Assessing remission in major depressive disorder using a functional-structural data fusion pipeline: A CAN-BIND-1 study

Neural network-level changes underlying symptom remission in major depressive disorder (MDD) are often studied from a single perspective. Multimodal approaches to assess neuropsychiatric disorders are evolving, as they offer richer information about brain networks. A FATCAT-awFC pipeline was developed to integrate a computationally intense data fusion method with a toolbox, to produce a faster and more intuitive pipeline for combining functional connectivity with structural connectivity (denoted as anatomically weighted functional connectivity (awFC)). Ninety-three participants from the Canadian Biomarker Integration Network for Depression study (CAN-BIND-1) were included. Patients with MDD were treated with 8 weeks of escitalopram and adjunctive aripiprazole for another 8 weeks. Between-group connectivity (SC, FC, awFC) comparisons contrasted remitters (REM) with non-remitters (NREM) at baseline and 8 weeks. Additionally, a longitudinal study analysis was performed to compare connectivity changes across time for REM, from baseline to week-8. Association between cognitive variables and connectivity were also assessed. REM were distinguished from NREM by lower awFC within the default mode, frontoparietal, and ventral attention networks. Compared to REM at baseline, REM at week-8 revealed increased awFC within the dorsal attention network and decreased awFC within the frontoparietal network. A medium effect size was observed for most results. AwFC in the frontoparietal network was associated with neurocognitive index and cognitive flexibility for the NREM group at week-8. In conclusion, the FATCAT-awFC pipeline has the benefit of providing insight on the 'full picture' of connectivity changes for REMs and NREMs while making for an easy intuitive approach.

Non-binary gender, vulnerable populations and mental health during the COVID-19 pandemic: Data from the COVID-19 MEntal health inTernational for the general population (COMET-G) study

BACKGROUND

The COVID-19 pandemic has brought significant mental health challenges, particularly for vulnerable populations, including non-binary gender individuals. The COMET international study aimed to investigate specific risk factors for clinical depression or distress during the pandemic, also in these special populations.

METHODS

Chi-square tests were used for initial screening to select only those variables which would show an initial significance. Risk Ratios (RR) were calculated, and a Multiple Backward Stepwise Linear Regression Analysis (MBSLRA) was followed with those variables given significant results at screening and with the presence of distress or depression or the lack of both of them.

RESULTS

The most important risk factors for depression were female (RR = 1.59-5.49) and non-binary gender (RR = 1.56-7.41), unemployment (RR = 1.41-6.57), not working during lockdowns (RR = 1.43-5.79), bad general health (RR = 2.74-9.98), chronic somatic disorder (RR = 1.22-5.57), history of mental disorders (depression RR = 2.31-9.47; suicide attempt RR = 2.33-9.75; psychosis RR = 2.14-10.08; Bipolar disorder RR = 2.75-12.86), smoking status (RR = 1.15-5.31) and substance use (RR = 1.77-8.01). The risk factors for distress or depression that survived MBSLRA were younger age, being widowed, living alone, bad general health, being a carer, chronic somatic disorder, not working during lockdowns, being single, self-reported history of depression, bipolar disorder, self-harm, suicide attempts and of other mental disorders, smoking, alcohol, and substance use.

CONCLUSIONS

Targeted preventive interventions are crucial to safeguard the mental health of vulnerable groups, emphasizing the importance of diverse samples in future research.

LIMITATIONS

Online data collection may have resulted in the underrepresentation of certain population groups.

Atypical brain aging and its association with working memory performance in major depressive disorder

BACKGROUND

Patients with major depressive disorder (MDD) can present with altered brain structure and deficits in cognitive function similar to aging. Yet, the interaction between age-related brain changes and brain development in MDD remains understudied. In a cohort of adolescents and adults with and without MDD, we assessed brain aging differences and associations through a newly developed tool quantifying normative neurodevelopmental trajectories.

METHODS

304 MDD participants and 236 non-depressed controls were recruited and scanned from three studies under the Canadian Biomarker Integration Network for Depression. Volumetric data were used to generate brain centile scores, which were examined for: a) differences in MDD relative to controls; b) differences in individuals with versus without severe childhood maltreatment; and c) correlations with depressive symptom severity, neurocognitive assessment domains, or escitalopram treatment response.

RESULTS

Brain centiles were significantly lower in the MDD group compared to controls. It was also significantly correlated with working memory in controls, but not the MDD group. No significant associations were observed in depression severity or antidepressant treatment response with brain centiles. Likewise, childhood maltreatment history did not significantly affect brain centiles.

CONCLUSIONS

Consistent with prior work on machine learning models that predict "brain age", brain centile scores differed in people diagnosed with MDD, and MDD was associated with differential relationships between centile scores and working memory. The results support the notion of atypical development and aging in MDD, with implications on neurocognitive deficits associated with aging-related cognitive function.

Somatic multicomorbidity and disability in patients with psychiatric disorders in comparison to the general population: a quasi-epidemiological investigation in 54,826 subjects from 40 countries (COMET-G study)

BACKGROUND

The prevalence of medical illnesses is high among patients with psychiatric disorders. The current study aimed to investigate multi-comorbidity in patients with psychiatric disorders in comparison to the general population. Secondary aims were to investigate factors associated with metabolic syndrome and treatment appropriateness of mental disorders.

METHODS

The sample included 54,826 subjects (64.73% females; 34.15% males; 1.11% nonbinary gender) from 40 countries (COMET-G study). The analysis was based on the registration of previous history that could serve as a fair approximation for the lifetime prevalence of various medical conditions.

RESULTS

About 24.5% reported a history of somatic and 26.14% of mental disorders. Mental disorders were by far the most prevalent group of medical conditions. Comorbidity of any somatic with any mental disorder was reported by 8.21%. One-third to almost two-thirds of somatic patients were also suffering from a mental disorder depending on the severity and multicomorbidity. Bipolar and psychotic patients and to a lesser extent depressives, manifested an earlier (15-20 years) manifestation of somatic multicomorbidity, severe disability, and probably earlier death. The overwhelming majority of patients with mental disorders were not receiving treatment or were being treated in a way that was not recommended. Antipsychotics and antidepressants were not related to the development of metabolic syndrome.

CONCLUSIONS

The finding that one-third to almost two-thirds of somatic patients also suffered from a mental disorder strongly suggests that psychiatry is the field with the most trans-specialty and interdisciplinary value and application points to the importance of teaching psychiatry and mental health in medical schools and also to the need for more technocratically oriented training of psychiatric residents.

Influence of CYP2C19, CYP2D6, and ABCB1 Gene Variants and Serum Levels of Escitalopram and Aripiprazole on Treatment-Emergent Sexual Dysfunction: A Canadian Biomarker Integration Network in Depression 1 (CAN-BIND 1) Study

OBJECTIVES

Treatment-emergent sexual dysfunction is frequently reported by individuals with major depressive disorder (MDD) on antidepressants, which negatively impacts treatment adherence and efficacy. We investigated the association of polymorphisms in pharmacokinetic genes encoding cytochrome-P450 drug-metabolizing enzymes, CYP2C19 and CYP2D6, and the transmembrane efflux pump, P-glycoprotein (i.e., ABCB1), on treatment-emergent changes in sexual function (SF) and sexual satisfaction (SS) in the Canadian Biomarker Integration Network in Depression 1 (CAN-BIND-1) sample.

METHODS

A total of 178 adults with MDD received treatment with escitalopram (ESC) from weeks 0-8 (Phase I). At week 8, nonresponders were augmented with aripiprazole (ARI) (i.e., ESC + ARI, n = 91), while responders continued ESC (i.e., ESC-Only, n = 80) from weeks 8-16 (Phase II). SF and SS were evaluated using the sex effects (SexFX) scale at weeks 0, 8, and 16. We assessed the primary outcomes, SF and SS change for weeks 0-8 and 8-16, using repeated measures mixed-effects models.

RESULTS

In ESC-Only, CYP2C19 intermediate metabolizer (IM) + poor metabolizers (PMs) showed treatment-related improvements in sexual arousal, a subdomain of SF, from weeks 8-16, relative to CYP2C19 normal metabolizers (NMs) who showed a decline, F(2,54) = 8.00, p < 0.001, q = 0.048. Specifically, CYP2C19 IM + PMs reported less difficulty with having and sustaining vaginal lubrication in females and erection in males, compared to NMs. Furthermore, ESC-Only females with higher concentrations of ESC metabolite, S-desmethylcitalopram (S-DCT), and S-DCT/ESC ratio in serum demonstrated more decline in SF (r = -0.42, p = 0.004, q = 0.034) and SS (r = -0.43, p = 0.003, q = 0.034), respectively, which was not observed in males. ESC-Only females also demonstrated a trend for a correlation between S-DCT and sexual arousal change in the same direction (r = -0.39, p = 0.009, q = 0.052).

CONCLUSIONS

CYP2C19 metabolizer phenotypes may be influencing changes in sexual arousal related to ESC monotherapy. Thus, preemptive genotyping of CYP2C19 may help to guide selection of treatment that circumvents selective serotonin reuptake inhibitor-related sexual dysfunction thereby improving outcomes for patients. Additionally, further research is warranted to clarify the role of S-DCT in the mechanisms underlying ESC-related changes in SF and SS. This CAN-BIND-1 study was registered on clinicaltrials.gov (Identifier: NCT01655706) on 27 July 2012.

Randomized, double-blind, controlled trial of a combination of alpha-lipoic acid and pregabalin for neuropathic pain: the PAIN-CARE trial

ABSTRACT

We compared a combination of the nonsedating antioxidant, alpha-lipoic acid (ALA), with the sedating anticonvulsant, pregabalin, vs each monotherapy to treat neuropathic pain due to peripheral neuropathies. In this randomized, double-blind, 3-period crossover trial, participants received oral ALA, pregabalin, and their combination-each for 6 weeks. The primary outcome was mean daily pain intensity at maximal tolerated doses (MTD); secondary outcomes included quality of life (SF-36), sleep (Medical Outcomes Study-Sleep Scale), adverse effects, drug doses, and other measures. Of 55 participants randomized (20-diabetic neuropathy, 19-small fiber neuropathy, and 16-other neuropathies), 46 completed 2 periods, and 44 completed 3. At MTD, the primary outcome of mean pain intensity (0-10) was 5.32 (standard error, SE = 0.18), 3.96 (0.25), 3.25 (0.25), and 3.16 (0.25) at baseline, ALA, pregabalin, and combination, respectively ( P < 0.01 for ALA vs combination and pregabalin). Treatment differences were similar in subgroups with diabetic neuropathy and with other neuropathies. SF-36 total scores (higher number indicates better quality of life) were 66.6 (1.88), 70.1 (1.88), and 69.4 (1.87) with ALA, pregabalin, and combination ( P < 0.05 for ALA vs combination and pregabalin). At MTD, there were no statistically significant treatment differences in adverse effects or drug doses. This trial demonstrates superiority of pregabalin vs ALA but provides no evidence to suggest added benefit of combining ALA with pregabalin to treat neuropathic pain.

Predicting recurrence of major depressive episodes using the Depression Implicit Association Test: A Canadian biomarker integration network in depression (CAN-BIND) report

Identifying clinically relevant predictors of depressive recurrence following treatment for Major Depressive Disorder (MDD) is critical for relapse prevention. Implicit self-depressed associations (SDAs), defined as implicit cognitive associations between elements of depression (e.g., sad, miserable) and oneself, often persist following depressive episodes and may represent a cognitive biomarker for future recurrences. Thus, we examined whether SDAs, and changes in SDAs over time, prospectively predict depressive recurrence among treatment responders in the CAN-BIND Wellness Monitoring for MDD Study, a prospective cohort study conducted across 5 clinical centres. A total of 96 patients with MDD responding to various treatments were followed an average of 1.01 years. Participants completed the Depression Implicit Association Test (DIAT) - a computer-based measure of SDAs - every 8 weeks on a tablet device. Survival analyses indicated that greater SDAs at baseline and increases in SDAs over time predicted shorter time to MDD recurrence, even after accounting for depressive symptom severity. The findings show that SDAs are a robust prognostic indicator of risk for MDD recurrence, and that the DIAT may be a feasible and low-cost clinical screening tool. SDAs also represent a potential mechanism underlying the course of recurrent depression and are a promising target for relapse prevention interventions.

Competencies for Repetitive Transcranial Magnetic Stimulation in Postgraduate Medical Education: Expert Consensus Using a Modified Delphi Process

BACKGROUND

Repetitive transcranial magnetic stimulation (rTMS) is recommended in Canadian guidelines as a first-line treatment for major depressive disorder. With the shift towards competency-based medical education, it remains unclear how to determine when a resident is considered competent in applying knowledge of rTMS to patient care. Given inconsistencies between postgraduate training programmes with regards to training requirements, defining competencies will improve the standard of care in rTMS delivery.

OBJECTIVE

The goal of this study was to develop competencies for rTMS that can be implemented into a competency-based training curriculum in postgraduate training programmes.

METHODS

A working group drafted competencies for postgraduate psychiatry trainees. Fourteen rTMS experts from across Canada were invited to participate in the modified Delphi process.

RESULTS

Ten experts participated in all three rounds of the modified Delphi process. A total of 20 items reached a consensus. There was improvement in the Cronbach's alpha over the rounds of modified Delphi process (Cronbach's alpha increased from 0.554 to 0.824) suggesting improvement in internal consistency. The intraclass correlation coefficient (ICC) increased from 0.543 to 0.805 suggesting improved interrater agreement.

CONCLUSIONS

This modified Delphi process resulted in expert consensus on competencies to be acquired during postgraduate medical education programmes where a learner is training to become competent as a consultant and/or practitioner in rTMS treatment. This is a field that still requires development, and it is expected that as more evidence emerges the competencies will be further refined. These results will help the development of other curricula in interventional psychiatry.

Pro-inflammatory markers are associated with response to sequential pharmacotherapy in major depressive disorder: a CAN-BIND-1 report

OBJECTIVE

There is limited literature on associations between inflammatory tone and response to sequential pharmacotherapies in major depressive disorder (MDD).

METHODS

In a 16-week open-label clinical trial, 211 participants with MDD were treated with escitalopram 10-20 mg daily for 8 weeks. Responders continued escitalopram while non-responders received adjunctive aripiprazole 2-10 mg daily for 8 weeks. Plasma levels of pro-inflammatory markers-C-reactive protein, interleukin (IL)-1β, IL-6, IL-17, interferon-gamma (IFN)-Γ, tumor necrosis factor (TNF)-α, and Chemokine C-C motif ligand-2 (CCL-2)-measured at baseline, and after 2, 8 and 16 weeks were included in logistic regression analyzes to assess associations between inflammatory markers and treatment response.

RESULTS

Pre-treatment IFN-Γ and CCL-2 levels were significantly associated with a lower of odds of response to escitalopram at 8 weeks. Increases in CCL-2 levels from weeks 8 to 16 in escitalopram non-responders were significantly associated with higher odds of non-response to adjunctive aripiprazole at week 16.

CONCLUSION

Higher pre-treatment levels of IFN-Γ and CCL-2 were associated with non-response to escitalopram. Increasing levels of these pro-inflammatory markers may be associated with non-response to adjunctive aripiprazole. These findings require validation in independent clinical populations.

The concept of "metabolic jet lag" in the pathophysiology of bipolar disorder: implications for research and clinical care

Bipolar disorder (BD) is a potentially chronic mental disorder marked by recurrent depressive and manic episodes, circadian rhythm disruption, and changes in energetic metabolism. "Metabolic jet lag" refers to a state of shift in circadian patterns of energy homeostasis, affecting neuroendocrine, immune, and adipose tissue function, expressed through behavioral changes such as irregularities in sleep and appetite. Risk factors include genetic variation, mitochondrial dysfunction, lifestyle factors, poor gut microbiome health and abnormalities in hunger, satiety, and hedonistic function. Evidence suggests metabolic jet lag is a core component of BD pathophysiology, as individuals with BD frequently exhibit irregular eating rhythms and circadian desynchronization of their energetic metabolism, which is associated with unfavorable clinical outcomes. Although current diagnostic criteria lack any assessment of eating rhythms, technological advancements including mobile phone applications and ecological momentary assessment allow for the reliable tracking of biological rhythms. Overall, methodological refinement of metabolic jet lag assessment will increase knowledge in this field and stimulate the development of interventions targeting metabolic rhythms, such as time-restricted eating.

A standardized workflow for long-term longitudinal actigraphy data processing using one year of continuous actigraphy from the CAN-BIND Wellness Monitoring Study

Monitoring sleep and activity through wearable devices such as wrist-worn actigraphs has the potential for long-term measurement in the individual's own environment. Long periods of data collection require a complex approach, including standardized pre-processing and data trimming, and robust algorithms to address non-wear and missing data. In this study, we used a data-driven approach to quality control, pre-processing and analysis of longitudinal actigraphy data collected over the course of 1 year in a sample of 95 participants. We implemented a data processing pipeline using open-source packages for longitudinal data thereby providing a framework for treating missing data patterns, non-wear scoring, sleep/wake scoring, and conducted a sensitivity analysis to demonstrate the impact of non-wear and missing data on the relationship between sleep variables and depressive symptoms. Compliance with actigraph wear decreased over time, with missing data proportion increasing from a mean of 4.8% in the first week to 23.6% at the end of the 12 months of data collection. Sensitivity analyses demonstrated the importance of defining a pre-processing threshold, as it substantially impacts the predictive value of variables on sleep-related outcomes. We developed a novel non-wear algorithm which outperformed several other algorithms and a capacitive wear sensor in quality control. These findings provide essential insight informing study design in digital health research.

Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication

Importance

Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging.

Objective

To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications.

Design, Setting, and Participants

This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022.

Exposures

In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment.

Main Outcomes and Measures

The model's performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity.

Results

The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model's specificity in predicting SSRIs treatment response.

Conclusions and Relevance

In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.

Response trajectories during escitalopram treatment of patients with major depressive disorder

Depression is a leading global cause of disability, yet about half of patients do not respond to initial antidepressant treatment. This treatment difficulty may be in part due to the heterogeneity of depression and corresponding response to treatment. Unsupervised machine learning allows underlying patterns to be uncovered, and can be used to understand this heterogeneity by finding groups of patients with similar response trajectories. Prior studies attempting this have clustered patients using a narrow range of data primarily from depression scales. In this work, we used unsupervised machine learning to cluster patients receiving escitalopram therapy using a wide variety of subjective and objective clinical features from the first eight weeks of the Canadian Biomarker Integration Network in Depression-1 trial. We investigated how these clusters responded to treatment by comparing changes in symptoms and symptom categories, and by using Principal Component Analysis (PCA). Our algorithm found three clusters, which broadly represented non-responders, responders, and remitters. Most categories of features followed this response pattern except for objective cognitive features. Using PCA with our clusters, we found that subjective mood state/anhedonia is the core feature of response with escitalopram, but there exists other distinct patterns of response around neurovegetative symptoms, activation, and cognition.

Prediction of depression treatment outcome from multimodal data: a CAN-BIND-1 report

BACKGROUND

Prediction of treatment outcomes is a key step in improving the treatment of major depressive disorder (MDD). The Canadian Biomarker Integration Network in Depression (CAN-BIND) aims to predict antidepressant treatment outcomes through analyses of clinical assessment, neuroimaging, and blood biomarkers.

METHODS

In the CAN-BIND-1 dataset of 192 adults with MDD and outcomes of treatment with escitalopram, we applied machine learning models in a nested cross-validation framework. Across 210 analyses, we examined combinations of predictive variables from three modalities, measured at baseline and after 2 weeks of treatment, and five machine learning methods with and without feature selection. To optimize the predictors-to-observations ratio, we followed a tiered approach with 134 and 1152 variables in tier 1 and tier 2 respectively.

RESULTS

A combination of baseline tier 1 clinical, neuroimaging, and molecular variables predicted response with a mean balanced accuracy of 0.57 (best model mean 0.62) compared to 0.54 (best model mean 0.61) in single modality models. Adding week 2 predictors improved the prediction of response to a mean balanced accuracy of 0.59 (best model mean 0.66). Adding tier 2 features did not improve prediction.

CONCLUSIONS

A combination of clinical, neuroimaging, and molecular data improves the prediction of treatment outcomes over single modality measurement. The addition of measurements from the early stages of treatment adds precision. Present results are limited by lack of external validation. To achieve clinically meaningful prediction, the multimodal measurement should be scaled up to larger samples and the robustness of prediction tested in an external validation dataset.

Results of the COVID-19 mental health international for the health professionals (COMET-HP) study: depression, suicidal tendencies and conspiracism

INTRODUCTION

The current study aimed to investigate the rates of anxiety, clinical depression, and suicidality and their changes in health professionals during the COVID-19 outbreak.

MATERIALS AND METHODS

The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Clinical depression was detected in 13.16% with male doctors and 'non-binary genders' having the lowest rates (7.89 and 5.88% respectively) and 'non-binary gender' nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p < 0.0001). Suicidal tendencies were at least doubled in terms of RASS scores. Approximately one-third of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop clinical depression was associated with a history of Bipolar disorder (RR = 4.23).

CONCLUSIONS

The current study reported findings in health care professionals similar in magnitude and quality to those reported earlier in the general population although rates of clinical depression, suicidal tendencies, and adherence to conspiracy theories were much lower. However, the general model of factors interplay seems to be the same and this could be of practical utility since many of these factors are modifiable.

Duration of Adjunctive Antidepressant Maintenance in Bipolar I Depression

BACKGROUND

Antidepressants are used to treat acute depression in patients with bipolar I disorder, but their effect as maintenance treatment after the remission of depression has not been well studied.

METHODS

We conducted a multisite, double-blind, randomized, placebo-controlled trial of maintenance of treatment with adjunctive escitalopram or bupropion XL as compared with discontinuation of antidepressant therapy in patients with bipolar I disorder who had recently had remission of a depressive episode. Patients were randomly assigned in a 1:1 ratio to continue treatment with antidepressants for 52 weeks after remission or to switch to placebo at 8 weeks. The primary outcome, assessed in a time-to-event analysis, was any mood episode, as defined by scores on scales measuring symptoms of hypomania or mania, depression, suicidality, and mood-episode severity; additional treatment or hospitalization for mood symptoms; or attempted or completed suicide. Key secondary outcomes included the time to an episode of mania or hypomania or depression.

RESULTS

Of 209 patients with bipolar I disorder who participated in an open-label treatment phase, 150 who had remission of depression were enrolled in the double-blind phase in addition to 27 patients who were enrolled directly. A total of 90 patients were assigned to continue treatment with the prescribed antidepressant for 52 weeks (52-week group) and 87 were assigned to switch to placebo at 8 weeks (8-week group). The trial was stopped before full recruitment was reached owing to slow recruitment and funding limitations. At 52 weeks, 28 of the patients in the 52-week group (31%) and 40 in the 8-week group (46%) had a primary-outcome event. The hazard ratio for time to any mood episode in the 52-week group relative to the 8-week group was 0.68 (95% confidence interval [CI], 0.43 to 1.10; P = 0.12 by log-rank test). A total of 11 patients in the 52-week group (12%) as compared with 5 patients in the 8-week group (6%) had mania or hypomania (hazard ratio, 2.28; 95% CI, 0.86 to 6.08), and 15 patients (17%) as compared with 35 patients (40%) had recurrence of depression (hazard ratio, 0.43; 95% CI, 0.25 to 0.75). The incidence of adverse events was similar in the two groups.

CONCLUSIONS

In a trial involving patients with bipolar I disorder and a recently remitted depressive episode, adjunctive treatment with escitalopram or bupropion XL that continued for 52 weeks did not show a significant benefit as compared with treatment for 8 weeks in preventing relapse of any mood episode. The trial was stopped early owing to slow recruitment and funding limitations. (Funded by the Canadian Institutes of Health Research; ClinicalTrials.gov number, NCT00958633.).

Does the Ranking Matter? A Retrospective Cohort Study Investigating the Impact of the 2018 CANMAT and ISBD Guidelines for the Management of Patients with Bipolar Disorder Treatment Recommendations for Acute Mania on Rehospitalization Rates

OBJECTIVE

There is limited data about the impact of mood disorders treatment guidelines on clinical outcomes. The objective of this study was to investigate the impact of prescribers' adherence to the 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) treatment guidelines recommendations on the readmission rates of patients hospitalized for mania.

METHOD

A retrospective cohort of all individuals admitted due to acute mania to Kingston General Hospital, Kingston, ON, from January 2018 to July 2021 was included in this study. Patient variables and data regarding index admission and subsequent hospitalizations were extracted from medical records up to December 31, 2021. Treatment regimens were classified as first-line, second-line, noncompliant, or no treatment. We explored the associations between treatment regimens and the risk of readmissions using univariate, multivariate, and survival analysis.

RESULTS

We identified 211 hospitalizations related to 165 patients. The mean time-to-readmission was 211.8 days (standard deviation [SD]  =  247.1); the 30-day rehospitalization rate was 13.7%, and any rehospitalization rate was 40.3%. Compared to no treatment, only first-line treatments were associated with a statistically significant decreased risk of 30-day readmission (odds ratio [OR] = 0.209; 95% CI, 0.058 to 0.670). The risk of any readmission was reduced by first-line (OR = 0.387; 95% CI, 0.173 to 0.848) and noncompliant regimens (OR = 0.414; 95% CI, 0.174 to 0.982) compared to no treatment. On survival analysis, no treatment group was associated with shorter time-to-readmission (log-rank test, p  =  0.014) and increased risk of readmission (hazard ratio = 2.27; 95% CI, 1.30 to 3.96) when compared to first-line medications.

CONCLUSIONS

Treatment with first-line medications was associated with lower 30-day rehospitalization rates and longer time-to-readmission. Physicians' adherence to treatments with higher-ranked evidence for efficacy, safety, and tolerability may improve bipolar disorder outcomes.

SNORD90 induces glutamatergic signaling following treatment with monoaminergic antidepressants

Pharmacotherapies for the treatment of major depressive disorder were serendipitously discovered almost seven decades ago. From this discovery, scientists pinpointed the monoaminergic system as the primary target associated with symptom alleviation. As a result, most antidepressants have been engineered to act on the monoaminergic system more selectively, primarily on serotonin, in an effort to increase treatment response and reduce unfavorable side effects. However, slow and inconsistent clinical responses continue to be observed with these available treatments. Recent findings point to the glutamatergic system as a target for rapid acting antidepressants. Investigating different cohorts of depressed individuals treated with serotonergic and other monoaminergic antidepressants, we found that the expression of a small nucleolar RNA, SNORD90, was elevated following treatment response. When we increased Snord90 levels in the mouse anterior cingulate cortex (ACC), a brain region regulating mood responses, we observed antidepressive-like behaviors. We identified neuregulin 3 (NRG3) as one of the targets of SNORD90, which we show is regulated through the accumulation of N6-methyladenosine modifications leading to YTHDF2-mediated RNA decay. We further demonstrate that a decrease in NRG3 expression resulted in increased glutamatergic release in the mouse ACC. These findings support a molecular link between monoaminergic antidepressant treatment and glutamatergic neurotransmission.

Practices, knowledge, and attitudes about lithium treatment: Results of online surveys completed by clinicians and lithium-treated patients

BACKGROUND

Lithium remains the gold-standard medication for acute and prophylactic treatment of bipolar disorder. Understanding clinicians' practices and patients' experiences, knowledge and attitudes about lithium may improve its clinical use.

METHODS

Online anonymous surveys collected information about clinician's practices and level of confidence in managing lithium and patients' experiences with lithium treatment and information received about benefits and side effects. Knowledge and attitudes regarding lithium were assessed with the Lithium Knowledge Test (LKT) and the Lithium Attitudes Questionnaire (LAQ).

RESULTS

Among 201 clinicians, 64.2% endorsed often treating patients with lithium and reported high levels of confidence in assessing and managing lithium. Practices concerning clinical indications, drug titration, and serum levels were guideline-concordant, but compliance with monitoring recommendations was less frequent. Practitioners were interested in receiving more education about lithium. The patients' survey recruited 219 participants with 70.3% being current lithium users. Most patients (68%) found lithium helpful and 71% reported experiencing any kind of side effect. Most responders did not receive information about side effects or other benefits of lithium. Patients with higher scores on the LKT were more likely to have positive attitudes about lithium.

LIMITATIONS

Cross-sectional design with predominantly English-speaking participants from Brazil and North America.

CONCLUSIONS

There is a discrepancy between guidelines, clinician confidence and knowledge of lithium use and practice. A deeper understanding of how to monitor, prevent and manage long-term side effects and which patients are most likely to benefit from lithium may narrow the gap between knowledge and use.

Rasch analyses of the Quick Inventory of Depressive Symptomatology Self-Report in neurodegenerative and major depressive disorders

Background

Symptoms of depression are present in neurodegenerative disorders (ND). It is important that depression-related symptoms be adequately screened and monitored in persons living with ND. The Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) is a widely-used self-report measure to assess and monitor depressive severity across different patient populations. However, the measurement properties of the QIDS-SR have not been assessed in ND.

Aim

To use Rasch Measurement Theory to assess the measurement properties of the Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) in ND and in comparison to major depressive disorder (MDD).

Methods

De-identified data from the Ontario Neurodegenerative Disease Research Initiative (NCT04104373) and Canadian Biomarker Integration Network in Depression (NCT01655706) were used in the analyses. Five hundred and twenty participants with ND (Alzheimer's disease or mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia and Parkinson's disease) and 117 participants with major depressive disorder (MDD) were administered the QIDS-SR. Rasch Measurement Theory was used to assess measurement properties of the QIDS-SR, including unidimensionality and item-level fit, category ordering, item targeting, person separation index and reliability and differential item functioning.

Results

The QIDS-SR fit well to the Rasch model in ND and MDD, including unidimensionality, satisfactory category ordering and goodness-of-fit. Item-person measures (Wright maps) showed gaps in item difficulties, suggesting poor precision for persons falling between those severity levels. Differences between mean person and item measures in the ND cohort logits suggest that QIDS-SR items target more severe depression than experienced by the ND cohort. Some items showed differential item functioning between cohorts.

Conclusion

The present study supports the use of the QIDS-SR in MDD and suggest that the QIDS-SR can be also used to screen for depressive symptoms in persons with ND. However, gaps in item targeting were noted that suggests that the QIDS-SR cannot differentiate participants falling within certain severity levels. Future studies would benefit from examination in a more severely depressed ND cohort, including those with diagnosed clinical depression.

Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression: a Canadian biomarker integration network for depression study

Cognitive behavioral therapy (CBT) is often recommended as a first-line treatment in depression. However, access to CBT remains limited, and up to 50% of patients do not benefit from this therapy. Identifying biomarkers that can predict which patients will respond to CBT may assist in designing optimal treatment allocation strategies. In a Canadian Biomarker Integration Network for Depression (CAN-BIND) study, forty-one adults with depression were recruited to undergo a 16-week course of CBT with thirty having resting-state electroencephalography (EEG) recorded at baseline and week 2 of therapy. Successful clinical response to CBT was defined as a 50% or greater reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score from baseline to post-treatment completion. EEG relative power spectral measures were analyzed at baseline, week 2, and as early changes from baseline to week 2. At baseline, lower relative delta (0.5-4 Hz) power was observed in responders. This difference was predictive of successful clinical response to CBT. Furthermore, responders exhibited an early increase in relative delta power and a decrease in relative alpha (8-12 Hz) power compared to non-responders. These changes were also found to be good predictors of response to the therapy. These findings showed the potential utility of resting-state EEG in predicting CBT outcomes. They also further reinforce the promise of an EEG-based clinical decision-making tool to support treatment decisions for each patient.

Canadian Network for Mood and Anxiety Treatments (CANMAT) Task Force Report: A Systematic Review and Recommendations of Cannabis use in Bipolar Disorder and Major Depressive Disorder

BACKGROUND

Given the increasing acceptability and legalization of cannabis in some jurisdictions, clinicians need to improve their understanding of the effect of cannabis use on mood disorders.

OBJECTIVE

The purpose of this task force report is to examine the association between cannabis use and incidence, presentation, course and treatment of bipolar disorder and major depressive disorder, and the treatment of comorbid cannabis use disorder.

METHODS

We conducted a systematic literature review using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed, Embase, PsycINFO, CINAHL and Cochrane Central Register of Controlled Trials from inception to October 2020 focusing on cannabis use and bipolar disorder or major depressive disorder, and treatment of comorbid cannabis use disorder. The Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach was used to evaluate the quality of evidence and clinical considerations were integrated to generate Canadian Network for Mood and Anxiety Treatments recommendations.

RESULTS

Of 12,691 publications, 56 met the criteria: 23 on bipolar disorder, 21 on major depressive disorder, 11 on both diagnoses and 1 on treatment of comorbid cannabis use disorder and major depressive disorder. Of 2,479,640 participants, 12,502 were comparison participants, 73,891 had bipolar disorder and 408,223 major depressive disorder without cannabis use. Of those with cannabis use, 2,761 had bipolar disorder and 5,044 major depressive disorder. The lifetime prevalence of cannabis use was 52%-71% and 6%-50% in bipolar disorder and major depressive disorder, respectively. Cannabis use was associated with worsening course and symptoms of both mood disorders, with more consistent associations in bipolar disorder than major depressive disorder: increased severity of depressive, manic and psychotic symptoms in bipolar disorder and depressive symptoms in major depressive disorder. Cannabis use was associated with increased suicidality and decreased functioning in both bipolar disorder and major depressive disorder. Treatment of comorbid cannabis use disorder and major depressive disorder did not show significant results.

CONCLUSION

The data indicate that cannabis use is associated with worsened course and functioning of bipolar disorder and major depressive disorder. Future studies should include more accurate determinations of type, amount and frequency of cannabis use and select comparison groups which allow to control for underlying common factors.

Evaluating the efficacy of Web-Based Cognitive Behavioural Therapy for the treatment of patients with Bipolar II Disorder and residual depressive symptoms: Protocol for a randomized controlled trial

BACKGROUND

Bipolar disorder (BD) is a highly prevalent psychiatric condition that can significantly impact every aspect of a person's life if left untreated. A subtype of BD, Bipolar disorder II (BD-II) is characterized by long depressive episodes and residual depression symptoms, with short-lived hypomanic episodes. Medication and psychotherapy, such as cognitive behavioral therapy (CBT), are the main treatment options for BD-II. CBT specific for BD-II involves the recognition of warning signs, potentially triggering stimuli, and the development of coping skills to increase euthymic periods and improve global functioning. However, access to in-person CBT may be limited by several barriers including low availability, high costs and geographical limitations. Thus, online adaptations of CBT (e-CBT) have become a promising solution to address these treatment barriers Nevertheless, e-CBT for the treatment of BD-II remains understudied.

OBJECTIVE

The proposed study aims to establish the first e-CBT program specific for the treatment of BD-II with residual depressive symptoms. The primary objective of this study will be to determine the effect of e-CBT in managing BD symptomatology. The secondary objective will be to assess the effects of this e-CBT program on quality of life, and resilience. The tertiary objective will involve gathering user feedback using a post-treatment survey to support the continuous improvement and optimization of the proposed program.

METHODS

Adult participants (n = 170) with a confirmed diagnosis of BD-II experiencing residual depressive symptoms will be randomly assigned to either the e-CBT plus TAU (n = 85) group or the TAU (n = 85) control group. Participants in the control group will be able to participate in the online program after the first 13 weeks. The e-CBT program will consist of 13 weekly online modules designed following a validated CBT framework. Participants will complete module-related homework and receive asynchronous personalized feedback from a therapist. TAU will consist of standard treatment services conducted outside of the current research study. Depression and manic symptoms quality of life and resiliency will be assessed using clinically validated symptomatology questionnaires at baseline, week 6, and week 13.

RESULTS

The study received ethics approval in March 2020 and participant recruitment is expected to begin in February 2023 through targeted advertisements and physician referrals. Data collection and analysis are expected to conclude by December 2024. Linear and binomial regression (continuous and categorical outcomes respectively) will be conducted along with qualitative interpretive methods.

CONCLUSIONS

The findings will be the first on the effectiveness of delivering e-CBT for patients with BD-II with residual depressive symptoms. This approach can provide an innovative method to address barriers to in-person psychotherapy by increasing accessibility and decreasing costs.

CLINICALTRIAL

clinicaltrials.gov (NCT04664257); clinicaltrials.gov/ct2/show/NCT04664257.

Mental health challenges, treatment experiences, and care needs of post-secondary students: a cross-sectional mixed-methods study

BACKGROUND

Post-secondary students frequently experience high rates of mental health challenges. However, they present meagre rates of treatment-seeking behaviours. This elevated prevalence of mental health problems, particularly after the COVID-19 pandemic, can lead to distress, poor academic performance, and lower job prospects following the completion of education. To address the needs of this population, it is important to understand students' perceptions of mental health and the barriers preventing or limiting their access to care.

METHODS

A broad-scoping online survey was publicly distributed to post-secondary students, collecting demographic, sociocultural, economic, and educational information while assessing various components of mental health.

RESULTS

In total, 448 students across post-secondary institutions in Ontario, Canada, responded to the survey. Over a third (n = 170; 38.6%) of respondents reported a formal mental health diagnosis. Depression and generalized anxiety disorder were the most commonly reported diagnoses. Most respondents felt that post-secondary students did not have good mental health (n = 253; 60.5%) and had inadequate coping strategies (n = 261; 62.4%). The most frequently reported barriers to care were financial (n = 214; 50.5%), long wait times (n = 202; 47.6%), insufficient resources (n = 165; 38.9%), time constraints (n = 148; 34.9%), stigma (n = 133; 31.4%), cultural barriers (n = 108; 25.5%), and past negative experiences with mental health care (n = 86; 20.3%). The majority of students felt their post-secondary institution needed to increase awareness (n = 231; 56.5%) and mental health resources (n = 306; 73.2%). Most viewed in-person therapy and online care with a therapist as more helpful than self-guided online care. However, there was uncertainty about the helpfulness and accessibility of different forms of treatment, including online interventions. The qualitative findings highlighted the need for personal strategies, mental health education and awareness, and institutional support and services.

CONCLUSIONS

Various barriers to care, perceived lack of resources, and low knowledge of available interventions may contribute to compromised mental health in post-secondary students. The survey findings indicate that upstream approaches such as integrating mental health education for students may address the varying needs of this critical population. Therapist-involved online mental health interventions may be a promising solution to address accessibility issues.

Neurocognition and Depressive Symptoms have Unique Pathways to Predicting Different Domains of Functioning in Major Depressive Disorder

BACKGROUND

Research has established the independent relationships between depressive symptoms to cognition and functioning in depression; however, little is known about the role of mediators in this relationship. We explored the role of neurocognitive abilities, depressive symptom severity, dysfunctional attitudes, and functional capacity in predicting two dimensions of daily functioning in individuals with major depressive disorder (MDD).

METHODS

One hundred and twenty-four participants (mean age  =  46.26, SD  =  12.27; 56% female) with a diagnosis of MDD were assessed on a standard neurocognitive battery, self-reported depressive symptoms, dysfunctional attitudes, and clinician-rated functional impairment. They completed a performance-based assessment of functional competence.

RESULTS

Confirmatory path analyses were used to model the independent and mediated effects of variables on two domains of functioning: social (relationships and social engagement) and productive (household and community activities). Cognition and depressive symptoms both predicted productive functioning, and dysfunctional attitudes mediated each of these relationships. Functional competence was a significant mediator in the relationship between neurocognition and productive functioning. Depressive symptoms and cognition were direct predictors of social functioning with no significant mediators.

CONCLUSIONS

There are divergent pathways to different dimensions of daily functioning in MDD. Measurement implications include the consideration of multiple levels of predicting productive activities and more direct relationships with social outcomes. Treatments that directly target depressive symptoms and cognition might not generalize to improvements in everyday functioning if additional pathways to functioning are not addressed.

Using electronically delivered therapy and brain imaging to understand OCD pathology: A pilot feasibility study

Background

Obsessive–compulsive disorder (OCD) is a debilitating mental health disorder with current psychotherapeutic treatments, while somewhat effective, yielding low accessibility and scalability. A lack of knowledge regarding the neural pathology of OCD may be hindering the development of innovative treatments. Previous research has observed baseline brain activation patterns in OCD patients, elucidating some understanding of the implications. However, by using neuroimaging to observe the effects of treatment on brain activation, a more complete picture of OCD can be drawn. Currently, the gold standard treatment is cognitive behavioral therapy (CBT). However, CBT is often inaccessible, time-consuming, and costly. Fortunately, it can be effectively delivered electronically (e-CBT).

Objectives

This pilot study implemented an e-CBT program for OCD and observed its effects on cortical activation levels during a symptom provocation task. It was hypothesized that abnormal activations could be attenuated following treatment.

Methods

OCD patients completed a 16-week e-CBT program administered through an online platform, mirroring in-person content. Treatment efficacy was evaluated using behavioral questionnaires and neuroimaging. Activation levels were assessed at the resting state and during the symptom provocation task.

Results

In this pilot, seven participants completed the program, with significant improvements (p &lt; 0.05) observed between baseline and post-treatment for symptom severity and levels of functioning. No statistically significant (p = 0.07) improvement was observed in the quality of life. Participants had mostly positive qualitative feedback, citing accessibility benefits, comprehensive formatting, and relatable content. No significant changes in cortical activation were observed between baseline and post-treatment.

Conclusion

This project sheds light on the application of e-CBT as a tool to evaluate the effects of treatment on cortical activation, setting the stage for a larger-scale study. The program showed great promise in feasibility and effectiveness. While there were no significant findings regarding changes in cortical activation, the trends were in agreeance with previous literature, suggesting future work could provide insight into whether e-CBT offers comparable cortical effects to in-person psychotherapy. Applying a greater knowledge of the neural mechanisms of action in OCD can help develop novel treatment plans in the future.

Systematic review of probiotics as an adjuvant treatment for psychiatric disorders

Introduction

Many psychiatric illnesses have been linked to the gut microbiome, with supplements such as probiotics showing some efficacy in alleviating the symptoms of some psychiatric illnesses. The aim of this review is to evaluate the current literature investigating the effects of adjuvant probiotic or synbiotic administration in combination with first-line treatments for psychiatric illnesses.

Method

A systematic search of four databases was conducted using key terms related to treatments for psychiatric illnesses, the gut microbiome, and probiotics. All results were then evaluated based on specific eligibility criteria.

Results

Eight studies met eligibility criteria and were analyzed for reported changes in outcome measures used to assess the symptoms of psychiatric illness and the tolerability of treatment. All Major Depressive Disorder (MDD) (n = 5) and Generalized Anxiety Disorder (GAD) (n = 1) studies found adjuvant probiotic or synbiotic treatment to be more efficacious in improving the symptoms of psychiatric illness than the first-line treatment alone or with placebo. The schizophrenia studies (n = 2) found adjuvant probiotic treatment to have no significant difference in clinical outcomes, but it was found to improve the tolerability of first-line antipsychotics.

Discussion and conclusion

The findings of the studies included in this review suggest the use of adjuvant probiotic treatment with selective serotonin reuptake inhibitors (SSRIs) for MDD and GAD to be superior to SSRI treatment alone. Probiotic adjuvant treatment with antipsychotics could be beneficial for improving the tolerability of the antipsychotics, but these findings do not suggest that adjuvant probiotic treatment would result in improved clinical outcomes for symptoms of schizophrenia.

Response Inhibition and Predicting Response to Pharmacological and Cognitive Behavioral Therapy Treatments for Major Depressive Disorder: A Canadian Biomarker Integration Network for Depression Study

BACKGROUND

Major depressive disorder (MDD) is associated with various cognitive impairments, including response inhibition. Deficits in response inhibition may also underlie poor antidepressant treatment response. Recent studies revealed that the neurobiological correlates of response inhibition can predict response to pharmacological treatments. However, the generalizability of this finding to first-line nonpharmacological treatments, particularly cognitive behavioral therapy, remains to be investigated.

METHODS

Data from two independent treatment protocols were combined, one in which 65 patients with MDD underwent treatment with escitalopram, and the other in which 41 patients with MDD underwent a course of cognitive behavioral therapy. A total of 25 healthy control subjects were also recruited. Neural correlates of response inhibition were captured by participants completing a Go/NoGo task during electroencephalography recording. Response inhibition-related measures of interest included the amplitudes of the N2 and P3 event-related potentials.

RESULTS

Pretreatment P3 amplitude, which has been linked to both the motor and cognitive aspects of response inhibition, was a significant predictor of change in depressive symptoms following escitalopram and cognitive behavioral therapy treatment. A greater pretreatment P3 amplitude was associated with a greater reduction in depressive severity. In addition, the pretreatment P3 amplitude was found to be significantly greater at baseline in remitters than in nonremitters and healthy control subjects.

CONCLUSIONS

The integrity of response inhibition may be critical for a successful course of pharmacological or psychological treatment for MDD. Electrophysiological correlates of response inhibition may have utility as a general prognostic marker of treatment response in MDD. Future studies may investigate the benefit of preceding first-line treatments with interventions that improve response inhibition in MDD.

ABCB1 Gene Variants and Antidepressant Treatment Outcomes: A Systematic Review and Meta-Analysis Including Results from the CAN-BIND-1 Study

The P-glycoprotein efflux pump, encoded by the ABCB1 gene, has been shown to alter concentrations of various antidepressants in the brain. In this study, we conducted a systematic review and meta-analysis to investigate the association between six ABCB1 single-nucleotide polymorphisms (SNPs; rs1045642, rs2032582, rs1128503, rs2032583, rs2235015, and rs2235040) and antidepressant treatment outcomes in individuals with major depressive disorder (MDD), including new data from the Canadian Biomarker and Integration Network for Depression (CAN-BIND-1) cohort. For the CAN-BIND-1 sample, we applied regression models to investigate the association between ABCB1 SNPs and antidepressant treatment response, remission, tolerability, and antidepressant serum levels. For the meta-analysis, we systematically summarized pharmacogenetic evidence of the association between ABCB1 SNPs and antidepressant treatment outcomes. Studies were included in the meta-analysis if they investigated at least one ABCB1 SNP in individuals with MDD treated with at least one antidepressant. We did not find a significant association between ABCB1 SNPs and antidepressant treatment outcomes in the CAN-BIND-1 sample. A total of 39 studies were included in the systematic review. In the meta-analysis, we observed a significant association between rs1128503 and treatment response (T vs. C-allele, odds ratio = 1.30, 95% confidence interval = 1.15-1.48, P value (adjusted) = 0.024, n = 2,526). We did not find associations among the six SNPs and treatment remission nor tolerability. Our findings provide limited evidence for an association between common ABCB1 SNPs and antidepressant outcomes, which do not support the implementation of ABCB1 genotyping to inform antidepressant treatment at this time. Future research, especially on rs1128503, is recommended.

Gray matter volume drives the brain age gap in schizophrenia: a SHAP study

Neuroimaging-based brain age is a biomarker that is generated by machine learning (ML) predictions. The brain age gap (BAG) is typically defined as the difference between the predicted brain age and chronological age. Studies have consistently reported a positive BAG in individuals with schizophrenia (SCZ). However, there is little understanding of which specific factors drive the ML-based brain age predictions, leading to limited biological interpretations of the BAG. We gathered data from three publicly available databases - COBRE, MCIC, and UCLA - and an additional dataset (TOPSY) of early-stage schizophrenia (82.5% untreated first-episode sample) and calculated brain age with pre-trained gradient-boosted trees. Then, we applied SHapley Additive Explanations (SHAP) to identify which brain features influence brain age predictions. We investigated the interaction between the SHAP score for each feature and group as a function of the BAG. These analyses identified total gray matter volume (group × SHAP interaction term β = 1.71 [0.53; 3.23]; p_corr < 0.03) as the feature that influences the BAG observed in SCZ among the brain features that are most predictive of brain age. Other brain features also presented differences in SHAP values between SCZ and HC, but they were not significantly associated with the BAG. We compared the findings with a non-psychotic depression dataset (CAN-BIND), where the interaction was not significant. This study has important implications for the understanding of brain age prediction models and the BAG in SCZ and, potentially, in other psychiatric disorders.

The Canadian Network for Mood and Anxiety Treatments (CANMAT) Task Force Report: Serotonergic Psychedelic Treatments for Major Depressive Disorder

OBJECTIVE

Serotonergic psychedelics are re-emerging as potential novel treatments for several psychiatric disorders including major depressive disorder. The Canadian Network for Mood and Anxiety Treatments (CANMAT) convened a task force to review the evidence and provide a consensus recommendation for the clinical use of psychedelic treatments for major depressive disorder.

METHODS

A systematic review was conducted to identify contemporary clinical trials of serotonergic psychedelics for the treatment of major depressive disorder and cancer-related depression. Studies published between January 1990 and July 2021 were identified using combinations of search terms, inspection of bibliographies and review of other psychedelic reviews and consensus statements. The levels of evidence for efficacy were graded according to the Canadian Network for Mood and Anxiety Treatments criteria.

RESULTS

Only psilocybin and ayahuasca have contemporary clinical trials evaluating antidepressant effects. Two pilot studies showed preliminary positive effects of single-dose ayahuasca for treatment-resistant depression (Level 3 evidence). Small randomized controlled trials of psilocybin combined with psychotherapy showed superiority to waitlist controls and comparable efficacy and safety to an active comparator (escitalopram with supportive psychotherapy) in major depressive disorder, with additional randomized controlled trials showing efficacy specifically in cancer-related depression (Level 3 evidence). There was only one open-label trial of psilocybin in treatment-resistant unipolar depression (Level 4 evidence). Small sample sizes and functional unblinding were major limitations in all studies. Adverse events associated with psychedelics, including psychological (e.g., psychotomimetic effects) and physical (e.g., nausea, emesis and headaches) effects, were generally transient.

CONCLUSIONS

There is currently only low-level evidence to support the efficacy and safety of psychedelics for major depressive disorder. In Canada, as of 2022, psilocybin remains an experimental option that is only available through clinical trials or the special access program. As such, Canadian Network for Mood and Anxiety Treatments considers psilocybin an experimental treatment and recommends its use primarily within clinical trials, or, less commonly, through the special access program in rare, special circumstances.

A detailed manual segmentation procedure for the hypothalamus for 3T T1-weighted MRI

The hypothalamus is a small grey matter structure which plays a crucial role in many physiological functions. Some studies have found an association between hypothalamic volume and psychopathology, which stresses the need for a standardized method to maximize segmentation accuracy. Here, we provide a detailed step-by-step method outlining the procedures to manually segment the hypothalamus using anatomical T1w images from 3T scanners, which many neuroimaging studies collect as a standard anatomical reference image. We compared volumes generated by manual segmentation and those generated by an automatic algorithm, observing a significant difference between automatically and manually segmented hypothalamus volumes on both sides (left: U = 222842, p-value < 2.2e-16; right: U = 218520, p- value < 2.2e-16).•Significant difference exists between existing automatic segmentation methods and the manual segmentation procedure.•We discuss potential drift effects, segmentation quality issues, and suggestions on how to mitigate them.•We demonstrate that the present manual segmentation procedure using standard T1-weighted MRI may be significantly more accurate than automatic segmentation outputs.

"One Degree of Separation": A Mixed-Methods Evaluation of Canadian Mental Health Care User and Provider Experiences With Remote Care During COVID-19

OBJECTIVES

The COVID-19 pandemic has contributed to a shift from in-person to remote mental health care. While remote care methods have long existed, their widespread use is unprecedented. There is little research about mental health care user and provider experiences with this transition, and no published studies to date have compared satisfaction between these groups.

METHODS

Canadian mental health care users (n = 332) and providers (n = 107) completed an online self-report survey from October 2020 to February 2021 hosted by the Canadian Biomarker Integration Network in Depression. Using a mixed-methods approach, participants were asked about their use of remote care, including satisfaction, barriers to use, helpful and unhelpful factors, and suggestions for improvement.

RESULTS

Overall, 59% to 63% of health care users and 59% of health care providers were satisfied with remote care. Users reported the greatest satisfaction with the convenience of remote care, while providers were most satisfied with the speed of provision of care; all groups were least satisfied with therapeutic rapport. Health care providers were less satisfied with the user-friendliness of remote care (P < 0.001) than users, while health care users were less satisfied than providers with continuity of care (P < 0.001). The use of a video-based platform was associated with remote care satisfaction among health care users (P < 0.02), and qualitative responses support the importance of visual cues in maintaining therapeutic rapport remotely. The majority of users (55%) and providers (87%) reported a likelihood of using remote care after the pandemic.

CONCLUSIONS

Remote mental health care is generally accepted by both users and providers, and the majority would consider using remote care following the pandemic. Suggestions for improvement include greater use of video, increased attention to body language and eye contact, consistency with in-person care, as well as increased provider training and administrative support.

The effect of different degrees of lockdown and self-identified gender on anxiety, depression and suicidality during the COVID-19 pandemic: Data from the international COMET-G study

INTRODUCTION

During the COVID-19 pandemic various degrees of lockdown were applied by countries around the world. It is considered that such measures have an adverse effect on mental health but the relationship of measure intensity with the mental health effect has not been thoroughly studied. Here we report data from the larger COMET-G study pertaining to this question.

MATERIAL AND METHODS

During the COVID-19 pandemic, data were gathered with an online questionnaire from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Anxiety was measured with the STAI, depression with the CES-D and suicidality with the RASS. Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

It included the calculation of Relative Risk (RR), Factorial ANOVA and Multiple backwards stepwise linear regression analysis RESULTS: Approximately two-thirds were currently living under significant restrictions due to lockdown. For both males and females the risk to develop clinical depression correlated significantly with each and every level of increasing lockdown degree (RR 1.72 and 1.90 respectively). The combined lockdown and psychiatric history increased RR to 6.88 The overall relationship of lockdown with severity of depression, though significant was small.

CONCLUSIONS

The current study is the first which reports an almost linear relationship between lockdown degree and effect in mental health. Our findings, support previous suggestions concerning the need for a proactive targeted intervention to protect mental health more specifically in vulnerable groups.

Association of intranasal esketamine, a novel 'standard of care' treatment and outcomes in the management of patients with treatment-resistant depression: protocol of a prospective cohort observational study of naturalistic clinical practice

INTRODUCTION

Esketamine is the S-enantiomer of racemic ketamine and has been approved by the Food and Drug Administration for the management of treatment resistant depression, demonstrating effective and long-lasting benefits. The objective of this observational study is to elucidate the association of intranasal (IN) esketamine with beneficial and negative outcomes in the management of treatment resistant major depressive disorder.

METHODS AND ANALYSIS

This is a multicentre prospective cohort observational study of naturalistic clinical practice. We expect to recruit 10 patients per research centre (6 centres, total 60 subjects). After approval to receive IN esketamine as part of their standard of care management of moderate to severe treatment resistant depression, patients will be invited to participate in this study. Association of esketamine treatment with outcomes in the management of depression will be assessed by measuring the severity of depression symptoms using the Montgomery-Åsberg Depression Rating Scale (MADRS), and tolerability by systematically tracking common side effects of ketamine treatment, dissociation using the simplified 6-Item Clinician Administered Dissociative Symptom Scale and potential for abuse using the Likeability and Craving Questionnaire (LCQ). Change in depressive symptoms (MADRS total scores) over time will be evaluated by within-subject repeated measures analysis of variance. We will calculate the relative risk associated with the beneficial (reduction in total scores for depression) outcomes, and the side effect and dropout rates (tolerability) of adding IN esketamine to patients' current pharmacological treatments. Covariate analysis will assess the impact of site and demographic variables on treatment outcomes.

ETHICS AND DISSEMINATION

Approval to perform this study was obtained through the Health Sciences Research Ethics Board at Queen's University. Findings will be shared among collaborators, through departmental meetings, presented on different academic venues and publishing our manuscript.

Identification of Endocannabinoid Predictors of Treatment Outcomes in Major Depressive Disorder: A Secondary Analysis of the First Canadian Biomarker Integration Network in Depression (CAN-BIND 1) Study

INTRODUCTION

An increasing number of studies are examining the link between the endocannabinoidome and major depressive disorder (MDD). We conducted an exploratory analysis of this system to identify potential markers of treatment outcomes.

METHODS

The dataset of the Canadian Biomarker Integration Network in Depression-1 study, consisting of 180 patients with MDD treated for eight weeks with escitalopram followed by eight weeks with escitalopram alone or augmented with aripiprazole was analyzed. Association between response Montgomery-Asberg Depression Rating Scale (MADRS; score reduction≥50%) or remission (MADRS score≤10) at weeks 8 and 16 and single nucleotide polymorphisms (SNPs), methylation, and mRNA levels of 33 endocannabinoid markers were examined. A standard genome-wide association studies protocol was used for identifying SNPs, and logistic regression was used to assess methylation and mRNA levels.

RESULTS

Lower methylation of CpG islands of the diacylglycerol lipase alpha gene (DAGLA) was associated with non-remission at week 16 (DAGLA; OR=0.337, p<0.003, q=0.050). Methylation of DAGLA was correlated with improvement in Clinical Global Impression (p=0.026), Quick Inventory of Depressive Symptomatology (p=0.010), and Snaith-Hamilton Pleasure scales (p=0.028). We did not find any association between SNPs or mRNA levels and treatment outcomes.

DISCUSSION

Methylation of DAGLA is a promising candidate as a marker of treatment outcomes for MDD and needs to be explored further.

What not to use in bipolar disorders: A systematic review of non-recommended treatments in clinical practice guidelines

BACKGROUND

Clinical practice guidelines (CPG) are an important tool for implementation of evidence-based clinical care. Despite clinical trials showing lack of efficacy of some agents in bipolar disorder (BD), they are still frequently prescribed in clinical practice. The objective of this study was to systematically review the CPG recommendations on pharmacological interventions with evidence against their use due to lack of efficacy data and/or due to serious safety concerns.

METHODS

A systematic literature search identified 29 guidelines published by national and international organizations during the 1994-2020 period. Information was extracted regarding how the recommendations framed non-use of treatments in particular clinical situations as well as the actual recommendation in the guideline.

RESULTS

Twenty-three guidelines (79%) mentioned at least one non-recommended treatment. The terms used to qualify recommendations varied amongst guidelines and included: "not recommended" "no recommendation" and "negative evidence". Lamotrigine, topiramate and gabapentin were commonly cited as non-recommended treatments for mania and most CPG did not recommend monotherapy with antidepressants, aripiprazole, risperidone, and ziprasidone for treatment of acute bipolar depression. Most guidelines made recommendations about lack of efficacy data or potential harm in treatments for BD but there is a significant variation in the way this information is conveyed to the reader.

LIMITATIONS

Non-recommended treatments were based on their use for BD episodes or maintenance but specific medications may benefit patients when treating comorbid conditions.

CONCLUSIONS

The absence of a uniform language and recommendations in current guidelines may be an additional complicating factor in the implementation of evidence-based treatments in BD.

Microbial Ecosystem Therapeutic-2 Intervention in People With Major Depressive Disorder and Generalized Anxiety Disorder: Phase 1, Open-Label Study

Background

Recent studies have investigated the potential of treatments that modify the gut microbiome, such as fecal microbiota transplantation and probiotics, in individuals with psychiatric illnesses.

Objective

The aim of this study was to investigate the safety, tolerability, and efficacy of a novel gut microbiome therapeutic, Microbial Ecosystem Therapuetic-2 (MET-2), in people with depression and anxiety.

Methods

In this phase 1, open-label trial, 12 adults diagnosed with major depressive disorder, generalized anxiety disorder, or both were recruited. Over 8 weeks, participants consumed three capsules per day, orally, of an encapsulated microbial therapeutic (MET-2), which contained 40 strains of bacteria that were purified and lab-grown from the stool of a single healthy donor. Participants were assessed biweekly using clinical scales and questionnaires in order to evaluate the safety, efficacy, and tolerability of the therapeutic.

Results

The therapeutic was found to be generally safe and tolerable, with limited adverse events and side effects and no serious adverse events. Of the 12 individuals included in this study, 9 (75%) responded to treatment (50% improvement in Montgomery-Asberg Depression Rating Scale [MADRS] scores, 7-item Generalized Anxiety Disorder scale [GAD-7] scores, or both, from baseline to the week-8 visit). Over the course of 10 weeks, MET-2 significantly decreased mean MADRS and GAD-7 scores (MADRS: F2.731, 30.05=8.784, P<.001; GAD-7: F2.778, 30.55= 9.638, P<.001). Multiple comparisons with Bonferroni adjustments showed a significant reduction in MADRS scores from baseline (mean 19.00, SD 4.843) to week 6 (mean 11.25, SD 8.001; P=.009), week 8 (mean 8.667, SD 8.732; P=.002), and week 10 (mean 8.250, SD 9.304; P=.006). Multiple comparisons showed a significant reduction in GAD-7 scores from baseline (mean 13.58, SD 4.010) to week 4 (mean 9.167, SD 5.096; P=.03), week 6 (mean 7.667, SD 4.539; P=.004), week 8 (mean 7.333, SD 6.583; P=.03), and week 10 (mean 7.500, SD 6.448; P=.03).

Conclusions

The findings from this study are the first to provide evidence for the role of microbial ecosystem therapy in treating depression and anxiety. However, a double-blind, randomized controlled trial with a larger sample size is needed for more conclusive results.

Trial Registration

ClinicalTrials.gov NCT04052451; https://www.clinicaltrials.gov/ct2/show/NCT04052451

International Registered Report Identifier (IRRID)

RR2-10.2196/17223

COVID-19 Pandemic: The Impact of COVID-19 on Mental Health and Life Habits in the Canadian Population

OBJECTIVES

The study aims to investigate the rate of clinical depression in the adult population during the COVID-19 pandemic, as well as the changes in anxiety, distress, suicidal ideation, and their relations with several personal and interpersonal/social variables.

METHODS

This is an epidemiological, non-interventional study. It is part of an international multi-center study, with the main site at the Aristotle University of Thessaloniki, in Greece (COMET-G Study). We are presenting aspects of the research involving the Canadian site, based on 508 Canadian responders to the online survey (QAIRE).

RESULTS

Of the 508 responders, 72.2% were females aged 42.57 ± 14.00 years; 27.2% were males aged 42.24 ± 15.49 years; and 0.6% were others aged 46.33 ± 17.79 years. Increased anxiety during the lockdown was reported by 69.3% of those surveyed. The rate of suicidal thoughts increased in 19.5% of participants during the lockdown. Depression was reported by 22% of responders, while distress was present in 18.4%. We found a greater prevalence of depression, but not distress, in individuals with a history of any mental disorder. Based on the multiple regression analysis, we found four CORE factors equally influencing the changes in mental health during the lockdown (gender, quality of sleep, family conflicts, and changes in daily routine). In the Canadian population, two major changes acted as protective factors, significantly expressed when compared with the worldwide tendencies: fewer financial difficulties; and an increase in religious beliefs.

CONCLUSION

The rate of major depression, distress, and suicidal ideation was higher in Canadians than in the worldwide population (per COMET-G), but the relative risk to develop depression in the presence of a history of mental disorders was lower. Almost 90% of Canadians believed in the real story of COVID source of provenience.

Predicting escitalopram treatment response from pre-treatment and early response resting state fMRI in a multi-site sample: A CAN-BIND-1 report

•

Baseline measures alone not able to predict escitalopram response above default.

•

This poor baseline performance contradicts results from smaller studies.

•

Accuracy improved using change in functional connectivity from baseline to week 2.

•

Measures of early change following treatment may be crucial for accurate prediction.

Many previous intervention studies have used functional magnetic resonance imaging (fMRI) data to predict the antidepressant response of patients with major depressive disorder (MDD); however, practical constraints have limited many of those attempts to small, single centre studies which may not adequately reflect how these models will generalize when used in clinical practice. Not only does the act of collecting data at multiple sites generally increase sample sizes (a critical point in machine learning development) it also generates a more heterogeneous dataset due to systematic differences in scanners at different sites, and geographical differences in patient populations. As part of the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) study, 144 MDD patients from six sites underwent resting state fMRI prior to starting escitalopram treatment, and again two weeks after the start. Here, we consider ways to use machine learning techniques to produce models that can predict response (measured at eight weeks after initiation), based on various parcellations, functional connectivity (FC) metrics, dimensionality reduction algorithms, and base learners, and also whether to use scans from one or both time points. Models that use only baseline (pre-treatment) or only week 2 (early-response) whole-brain FC features consistently failed to perform significantly better than default models. Utilizing the change in FC between these two time points, however, yielded significant results, with the best performing analytical pipeline achieving 69.6% (SD 10.8) accuracy. These results appear contrary to findings from many smaller single-site studies, which report substantially higher predictive accuracies from models trained on only baseline resting state FC features, suggesting these models may not generalize well beyond data used for development. Further, these results indicate the potential value of collecting data both before and shortly after treatment initiation.

Results of the COVID-19 mental health international for the general population (COMET-G) study

INTRODUCTION

There are few published empirical data on the effects of COVID-19 on mental health, and until now, there is no large international study.

MATERIAL AND METHODS

During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.

STATISTICAL ANALYSIS

Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.

RESULTS

Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.

CONCLUSIONS

The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

Structural covariance pattern abnormalities of insula in major depressive disorder: A CAN-BIND study report

BACKGROUND AND METHODS

Investigation of the insula may inform understanding of the etiopathogenesis of major depressive disorder (MDD). In the present study, we introduced a novel gray matter volume (GMV) based structural covariance technique, and applied it to a multi-centre study of insular subregions of 157 patients with MDD and 93 healthy controls from the Canadian Biomarker Integration Network in Depression (CAN-BIND, https://www.canbind.ca/). Specifically, we divided the unilateral insula into three subregions, and investigated their coupling with whole-brain GMV-based structural brain networks (SBNs). We compared between-group difference of the structural coupling patterns between the insular subregions and SBNs.

RESULTS

The insula was divided into three subregions, including an anterior one, a superior-posterior one and an inferior-posterior one. In the comparison between MDD patients and controls we found that patients' right anterior insula showed increased inter-network coupling with the default mode network, and it showed decreased inter-network coupling with the central executive network; whereas patients' right ventral-posterior insula showed decreased inter-network coupling with the default mode network, and it showed increased inter-network coupling with the central executive network. We also demonstrated that patients' loading parameters of the right ventral-posterior insular structural covariance negatively correlated with their suicidal ideation scores; and controls' loading parameters of the right ventral-posterior insular structural covariance positively correlated with their motor and psychomotor speed scores, whereas these phenomena were not found in patients. Additionally, we did not find significant inter-network coupling between the whole-brain SBNs, including salience network, default mode network, and central executive network.

CONCLUSIONS

Our work proposed a novel technique to investigate the structural covariance coupling between large-scale structural covariance networks, and provided further evidence that MDD is a system-level disorder that shows disrupted structural coupling between brain networks.

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

BACKGROUND

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

METHODS

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

RESULTS

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

CONCLUSIONS

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Magnetic Resonance Imaging Sequence Identification Using a Metadata Learning Approach

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration (Brain-CODE) data platform, each involving MRI data from multiple research institutes, are used to build and test our model. The preliminary results show that a random forest model can be trained to accurately identify MRI sequence types, and to recognize MRI scans that do not belong to any of the known sequence types. Therefore the proposed approach can be used to automate processing of MRI data that involves a large number of variations in sequence names, and to help standardize sequence naming in ongoing data collections. This study highlights the potential of the machine learning approaches in helping manage health data.

Interactions between neuroticism and stressful life events predict response to pharmacotherapy for major depression: A CAN-BIND 1 report

Exposure to stressful life events and individual differences in the personality trait neuroticism are important risk factors that interact to predict major depressive disorder (MDD). Less is known about their effect on treatment response in depression. Here, we examine whether stressful life events experienced prior to and during treatment interact with neuroticism to predict response to 16-week pharmacotherapy for MDD. Participants included 159 outpatients with MDD who were initially treated with 8 weeks of escitalopram. Those who responded to the initial treatment continued on escitalopram monotherapy, whereas non-responders received 8 weeks of adjunctive aripiprazole. Personality was assessed using the NEO-Five Factor Inventory, and stressful life events were assessed using the Life Events and Difficulties Schedule, a rigorous contextual interview that includes independent ratings of threatening life events. High baseline neuroticism was associated with a lower likelihood of response when patients experienced one or more negative life events before treatment. Secondary analyses indicated that this effect was specific to neuroticism, and not better accounted for by its self-criticism or negative affect facets. Our results suggest that assessing personality and stressful life events at baseline can help clinicians assess which patients will respond to antidepressant therapy and which may need treatment augmentation.

Accelerated brain aging in major depressive disorder and antidepressant treatment response: A CAN-BIND report

OBJECTIVES

Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response.

METHODS

Individuals in a major depressive episode received escitalopram treatment (10-20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2-10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data.

RESULTS

Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD.

CONCLUSION

We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.