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In many research settings, the intervention implied by the average causal effect of a time-varying exposure is impractical or unrealistic, and we might instead prefer a more realistic target estimand. Instead of requiring all individuals to be always exposed versus unexposed, incremental effects quantify the impact of merely shifting each individual's probability of being exposed.We demonstrate the estimation of incremental effects in the time-varying setting, using data from the Effects of Aspirin in Gestation and Reproduction trial, which assessed the effect of preconception low-dose aspirin on pregnancy outcomes. Compliance to aspirin or placebo was summarized weekly and was affected by time-varying confounders such as bleeding or nausea. We sought to estimate what the incidence of pregnancy by 26 weeks postrandomization would have been if we shifted each participant's probability of taking aspirin or placebo each week by odds ratios (OR) between 0.30 and 3.00.Under no intervention (OR = 1), the incidence of pregnancy was 77% (95% CI: 74%, 80%). Decreasing women's probability of complying with aspirin had little estimated effect on pregnancy incidence. When we increased women's probability of taking aspirin, estimated incidence of pregnancy increased, from 83% (95% confidence interval [CI] = 79%, 87%) for OR = 2 to 89% (95% CI = 84%, 93%) for OR=3. We observed similar results when we shifted women's probability of complying with a placebo.These results estimated that realistic interventions to increase women's probability of taking aspirin would have yielded little to no impact on the incidence of pregnancy, relative to similar interventions on placebo.

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.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.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.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.

Objectives: Because health care personnel (HCP) are potentially at increased risk of contracting COVID-19, high vaccination rates in this population are essential. The objective of this study was to assess vaccination status, barriers to vaccination, reasons for vaccine acceptance, and concerns about COVID-19 vaccination among HCP. Methods: We conducted an anonymous online survey at a large US health care system from April 9 through May 4, 2021, to assess COVID-19 vaccination status and endorsement of reasons for acceptance and concerns related to vaccination (based on selections from a provided list). Results: A total of 4603 HCP (12.2% response rate) completed the survey, 3947 (85.7%) had received at least 1 dose of a COVID-19 vaccine at the time of the survey, and 550 (11.9%) reported no plans to receive the vaccine. Unvaccinated HCP were 30 times more likely than vaccinated HCP to endorse religious or personal beliefs as a vaccine concern (odds ratio = 30.95; 95% CI, 21.06-45.48) and 15 times more likely to believe that personal vaccination is not needed if enough others are vaccinated (odds ratio = 14.99; 95% CI, 10.84-20.72). The more reasons endorsed for vaccination (ß = 0.60; P < .001), the higher the likelihood of having received the vaccine. However, the number of concerns about COVID-19 vaccine was not related to vaccination status (ß = 1.01; P = .64). Conclusions: Our findings suggest that reasons for vaccination acceptance and concerns about vaccination need to be considered to better understand behavioral choices related to COVID-19 vaccination among HCP, because these beliefs may affect vaccination advocacy, responses to vaccine mandates, and promotion of COVID-19 vaccine boosters.

In treatment studies of major depressive disorder (MDD), exposure to major life events predicts less symptom improvement and greater likelihood of relapse. In contrast, the impact of minor life events has received less attention. We hypothesized that the impact of minor events on symptom improvement and risk of relapse would be heightened in the presence of concurrent chronic stress. We also hypothesized that major events would predict less symptom improvement and greater risk of relapse independently of chronic stress.Adult patients experiencing an episode of MDD were enrolled into a 16-week trial with antidepressant treatments (n = 156). Forty-three fully remitted patients agreed to participate in a naturalistic 18-month follow-up, and 30 had full data for analyses. Life events and chronic stressors were assessed using a contextual life stress interview.Greater exposure to minor events predicted greater improvement in symptoms during acute treatment, but this relation was specific to those who reported greater severity of chronic stress. During follow-up, however, major life events predicted increased risk of relapse, and this effect was not moderated by chronic stress.High attrition rates led to a small sample size for the follow-up analyses.Exposure to minor events may provide an opportunity to practice problem-solving skills, thereby facilitating symptom improvement. Nevertheless, acute treatment did not protect patients from relapse when they subsequently faced major events during follow-up. Therefore, adjunctive strategies may be needed to enhance outcomes during pharmacotherapy, consolidating benefits from acute treatment and providing skills to prevent relapse.

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.

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

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.

Background Major Depressive Disorder (MDD) is associated with interoceptive deficits expressed throughout the body, particularly the facial musculature. According to the facial feedback hypothesis, afferent feedback from the facial muscles suffices to alter the emotional experience. Thus, manipulating the facial muscles could provide a new “mind-body” intervention for MDD. This article provides a conceptual overview of functional electrical stimulation (FES), a novel neuromodulation-based treatment modality that can be potentially used in the treatment of disorders of disrupted brain connectivity, such as MDD. Methods A focused literature search was performed for clinical studies of FES as a modulatory treatment for mood symptoms. The literature is reviewed in a narrative format, integrating theories of emotion, facial expression, and MDD. Results A rich body of literature on FES supports the notion that peripheral muscle manipulation in patients with stroke or spinal cord injury may enhance central neuroplasticity, restoring lost sensorimotor function. These neuroplastic effects suggest that FES may be a promising innovative intervention for psychiatric disorders of disrupted brain connectivity, such as MDD. Recent pilot data on repetitive FES applied to the facial muscles in healthy participants and patients with MDD show early promise, suggesting that FES may attenuate the negative interoceptive bias associated with MDD by enhancing positive facial feedback. Neurobiologically, the amygdala and nodes of the emotion-to-motor transformation loop may serve as potential neural targets for facial FES in MDD, as they integrate proprioceptive and interoceptive inputs from muscles of facial expression and fine-tune their motor output in line with socio-emotional context. Conclusions Manipulating facial muscles may represent a mechanistically novel treatment strategy for MDD and other disorders of disrupted brain connectivity that is worthy of investigation in phase II/III trials.

Background Electroconvulsive therapy (ECT) is a procedural treatment that is potentially life-saving for some patients with severe psychiatric illness. At the start of the global coronavirus disease 2019 (COVID-19) pandemic, ECT practice was remarkably disrupted, putting vulnerable individuals at increased risk of symptom exacerbation and death by suicide. This study aimed to capture the self-reported experiences of psychiatrists based at healthcare facilities across Canadian provinces who were delivering ECT treatments during the first phase of the COVID-19 pandemic (i.e., from mid-March 2020 to mid-May 2020). Methods A multidisciplinary team of experts developed a survey focusing on five domains: ECT unit operations, decision-making, hospital resources, ECT procedure, and mitigating patient impact. Responses were collected from psychiatrists providing ECT at 67 ECT centres in Canada, grouped by four geographical regions (Ontario, Quebec, Atlantic Canada, and Western Canada). Results Clinical operations of ECT programs were disrupted across all four regions – however, centres in Atlantic Canada were able to best preserve outpatient and maintenance care, while centres in Western Canada were able to best preserve inpatient and acute care. Similarly, Atlantic and Western Canada demonstrated the best decision-making practices of involving the ECT team and clinical ethicists in the development of pandemic-related guidelines. Across all four regions, ECT practice was affected by the redeployment of professionals, the shortage of personal protective equipment, and the need to enforce social distancing. Attempts to introduce modifications to the ECT delivery room and minimize bag-valve-mask ventilation were consistently reported. All four regions developed a new patient prioritization framework, and Western Canada, notably, aimed to provide ECT to only the most severe cases. Conclusions The results suggest that ECT provision was disproportionately affected across different parts of Canada. Possible factors that could explain these interregional differences include population, distribution of urban vs. rural areas, pre-pandemic barriers in access to ECT, number of cases, ability to control the spread of infection, and the general reduction in physicians’ activities across different areas of health care. Studying these factors in the future will inform how medical centres should respond to public health emergencies and pandemic-related circumstances in the context of procedural treatments.

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

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.

Publicly available and validated DNA reference sequences useful for phylogeny estimation and identification of fungal pathogens are an increasingly important resource in the efforts of plant protection organizations to facilitate safe international trade of agricultural commodities. Colletotrichum species are among the most frequently encountered and regulated plant pathogens at U.S. ports-of-entry. The RefSeq Targeted Loci (RTL) project at NCBI (BioProject no. PRJNA177353) contains a database of curated fungal internal transcribed spacer (ITS) sequences that interact extensively with NCBI Taxonomy, resulting in verified name−strain−sequence type associations for >12,000 species. We present a publicly available dataset of verified and curated name−type strain−sequence associations for all available Colletotrichum species. This includes an updated GenBank Taxonomy for 238 species associated with up to 11 protein coding loci and an updated RTL ITS dataset for 226 species. We demonstrate that several marker loci are well suited for phylogenetic inference and identification. We improve understanding of phylogenetic relationships among verified species, verify or improve phylogenetic circumscriptions of 14 species complexes, and reveal that determining relationships among these major clades will require additional data. We present detailed comparisons between phylogenetic and similarity-based approaches to species identification, revealing complex patterns among single marker loci that often lead to misidentification when based on single-locus similarity approaches. We also demonstrate that species-level identification is elusive for a subset of samples regardless of analytical approach, which may be explained by novel species diversity in our dataset and incomplete lineage sorting and lack of accumulated synapomorphies at these loci.

To systematically review published research studies and ongoing clinical trials investigating nitrous oxide (NA comprehensive literature search was conducted for studies published until June 2021 using the OVID databases (MEDLINE, Embase, APA PsycInfo) and the clinical trial registries (ClinicalTrials.gov, ICTRP).In total, five relevant published articles were identified, among which four investigated NPreliminary studies support the feasibility of administering N

Youth at clinical high risk (CHR) for psychosis can present not only with characteristic attenuated psychotic symptoms but also may have other comorbid conditions, including anxiety and depression. These undifferentiated mood symptoms can overlap with the clinical presentation of youth with Distress syndromes. Increased resting-state functional connectivity within cerebello-thalamo-cortical (CTC) pathways has been proposed as a trait-specific biomarker for CHR. However, it is unclear whether this functional neural signature remains specific when compared to a different risk group: youth with Distress syndromes. The purpose of the present work was to describe CTC alterations that distinguish between CHR and Distressed individuals. Using machine learning algorithms, we analyzed CTC connectivity features of CHR (n = 51), Distressed (n = 41), and healthy control (n = 36) participants. We found four cerebellar (lobes VII and left Crus II anterior/posterior) and two basal ganglia (right putamen and right thalamus) nodes containing a set of specific connectivity features that distinguished between CHR, Distressed and healthy control groups. Hyperconnectivity between medial lobule VIIb, somatomotor network and middle temporal gyrus was associated with CHR status and more severe symptoms. Detailed atlas parcellation suggested that CHR individuals may have dysfunction mainly within the associative (cognitive) pathways, particularly, between those brain areas responsible for the multi-sensory signal integration.

Neuroimaging studies have demonstrated aberrant structure and function of the "cognitive-affective cerebellum" in major depressive disorder (MDD), although the specific role of the cerebello-cerebral circuitry in this population remains largely uninvestigated. The objective of this study was to delineate the role of cerebellar functional networks in depression. A total of 308 unmedicated participants completed resting-state functional magnetic resonance imaging scans, of which 247 (148 MDD; 99 healthy controls, HC) were suitable for this study. Seed-based resting-state functional connectivity (RsFc) analysis was performed using three cerebellar regions of interest (ROIs): ROI

Causal effects are often characterized with averages, which can give an incomplete picture of the underlying counterfactual distributions. Here we consider estimating the entire counterfactual density and generic functionals thereof. We focus on two kinds of target parameters: density approximations and the distance between counterfactual densities. We study nonparametric efficiency bounds, giving results for smooth but otherwise generic models and distances. Importantly, we show how these bounds connect to means of particular nontrivial functions of counterfactuals, linking the problems of density and mean estimation. We propose doubly robust-style estimators, and study their rates of convergence, showing that they can be optimally efficient in large nonparametric models. We also give analogous methods for model selection and aggregation, when many models may be available and of interest. Our results all hold for generic models and distances, but we highlight results for L2 projections on linear models and KL projections on exponential families. Finally, we illustrate our method by estimating the density of CD4 count among patients with HIV, had all been treated with combination therapy versus zidovudine alone, as well as a density effect. Our methods are implemented in the R package npcausal on GitHub.

International audience; 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 N 6 -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.

Background Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. Methods We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. Results We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. Conclusion We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project.

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.

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]; pcorr

Americans are politically polarized in their views on environmental protection, and scholars have identified structural and cultural drivers of this polarity. Missing from these theories is a consideration of the emotional dynamics at play in environmentally relevant interactions between liberals and conservatives. Based on analyses of in-depth interviews conducted with 63 politically and socioeconomically diverse residents of four communities in Washington State, we find evidence of important common ground across the political spectrum. Our participants voice support and respect for environmental protection and convey a shared image of an ideal environmentalist: a conscious, caring, and committed individual who seeks to reduce their personal environmental impact. We see political differences arise when our participants evaluate their own relationship with the environment against this ideal environmentalist. Liberals are more likely to align with or admire the ideal environmentalist and conservatives are more likely to challenge or denigrate the ideal. Emotions and competing claims for moral worth, we suggest, play a role in making these political differences polarizing.

Objectives The 2018 Canadian Network for Mood and Anxiety Treatments (CANMAT) and International Society for Bipolar Disorders (ISBD) guidelines provided clinicians with pragmatic treatment recommendations for bipolar disorder (BD). While these guidelines included commentary on how mixed features may direct treatment selection, specific recommendations were not provided- a critical gap which the current update aims to address. Method Overview of research regarding mixed presentations in BD, with treatment recommendations developed using a modified CANMAT/ISBD rating methodology. Limitations are discussed, including the dearth of high-quality data and reliance on expert opinion. Results No agents met threshold for first line treatment of DSM-5 manic or depressive episodes with mixed features. For mania + mixed features second line treatment options include asenapine, cariprazine, divalproex, and aripiprazole. In depression + mixed features, cariprazine and lurasidone are recommended as second line options. For DSM-IV defined mixed episodes, with a longer history of research, asenapine and aripiprazole are first line, and olanzapine (monotherapy or combination), carbamazepine, and divalproex are second line. Research on maintenance treatments following a DSM-5 mixed presentation is extremely limited, with third-line recommendations based on expert opinion. For maintenance treatment following a DSM-IV mixed episode, quetiapine (monotherapy or combination) is first line, and lithium and olanzapine identified as second line options. Conclusion The CANMAT and ISBD groups hope these guidelines provide valuable support for clinicians providing care to patients experiencing mixed presentations, as well as further influence investment in research to improve diagnosis and treatment of this common and complex clinical state.

BackgroundMultiple 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.MethodsOf 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.ResultsEight 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.ConclusionsThe 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.

Introduction Cognitive behavioural therapy (CBT) is an established first-line treatment for depression; however, it remains unclear which factors predict a positive outcome with this approach. Prior work suggests that co-morbid obesity predicts a poorer response to antidepressant medication. The current study examined whether there is an association between weight parameters and improvement of depressive symptoms with CBT. Methods This was a secondary analysis of data from the “Clinical and Biological Markers of Response to Cognitive Behavioural Therapy for Depression – 6” (CANBIND-6; https://clinicaltrials.gov/ct2/show/NCT02883257 ) study. Adult participants (n = 41) with a diagnosis of Major Depressive Disorder (MDD) or Persistent Depressive Disorder (PDD) were recruited from an outpatient tertiary psychiatric centre in Canada. Participants completed 20 individual sessions of CBT over 16 weeks. The primary measure for treatment outcome was the Montgomery–Asberg Depression Rating Scale (MADRS) total score at week 16. Results Thirty-seven participants completed assessments pre and post CBT. Baseline weight parameters were not correlated with treatment response to CBT in the entire group. There was a significant sex*waist circumference (WC) (B:-1.34; p = 0.004) and sex*body mass index (BMI) interaction (B:-2.03; p:0.009). In female participants, baseline waist circumference, but not BMI, significantly predicted week 16 MADRS after controlling for age and baseline MADRS (B:0.422 p:0.049). Limitations The major limitation of our preliminary finding is the small sample size. Conclusion Our preliminary findings suggest that higher waist circumference may be associated with a better treatment response to CBT for depression in females. This result could be of clinical relevance and warrants further investigation in larger and independent samples.

Quality control of morphometric neuroimaging data is essential to improve reproducibility. Owing to the complexity of neuroimaging data and subsequently the interpretation of their results, visual inspection by trained raters is the most reliable way to perform quality control. Here, we present a protocol for visual quality control of the anatomical accuracy of FreeSurfer parcellations, based on an easy-to-use open-source tool called VisualQC. We comprehensively evaluate its utility in terms of error detection rate and inter-rater reliability on two large multi-site datasets and discuss site differences in error patterns. This evaluation shows that VisualQC is a practically viable protocol for community adoption.

Longitudinal observational data pose a challenge for causal inference when the exposure of interest varies over time alongside time-dependent confounders, which often occurs in trauma research. We describe marginal structural models (MSMs) using inverse probability weighting as a useful solution under several assumptions that are well-suited to estimating causal effects in trauma research.We illustrate the application of MSMs by estimating the joint effects of community violence exposure across time on youths' internalizing and externalizing symptoms. Our sample included 4,327 youth (50% female, 50% male; 1.4% Asian American or Pacific Islander, 34.7% Black, 46.9% Hispanic, .8% Native American, 14.3%, White, 1.5%, Other race/ethnicity;Wave 3 internalizing symptoms increased linearly with increases in Wave 2 and Wave 3 community violence exposure, whereas effects on externalizing symptoms were quadratic for Wave 2 community violence exposure and linear for Wave 3. These results fail to provide support for the desensitization model of community violence exposure.MSMs are a useful tool for researchers who rely on longitudinal observational data to estimate causal effects of time-varying exposures, as is often the case in the study of psychological trauma. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

OBJECTIVE To evaluate whether accelerated brain aging occurs in individuals with mood or psychotic disorders. METHODS A systematic review following PRISMA guidelines was conducted. A meta-analysis was then performed to assess neuroimaging-derived brain age gap in three independent groups: (1) schizophrenia and first-episode psychosis, (2) major depressive disorder, and (3) bipolar disorder. RESULTS A total of 18 papers were included. The random-effects model meta-analysis showed a significantly increased neuroimaging-derived brain age gap relative to age-matched controls for the three major psychiatric disorders, with schizophrenia (3.08; 95%CI [2.32; 3.85]; p

Objectives The COVID-19 pandemic has disrupted the provision of essential and potentially life-saving procedural treatments such as electroconvulsive therapy (ECT). We surveyed ECT providers across Canada to understand how the first wave of the pandemic affected ECT delivery between mid-March 2020 and mid-May 2020. Methods The survey was administered to ECT team members and decision makers at 107 Canadian health care centers with a focus on 5 domains: operations, decision-making, hospital resources, ECT procedure, and patient impact. Responses were obtained from 72 institutions, and collected answers were used to derive representative responses reflecting the situation at each ECT center. For specific domains, responses were split into 2 databases representing the perspective of psychiatrists (n = 67 centers) and anesthesiologists (n = 24 centers). Results Provision of ECT decreased in 64% centers and was completely suspended in 27% of centers after the onset of the pandemic. Outpatient and maintenance ECT were more affected than inpatient and acute ECT. Programs reported a high level of collaboration between psychiatry and hospital leadership (59%) but a limited input from clinical ethicists (18%). Decisions were mostly made ad hoc leading to variability across institutions in adopted resource allocation, physical location of ECT delivery, and triaging frameworks. The majority of centers considered ECT to be aerosol-generating and incorporated changes to airway management. Conclusions Electroconvulsive therapy services in Canada were markedly disrupted by the COVID-19 pandemic. The variability in decision-making across centers warrants the development of a rational approach toward offering ECT in pandemic contexts.

Previous work has demonstrated that microRNAs (miRNAs) change as a function of antidepressant treatment (ADT) response. However, it is unclear how representative these peripherally detected miRNA changes are to those occurring in the brain. This study aimed to use peripherally extracted neuron-derived extracellular vesicles (NDEV) to circumvent these limitations and investigate neuronal miRNA changes associated with antidepressant response. Samples were collected at two time points (baseline and after 8 weeks of follow-up) from depressed patients who responded (N = 20) and did not respond (N = 20) to escitalopram treatment, as well as controls (N = 20). Total extracellular vesicles (EVs) were extracted from plasma, and then further enriched for NDEV by immunoprecipitation with L1CAM. EVs and NDEVs were characterized, and NDEV miRNA cargo was extracted and sequenced. Subsequently, studies in cell lines and postmortem tissue were conducted. Characterization of NDEVs revealed that they were smaller than other EVs isolated from plasma (p

An increasing number of recent studies have suggested that doubly robust estimators with cross-fitting should be used when estimating causal effects with machine learning methods. However, not all existing programs that implement doubly robust estimators support machine learning methods and cross-fitting, or provide estimates on multiplicative scales. To address these needs, we developed AIPW, a software package implementing augmented inverse probability weighting (AIPW) estimation of average causal effects in R (R Foundation for Statistical Computing, Vienna, Austria). Key features of the AIPW package include cross-fitting and flexible covariate adjustment for observational studies and randomized controlled trials (RCTs). In this paper, we use a simulated RCT to illustrate implementation of the AIPW estimator. We also perform a simulation study to evaluate the performance of the AIPW package compared with other doubly robust implementations, including CausalGAM, npcausal, tmle, and tmle3. Our simulation showed that the AIPW package yields performance comparable to that of other programs. Furthermore, we also found that cross-fitting substantively decreases the bias and improves the confidence interval coverage for doubly robust estimators fitted with machine learning algorithms. Our findings suggest that the AIPW package can be a useful tool for estimating average causal effects with machine learning methods in RCTs and observational studies.

Cytochrome P450 drug-metabolizing enzymes may contribute to interindividual differences in antidepressant outcomes. We investigated the effects of CYP2C19 and CYP2D6 gene variants on response, tolerability, and serum concentrations. Patients (N = 178) were treated with escitalopram (ESC) from weeks 0–8 (Phase I), and at week 8, either continued ESC if they were responders or were augmented with aripiprazole (ARI) if they were non-responders (CYP2C19 intermediate and poor metabolizers (IM + PMs), with reduced or null enzyme function, trended towards significantly lower symptom improvement during Phase II compared to normal metabolizers (NMs), which was not observed in ESC + ARI. We further showed that CYP2D6 NMs and IM + PMs had a higher likelihood of reporting a treatment-related central nervous system side effect in ESC-Only and ESC + ARI, respectively. The differences in the findings between ESC-Only and ESC + ARI may be due to the altered pharmacokinetics of ESC by ARI coadministration in ESC + ARI. We provided evidence for this postulation when we showed that in ESC-Only, CYP2C19 and CYP2D6 IM + PMs demonstrated significantly higher ESC concentrations at Weeks 10 and 16 compared to NMs. In contrast, ESC + ARI showed an association with CYP2C19 but not with CYP2D6 metabolizer group. Instead, ESC + ARI showed an association between CYP2D6 metabolizer group and ARI metabolite-to-drug ratio suggesting potential competition between ESC and ARI for CYP2D6. Our findings suggest that dosing based on CYP2C19 and CYP2D6 genotyping could improve safety and outcome in patients on ESC monotherapy.

Caring Contacts are an emerging intervention that aims to reduce distress and suicide risk after acute psychiatric care. This trial aimed to determine whether, during a pandemic, there was any evidence that the mental health benefits and reduction in suicidal ideation (SI) associated with delivering Caring Contacts to recently discharged psychiatric patients were greater than a control communication. The secondary objective was to identify whether the predicted benefits were greater among people living alone or those diagnosed with depression.A single-site pilot randomized clinical trial (Both groups experienced a significant worsening of mental health symptoms at all time points post-discharge relative to baseline. There were no significant differences between groups at any time point, however, on day 4 there was a 24.2% and 72.6% attenuated worsening in the Caring Contact group compared to the control group for total symptom severity and SI, respectively. There was no significant interaction effect for the depression subgroup or those living alone.While this pilot study was not powered to identify significant differences between groups, results are indicative of feasibility and acceptability of the intervention and provide some indication that Caring Contacts may have benefited patients in the days following discharge, supporting the need for larger-scale trials. The study was registered with clinicaltrials.gov (study ID NCT04456062).Les contacts attentionnés sont une nouvelle intervention qui vise à réduire le risque de détresse et de suicide après des soins intensifs psychiatriques. Le présent essai visait à déterminer si, durant une pandémie, des données probantes indiquaient que les avantages pour la santé mentale et la réduction de l'idéation suicidaire (IS) associés à la prestation de soins attentionnés à des patients psychiatriques récemment mis en congé étaient plus puissants que le contrôle des communications. L'objectif secondaire était d'identifier si les avantages escomptés étaient plus grands chez les personnes vivant seules ou chez celles où la dépression était diagnostiquée.Un essai clinique randomisé (n = 100) d'un seul site, dont les patients étaient recrutés à l'Unité de psychiatrie pour patients hospitalisés adultes du Centre des sciences de la santé Sunnybrook de Toronto, Canada, entre août 2020 et mai 2021. Les participants ont été randomisés (1:1) aux Contacts attentionnés ou au groupe témoin. Les participants ont reçu trois communications des Contacts attentionnés ou des témoins par courriel ou courrier (aux jours 4, 21, 56 après-congé). Les symptômes de santé mentale ont été évalués à l'aide des scores auto-déclarés à la liste de contrôle des symptômes de Hopkins-25 (HSCL-25) au congé (à la base) et quand les participants ont reçu chaque communication. ANOVA a été utilisée pour les comparaisons primaires, et les analyses exploratoires pour les sous-groupes.Les deux groupes ont connu une aggravation significative des symptômes de santé mentale à différents moments après le congé relativement à la base. Il n'y avait pas de différences significatives entre les groupes à tout moment, cependant au jour 4 il y avait une aggravation atténuée de 24,2% et de 72,6% dans le groupe des Contacts attentionnés comparé au groupe témoin pour la gravité totale des symptômes et les IS respectivement. Il n'y avait pas d'effet d'interaction significatif pour le sous-groupe de la dépression ou de ceux vivant seuls.Bien que cette étude pilote n'ait pas été mandatée pour identifier les différences significatives entre les groupes, les résultats indiquent la faisabilité et l'acceptabilité de l'intervention et offrent certaines indications à l'effet que Contacts attentionnés puissent avoir été bénéfiques pour les patients dans les jours suivant le congé, appuyant ainsi le besoin d'essais à plus grande échelle. L'étude a été enregistrée à clinicaltrials.gov (ID de l'étude NCT04456062).

Lactation is critical to infant short-term and long-term health and protects mothers from breast cancer, ovarian cancer and type 2 diabetes mellitus. The mammary gland is a dynamic organ, regulated by the coordinated actions of reproductive and metabolic hormones. These hormones promote gland development from puberty onwards and induce the formation of a branched, epithelial, milk-secreting organ by the end of pregnancy. Progesterone withdrawal following placental delivery initiates lactation, which is maintained by increased pituitary secretion of prolactin and oxytocin, and stimulated by infant suckling. After weaning, local cytokine production and decreased prolactin secretion trigger large-scale mammary cell loss, leading to gland involution. Here, we review advances in the molecular endocrinology of mammary gland development and milk synthesis. We discuss the hormonal functions of the mammary gland, including parathyroid hormone-related peptide secretion that stimulates maternal calcium mobilization for milk synthesis. We also consider the hormonal composition of human milk and its associated effects on infant health and development. Finally, we highlight endocrine and metabolic diseases that cause lactation insufficiency, for example, monogenic disorders of prolactin and prolactin receptor mutations, maternal obesity and diabetes mellitus, interventions during labour and delivery, and exposure to endocrine-disrupting chemicals such as polyfluoroalkyl substances in consumer products and other oestrogenic compounds.