Improving the prediction of treatment response in depression: integration of clinical, cognitive, psychophysiological, neuroimaging, and genetic measures

Prediction of remission of pharmacologically treated psychotic depression: A machine learning approach

BACKGROUND

The combination of antidepressant and antipsychotic medication is an effective treatment for major depressive disorder with psychotic features ('psychotic depression'). The present study aims to identify sociodemographic and clinical predictors of remission of psychotic depression treated with combination pharmacotherapy and determine the accuracy of prediction models.

METHODS

Two hundred and sixty-nine participants aged 18 to 85 years with psychotic depression were acutely treated with protocolized sertraline plus olanzapine for up to 12 weeks. Three cross-validated machine learning models were implemented to predict remission based on 74 sociodemographic and clinical variables measured at acute baseline. The optimal model for each method was selected by the average fold C-index. Based on the performance of each method, grouped elastic net (cox) regression was chosen to examine the association of each predictor with remission of psychotic depression.

RESULTS

Of the 269 participants, 145 (53.9 %) experienced full remission of the depressive episode and psychotic features. Multivariable models had 65.1 % to 67.4 % accuracy in predicting remission. In the grouped elastic net (cox) regression model, longer duration of index episode, somatic or tactile hallucinations, higher burden of comorbid physical problems, and single or divorced marital status were independent predictors of longer time to remission. A higher number of lifetime depressive episodes and peripheral vascular or cardiovascular disease were predictors of shorter time to remission.

CONCLUSIONS

Future research needs to determine whether the addition of biomarkers to clinical and sociodemographic variables can improve model accuracy in predicting remission of pharmacologically-treated psychotic depression.

Cognitive impairment and therapeutic response in resistant depression

OBJECTIVES

Therapeutic response in depression is a major challenge since more than one third of patients are not in remission after two attempts of antidepressant treatment and will present a treatment-resistant depression. In order to better adapt therapeutic strategies for treatment-resistant patients, predictive indicators and markers of therapeutic response still need to be identified. In parallel, patients with depression exhibit disturbances in cognitive functioning. This study aims to describe and compare cognitive performances collected at inclusion of patients presenting treatment-resistant depression who will be responders at 6 months to those of non-responders, and to evaluate the predictive value of cognitive indicators on clinical therapeutic response at 6 months after a therapeutic modification.

METHODS

Observational study. Patients were evaluated at the clinical (HDRS and BDI-II) and cognitive levels using standardized tools assessing memory, executive functions, attention, and social cognition, prior to a change in antidepressant treatment. Six months after inclusion, they were reassessed and classified into two groups based on the presence or absence of therapeutic response, defined by a 50% improvement on HDRS and BDI-II. The cognitive scores collected at inclusion were then compared. Additionally, univariate logistic regression models were used.

RESULTS

Thirty patients were included in this study. Only 13 could be evaluated at 6 months. Among these patients, four had responded to the new treatment while nine were non-responders. Both groups of patients presented deviant cognitive performances compared to norms on tests evaluating executive functions and attention. Statistical analyses did not reveal any difference between the cognitive performances of responders and non-responders at 6 months. Regression analyses showed no association between cognitive scores and therapeutic response at 6 months.

CONCLUSION

Executive functioning plays a significant role in treatment-resistant depression. In order to improve the understanding and identification of subtypes of depression, cognitive indicators should be systematically integrated into future research.

Surface-based analysis of early cortical gyrification and thickness alterations in treatment-Naïve, first-episode depressive patients during emerging adulthood

BACKGROUND

Extensive research, predominantly in adults, has highlighted structural brain variations among patients with major depressive disorder (MDD). However, emerging adults, who undergo significant cortical reshaping and are highly vulnerable to depression, receive relatively little attention, despite reporting a higher prevalence of childhood trauma experiences. This study examines cortical gyrification and thickness in emerging adults with first-episode, treatment-naïve MDD, with the objective of investigating their association with childhood trauma.

METHODS

Eighty-six emerging adults diagnosed with MDD, aged 18 to 25, and eighty-one healthy controls (HCs), underwent T1-MRI scans. We compared the local gyrification index (LGI) and cortical thickness (CT) between the two groups. Subsequently, we examined the relationship between the LGI and CT in clusters showing differences and childhood trauma as well as clinical characteristics in emerging adults with MDD.

RESULTS

Compared to HCs, MDD showed decreased LGI in the bilateral superior frontal cortices (SFC) and CT in the left pericalcarine cortex (PCC), while an increase in CT was observed in the left lateral orbitofrontal cortex (OFC). The reduction in LGI of the right SFC and the decrease in CT of the left PCC are associated with childhood trauma. Notably these brain abnormalities were not significantly associated with depressive and anxiety symptoms, or the duration of illness.

CONCLUSION

Abnormal cortical development observed in emerging adults with first episode depression may act as a predisposing factor for depression, irrespective of clinical manifestations, and may be linked to childhood trauma.

Mapping Knowledge Landscapes and Evolving Trends of Clinical Hypnotherapy Practice: A Bibliometrics-Based Visualization Analysis

Background and Aims

Increasing interest in hypnotherapy's application for a wide range of health conditions has spurred a rise in global research and publications. This study aims to visualize development patterns and current research hotspots in clinical hypnotherapy practice using scientometric methods, and to predict future research directions based on the keyword trending topics analysis.

Methods

Data on hypnotherapy applications and mechanisms in clinical settings between 1994 and 2023 were gathered from Scopus, Web of Science, and PubMed, followed by analysis and visualization using the VOSviewer, Bibliometrix package in R, and CiteSpace.

Results

A total of 1,549 publications were examined, indicating a steady annual increase with an average growth rate of 8.5%, reaching a high of 134 publications in 2022. The United States was the primary research hub. Collectively, 1,464 distinct institutions involving 3,195 scholars contributed to this research theme. Collaboration was predominantly confined to the same country, institution, and/or research team. High-frequency keywords included "Pain", "Irritable Bowel Syndrome (IBS)", and "Anxiety". Systematic review and/or meta-analysis have emerged as favored research methods. fMRI and EEG were commonly used techniques for exploring the neuropsychological mechanisms underlying hypnotherapy. "Self-Hypnosis", "Virtual Reality", and "Meditation" were predicted as trending topics, indicating that patients' self-managed hypnosis practice, virtual reality hypnotherapy, and exploration of the variations in mechanisms between meditation and hypnotherapy might be emerging topics and/or future key research directions within the current field.

Conclusion

The use of hypnotherapy for diverse clinical issues, particularly pain, IBS, and comorbid anxiety, is garnering global attention. The evidence-based approach is widely used to assess the quality of clinical evidence for hypnotherapy. Researchers are keen on innovating traditional hetero-hypnosis, with a shift towards more cost-effective self-hypnosis and immersive virtual reality hypnotherapy. Promoting and reinforcing collaborative research efforts across countries, institutions, and teams is warranted.

Symptom clusters in adolescent depression and differential responses of clusters to pharmacologic treatment

OBJECTIVE

Symptoms of depression in adolescents are widely variable, but they are often interactive and clustered. The analysis of interactions and clusters among individual symptoms may help predict treatment outcomes. We aimed to determine clusters of individual symptoms in adolescent depression and their changes in the response to pharmacological treatment.

METHOD

A total of 95 adolescents, aged 12-17 years, with major depressive disorder were included. Participants were treated with escitalopram, and depressive symptoms were assessed at baseline (V1) and 1, 2, 4, 6, and 8 weeks (V6). The severity of depression was assessed using the Children's Depression Rating Scale-Revised. To construct network and clustering structures among symptoms, the Gaussian graphical model and Exploratory Graph Analysis with the tuning parameter to minimize the extended Bayesian information criterion were adopted.

RESULTS

Exploratory Graph Analysis revealed that symptoms of depression comprised four clusters: impaired activity, somatic concerns, subjective mood, and observed affect. The main effect of visit with decreased symptom severity was significant in all four clusters; however, the degree of symptom improvement differed among the four clusters. The effect size of score differences from V1 to V6 was the highest in the subjective mood (Cohen's d = 1.075), and lowest in impaired activity (d = 0.501) clusters.

CONCLUSION

The present study identified four symptom clusters associated with adolescent depression and their differential changes related to antidepressant treatment. This finding suggests that escitalopram was the most effective at improving subjective mood among different clusters. However, other therapeutic modalities may be needed to improve other clusters of symptoms, consequently leading to increased overall improvement of depression in adolescents.

Personality traits predict treatment outcome of an antidepressant in untreated adolescents with depression: An 8-week, open-label, flexible-dose study

BACKGROUND

Antidepressant response in adults with major depressive disorder (MDD) is probably influenced by personality dimensions. However, personality dimensions in depression and their association with antidepressant treatment in adolescents are relatively unknown. We sought to investigate whether personality traits (PTs) can influence antidepressant treatment response in adolescents with depression.

METHODS

Eighty-two adolescents with MDD who had completed the 8 weeks of treatment with selective serotonin reuptake inhibitors (SSRI) were enrolled. The Revised NEO Five-Factor Inventory (NEO-FFI-R) was used to measure their personality at baseline, and the 17-item Hamilton Depression Rating Scale (HAMD-17) and Children's Depression Rating Scale-Revised (CDRS-R) were used to evaluate depressive symptoms at baseline and 8 weeks. Moreover, logistic regression was performed to investigate the relationship between personality dimensions and antidepressant response. Receiver operating characteristic analyses were employed to determine the accuracy of a PT-based model in predicting the antidepressant response rate.

RESULTS

Adolescents with MDD had significantly different PTs at baseline. Multivariable logistic regression analysis showed that extroversion scores were associated with response to antidepressant treatment, the lower the extroversion score, the better the response to antidepressant treatment, after correcting for variables with significant differences and trends or all potential confounding variables. It was also found that the combination of disease duration, extraversion-gregariousness, and agreeableness-trust effectively predicted antidepressant response in adolescents with MDD, with a sensitivity of 79.4 % and specificity of 68.7 %.

CONCLUSION

Personality dysfunction in adolescents is associated with MDD. The antidepressant treatment response is influenced by the degree of extroversion in adolescents with MDD.

Links among genetic variants and hierarchical brain structural and functional networks for antidepressant treatment: A multivariate study

BACKGROUND

Antidepressant treatment effects are strongly heritable and have substantial effects on brain function and structure, but the underlying mechanisms are still poorly understood. In this research, we aimed to evaluate the factors of single nucleotide polymorphisms (SNPs) and hierarchical brain structural and functional networks that were associated with antidepressant treatment. Moreover, we further explored the correlations and mediation pattern among "brain structure-brain function-gene" in major depressive disorder (MDD).

METHODS

We analysed 405 SNPs and rich club/feeder/local connections of hierarchical structural and functional networks with three-way parallel independent component analysis in 179 MDD patients. The group-discriminative independent components of the three modalities between responders and non-responders of antidepressant treatment were identified. Pearson correlations and mediation analysis were further utilized to investigate the associations among SNPs and connections of the structural and functional networks.

RESULTS

Notably, correlations with antidepressant treatment outcomes were found in structural, functional and SNP modalities simultaneously. The features of group-discriminative independent components included the shared feeder connections of hub regions with the inferior frontal orbital gyrus and amygdala in structural and functional modalities and genes enriched in circadian rhythmic processes and dopaminergic synapse pathways. The structural feeder network displayed close correlations with SNPs and the functional feeder network. Furthermore, the structural feeder network could mediate the association between SNPs and the functional feeder network, implying that genetic variants might influence brain function by affecting brain structure in MDD.

CONCLUSIONS

These findings provide potential biomarkers for antidepressant therapy and provide a better grasp of the associations among SNPs and hierarchical structural and functional networks in MDD.

A common symptom geometry of mood improvement under sertraline and placebo associated with distinct neural patterns

Importance

Understanding the mechanisms of major depressive disorder (MDD) improvement is a key challenge to determine effective personalized treatments.

Objective

To perform a secondary analysis quantifying neural-to-symptom relationships in MDD as a function of antidepressant treatment.

Design

Double blind randomized controlled trial.

Setting

Multicenter.

Participants

Patients with early onset recurrent depression from the public Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.

Interventions

Either sertraline or placebo during 8 weeks (stage 1), and according to response a second line of treatment for 8 additional weeks (stage 2).

Main Outcomes and Measures

To identify a data-driven pattern of symptom variations during these two stages, we performed a Principal Component Analysis (PCA) on the variations of individual items of four clinical scales measuring depression, anxiety, suicidal ideas and manic-like symptoms, resulting in a univariate measure of clinical improvement. We then investigated how initial clinical and neural factors predicted this measure during stage 1. To do so, we extracted resting-state global brain connectivity (GBC) at baseline at the individual level using a whole-brain functional network parcellation. In turn, we computed a linear model for each brain parcel with individual data-driven clinical improvement scores during stage 1 for each group.

Results

192 patients (127 women), age 37.7 years old (standard deviation: 13.5), were included. The first PC (PC1) capturing 20% of clinical variation was similar across treatment groups at stage 1 and stage 2, suggesting a reproducible pattern of symptom improvement. PC1 patients' scores significantly differed according to treatment during stage 1, whereas no difference of response was evidenced between groups with the Clinical Global Impressions (CGI). Baseline GBC correlated to stage 1 PC1 scores in the sertraline, but not in the placebo group.

Conclusions and Relevance

Using data-driven reduction of symptoms scales, we identified a common profile of symptom improvement across placebo and sertraline. However, the neural patterns of baseline that mapped onto symptom improvement distinguished between treatment and placebo. Our results underscore that mapping from data-driven symptom improvement onto neural circuits is vital to detect treatment-responsive neural profiles that may aid in optimal patient selection for future trials.

Integration of a pharmacist-led pharmacogenomic service in a geriatric clinic: Barriers and outcomes

OBJECTIVES

The primary objective was to identify the proportion of patients who successfully completed PGx testing. Secondary objectives included determining the proportion of patients with actionable PGx results, determining the proportion of patients with a baseline medication intervention within 6 months of successfully completing PGx testing, and identifying barriers for not completing testing.

DESIGN

This was a single center, non-interventional, retrospective cohort study, approved by the institutional review board.

SETTING AND PARTICIPANTS

Patients included were 65 years of age or older and referred for PGx testing from geriatric outpatient clinics between May 1, 2019 and July 31, 2020.

OUTCOME MEASURES

This study aimed to assess the implementation of pharmacist-led pharmacogenomics (PGx) in the care of community-dwelling older adults in an outpatient clinic. Little is known about the acceptance and impact of this type of service within this population.

RESULTS

Of the 67 patients included, majority were female (78%), white (76%), and an average age of 78 years ± 5.98 SD. Majority were insured by Original Medicare or Medicaid (61%), had a history of cognitive impairment (84%), had a referring diagnosis of anxiety (40%) or depression (67%), and were prescribed a selective serotonin reuptake inhibitor (69%) at baseline. Majority successfully completed PGx testing (72%), with 72% having actionable PGx findings and 83% having a pharmacological intervention made thereafter. Nineteen patients did not complete testing (28%), with the primary barrier being not having an appointment scheduled (63%).

CONCLUSION

This study demonstrated majority of older adults were accepting of PGx testing and majority of findings were relevant to clinical care of anxiety, depression, or cognitive impairment.

Evolutionary Aspects of Diverse Microbial Exposures and Mental Health: Focus on "Old Friends" and Stress Resilience

The prevalence of inflammatory disease conditions, including allergies, asthma, and autoimmune disorders, increased during the latter half of the twentieth century, as societies transitioned from rural to urban lifestyles. A number of hypotheses have been put forward to explain the increasing prevalence of inflammatory disease in modern urban societies, including the hygiene hypothesis and the "Old Friends" hypothesis. In 2008, Rook and Lowry proposed, based on the evidence that increased inflammation was a risk factor for stress-related psychiatric disorders, that the hygiene hypothesis or "Old Friends" hypothesis may be relevant to psychiatric disorders. Since then, it has become more clear that chronic low-grade inflammation is a risk factor for stress-related psychiatric disorders, including anxiety disorders, mood disorders, and trauma- and stressor-related disorders, such as posttraumatic stress disorder (PTSD). Evidence now indicates that persons raised in modern urban environments without daily contact with pets, relative to persons raised in rural environments in proximity to farm animals, respond with greater systemic inflammation to psychosocial stress. Here we consider the possibility that increased inflammation in persons living in modern urban environments is due to a failure of immunoregulation, i.e., a balanced expression of regulatory and effector T cells, which is known to be dependent on microbial signals. We highlight evidence that microbial signals that can drive immunoregulation arise from phylogenetically diverse taxa but are strain specific. Finally, we highlight Mycobacterium vaccae NCTC 11659, a soil-derived bacterium with anti-inflammatory and immunoregulatory properties, as a case study of how single strains of bacteria might be used in a psychoneuroimmunologic approach for prevention and treatment of stress-related psychiatric disorders.

Structural and Functional Neural Correlates of Treatment Response for Interpersonal Psychotherapy for Depressed Adolescents

Precision medicine approaches hold tremendous promise to advance current clinical practice by providing information about which individuals will benefit from which treatments. This pilot study evaluated if baseline structure and function of the salience and emotion brain regions implicated in adolescent depression, specifically the amygdala and anterior cingulate cortex (ACC), predict response to Interpersonal Psychotherapy for Depressed Adolescents (IPT-A). Adolescents (n = 15; mean age = 14.5 (1.6); 80.0% female) diagnosed with a depressive disorder completed brain scans before the start of a 16 week trial of IPT-A. Clinical measures assessing depressive symptoms were completed before, during, and after a trial of therapy. Results show that at baseline, greater ACC activation in the context of an emotion-matching task and greater amygdala-ACC resting-state functional connectivity was related to greater improvement in depression symptoms. There was minimal evidence that brain structure predicted changes in depressive symptoms. The present study is the first to evaluate neural predictors of IPT-A response. While the results are preliminary, these findings suggest some avenues for future research to pursue in the hopes that more will benefit from treatment.

A Whole Transcriptome Analysis in Peripheral Blood Suggests That Energy Metabolism and Inflammation Are Involved in Major Depressive Disorder

INTRODUCTION

Previous studies on transcriptional profiles suggested dysregulation of multiple RNA species in major depressive disorder (MDD). However, the interaction between different types of RNA was neglected. Therefore, integration of different RNA species in transcriptome analysis would be helpful for interpreting the functional readout of the transcriptome in MDD.

METHODS

A whole transcriptome sequencing were performed on the peripheral blood of 15 patients with MDD and 15 matched healthy controls (HCs). The differential expression of miRNAs, lncRNAs, circRNAs, and mRNAs was examined between MDD and HCs using empirical analysis of digital gene expression data in R (edgeR). Weighted correlation network analysis (WGCNA) was used to identify RNA co-expression modules associated with MDD. A ceRNA network was constructed for interpretation of interactions between different RNA species. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to explore potential biological mechanisms associated with MDD.

RESULTS

Multiple RNAs and co-expression modules were identified to be significantly dysregulated in MDD compared to HCs. Based on the differential RNAs, a ceRNA network that were dysregulated in MDD were constructed. The pathway networks that related to oxidative phosphorylation and the chemokine signaling were found to be associated with MDD.

CONCLUSION

Our results suggested that the processes of energy metabolism and inflammation may be involved in the pathophysiology of MDD.

P300 amplitude during a monetary incentive delay task predicts future therapy completion in individuals with major depressive disorder

INTRODUCTION

Treatment effectiveness for major depressive disorder (MDD) is often affected by client non-adherence, dropout, and non-response. Identification of client characteristics predicting successful treatment completion and/or response (i.e., symptom reduction) may be an important tool to increase intervention effectiveness. It is unclear whether neural attenuations in reward processing associated with MDD predict behavioral treatment outcome.

METHODS

This study aimed to determine whether blunted neural responses to reward at baseline differentiate MDD (n = 60; 41 with comorbid anxiety) and healthy control (HC; n = 40) groups; and predict MDD completion of and response to 7-10 sessions of behavior therapy. Participants completed a monetary incentive delay (MID) task. The N200, P300, contingent negative variation (CNV) event related potentials (ERPs) and behavioral responses (reaction time [RT], correct hits) were quantified and extracted for cross-sectional group analyses. ERPs and behavioral responses demonstrating group differences were then used to predict therapy completion and response within MDD.

RESULTS

MDD exhibited faster RT and smaller P300 amplitudes than HC across conditions. Within the MDD group, treatment completers (n = 37) exhibited larger P300 amplitudes than non-completers (n = 21).

LIMITATIONS

This study comprises secondary analyses of EEG data; thus task parameters are not optimized to examine feedback ERPs from the paradigm. We did not examine heterogenous presentations of MDD; however, severity and comorbidity did not influence findings.

CONCLUSIONS

Previous studies suggest that P300 is an index of motivational salience and stimulus resource allocation. In sum, individuals who deploy greater neural resources to task demands are more likely to persevere in behavioral therapy.

Impact of gender, depression severity and type of depressive episode on efficacy and safety of escitalopram: an observational study on major depressive disorder patients in southern India

Background

Antidepressant response is a complex trait influenced by clinical, demographic and genetic factors.

Objectives

To explore the influences of baseline depression severity, gender and type of depressive episode on efficacy and safety of escitalopram (10–20 mg/day) in South Indian patients with major depressive disorder (MDD).

Methods

The study was conducted on 18–65-year-old patients (n = 151) suffering from a first or recurrent episode of MDD with a 17-item Hamilton Depression Rating Scale (HDRS-17) score of ≥ 18 at baseline. Efficacy assessments were done using HDRS-17, Montgomery-Asberg Depression Rating Scale (MADRS), and Clinical Global Impression (CGI) at baseline and weeks 4, 8 and 12. Patients were monitored for adverse drug reactions (ADRs). Clinical outcomes were compared among various groups based on gender, type of depressive episode (first or recurrent episode) and baseline HDRS-17 scores (moderate depression—score between 17 and 23; severe depression—score ≥ 24).

Results

Among the 148 subjects who completed the 12-week study, 43.9% and 42.6% achieved response and remission, respectively. The decline in HDRS-17 and MADRS scores from baseline was significant (p value < 0.05) at all follow-up visits and a similar pattern was seen with CGI. Efficacy outcomes were better in the moderate baseline depression group compared with severe depression. There were no associations of efficacy with gender and type of depressive episode. A total of 247 adverse drug reactions (ADR) were reported and 119 (80.41%) subjects experienced at least one ADR during the study period. No serious ADR was reported. Male patients experienced more ADRs compared with females. The safety profile of escitalopram was similar across various groups based on baseline depression severity and type of depressive episode.

Conclusion

The study revealed that escitalopram is efficacious in south Indian MDD patients with a favourable safety profile. The efficacy was influenced by baseline depression severity whereas more ADRs were reported by male patients.

Pharmacogenomics in Older Adults: An Integrative Review

Through pharmacogenomics testing, identifying genetic variants that influence how individuals respond to medications could potentially decrease the "trial and error" approach to prescribing medications, maximize beneficial effects, and reduce risks of adverse drug events. Yet, pharmacogenomics testing is still subject to an ongoing debate over its clinical validity and utility. The purpose of the current integrative review was to examine and synthesize evidence on the clinical application of pharmacogenomics in medication management among older adults. Gaps were found, such as lack of studies investigating the prospective use of pharmacogenomics testing to improve clinical outcomes and lack of strong evidence on the clinical validity and utility of pharmacogenomics testing in the medication management of older adults. However, the review identified evidence for the potential benefits of pharmacogenomics testing to improve older adults' clinical outcomes that warrant further investigation. [Research in Gerontological Nursing, 14(4), 211-220.].

Association between psychomotor disturbance and treatment outcome in psychotic depression: a STOP-PD II report

BACKGROUND

Little is known about the relationship between psychomotor disturbance (PMD) and treatment outcome of psychotic depression. This study examined the association between PMD and subsequent remission and relapse of treated psychotic depression.

METHODS

Two hundred and sixty-nine men and women aged 18-85 years with an episode of psychotic depression were treated with open-label sertraline plus olanzapine for up to 12 weeks. Participants who remained in remission or near-remission following an 8-week stabilization phase were eligible to participate in a 36-week randomized controlled trial (RCT) that compared the efficacy and tolerability of sertraline plus olanzapine (n = 64) with sertraline plus placebo (n = 62). PMD was measured with the psychiatrist-rated sign-based CORE at acute phase baseline and at RCT baseline. Spearman's correlations and logistic regression analyses were used to analyze the association between CORE total score at acute phase baseline and remission/near-remission and CORE total score at RCT baseline and relapse.

RESULTS

Higher CORE total score at acute phase baseline was associated with lower frequency of remission/near-remission. Higher CORE total score at RCT baseline was associated with higher frequency of relapse, in the RCT sample as a whole, as well as in each of the two randomized groups.

CONCLUSIONS

PMD is associated with poorer outcome of psychotic depression treated with sertraline plus olanzapine. Future research needs to examine the neurobiology of PMD in psychotic depression in relation to treatment outcome.

In-vivo imaging of targeting and modulation of depression-relevant circuitry by transcranial direct current stimulation: a randomized clinical trial

Recent clinical trials of transcranial direct current stimulation (tDCS) in depression have shown contrasting results. Consequently, we used in-vivo neuroimaging to confirm targeting and modulation of depression-relevant neural circuitry by tDCS. Depressed participants (N = 66, Baseline Hamilton Depression Rating Scale (HDRS) 17-item scores ≥14 and <24) were randomized into Active/Sham and High-definition (HD)/Conventional (Conv) tDCS groups using a double-blind, parallel design, and received tDCS individually targeted at the left dorsolateral prefrontal cortex (DLPFC). In accordance with Ampere's Law, tDCS currents were hypothesized to induce magnetic fields at the stimulation-target, measured in real-time using dual-echo echo-planar-imaging (DE-EPI) MRI. Additionally, the tDCS treatment trial (consisting of 12 daily 20-min sessions) was hypothesized to induce cerebral blood flow (CBF) changes post-treatment at the DLPFC target and in the reciprocally connected anterior cingulate cortex (ACC), measured using pseudo-continuous arterial spin labeling (pCASL) MRI. Significant tDCS current-induced magnetic fields were observed at the left DLPFC target for both active stimulation montages (Brodmann's area (BA) 46: p_HD = 0.048, Cohen's d_HD = 0.73; p_Conv = 0.018, d_Conv = 0.86; BA 9: p_HD = 0.011, d_HD = 0.92; p_Conv = 0.022, d_Conv = 0.83). Significant longitudinal CBF increases were observed (a) at the left DLPFC stimulation-target for both active montages (p_HD = 3.5E-3, d_HD = 0.98; p_Conv = 2.8E-3, d_Conv = 1.08), and (b) at ACC for the HD-montage only (p_HD = 2.4E-3, d_HD = 1.06; p_Conv = 0.075, d_Conv = 0.64). These results confirm that tDCS-treatment (a) engages the stimulation-target, and (b) modulates depression-relevant neural circuitry in depressed participants, with stronger network-modulations induced by the HD-montage. Although not primary outcomes, active HD-tDCS showed significant improvements of anhedonia relative to sham, though HDRS scores did not differ significantly between montages post-treatment.

Utilization of Machine Learning-Based Computer Vision and Voice Analysis to Derive Digital Biomarkers of Cognitive Functioning in Trauma Survivors

Background

Alterations in multiple domains of cognition have been observed in individuals who have experienced a traumatic stressor. These domains may provide important insights in identifying underlying neurobiological dysfunction driving an individual's clinical response to trauma. However, such assessments are burdensome, costly, and time-consuming. To overcome barriers, efforts have emerged to measure multiple domains of cognitive functioning through the application of machine learning (ML) models to passive data sources.

Methods

We utilized automated computer vision and voice analysis methods to extract facial, movement, and speech characteristics from semi-structured clinical interviews in 81 trauma survivors who additionally completed a cognitive assessment battery. A ML-based regression framework was used to identify variance in visual and auditory measures that relate to multiple cognitive domains.

Results

Models derived from visual and auditory measures collectively accounted for a large variance in multiple domains of cognitive functioning, including motor coordination (R² = 0.52), processing speed (R² = 0.42), emotional bias (R² = 0.52), sustained attention (R² = 0.51), controlled attention (R² = 0.44), cognitive flexibility (R² = 0.43), cognitive inhibition (R² = 0.64), and executive functioning (R² = 0.63), consistent with the high test-retest reliability of traditional cognitive assessments. Face, voice, speech content, and movement have all significantly contributed to explaining the variance in predicting functioning in all cognitive domains.

Conclusions

The results demonstrate the feasibility of automated measurement of reliable proxies of cognitive functioning through low-burden passive patient evaluations. This makes it easier to monitor cognitive functions and to intervene earlier and at a lower threshold without requiring a time-consuming neurocognitive assessment by, for instance, a licensed psychologist with specialized training in neuropsychology.

Changes in Regional Homogeneity of Medication-Free Major Depressive Disorder Patients With Different Onset Ages

Background: Neurobiological mechanisms underlying the development of major depressive disorder (MDD) may differ depending on onset ages. Our aim was to determine whether regional homogeneity (ReHo) changes in early-onset depression (EOD) and late-onset depression (LOD) are different, which could also delineate EOD and LOD. Methods: Ninety-one MDD patients and 115 healthy controls (HCs) were recruited, and resting-state functional magnetic resonance imaging data were collected. The ReHo comparison was conducted using analysis of variance. Results: Compared with HCs, MDD patients showed decreased ReHo in the left precentral gyrus and the left middle cingulum area, and increased ReHo in the left middle orbital frontal gyrus and superior temporal gyrus. Compared with LOD patients, young HC separately, EOD patients had significantly increased ReHo in the right inferior frontal triangular gyrus and the left postcentral gyrus. However, compared with young HC, EOD patients showed decreased ReHo in the right superior frontal gyrus/supplementary motor area and the right medial frontal gyrus. ReHo in the right inferior frontal triangular gyrus was negatively correlated with the severity of cognitive disturbance in LOD patients (r = -0.47, p = 0.002), but not in EOD patients (r = 0.21, p = 0.178). Conclusion: MDD patients with different onset ages may have different pathophysiological mechanisms; the EOD patients had more abnormal ReHo than LOD patients in the prefrontal lobe, especially the right inferior frontal triangular gyrus.

Resting State Functional Connectivity Biomarkers of Treatment Response in Mood Disorders: A Review

There are currently no validated treatment biomarkers in psychiatry. Resting State Functional Connectivity (RSFC) is a popular method for investigating the neural correlates of mood disorders, but the breadth of the field makes it difficult to assess progress toward treatment response biomarkers. In this review, we followed general PRISMA guidelines to evaluate the evidence base for mood disorder treatment biomarkers across diagnoses, brain network models, and treatment modalities. We hypothesized that no treatment biomarker would be validated across these domains or with independent datasets. Results are organized, interpreted, and discussed in the context of four popular analytic techniques: (1) reference region (seed-based) analysis, (2) independent component analysis, (3) graph theory analysis, and (4) other methods. Cortico-limbic connectivity is implicated across studies, but there is no single biomarker that spans analyses or that has been replicated in multiple independent datasets. We discuss RSFC limitations and future directions in biomarker development.

Repetitive Transcranial Magnetic Stimulation for Treatment-Resistant Depression in Active-Duty Service Members Improves Depressive Symptoms

OBJECTIVES

Current research on the efficacy of repetitive transcranial magnetic stimulation (rTMS) over left dorsolateral prefrontal cortex as a noninvasive therapy for treatment-resistant depression is largely settled science. However, little is known about its efficacy with active-duty service members (ADSMs) with major depressive disorder. In a retrospective chart review, we examined depressive symptom ratings in ADSMs seeking treatment at the US Army Outpatient Behavioral Health Service Clinic at Eisenhower Army Medical Center, Fort Gordon, Ga.

METHODS

We reviewed 121 consecutive outpatient charts, which yielded 61 ADSMs who completed a minimum of 20 rTMS sessions for refractory depression, and for whom both pretreatment and posttreatment depressive symptom ratings were available. Pre- and post-Patient Health Questionnaire 9 (PHQ-9) scores were subjected to a paired t test, and Reliable Change Indices were calculated to determine both reliable and clinical significance.

RESULTS

Average (SD) pretreatment and posttreatment PHQ-9 scores were 15.8 (6.2) and 12.6 (7.6), respectively. Statistically significant reduction in post-PHQ-9 was demonstrated (P < 0.001), with 69% of patients lowering their ratings and 31% demonstrating reliable change (improvement >5.64). Additionally, 20% demonstrated a reliable change that placed them in the nondysfunctional range (post-PHQ-9 <9.6), demonstrating clinical significance.

CONCLUSIONS

These data confirm a course of standard rTMS to ADSMs with major depression is promising in reducing depressive symptoms. Given that success and completion rates from this clinic are similar to those reported in civilian populations (80%), rTMS may be an adequate additional treatment or augmentation strategy for refractory depression in ADSMs.

Grant Report on SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics

We report on the newly started project "SCH: Personalized Depression Treatment Supported by Mobile Sensor Analytics". The current best practice guidelines for treating depression call for close monitoring of patients, and periodically adjusting treatment as needed. This project will advance personalized depression treatment by developing a system, DepWatch, that leverages mobile health technologies and machine learning tools. The objective of DepWatch is to assist clinicians with their decision making process in the management of depression. The project comprises two studies. Phase I collects sensory data and other data, e.g., clinical data, ecological momentary assessments (EMA), tolerability and safety data from 250 adult participants with unstable depression symptomatology initiating depression treatment. The data thus collected will be used to develop and validate assessment and prediction models, which will be incorporated into DepWatch system. In Phase II, three clinicians will use DepWatch to support their clinical decision making process. A total of 128 participants under treatment by the three participating clinicians will be recruited for the study. A number of new machine learning techniques will be developed.

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.

Reverse translation of major depressive disorder symptoms: A framework for the behavioural phenotyping of putative biomarkers

BACKGROUND

Reverse translating putative biomarkers of depression from patients to animals is complex because Major Depressive Disorder (MDD) is a highly heterogenous condition. This review proposes an approach to reverse translation based on relating relevant bio-behavioural functions in laboratory rodents to MDD symptoms.

METHODS

This systematic review outlines symptom clusters assessed by psychometric tests of MDD and antidepressant treatment response including the Montgomery-Åsberg Depression Rating Scale, the Hamilton Depression Rating Scale, and the Beck Depression Inventory. Symptoms were related to relevant behavioural assays in laboratory rodents.

RESULTS

The resulting battery of tests includes passive coping, anxiety-like behaviours, sleep, caloric intake, cognition, psychomotor functions, hedonic reactivity and aversive learning. These assays are discussed alongside relevant clinical symptoms of MDD, providing a framework through which reverse translation of a biomarker can be interpreted.

LIMITATIONS

Certain aspects of MDD may not be quantified by tests in laboratory rodents, and their biological significance may not always be of clinical relevance.

CONCLUSIONS

Using this reverse translation approach, it is possible to clarify the functional significance of a putative biomarker in rodents and hence translate its contribution to specific clinical symptoms, or clusters of symptoms.

Animal models of post-traumatic stress disorder and novel treatment targets

Understanding the neurobiological basis of post-traumatic stress disorder (PTSD) is fundamental to accurately diagnose this neuropathology and offer appropriate treatment options to patients. The lack of pharmacological effects, too often observed with the most currently used drugs, the selective serotonin reuptake inhibitors (SSRIs), makes even more urgent the discovery of new pharmacological approaches. Reliable animal models of PTSD are difficult to establish because of the present limited understanding of the PTSD heterogeneity and of the influence of various environmental factors that trigger the disorder in humans. We summarize knowledge on the most frequently investigated animal models of PTSD, focusing on both their behavioral and neurobiological features. Most of them can reproduce not only behavioral endophenotypes, including anxiety-like behaviors or fear-related avoidance, but also neurobiological alterations, such as glucocorticoid receptor hypersensitivity or amygdala hyperactivity. Among the various models analyzed, we focus on the social isolation mouse model, which reproduces some deficits observed in humans with PTSD, such as abnormal neurosteroid biosynthesis, changes in GABAA receptor subunit expression and lack of pharmacological response to benzodiazepines. Neurosteroid biosynthesis and its interaction with the endocannabinoid system are altered in PTSD and are promising neuronal targets to discover novel PTSD agents. In this regard, we discuss pharmacological interventions and we highlight exciting new developments in the fields of research for novel reliable PTSD biomarkers that may enable precise diagnosis of the disorder and more successful pharmacological treatments for PTSD patients.

Use of Machine Learning for Predicting Escitalopram Treatment Outcome From Electroencephalography Recordings in Adult Patients With Depression

IMPORTANCE

Social and economic costs of depression are exacerbated by prolonged periods spent identifying treatments that would be effective for a particular patient. Thus, a tool that reliably predicts an individual patient's response to treatment could significantly reduce the burden of depression.

OBJECTIVE

To estimate how accurately an outcome of escitalopram treatment can be predicted from electroencephalographic (EEG) data on patients with depression.

DESIGN, SETTING, AND PARTICIPANTS

This prognostic study used a support vector machine classifier to predict treatment outcome using data from the first Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study. The CAN-BIND-1 study comprised 180 patients (aged 18-60 years) diagnosed with major depressive disorder who had completed 8 weeks of treatment. Of this group, 122 patients had EEG data recorded before the treatment; 115 also had EEG data recorded after the first 2 weeks of treatment.

INTERVENTIONS

All participants completed 8 weeks of open-label escitalopram (10-20 mg) treatment.

MAIN OUTCOMES AND MEASURES

The ability of EEG data to predict treatment outcome, measured as accuracy, specificity, and sensitivity of the classifier at baseline and after the first 2 weeks of treatment. The treatment outcome was defined in terms of change in symptom severity, measured by the Montgomery-Åsberg Depression Rating Scale, before and after 8 weeks of treatment. A patient was designated as a responder if the Montgomery-Åsberg Depression Rating Scale score decreased by at least 50% during the 8 weeks and as a nonresponder if the score decrease was less than 50%.

RESULTS

Of the 122 participants who completed a baseline EEG recording (mean [SD] age, 36.3 [12.7] years; 76 [62.3%] female), the classifier was able to identify responders with an estimated accuracy of 79.2% (sensitivity, 67.3%; specificity, 91.0%) when using only the baseline EEG data. For a subset of 115 participants who had additional EEG data recorded after the first 2 weeks of treatment, use of these data increased the accuracy to 82.4% (sensitivity, 79.2%; specificity, 85.5%).

CONCLUSIONS AND RELEVANCE

These findings demonstrate the potential utility of EEG as a treatment planning tool for escitalopram therapy. Further development of the classification tools presented in this study holds the promise of expediting the search for optimal treatment for each patient.

Predictors of Treatment Resistance Across Different Clinical Subtypes of Depression: Comparison of Unipolar vs. Bipolar Cases

OBJECTIVE

Treatment-resistant depression (TRD) and treatment-resistant bipolar depression (TRBD) poses a significant clinical and societal burden, relying on different operational definitions and treatment approaches. The detection of clinical predictors of resistance is elusive, soliciting clinical subtyping of the depressive episodes, which represents the goal of the present study.

METHODS

A hundred and thirty-one depressed outpatients underwent psychopathological evaluation using major rating tools, including the Hamilton Rating Scale for Depression, which served for subsequent principal component analysis, followed-up by cluster analysis, with the ultimate goal to fetch different clinical subtypes of depression.

RESULTS

The cluster analysis identified two clinically interpretable, yet distinctive, groups among 53 bipolar (resistant cases = 15, or 28.3%) and 78 unipolar (resistant cases = 20, or 25.6%) patients. Among the MDD patients, cluster "1" included the following components: "Psychic symptoms, depressed mood, suicide, guilty, insomnia" and "genitourinary, gastrointestinal, weight loss, insight". Altogether, with broadly defined "mixed features," this latter cluster correctly predicted treatment outcome in 80.8% cases of MDD. The same "broadly-defined" mixed features of depression (namely, the standard Diagnostic and Statistical Manual for Mental Disorders, Fifth Edition-DSM-5-specifier plus increased energy, psychomotor activity, irritability) correctly classified 71.7% of BD cases, either as TRBD or not.

LIMITATIONS

Small sample size and high rate of comorbidity.

CONCLUSIONS

Although relying on different operational criteria and treatment history, TRD and TRBD seem to be consistently predicted by broadly defined mixed features among different clinical subtypes of depression, either unipolar or bipolar cases. If replicated by upcoming studies to encompass also biological and neuropsychological measures, the present study may aid in precision medicine and informed pharmacotherapy.

Covariation bias in depression - a predictor of treatment response?

Covariation bias, defined as an overestimation of the relationship between fear-relevant stimuli and aversive consequences, is a well-investigated cognitive bias in anxiety disorders. As patients with affective disorders also show biased information processing, the aim of the present study was to investigate whether depressed patients also display a covariation bias between negative stimuli and aversive consequences. Covariation estimates of 62 inpatients with a current severe depressive episode were assessed at admission (n = 31) or after 6 weeks of treatment (n = 31) and were compared in a between-group design with 31 age- and sex-matched healthy controls. All participants showed a covariation bias for the relationship between negative stimuli and aversive consequences. Moreover, covariation bias at admission was significantly associated with various clinician- and self-reported dimensional measures of treatment response assessed 6 weeks later (Global Assessment of Functioning, Clinical Global Impression Scale, and Beck Depression Inventory), i.e., patients with a stronger bias showed greater impairment after 6 weeks of treatment. Categorical analyses revealed that overall, treatment non-responders-but not responders-were characterized by a covariation bias. The naturalistic study design without standardized pharmacological and psychotherapeutic treatments is a central limitation. We conclude that the covariation bias may constitute a possible marker in the field of emotional information processing in the search for effective predictors of therapy outcome.

Stress, psychiatric disorders, molecular targets, and more

Mental health is central to normal health outcomes. A widely accepted theory is that chronic persistent stress during adulthood as well as during early life triggers onset of neuropsychiatric ailments. However, questions related to how that occurs, and why are some individuals resistant to stress while others are not, remain unanswered. An integrated, multisystemic stress response involving neuroinflammatory, neuroendocrine, epigenetic and metabolic cascades have been suggested to have causative links. Several theories have been proposed over the years to conceptualize this link including the cytokine hypothesis, the endocrine hypothesis, the oxidative stress hypothesis and the oxido-neuroinflammation hypothesis. The data discussed in this review describes potential biochemical basis of the link between stress, and stress-induced neuronal, behavioral and emotional deficits, providing insights into potentially novel drug targets.

Animal Models of PTSD: The Socially Isolated Mouse and the Biomarker Role of Allopregnanolone

Post-traumatic stress disorder (PTSD) is a debilitating undertreated condition that affects 8%-13% of the general population and 20%-30% of military personnel. Currently, there are no specific medications that reduce PTSD symptoms or biomarkers that facilitate diagnosis, inform treatment selection or allow monitoring drug efficacy. PTSD animal models rely on stress-induced behavioral deficits that only partially reproduce PTSD neurobiology. PTSD heterogeneity, including comorbidity and symptoms overlap with other mental disorders, makes this attempt even more complicated. Allopregnanolone, a neurosteroid that positively, potently and allosterically modulates GABAA receptors and, by this mechanism, regulates emotional behaviors, is mainly synthesized in brain corticolimbic glutamatergic neurons. In PTSD patients, allopregnanolone down-regulation correlates with increased PTSD re-experiencing and comorbid depressive symptoms, CAPS-IV scores and Simms dysphoria cluster scores. In PTSD rodent models, including the socially isolated mouse, decrease in corticolimbic allopregnanolone biosynthesis is associated with enhanced contextual fear memory and impaired fear extinction. Allopregnanolone, its analogs or agents that stimulate its synthesis offer treatment approaches for facilitating fear extinction and, in general, for neuropsychopathologies characterized by a neurosteroid biosynthesis downregulation. The socially isolated mouse model reproduces several other deficits previously observed in PTSD patients, including altered GABAA receptor subunit subtypes and lack of benzodiazepines pharmacological efficacy. Transdiagnostic behavioral features, including expression of anxiety-like behavior, increased aggression, a behavioral component to reproduce behavioral traits of suicidal behavior in humans, as well as alcohol consumption are heightened in socially isolated rodents. Potentials for assessing novel biomarkers to predict, diagnose, and treat PTSD more efficiently are discussed in view of developing a precision medicine for improved PTSD pharmacological treatments.

Brain-derived neurotrophic factor as a possible predictor of electroconvulsive therapy outcome

While brain-derived neurotrophic factor (BDNF) has been shown to predict response to pharmacotherapy in depression, studies in electroconvulsive therapy (ECT) are small and report conflicting results. This study assesses the association between pre-treatment BDNF levels and ECT outcome in severe late-life unipolar depression (LLD). The potential of BDNF as a clinical predictor of ECT outcome was subsequently evaluated. Characteristics associated with low and high BDNF subgroups were determined as well. Ninety-four patients diagnosed with LDD referred for ECT were included. Fasting serum BDNF levels were determined before ECT. Remission and response, measured with the Montgomery-Åsberg Depression Rating Scale, were the outcomes. The association between BDNF and ECT outcome was analysed with logistic regression and Cox regression. The clinical usefulness of BDNF was evaluated using the receiver operating characteristic (ROC) curve. Associations between clinical characteristics and low versus high BDNF levels were examined with T tests, chi-squared tests and Mann-Whitney tests. The odds of remission decreased with 33% for every five units increase of BDNF levels (OR 0.67, 95% confidence interval 0.47-0.96; p = 0.03); however, neither the association with time to remission nor the associations with response nor the adjusted models were significant. The area under the ROC (0.66) implied a poor accuracy of BDNF as a clinical test. Clinical characteristics associated with BDNF were inclusion site, physical comorbidities and duration of the index episode. To conclude, although there is an association between pre-treatment BDNF levels and ECT outcome, BDNF cannot be considered an eligible biomarker for ECT outcome in clinical practice.

Current understanding of fear learning and memory in humans and animal models and the value of a linguistic approach for analyzing fear learning and memory in humans

Fear is an emotion that serves as a driving factor in how organisms move through the world. In this review, we discuss the current understandings of the subjective experience of fear and the related biological processes involved in fear learning and memory. We first provide an overview of fear learning and memory in humans and animal models, encompassing the neurocircuitry and molecular mechanisms, the influence of genetic and environmental factors, and how fear learning paradigms have contributed to treatments for fear-related disorders, such as posttraumatic stress disorder. Current treatments as well as novel strategies, such as targeting the perisynaptic environment and use of virtual reality, are addressed. We review research on the subjective experience of fear and the role of autobiographical memory in fear-related disorders. We also discuss the gaps in our understanding of fear learning and memory, and the degree of consensus in the field. Lastly, the development of linguistic tools for assessments and treatment of fear learning and memory disorders is discussed.

A systematic meta-review of predictors of antidepressant treatment outcome in major depressive disorder

INTRODUCTION

The heterogeneity of symptoms and complex etiology of depression pose a significant challenge to the personalization of treatment. Meanwhile, the current application of generic treatment approaches to patients with vastly differing biological and clinical profiles is far from optimal. Here, we conduct a meta-review to identify predictors of response to antidepressant therapy in order to select robust input features for machine learning models of treatment response. These machine learning models will allow us to learn associations between patient features and treatment response which have predictive value at the individual patient level; this learning can be optimized by selecting high-quality input features for the model. While current research is difficult to directly apply to the clinic, machine learning models built using knowledge gleaned from current research may become useful clinical tools.

METHODS

The EMBASE and MEDLINE/PubMed online databases were searched from January 1996 to August 2017, using a combination of MeSH terms and keywords to identify relevant literature reviews. We identified a total of 1909 articles, wherein 199 articles met our inclusion criteria.

RESULTS

An array of genetic, immune, endocrine, neuroimaging, sociodemographic, and symptom-based predictors of treatment response were extracted, varying widely in clinical utility.

LIMITATIONS

Due to heterogeneous sample sizes, effect sizes, publication biases, and methodological disparities across reviews, we could not accurately assess the strength and directionality of every predictor.

CONCLUSION

Notwithstanding our cautious interpretation of the results, we have identified a multitude of predictors that can be used to formulate a priori hypotheses regarding the input features for a computational model. We highlight the importance of large-scale research initiatives and clinically accessible biomarkers, as well as the need for replication studies of current findings. In addition, we provide recommendations for future improvement and standardization of research efforts in this field.

Heart rate variability as a biomarker of anxious depression response to antidepressant medication

BACKGROUND

There is a need to identify biomarkers of treatment outcomes for major depressive disorder (MDD) that can be disseminated. We investigated the predictive utility of pretreatment heart rate variability (HRV) for outcomes of antidepressant medication in MDD, with pretreatment anxious depression as a hypothesized moderator of HRV effects.

METHODS

A large, randomized, multicenter practical trial (International Study to Predict Optimized Treatment in Depression) in patients with current nonpsychotic MDD (N = 1,008; 722 completers) had three arms: escitalopram, sertraline, and venlafaxine-extended release. At pretreatment, patients were defined as having anxious (N = 309) versus nonanxious (N = 413) depression and their resting high-frequency HRV (root mean square of successive differences) was assessed. Patients' usual treating clinicians managed medication. At 8 weeks, primary outcomes were clinician-rated depressive symptom response and remission; secondary outcomes were self-reported response and remission.

RESULTS

Pretreatment HRV predicted antidepressant outcomes as a function of anxious versus nonanxious depression. In anxious depression, patients with higher HRV had better outcomes, whereas patients with lower HRV had poorer outcomes. In nonanxious depression, patients with lower HRV had better outcomes, whereas patients with higher HRV had poorer outcomes. Some simple effects were not significant. Results did not differ by treatment arm and remained significant when controlling for important covariates.

CONCLUSIONS

These findings inform a precision medicine approach in which clinical and biological assessments may be integrated to facilitate treatment outcome prediction. Knowing about HRV may help determine which patients with anxious depression could benefit from antidepressants and which patients may require a different treatment approach.

Could a blood test for PTSD and depression be on the horizon?

INTRODUCTION

Depression and posttraumatic stress disorder (PTSD) are two complex and debilitating psychiatric disorders that result in poor life and destructive behaviors against self and others. Currently, diagnosis is based on subjective rather than objective determinations leading to misdiagnose and ineffective treatments. Advances in novel neurobiological methods have allowed assessment of promising biomarkers to diagnose depression and PTSD, which offers a new means of appropriately treating patients. Areas covered: Biomarkers discovery in blood represents a fundamental tool to predict, diagnose, and monitor treatment efficacy in depression and PTSD. The potential role of altered HPA axis, epigenetics, NPY, BDNF, neurosteroid biosynthesis, the endocannabinoid system, and their function as biomarkers for mood disorders is discussed. Insofar, we propose the identification of a biomarker axis to univocally identify and discriminate disorders with large comorbidity and symptoms overlap, so as to provide a base of support for development of targeted treatments. We also weigh in on the feasibility of a future blood test for early diagnosis. Expert commentary: Potential biomarkers have already been assessed in patients' blood and need to be further validated through multisite large clinical trial stratification. Another challenge is to assess the relation among several interdependent biomarkers to form an axis that identifies a specific disorder and secures the best-individualized treatment. The future of blood-based tests for PTSD and depression is not only on the horizon but, possibly, already around the corner.

Cost-Effectiveness of a Pharmacogenetic Test to Guide Treatment for Major Depressive Disorder

BACKGROUND

Recent clinical trials indicate that pharmacogenetic-guided treatment of major depressive disorder (MDD) results in higher treatment response rates by genetically matching patients to medications and avoiding a trial-and-error process.

OBJECTIVE

To evaluate the cost-effectiveness of a pharmacogenetic test (IDGx) that has demonstrated effectiveness compared with standard of care (SOC) medication management among patients with varied MDD severity.

METHODS

Data from a large prospective, randomized controlled trial of treatment-naive patients or patients with inadequately controlled MDD in general practice and psychiatric treatment settings were used to build a Markov state-transition probability model. Analyses were conducted from the societal perspective. Treatment response rates, mortality rates, direct and indirect medical costs, and utility inputs were derived from the reference study and published scientific literature. The cost of the pharmacogenetic test was $2,000. A 3% discount rate was used to discount costs and effects. Univariate one-way sensitivity analyses were performed to determine the effect of input parameters on net monetary benefit.

RESULTS

For moderate to severe MDD, the model estimated a cumulative effect over 3 years of 2.07 quality-adjusted life-years (QALYs) for the pharmacogenetic-guided treatment group and 1.97 QALYs for the SOC group, including a lower probability of death from suicide (0.328% and 0.351%, respectively). Total costs over 3 years were $44,697 (IDGx) and $47,295 (SOC). This difference includes a savings of $2,918 in direct medical costs and $1,680 in indirect costs. Results were more pronounced when only severely depressed patients were evaluated.

CONCLUSIONS

Pharmacogenetic testing among moderate to severe MDD patients improved QALYs and resulted in cost savings. Sensitivity analyses supported the robust nature of the current findings of the dominant IDGx test to guide treatment.

DISCLOSURES

Funding for this analysis was provided by AltheaDx, which is the manufacturer of the IDgenetix test. AltheaDx personnel assisted in the study design, data collection, and review of the manuscript. Maciel and Garces are employed by AltheaDx. Groessl has received funding as a consultant from American Specialty Health.

Use of combinatorial pharmacogenomic guidance in treating psychiatric disorders

Aim: To evaluate payer costs associated with treating psychiatric disorders utilizing a combinatorial pharmacogenomics test versus treatment-as-usual (TAU). Patients & methods: Administrative claims data were analyzed from health plan members whose treatment was guided by GeneSight® Psychotropic testing (CPGx® cohort) and those who received TAU (TAU cohort). Reimbursed costs were calculated over the 12-month pre-index and post-index event periods. Results: 205 CPGx and 478 TAU members were included. Post-index cost savings (US$5505) drove a per-member-per-month savings of US$0.07. Disease-specific analyses resulted in similar savings. Conclusion: Use of CPGx yielded reduced spending for a commercial health plan across the patient population with psychiatric disorders, as well as among high-cost subpopulations.

Longitudinal Estimation of the Clinically Significant Change in the Treatment of Major Depression Disorder

Background: Although major depressive disorder is usually treated with antidepressants, only 50-70% of the patients respond to this treatment. This study applied Jacobson and Truax's (1991) methodology (reliable change index, RCI) to a sample of depressive patients being treated with one of two antidepressants to evaluate their functioning and the effect of certain variables such as severity and age. Method: Seventy-three depressive patients medicated with Escitalopram (n = 37) or Duloxetine (n = 36) were assessed using the Hamilton depression rating scale over a 24-week period. Results: They indicate that the RCI stabilizes in an absolute way starting in week 16, and it is not until week 24 that all of the patients become part of the functional population. We found limited statistical significance with respect to the RCI and the external variables. Conclusion: Our study suggests the need to accompany the traditional statistical methodology with some other clinical estimation systems capable of going beyond a simple subtraction between pre and posttreatment values. Hence, it is concluded that RCI estimations could be stronger and more stable than the classical statistical techniques.

Personality traits and escitalopram treatment outcome in major depression

BACKGROUND

Selective serotonin re-uptake inhibitors (SSRI) have proven to be effective in treatment of depression. Still, treatment efficacy varies significantly from patient to patient and about 40% of patients do not respond to initial treatment. Personality traits have been considered one source of variability in treatment outcome.

AIM

Current study aimed at identifying specific personality traits that could be predictive of treatment response and/or the dynamics of symptom change in depressive patients.

METHOD

In a sample of 132 outpatients with major depressive disorder (MDD) treated with an SSRI-group antidepressant escitalopram, the Swedish universities Scales of Personality (SSP) were used in order to find predictive personality traits. For the assessment of the severity of depressive symptoms and the improvement rates, the Hamilton Depression Scale (HAM-D) and Montgomery-Åsberg Depression Rating Scale (MADRS) were used.

RESULTS

Escitalopram-treated MDD patients with higher social desirability achieved more rapid decrease in symptom severity. None of the studied traits predicted the end result of the treatment.

CONCLUSION

The findings suggest that specific personality traits may predict the trajectory of symptom change rather than the overall improvement rate.

Effect of Metabolic Syndrome on Late-Life Depression: Associations with Disease Severity and Treatment Resistance

BACKGROUND/OBJECTIVES

Metabolic syndrome (MetS) is the co-occurrence of obesity and metabolic derangements. Prior research implicates MetS in prolongation of the course of depression in older adults, but its effect on antidepressant response is unknown in this population. The objective was to determine whether MetS and related metabolic dyscrasias are associated with decreased rate of remission from depression in older adults treated pharmacologically for depression.

DESIGN

Secondary analysis of a randomized controlled trial.

SETTING

Three academic medical centers in North America.

PARTICIPANTS

Adults aged 60 and older (mean age 69.1) with major depressive disorder (MDD) (N = 435).

INTERVENTION

Open-label, protocolized treatment with extended-release venlafaxine for 12 or more weeks.

MEASUREMENTS

Time to remission from depression, with remission defined as a Montgomery-Åsberg Depression Rating Scale (MADRS) score of 10 or less at last two visits.

RESULTS

Two hundred twenty-two participants (51%) met criteria for MetS at baseline; MetS was associated with greater severity (MADRS score) and chronicity of depression at baseline. Remission was achieved in 182 participants (42%). In the unadjusted analysis, MetS was associated with prolonged time to remission (hazard ratio for remission = 0.71, 95% confidence interval = 0.52-0.95), but this relationship was not significant in the adjusted model; greater number of MetS components and lower high-density lipoprotein cholesterol had similar effects. Only diastolic blood pressure (DBP) was a significant predictor of time to remission before and after adjustment, with higher DBP predicting longer time to remission. Insulin sensitivity did not predict time to remission.

CONCLUSION

The presence of MetS in older adults with depression was associated with greater symptom severity and chronicity of depression, which appears to have accounted for the poorer antidepressant response observed in those with MetS. Additionally, our preliminary finding of an association between higher DBP and poorer antidepressant response bears further examination and replication.

Social Isolation in Early versus Late Adolescent Mice Is Associated with Persistent Behavioral Deficits That Can Be Improved by Neurosteroid-Based Treatment

Early trauma and stress exposure during a critical period of life may increase the risk of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) in adulthood. The first-choice treatment for MDD and PTSD are selective serotonin reuptake inhibitor (SSRI) antidepressants. Unfortunately, half of MDD and PTSD patients show resistance to the therapeutic effects of these drugs and more efficient treatments are essential. Both MDD and PTSD patients present reduced levels of allopregnanolone (Allo), a potent endogenous positive allosteric modulator of GABA action at GABA_A receptors which are normalized by SSRIs in treatment responders. Thus, Allo analogs or drugs that stimulate its levels may offer an alternative in treating SSRIs-non-responders. We tested several drugs on the aggressive behavior of early and late adolescent socially-isolated (SI) mice, a model of PTSD. Isolation in early adolescence (PND 21) induced more severe aggression than mice isolated at PND 45. A single non-sedating administration of S-fluoxetine (S-FLX; 0.375–1.5 mg/kg), or of the Allo analogs ganaxolone (GNX; 10 mg/kg), BR351 (1–5 mg/kg), or BR297 (0.3125–2.5 mg/kg), or of the endocannabinoid, N-palmitoylethanolamine (PEA; 5–20 mg/kg) all decreased aggression more effectively in late than early adolescent SI mice. Importantly, the number of drug non-responders was higher in early than late SI mice for all the drugs tested. The non-responder rate was more elevated (12–64%) after S-FLX treatment, while 100% of mice responded to a single administration of PEA at the dose range of 15–20 mg/kg. Moreover, GNX, BR351, and BR297’s antiaggressive effect persisted longer than S-FLX in both late and early SI mice. All drugs tested failed to alter locomotor activity of SI mice. Our results show that drugs that mimic Allo’s action or that induce Allo biosynthesis may be valuable for the treatment of “SSRIs non-responder” patients.

Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study

Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders.

Neurosteroid biosynthesis down-regulation and changes in GABAA receptor subunit composition: a biomarker axis in stress-induced cognitive and emotional impairment

By rapidly modulating neuronal excitability, neurosteroids regulate physiological processes, such as responses to stress and development. Excessive stress affects their biosynthesis and causes an imbalance in cognition and emotions. The progesterone derivative, allopregnanolone (Allo) enhances extrasynaptic and postsynaptic inhibition by directly binding at GABA_A receptors, and thus, positively and allosterically modulates the function of GABA. Allo levels are decreased in stress-induced psychiatric disorders, including depression and post-traumatic stress disorder (PTSD), and elevating Allo levels may be a valid therapeutic approach to counteract behavioural dysfunction. While benzodiazepines are inefficient, selective serotonin reuptake inhibitors (SSRIs) represent the first choice treatment for depression and PTSD. Their mechanisms to improve behaviour in preclinical studies include neurosteroidogenic effects at low non-serotonergic doses. Unfortunately, half of PTSD and depressed patients are resistant to current prescribed 'high' dosage of these drugs that engage serotonergic mechanisms. Unveiling novel biomarkers to develop more efficient treatment strategies is in high demand. Stress-induced down-regulation of neurosteroid biosynthesis and changes in GABA_A receptor subunit expression offer a putative biomarker axis to develop new PTSD treatments. The advantage of stimulating Allo biosynthesis relies on the variety of neurosteroidogenic receptors to be targeted, including TSPO and endocannabinoid receptors. Furthermore, stress favours a GABA_A receptor subunit composition with higher sensitivity for Allo. The use of synthetic analogues of Allo is a valuable alternative. Pregnenolone or drugs that stimulate its levels increase Allo but also sulphated steroids, including pregnanolone sulphate which, by inhibiting NMDA tonic neurotransmission, provides neuroprotection and cognitive benefits. In this review, we describe current knowledge on the effects of stress on neurosteroid biosynthesis and GABA_A receptor neurotransmission and summarize available pharmacological strategies that by enhancing neurosteroidogenesis are relevant for the treatment of SSRI-resistant patients. Linked Articles This article is part of a themed section on Pharmacology of Cognition: a Panacea for Neuropsychiatric Disease? To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v174.19/issuetoc.

Effects of Patient Preferences on Outcomes in the Predictors of Remission in Depression to Individual and Combined Treatments (PReDICT) Study

OBJECTIVE

The Predictors of Remission in Depression to Individual and Combined Treatments [PReDICT] study aimed to identify clinical and biological factors predictive of treatment outcomes in major depressive disorder among treatment-naive adults. The authors evaluated the efficacy of cognitive-behavioral therapy (CBT) and two antidepressant medications (escitalopram and duloxetine) in patients with major depression and examined the moderating effect of patients' treatment preferences on outcomes.

METHOD

Adults aged 18-65 with treatment-naive major depression were randomly assigned with equal likelihood to 12 weeks of treatment with escitalopram (10-20 mg/day), duloxetine (30-60 mg/day), or CBT (16 50-minute sessions). Prior to randomization, patients indicated whether they preferred medication or CBT or had no preference. The primary outcome was change in the 17-item Hamilton Depression Rating Scale (HAM-D), administered by raters blinded to treatment.

RESULTS

A total of 344 patients were randomly assigned, with a mean baseline HAM-D score of 19.8 (SD=3.8). The mean estimated overall decreases in HAM-D score did not significantly differ between treatments (CBT: 10.2, escitalopram: 11.1, duloxetine: 11.2). Last observation carried forward remission rates did not significantly differ between treatments (CBT: 41.9%, escitalopram: 46.7%, duloxetine: 54.7%). Patients matched to their preferred treatment were more likely to complete the trial but not more likely to achieve remission.

CONCLUSIONS

Treatment guidelines that recommend either an evidence-based psychotherapy or antidepressant medication for nonpsychotic major depression can be extended to treatment-naive patients. Treatment preferences among patients without prior treatment exposure do not significantly moderate symptomatic outcomes.

A Pilot Study of Possible Easy-to-Use Electrophysiological Index for Early Detection of Antidepressive Treatment Non-Response

INTRODUCTION

The evaluation of response to pharmacological treatment in MDD requires 4-8 weeks. Therefore, the ability to predict response, and especially lack of response to treatment, as early as possible after treatment onset or change, is of prime significance. Many studies have demonstrated significant results regarding the ability to use EEG and ERP markers, including attention-associated markers such as P300, for early prediction of response to treatment. But these markers are derived from long EEG/ERP samples, often from multiple channels, which render them impractical for frequent sampling.

METHODS AND RESULTS

We developed a new electrophysiological attention-associated marker from a single channel (two electrodes), using 1-min samples with auditory oddball stimuli. This work presents an initial evaluation of the ability to use this marker's dynamics between repetitive measures for early (<2 weeks) differentiation between responders and non-responders to antidepressive treatment, in 26 patients with various levels of depression and heterogeneous treatment interventions. The slope of change in the marker between early consecutive samples was negative in the non-responders, but not in the responders. This differentiation was stronger for patients suffering from severe depression (p < 0.001).

CONCLUSION

This pilot study supports the feasibility of the EEG marker for early recognition of treatment-resistant depression. If verified in large-scale prospective studies, it can contribute to research and clinical work.