsic brain networks may characterize acute recovery with antidepressants in depression.

Drug-Placebo Additivity in Randomized Clinical Trials

In randomized clinical trials (RCTs), it is assumed that nonspecific effects beyond action of pharmacological agents are roughly equivalent in drug and placebo treatment groups. Hence, since the inception of RCTs, drug efficacy is determined by comparing outcomes in active to those in placebo control arms. However, quantitation of efficacy is based on an unproven assumption, that drug and placebo responses are always additive. Response to treatment in RCTs can be differentially influenced by the perturbing effects of patient expectations, side effects, and pharmacogenomic interactions in both drug and placebo arms. Ability to control for these effects requires understanding of when and where they arise, how to mitigate, analyze, and even leverage their impact. Here, we examine three factors that influence additivity: expectation, side effects, and pharmacogenomics. Furthermore, to provide novel insights into nonadditivity and solutions for managing it, we introduce systems pharmacogenomics, a network approach to integrating and analyzing the effects of the numerous interacting perturbations to which a patient is exposed in RCTs.

Baseline functional connectivity may predict placebo responses to accelerated rTMS treatment in major depression

Although in theory sham repetitive transcranial magnetic stimulation (rTMS) has no inherent therapeutic value, nonetheless, such placebo stimulations may have relevant therapeutic effects in clinically depressed patients. On the other hand, antidepressant responses to sham rTMS are quite heterogeneous across individuals and its neural underpinnings have not been explored yet. The current brain imaging study aims to detect baseline neural fingerprints resulting in clinically beneficial placebo rTMS treatment responses. We collected resting‐state functional magnetic resonance imaging data prior to a registered randomized clinical trial of accelerated placebo stimulation protocol in patients documented with treatment‐resistant depression (http://clinicaltrials.gov/show/NCT01832805). In addition to global brain connectivity and rostral anterior cingulate cortex (rACC) seed‐based functional connectivity (FC), elastic‐net regression and cross‐validation procedures were used to identify baseline intrinsic brain connectivity biomarkers for sham‐rTMS responses. Placebo responses to accelerated sham rTMS were correlated with baseline global brain connectivity in the rACC/ventral medial prefrontal cortex (vmPFC). Concerning the rACC seed‐based FC analysis, the placebo response was associated positively with the precuneus/posterior cingulate (PCun/PCC) cortex and negatively with the middle frontal gyrus. Our findings provide first brain imaging evidence for placebo responses to sham stimulation being predictable from rACC rsFC profiles, especially in brain areas implicated in (re)appraisal and self‐focus processes.

Neural Predictors of the Antidepressant Placebo Response

The antidepressant placebo response remains a barrier to the development of novel therapies for depression, despite decades of efforts to identify and methodologically address its clinical correlates. This manuscript reviews recent neuroimaging studies that aim to identify the neural signature of antidepressant placebo response. Data captured in clinical trials have primarily focused on antidepressant efficacy or predicting antidepressant response and have reliably implicated the rostral anterior cingulate cortex (rACC) in antidepressant placebo response, but also in medication response. Imaging and electroencephalography (EEG) experiments specifically interrogating the mechanism of antidepressant placebo response, while few, suggest the reward network, including opiate neurotransmission, is also involved. Therefore, while the rACC is likely involved in the antidepressant placebo response, its observation in isolation is unlikely to prospectively distinguish antidepressant placebo from medication responders. Instead, future studies of antidepressant placebo response should probe the reward network as a whole and incorporate sophisticated computational analytical approaches.

Neuroimaging Studies of Antidepressant Placebo Effects: Challenges and Opportunities

Over the last two decades, neuroscientists have used antidepressant placebo probes to examine the biological mechanisms implicated in expectancies of mood improvement.However, findings from these studies have yet to elucidate a model-based theory that would explain the mechanisms through which antidepressant expectancies evolve to induce persistent mood changes. Compared to other fields, the development of experimental models of antidepressant placebo effects faces significant challenges, such as the delayed mechanism of action of conventional antidepressants and the complex internal dynamics of mood. Still, recent neuroimaging studies of antidepressant placebo effects have shown remarkable similarities to those observed in other disciplines (e.g., placebo analgesia), such as placebo-induced increased µ-opioid signaling and blood-oxygen-level dependent (BOLD) responses in areas involved in cognitive control, the representation of expected values and reward and emotional processing. This review will summarize these findings and the challenges and opportunities that arise from applying methodologies used in the field of placebo analgesia into the field of antidepressant placebo effects.

Pretreatment Rostral Anterior Cingulate Cortex Connectivity With Salience Network Predicts Depression Recovery: Findings From the EMBARC Randomized Clinical Trial

BACKGROUND

Baseline rostral anterior cingulate cortex (rACC) activity is a well-replicated nonspecific predictor of depression improvement. The rACC is a key hub of the default mode network, which prior studies indicate is hyperactive in major depressive disorder. Because default mode network downregulation is reliant on input from the salience network and frontoparietal network, an important question is whether rACC connectivity with these systems contributes to depression improvement.

METHODS

Our study evaluated this hypothesis in outpatients (N = 238; 151 female) enrolled in the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) 8-week randomized clinical trial of sertraline versus placebo for major depressive disorder. Depression severity was measured using the Hamilton Rating Scale for Depression, and electroencephalography was recorded at baseline and week 1. Exact low-resolution electromagnetic tomography was used to compute activity from the rACC, and key regions within the default mode network (posterior cingulate cortex), frontoparietal network (left dorsolateral prefrontal cortex), and salience network (right anterior insula [rAI]). Connectivity in the theta band (4.5-7 Hz) and beta band (12.5-21 Hz) was computed using lagged phase synchronization.

RESULTS

Stronger baseline theta-band rACC-rAI (salience network hub) connectivity predicted greater depression improvement across 8 weeks of treatment for both treatment arms (B = -0.57, 95% confidence interval = -1.07, -0.08, p = .03). Early increases in theta-band rACC-rAI connectivity predicted greater likelihood of achieving remission at week 8 (odds ratio = 2.90, p = .03).

CONCLUSIONS

Among patients undergoing treatment, theta-band rACC-rAI connectivity is a prognostic, albeit treatment-nonspecific, indicator of depression improvement, and early connectivity changes may predict clinically meaningful outcomes.

Childhood trauma history is linked to abnormal brain connectivity in major depression

Patients with major depressive disorder (MDD) present with heterogeneous symptom profiles, while neurobiological mechanisms are still largely unknown. Brain network studies consistently report disruptions of resting-state networks (RSNs) in patients with MDD, including hypoconnectivity in the frontoparietal network (FPN), hyperconnectivity in the default mode network (DMN), and increased connection between the DMN and FPN. Using a large, multisite fMRI dataset (n = 189 patients with MDD, n = 39 controls), we investigated network connectivity differences within and between RSNs in patients with MDD and healthy controls. We found that MDD could be characterized by a network model with the following abnormalities relative to controls: (i) lower within-network connectivity in three task-positive RSNs [FPN, dorsal attention network (DAN), and cingulo-opercular network (CON)], (ii) higher within-network connectivity in two intrinsic networks [DMN and salience network (SAN)], and (iii) higher within-network connectivity in two sensory networks [sensorimotor network (SMN) and visual network (VIS)]. Furthermore, we found significant alterations in connectivity between a number of these networks. Among patients with MDD, a history of childhood trauma and current symptoms quantified by clinical assessments were associated with a multivariate pattern of seven different within- and between-network connectivities involving the DAN, FPN, CON, subcortical regions, ventral attention network (VAN), auditory network (AUD), VIS, and SMN. Overall, our study showed that traumatic childhood experiences and dimensional symptoms are linked to abnormal network architecture in MDD. Our results suggest that RSN connectivity may explain underlying neurobiological mechanisms of MDD symptoms and has the potential to serve as an effective diagnostic biomarker.

Segregation of salience network predicts treatment response of depression to repetitive transcranial magnetic stimulation

Background

The present study tested the hypothesis that network segregation, a graph theoretic measure of functional organization of the brain, is correlated with treatment response in patients with major depressive disorder (MDD) undergoing repetitive transcranial magnetic stimulation (rTMS).

Methods

Network segregation, calculated from resting state functional magnetic resonance imaging scans, was measured in 32 patients with MDD who entered a sham-controlled, double-blinded, randomized trial of rTMS to the left dorsolateral prefrontal cortex, and a cohort of 20 healthy controls (HCs). Half of the MDD patients received sham treatment in the blinded phase, followed by active rTMS in the open-label phase. The analyses focused on segregation of the following networks: default mode (DMN), salience (SN), fronto-parietal (FPN), cingulo-opercular (CON), and memory retrieval (MRN).

Results

There was no differential change in network segregation comparing sham to active treatment. However, in the combined group of patients who completed active rTMS treatment (in the blinded plus open-label phases), higher baseline segregation of SN significantly predicted more symptom improvement after rTMS. Compared to HCs at baseline, MDD patients showed decreased segregation in DMN, and trend-level decreases in SN and MRN.

Conclusion

The results highlight the importance of network segregation in MDD, particularly in the SN, where more normal baseline segregation of SN may predict better treatment response to rTMS in depression.

•

We examined network segregation in a cohort of MDD patients receiving rTMS treatment.

•

More normal segregation of SN predicted better response of depression to rTMS.

•

Patients with MDD had decreased network segregation in DMN.

Development and validation of a prediction model for the probability of responding to placebo in antidepressant trials: a pooled analysis of individual patient data

BACKGROUND

Identifying potential placebo responders among apparent drug responders is critical to dissect drug-specific and nonspecific effects in depression.

OBJECTIVE

This project aimed to develop and test a prediction model for the probability of responding to placebo in antidepressant trials. Such a model will allow us to estimate the probability of placebo response among drug responders in antidepressants trials.

METHODS

We identified all placebo-controlled, double-blind randomised controlled trials (RCTs) of second generation antidepressants for major depressive disorder conducted in Japan and requested their individual patient data (IPD) to pharmaceutical companies. We obtained IPD (n=1493) from four phase II/III RCTs comparing mirtazapine, escitalopram, duloxetine, paroxetine and placebo. Out of 1493 participants in the four clinical trials, 440 participants allocated to placebo were included in the analyses. Our primary outcome was response, defined as 50% or greater reduction on Hamilton Rating Scale for Depression at study endpoint. We used multivariable logistic regression to develop a prediction model. All available candidate of predictor variables were tested through a backward variable selection and covariates were selected for the prediction model. The performance of the model was assessed by using Hosmer-Lemeshow test for calibration and the area under the ROC curve for discrimination.

FINDINGS

Placebo response rates differed between 31% and 59% (grand average: 43%) among four trials. Four variables were selected from all candidate variables and included in the final model: age at onset, age at baseline, bodily symptoms, and study-level difference. The final model performed satisfactorily in terms of calibration (Hosmer-Lemeshow p=0.92) and discrimination (the area under the ROC curve (AUC): 0.70).

CONCLUSIONS

Our model is expected to help researchers discriminate individuals who are more likely to respond to placebo from those who are less likely so.

CLINICAL IMPLICATIONS

A larger sample and more precise individual participant information should be collected for better performance. Examination of external validity in independent datasets is warranted.

TRIAL REGISTRATION NUMBER

CRD42017055912.

Resting State Functional Connectivity of Dorsal Raphe Nucleus and Ventral Tegmental Area in Medication-Free Young Adults With Major Depression

Background: This study has, for the first time, investigated the dorsal raphe nucleus (DRN) and ventral tegmental area (VTA) resting state whole-brain functional connectivity in medication-free young adults with major depression (MDD), at baseline and in relationship to treatment response. Method: A total of 119 subjects: 78 MDD (24 ± 4 years.) and 41 Healthy Controls (HC) (24 ± 3 years) were included in the analysis. DRN and VTA ROIs anatomical templates were used to extract resting state fluctuations and used to derive whole-brain functional connectivity. Differences between MDD and HCs were examined, as well as the correlation of baseline Hamilton Depression and Anxiety scale scores to the baseline DRN and VTA connectivity. The relationship to treatment response was examined by investigating the correlation of the percentage decrease in depression and anxiety scale scores with baseline connectivity measures. Results: There was a significant decrease (p = 0.05; cluster-wise corrected) in DRN connectivity with the prefrontal and mid-cingulate cortex in the MDD group, compared with the HC group. DRN connectivity with temporal areas, including the hippocampus and amygdala, positively correlated with baseline depression scores (p = 0.05; cluster-wise corrected). VTA connectivity with the cuneus-occipital areas correlated with a change in depression scores (p = 0.05; cluster-wise corrected). Conclusion: Our results indicate the presence of DRN-prefrontal and DRN-cingulate cortex connectivity abnormalities in young medication-free depressed subjects when compared to HCs and that the severity of depressive symptoms correlates with DRN-amygdala/hippocampus connectivity. VTA connectivity with the parietal and occipital areas is related to antidepressant treatment associated with a decrease in depressive symptoms. Future studies need to be carried out in larger and different age group populations to confirm the findings of the study.

Intrinsic Brain Network Biomarkers of Antidepressant Response: a Review

PURPOSE OF REVIEW

Poor treatment response is a hallmark of major depressive disorder. To tackle this problem, recent neuroimaging studies have sought to characterize antidepressant response in terms of pretreatment differences in intrinsic functional brain networks. Our aim is to review recent studies that predict antidepressant response using intrinsic network connectivity. We discuss current methodological limitations and directions for future antidepressant biomarker studies.

RECENT FINDINGS

Functional connectivity stemming from the subgenual and rostral anterior cingulate has shown particular consistency in predicting antidepressant response. Differences in this connectivity may prove fruitful in differentiating treatment responders to many antidepressant interventions. Future biomarker studies should integrate biological MDD subtypes to address the disorder's inherent clinical heterogeneity. These clinical and scientific advancements have the potential to address this population marked by limited treatment response. Methodological considerations, including patient selection, response criteria, and model overfitting, will require future investigation to ensure that biomarkers generalize for prospective prediction of treatment response.

Prefrontal expectancy and reinforcement-driven antidepressant placebo effects

Placebo responses in depression exemplify how expectancies and appraisals impact mood. Cognitive and neural mechanisms underlying these responses are still poorly understood, partly due to the difficulty of simulating antidepressant effects and manipulating mood experimentally. To address these challenges, we developed an acute antidepressant placebo experiment involving the intravenous administration of a "fast-acting antidepressant" and a trial-by-trial sham fMRI "neurofeedback" manipulation, purporting to reveal mood-relevant neural responses. Twenty volunteers with major depression underwent this experiment while rating their expected and actual mood improvement. Mixed-effects analyses of trial-by-trial ratings revealed that the "drug" infusion cues induced higher expectancies of mood improvement, while both the "drug" infusion cue and the sham neurofeedback induced a reported mood improvement. Neurofeedback of greater magnitude, compared to lower magnitude, recruited the lateral prefrontal cortex (lPFC). Individuals with greater lPFC responses to neurofeedback displayed: (1) greater effect of previous mood improvement on expectancy ratings and (2) greater effect of sham neurofeedback on mood improvement. Behavioral antidepressant placebo effects were additionally moderated by changes in peripheral β-endorphin plasma levels and depressive symptomatology. These data demonstrate the feasibility of trial-by-trial manipulation of antidepressant placebo-associated expectancies and their reinforcement. We provide initial insights into the role of the lPFC in the interplay between placebo-induced expectancies and mood, as well as preliminary evidence for the role of the opioid system in antidepressant placebo effects.

Pretreatment and early-treatment cortical thickness is associated with SSRI treatment response in major depressive disorder

To date, there are no biomarkers for major depressive disorder (MDD) treatment response in clinical use. Such biomarkers could allow for individualized treatment selection, reducing time spent on ineffective treatments and the burden of MDD. In search of such a biomarker, multisite pretreatment and early-treatment (1 week into treatment) structural magnetic resonance (MR) images were acquired from 184 patients with MDD randomized to an 8-week trial of the selective serotonin reuptake inhibitor (SSRI) sertraline or placebo. This study represents a large, multisite, placebo-controlled effort to examine the association between pretreatment differences or early-treatment changes in cortical thickness and treatment-specific outcomes. For standardization, a novel, robust site harmonization procedure was applied to structural measures in a priori regions (rostral and caudal anterior cingulate, lateral orbitofrontal, rostral middle frontal, and hippocampus), chosen based on previously published reports. Pretreatment cortical thickness or volume did not significantly associate with SSRI response. Thickening of the rostral anterior cingulate cortex in the first week of treatment was associated with better 8-week responses to SSRI (p = 0.010). These findings indicate that frontal lobe structural alterations in the first week of treatment may be associated with long-term treatment efficacy. While these associational findings may help to elucidate the specific neural targets of SSRIs, the predictive accuracy of pretreatment or early-treatment structural alterations in classifying treatment remitters from nonremitters was limited to 63.9%. Therefore, in this large sample of adults with MDD, structural MR imaging measures were not found to be clinically translatable biomarkers of treatment response to SSRI or placebo.

Pretreatment Rostral Anterior Cingulate Cortex Theta Activity in Relation to Symptom Improvement in Depression: A Randomized Clinical Trial

IMPORTANCE

Major depressive disorder (MDD) remains challenging to treat. Although several clinical and demographic variables have been found to predict poor antidepressant response, these markers have not been robustly replicated to warrant implementation in clinical care. Increased pretreatment rostral anterior cingulate cortex (rACC) theta activity has been linked to better antidepressant outcomes. However, no prior study has evaluated whether this marker has incremental predictive validity over clinical and demographic measures.

OBJECTIVE

To determine whether increased pretreatment rACC theta activity would predict symptom improvement regardless of randomization arm.

DESIGN, SETTING, AND PARTICIPANTS

A multicenter randomized clinical trial enrolled outpatients without psychosis and with chronic or recurrent MDD between July 29, 2011, and December 15, 2015 (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care [EMBARC]). Patients were consecutively recruited from 4 university hospitals: 634 patients were screened, 296 were randomized to receive sertraline hydrochloride or placebo, 266 had electroencephalographic (EEG) recordings, and 248 had usable EEG data. Resting EEG data were recorded at baseline and 1 week after trial onset, and rACC theta activity was extracted using source localization. Intent-to-treat analysis was conducted. Data analysis was performed from October 7, 2016, to January 19, 2018.

INTERVENTIONS

An 8-week course of sertraline or placebo.

MAIN OUTCOMES AND MEASURES

The 17-item Hamilton Rating Scale for Depression score (assessed at baseline and weeks 1, 2, 3, 4, 6, and 8).

RESULTS

The 248 participants (160 [64.5%] women, 88 [35.5%] men) with usable EEG data had a mean (SD) age of 36.75 (13.15) years. Higher rACC theta activity at both baseline (b = -1.05; 95% CI, -1.77 to -0.34; P = .004) and week 1 (b = -0.83; 95% CI, -1.60 to -0.06; P < .04) predicted greater depressive symptom improvement, even when controlling for clinical and demographic variables previously linked with treatment outcome. These effects were not moderated by treatment arm. The rACC theta marker, in combination with clinical and demographic variables, accounted for an estimated 39.6% of the variance in symptom change (with 8.5% of the variance uniquely attributable to the rACC theta marker).

CONCLUSIONS AND RELEVANCE

Increased pretreatment rACC theta activity represents a nonspecific prognostic marker of treatment outcome. This is the first study to date to demonstrate that rACC theta activity has incremental predictive validity.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT01407094.

Increased functional connectivity of the posterior cingulate cortex with the lateral orbitofrontal cortex in depression

To analyze the functioning of the posterior cingulate cortex (PCC) in depression, we performed the first fully voxel-level resting state functional-connectivity neuroimaging analysis of depression of the PCC, with 336 patients with major depressive disorder and 350 controls. Voxels in the PCC had significantly increased functional connectivity with the lateral orbitofrontal cortex, a region implicated in non-reward and which is thereby implicated in depression. In patients receiving medication, the functional connectivity between the lateral orbitofrontal cortex and PCC was decreased back towards that in the controls. In the 350 controls, it was shown that the PCC has high functional connectivity with the parahippocampal regions which are involved in memory. The findings support the theory that the non-reward system in the lateral orbitofrontal cortex has increased effects on memory systems, which contribute to the rumination about sad memories and events in depression. These new findings provide evidence that a key target to ameliorate depression is the lateral orbitofrontal cortex.

A Novel Strategy to Identify Placebo Responders: Prediction Index of Clinical and Biological Markers in the EMBARC Trial

BACKGROUND

One in three clinical trial patients with major depressive disorder report symptomatic improvement with placebo. Strategies to mitigate the effect of placebo responses have focused on modifying study design with variable success. Identifying and excluding or controlling for individuals with a high likelihood of responding to placebo may improve clinical trial efficiency and avoid unnecessary medication trials.

METHODS

Participants included those assigned to the placebo arm (n = 141) of the Establishing Moderators and Biosignatures for Antidepressant Response in Clinical Care (EMBARC) trial. The elastic net was used to evaluate 283 baseline clinical, behavioral, imaging, and electrophysiological variables to identify the most robust yet parsimonious features that predicted depression severity at the end of the double-blind 8-week trial. Variables retained in at least 50% of the 100 imputed data sets were used in a Bayesian multiple linear regression model to simultaneously predict the probabilities of response and remission.

RESULTS

Lower baseline depression severity, younger age, absence of melancholic features or history of physical abuse, less anxious arousal, less anhedonia, less neuroticism, and higher average theta current density in the rostral anterior cingulate predicted a higher likelihood of improvement with placebo. The Bayesian model predicted remission and response with an actionable degree of accuracy (both AUC > 0.73). An interactive calculator was developed predicting the likelihood of placebo response at the individual level.

CONCLUSION

Easy-to-measure clinical, behavioral, and electrophysiological assessments can be used to identify placebo responders with a high degree of accuracy. Development of this calculator based on these findings can be used to identify potential placebo responders.

Resting state brain network function in major depression - Depression symptomatology, antidepressant treatment effects, future research

The alterations of functional connectivity brain networks in major depressive disorder (MDD) have been subject of a large number of studies. Using different methodologies and focusing on diverse aspects of the disease, research shows heterogeneous results lacking integration. Disrupted network connectivity has been found in core MDD networks like the default mode network (DMN), the central executive network (CEN), and the salience network, but also in cerebellar and thalamic circuitries. Here we review literature published on resting state brain network function in MDD focusing on methodology, and clinical characteristics including symptomatology and antidepressant treatment related findings. There are relatively few investigations concerning the qualitative aspects of symptomatology of MDD, whereas most studies associate quantitative aspects with distinct resting state functional connectivity alterations. Such depression severity associated alterations are found in the DMN, frontal, cerebellar and thalamic brain regions as well as the insula and the subgenual anterior cingulate cortex. Similarly, different therapeutical options in MDD and their effects on brain function showed patchy results. Herein, pharmaceutical treatments reveal functional connectivity alterations throughout multiple brain regions notably the DMN, fronto-limbic, and parieto-temporal regions. Psychotherapeutical interventions show significant functional connectivity alterations in fronto-limbic networks, whereas electroconvulsive therapy and repetitive transcranial magnetic stimulation result in alterations of the subgenual anterior cingulate cortex, the DMN, the CEN and the dorsal lateral prefrontal cortex. While it appears clear that functional connectivity alterations are associated with the pathophysiology and treatment of MDD, future research should also generate a common strategy for data acquisition and analysis, as a least common denominator, to set the basis for comparability across studies and implementation of functional connectivity as a scientifically and clinically useful biomarker.

Treatment Associated Changes of Functional Connectivity of Midbrain/Brainstem Nuclei in Major Depressive Disorder

Previous functional magnetic resonance imaging (fMRI) studies demonstrated an abnormally coordinated network functioning in Major Depression Disorder (MDD) during rest. The main monoamine-producing nuclei within midbrain/brainstem are functionally integrated within these specific networks. Therefore, we aimed to investigate the resting-state functional connectivity (RSFC) of these nuclei in 45 MDD patients and differences between patients receiving two different classes of antidepressant drugs. Patients showed reduced RSFC from the ventral tegmental area (VTA) to dorsal anterior cingulate cortex (dACC) and stronger RSFC to the left amygdala and dorsolateral prefrontal cortex (DLPFC). Patients treated with antidepressants influencing noradrenergic and serotonergic neurotransmission showed different RSFC from locus coeruleus to DLPFC compared to patients treated with antidepressants influencing serotonergic neurotransmission only. In the opposite contrast patients showed stronger RSFC from dorsal raphe to posterior brain regions. Enhanced VTA-RSFC to amygdala as a central region of the salience network may indicate an over‐attribution of the affective salience to internally-oriented processes. Significant correlation between decreased VTA-dACC functional connectivity and the BDI-II somatic symptoms indicates an association with diminished volition and behavioral activation in MDD. The observed differences in the FC of the midbrain/brainstem nuclei between two classes of antidepressants suggest differential neural effects of SSRIs and SNRIs.

Nicotinic modulation of salience network connectivity and centrality in schizophrenia

Although functional abnormalities of the salience network are associated with schizophrenia, the acute effects of nicotine on its function and network dynamics during the resting state in patients are poorly understood. In this study, the effects of a 7 mg nicotine patch (vs. placebo) on salience network connectivity were examined in 17 patients with schizophrenia and 19 healthy subjects. We hypothesized abnormal connectivity between the salience network and other major networks (e.g. executive network) in patients under placebo administration and amelioration of this difference after nicotine. We also examined effects of nicotine on betweenness centrality (a measure of the influence of a region on information transfer throughout the brain) and local efficiency (a measure of local information transfer) of the network. A hybrid independent component analysis (ICA)/seed-based connectivity approach was implemented in which the salience network was extracted by ICA and cortical network peaks (anterior cingulate cortex (ACC), left and right insula) were used as seeds for whole-brain seed-to-voxel connectivity analysis. Significant drug X diagnosis interactions were observed between the ACC seed and superior parietal lobule and ventrolateral prefrontal cortex. A significant interaction effect was also observed between the left insula seed and middle cingulate cortex. During placebo conditions, abnormal connectivity predicted negative symptom severity and lower global functioning in patients. A significant drug X diagnosis interaction was also observed for betweenness centrality of the ACC. These results suggest that nicotine may target abnormalities in functional connectivity between salience and executive network areas in schizophrenia as well as affect the ability of the salience network to act as an integrator of global signaling in the disorder.

Bupropion Administration Increases Resting-State Functional Connectivity in Dorso-Medial Prefrontal Cortex

Background

Patients on the selective serotonergic reuptake inhibitors like citalopram report emotional blunting. We showed previously that citalopram reduces resting-state functional connectivity in healthy volunteers in a number of brain regions, including the dorso-medial prefrontal cortex, which may be related to its clinical effects. Bupropion is a dopaminergic and noradrenergic reuptake inhibitor and is not reported to cause emotional blunting. However, how bupropion affects resting-state functional connectivity in healthy controls remains unknown.

Methods

Using a within-subjects, repeated-measures, double-blind, crossover design, we examined 17 healthy volunteers (9 female, 8 male). Volunteers received 7 days of bupropion (150 mg/d) and 7 days of placebo treatment and underwent resting-state functional Magnetic Resonance Imaging. We selected seed regions in the salience network (amygdala and pregenual anterior cingulate cortex) and the central executive network (dorsal medial prefrontal cortex). Mood and anhedonia measures were also recorded and examined in relation to resting-state functional connectivity.

Results

Relative to placebo, bupropion increased resting-state functional connectivity in healthy volunteers between the dorsal medial prefrontal cortex seed region and the posterior cingulate cortex and the precuneus cortex, key parts of the default mode network.

Conclusions

These results are opposite to that which we found with 7 days treatment of citalopram in healthy volunteers. These results reflect a different mechanism of action of bupropion compared with selective serotonergic reuptake inhibitors. These results help explain the apparent lack of emotional blunting caused by bupropion in depressed patients.

Default mode network deactivation during emotion processing predicts early antidepressant response

Several previous functional magnetic resonance imaging (fMRI) studies have demonstrated the predictive value of brain activity during emotion processing for antidepressant response, with a focus on clinical outcome after 6-8 weeks. However, longitudinal studies emphasize the paramount importance of early symptom improvement for the course of disease in major depressive disorder (MDD). We therefore aimed to assess whether neural activity during the emotion discrimination task (EDT) predicts early antidepressant effects, and how these predictive measures relate to more sustained response. Twenty-three MDD patients were investigated once with ultrahigh-field 7T fMRI and the EDT. Following fMRI, patients received Escitalopram in a flexible dose schema and were assessed with the Hamilton Depression Rating Scale (HAMD) before, and after 2 and 4 weeks of treatment. Deactivation of the precuneus and posterior cingulate cortex (PCC) during the EDT predicted change in HAMD scores after 2 weeks of treatment. Baseline EDT activity was not predictive of HAMD change after 4 weeks of treatment. The precuneus and PCC are integral components of the default mode network (DMN). We show that patients who exhibit stronger DMN suppression during emotion processing are more likely to show antidepressant response after 2 weeks. This is, to our knowledge, the first study to show that DMN activity predicts early antidepressant effects. However, DMN deactivation did not predict response at 4 weeks, suggesting that our finding is representative of early, likely treatment-related, yet unspecific symptom improvement. Regardless, early effects may be harnessed for optimization of treatment regimens and patient care.