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Abstract Current pharmacological agents for depression have limited efficacy in achieving remission. Developing and validating new medications is challenging due to limited biological targets. This study aimed to link electrophysiological data and symptom improvement to better understand mechanisms underlying treatment response. Longitudinal changes in neural oscillations were assessed using resting-state electroencephalography (EEG) data from two Canadian Biomarker Integration Network in Depression studies, involving pharmacological and cognitive behavioral therapy (CBT) trials. Patients in the pharmacological trial received eight weeks of escitalopram, with treatment response defined as ≥ 50% decrease in Montgomery–Åsberg Depression Rating Scale (MADRS). Early (baseline to week 2) and late (baseline to week 8) changes in neural oscillation were investigated using relative power spectral measures. An association was found between an initial increase in theta and symptom improvement after 2 weeks. Additionally, late increases in delta and theta, along with a decrease in alpha, were linked to a reduction in MADRS after 8 weeks. These late changes were specifically observed in responders. To assess specificity, we extended our analysis to the independent CBT cohort. Responders exhibited an increase in delta and a decrease in alpha after 2 weeks. Furthermore, a late (baseline to week 16) decrease in alpha was associated with symptom improvement following CBT. Results suggest a common late decrease in alpha across both treatments, while modulatory effects in theta may be specific to escitalopram treatment. This study offers insights into electrophysiological markers indicating a favorable response to antidepressants, enhancing our comprehension of treatment response mechanisms in depression.

Abstract Evidence from preclinical animal models suggests that the stress-buffering function of the endocannabinoid (eCB) system may help protect against stress-related reductions in hippocampal volume, as is documented in major depressive disorder (MDD). However, stress exposure may also lead to dysregulation of this system. Thus, pathways from marked stress histories, such as childhood maltreatment (CM), to smaller hippocampal volumes and MDD in humans may depend on dysregulated versus intact eCB functioning. We examined whether the relation between MDD and peripheral eCB concentrations would vary as a function of CM history. Further, we examined whether eCBs moderate the relation of CM/MDD and hippocampal volume. Ninety-one adults with MDD and 62 healthy comparison participants (HCs) were recruited for a study from the Canadian Biomarker Integration Network in Depression program (CAN-BIND-04). The eCBs, anandamide (AEA) and 2-arachidonylglycerol (2-AG), were assessed from blood plasma. Severe CM history was assessed retrospectively via contextual interview. MDD was associated with eCBs, though not all associations were moderated by CM or in the direction expected. Specifically, MDD was associated with higher AEA compared to HCs regardless of CM history, a difference that could be attributed to psychotropic medications. MDD was also associated with higher 2-AG, but only for participants with CM. Consistent with hypotheses, we found lower left hippocampal volume in participants with versus without CM, but only for those with lower AEA, and not moderate or high AEA. Our study presents the first evidence in humans implicating eCBs in stress-related mechanisms involving reduced hippocampal volume in MDD.

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.

BackgroundTranscranial temporal interference stimulation (tTIS) is a new, emerging neurostimulation technology that utilizes two or more electric fields at specific frequencies to modulate the oscillations of neurons at a desired spatial location in the brain. The physics of tTIS offers the advantage of modulating deep brain structures in a non-invasive fashion and with minimal stimulation of the overlying cortex outside of a selected target. As such, tTIS can be effectively employed in the context of therapeutics for the psychiatric disease of disrupted brain connectivity, such as major depressive disorder (MDD). The subgenual anterior cingulate cortex (sgACC), a key brain center that regulates human emotions and influences negative emotional states, is a plausible target for tTIS in MDD based on reports of its successful neuromodulation with invasive deep brain stimulation.MethodsThis pilot, single-site, double-blind, randomized, sham-controlled interventional clinical trial will be conducted at St. Michael’s Hospital – Unity Health Toronto in Toronto, ON, Canada. The primary objective is to demonstrate target engagement of the sgACC with 130 Hz tTIS using resting-state magnetic resonance imaging (MRI) techniques. The secondary objective is to estimate the therapeutic potential of tTIS for MDD by evaluating the change in clinical characteristics of participants and electrophysiological outcomes and providing feasibility and tolerability estimates for a large-scale efficacy trial. Thirty participants (18–65 years) with unipolar, non-psychotic MDD will be recruited and randomized to receive 10 sessions of 130 Hz tTIS or sham stimulation (n = 15 per arm). The trial includes a pre- vs. post-treatment 3T MRI scan of the brain, clinical evaluation, and electroencephalography (EEG) acquisition at rest and during the auditory mismatch negativity (MMN) paradigm.DiscussionThis study is one of the first-ever clinical trials among patients with psychiatric disorders examining the therapeutic potential of repetitive tTIS and its neurobiological mechanisms. Data obtained from this trial will be used to optimize the tTIS approach and design a large-scale efficacy trial. Research in this area has the potential to provide a novel treatment option for individuals with MDD and circuitry-related disorders and may contribute to the process of obtaining regulatory approval for therapeutic applications of tTIS.Clinical Trial RegistrationClinicalTrials.gov, identifier NCT05295888.

Abstract Major depressive disorder (MDD) is a chronic illness wherein relapses contribute to significant patient morbidity and mortality. Near-term prediction of relapses in MDD patients has the potential to improve outcomes by helping implement a ‘predict and preempt’ paradigm in clinical care. In this study, we developed a novel personalized (N-of-1) encoder-decoder anomaly detection-based framework of combining anomalies in multivariate actigraphy features (passive) as triggers to utilize an active concurrent self-reported symptomatology questionnaire (core symptoms of depression and anxiety) to predict near-term relapse in MDD. The framework was evaluated on two independent longitudinal observational trials, characterized by regular bimonthly (every other month) in-person clinical assessments, weekly self-reported symptom assessments, and continuous activity monitoring data with two different wearable sensors for ≥ 1 year or until the first relapse episode. This combined passive-active relapse prediction framework achieved a balanced accuracy of ≥ 71%, false alarm rate of ≤ 2.3 alarm/patient/year with a median relapse detection time of 2–3 weeks in advance of clinical onset in both studies. The study results suggest that the proposed personalized N-of-1 prediction framework is generalizable and can help predict a majority of MDD relapses in an actionable time frame with relatively low patient and provider burden.

Abstract 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.

Background: Deep brain stimulation of the subcallosal cingulate area (SCC-DBS) is a promising neuromodulatory therapy for treatment-resistant depression (TRD). Biomarkers of optimal target engagement are needed to guide surgical targeting and stimulation parameter selection and to reduce variance in clinical outcome. Objective/Hypothesis: We aimed to characterize the relationship between stimulation location, white matter tract engagement, and clinical outcome in a large (n = 60) TRD cohort treated with SCC-DBS. A smaller cohort (n = 22) of SCC-DBS patients with differing primary indications (bipolar disorder/anorexia nervosa) was utilized as an out-of-sample validation cohort. Methods: Volumes of tissue activated (VTAs) were constructed in standard space using high-resolution structural MRI and individual stimulation parameters. VTA-based probabilistic stimulation maps (PSMs) were generated to elucidate voxelwise spatial patterns of efficacious stimulation. A whole-brain tractogram derived from Human Connectome Project diffusion-weighted MRI data was seeded with VTA pairs, and white matter streamlines whose overlap with VTAs related to outcome (‘discriminative’ streamlines; Puncorrected < 0.05) were identified using t-tests. Linear modelling was used to interrogate the potential clinical relevance of VTA overlap with specific structures. Results: PSMs varied by hemisphere: high-value left-sided voxels were located more anterosuperiorly and squarely in the lateral white matter, while the equivalent right-sided voxels fell more posteroinferiorly and involved a greater proportion of grey matter. Positive discriminative streamlines localized to the bilateral (but primarily left) cingulum bundle, forceps minor/rostrum of corpus callosum, and bilateral uncinate fasciculus. Conversely, negative discriminative streamlines mostly belonged to the right cingulum bundle and bilateral uncinate fasciculus. The best performing linear model, which utilized information about VTA volume overlap with each of the positive discriminative streamline bundles as well as the negative discriminative elements of the right cingulum bundle, explained significant variance in clinical improvement in the primary TRD cohort (R = 0.46, P

Background Identifying neuroimaging biomarkers of antidepressant response may help guide treatment decisions and advance precision medicine. Aims To examine the relationship between anhedonia and functional neurocircuitry in key reward processing brain regions in people with major depressive disorder receiving aripiprazole adjunct therapy with escitalopram. Method Data were collected as part of the CAN-BIND-1 study. Participants experiencing a current major depressive episode received escitalopram for 8 weeks; escitalopram non-responders received adjunct aripiprazole for an additional 8 weeks. Functional magnetic resonance imaging (on weeks 0 and 8) and clinical assessment of anhedonia (on weeks 0, 8 and 16) were completed. Seed-based correlational analysis was employed to examine the relationship between baseline resting-state functional connectivity (rsFC), using the nucleus accumbens (NAc) and anterior cingulate cortex (ACC) as key regions of interest, and change in anhedonia severity after adjunct aripiprazole. Results Anhedonia severity significantly improved after treatment with adjunct aripiprazole. There was a positive correlation between anhedonia improvement and rsFC between the ACC and posterior cingulate cortex, ACC and posterior praecuneus, and NAc and posterior praecuneus. There was a negative correlation between anhedonia improvement and rsFC between the ACC and anterior praecuneus and NAc and anterior praecuneus. Conclusions Eight weeks of aripiprazole, adjunct to escitalopram, was associated with improved anhedonia symptoms. Changes in functional connectivity between key reward regions were associated with anhedonia improvement, suggesting aripiprazole may be an effective treatment for individuals experiencing reward-related deficits. Future studies are required to replicate our findings and explore their generalisability, using other agents with partial dopamine (D2) agonism and/or serotonin (5-HT2A) antagonism.

IntroductionIn Canada, approximately 4,500 individuals die by suicide annually. Approximately 45% of suicide decedents had contact with their primary care provider within the month prior to their death. Current versus never smokers have an 81% increased risk of death by suicide. Those who smoke have additional risks for suicide such as depression, chronic pain, alcohol, and other substance use. They are more likely to experience adverse social determinants of health. Taken together, this suggests that smoking cessation programs in primary care could be facilitators of suicide prevention, but this has not been studied.Study objectivesThe objectives of the study are to understand barriers/facilitators to implementing a suicide prevention protocol within a smoking cessation program (STOP program), which is deployed by an academic mental health and addiction treatment hospital in primary care clinics and to develop and test implementation strategies to facilitate the uptake of suicide screening and assessment in primary care clinics across Ontario.MethodsThe study employed a three-phase sequential mixed-method design. Phase 1: Conducted interviews guided by the Consolidated Framework for Implementation Research exploring barriers to implementing a suicide prevention protocol. Phase 2: Performed consensus discussions to map barriers to implementation strategies using the Expert Recommendations for Implementing Change tool and rank barriers by relevance. Phase 3: Evaluated the feasibility and acceptability of implementation strategies using Plan Do Study Act cycles.ResultsEleven healthcare providers and four research assistants identified lack of training and the need of better educational materials as implementation barriers. Participants endorsed and tested the top three ranked implementation strategies, namely, a webinar, adding a preamble before depression survey questions, and an infographic. After participating in the webinar and reviewing the educational materials, all participants endorsed the three strategies as acceptable/very acceptable and feasible/very feasible.ConclusionAlthough there are barriers to implementing a suicide prevention protocol within primary care, it is possible to overcome them with strategies deemed both acceptable and feasible. These results offer promising practice solutions to implement a suicide prevention protocol in smoking cessation programs delivered in primary care settings. Future efforts should track implementation of these strategies and measure outcomes, including provider confidence, self-efficacy, and knowledge, and patient outcomes.

Abstract 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.

Abstract 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.

Abstract 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.

Abstract 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.

BackgroundAnhedonia is the core symptom of depression. Its presence has been linked to worsened prognosis. The Dimensional Anhedonia Rating Scale (DARS) is a scale measuring desire, motivation, effort and consummatory pleasure across different domains. The aim of this paper was to confirm factor structure, assess reliability and validity of the Polish adaptation of the DARS in a clinical sample of patients with mood disorders and healthy controls (HC).MethodsThe study sample included 161 participants aged 18–65 years - 34 HC, 72 patients with bipolar disorder and 55 with major depressive disorder (in depressive episode or remission). Reliability of the Polish adaptation of the DARS was assessed using Cronbach’s α and the average inter-item correlation (AIC). Convergent and divergent validity was established by Pearson’s correlations between the DARS and the Snaith-Hamilton Pleasure Scale (SHAPS), the Quick Inventory of Depressive Symptomatology- self-report (QIDS-SR), the Hospital Anxiety and Depression Scale (HADS). The structure of the scale was examined by factor analysis.ResultsThe factor structure was consistent with the original scale. Strong internal consistency for the DARS total score (Cronbach’s α = 0.95) and all subscales (0.86–0.93) was observed. The DARS demonstrated good convergent (moderate to strong correlations with measures of anhedonia and depression) and divergent validity (weak correlations with anxiety level).ConclusionThe Polish DARS demonstrated excellent internal consistency and very good validity. The scale is a valuable contribution to the psychometrics of anhedonia measures in patients with mood disorders.

Abstract 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.

Abstract Background Men who present to the emergency department (ED) with self-harm are at high risk of dying by suicide, with 2.7% of men dying in the year following their presentation, more than double the rate for women (1.2%). Despite this, care received after an ED visit is highly variable and many are not assessed for psychological needs. Furthermore, the limited psychological care that is available is often not covered by provincial health insurance. Even when referrals for follow-up care are made, engagement rates are low. Previous recommendations to improve engagement include written discharge plans, caring contacts, and focused interventions targeting middle-aged men at elevated risk of dying by suicide. Blended care, the incorporation of technology into traditional care, has also been proposed as a method to increase engagement in and clinical benefits from psychotherapy. This project aims to determine whether the delivery of an evidence based treatment (problem-solving therapy (PST)) is enhanced by the addition of a custom smartphone application (BEACON) compared to usual care. Due to the impact of the COVID-19 pandemic on site participation and the planned implementation, we have made several changes to the study design, primary outcome, and implementation. Method We originally proposed a cohort study nested within a larger cluster randomized trial wherein intervention sites would deliver the blended care, and control sites, whose personnel were not aware of their participation, would continue delivering usual care. The cohort study evaluated participant level outcomes as previously described by Hatcher et al. (2020). Due to pandemic-related constraints, our number of participating sites dropped to five potential sites which left the cohort study underpowered. As such, we changed the study design to a multi-site, individual randomized controlled trial (RCT) among the five remaining sites. Participants will be randomized to six sessions of therapy (PST) alone, or to the therapy plus BEACON, and followed up for 6 months. Our primary outcome was changed to evaluate feasibility and acceptability with the aim of designing a definitive RCT. Study implementation was reimagined to allow for completely virtual/online conduct to comply with local COVID-19 and institutional restrictions on in-person activities. Conclusion This updated protocol will provide strong results for the planning of a definitive RCT of the blended care intervention in the future, addressing areas of difficulty and concern prior to its implementation. We will evaluate the feasibility of the study intervention, assess recruitment and retention of participants, and address challenges with implementing the protocol. Lastly, we will evaluate the appropriateness of our primary outcome measure and accurately determine a sample size for a definitive RCT. Trial registration ClinicalTrials.gov, NCT03473535 . Registered on March 22, 2018.

Abstract Background Identifying early biomarkers of serious mental illness (SMI)—such as changes in brain structure and function—can aid in early diagnosis and treatment. Whole brain structural and functional connectomes were investigated in youth at risk for SMI. Methods Participants were classified as healthy controls (HC; n = 33), familial risk for serious mental illness (stage 0; n = 31), mild symptoms (stage 1a; n = 37), attenuated syndromes (stage 1b; n = 61), or discrete disorder (transition; n = 9) based on clinical assessments. Imaging data was collected from two sites. Graph-theory based analysis was performed on the connectivity matrix constructed from whole-brain white matter fibers derived from constrained spherical deconvolution of the diffusion tensor imaging (DTI) scans, and from the correlations between brain regions measured with resting state functional magnetic resonance imaging (fMRI) data. Results Linear mixed effects analysis and analysis of covariance revealed no significant differences between groups in global or nodal metrics after correction for multiple comparisons. A follow up machine learning analysis broadly supported the findings. Several non-overlapping frontal and temporal network differences were identified in the structural and functional connectomes before corrections. Conclusions Results suggest significant brain connectome changes in youth at transdiagnostic risk may not be evident before illness onset.

Abstract 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 (

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.

BackgroundSymptoms 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.AimTo 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).MethodsDe-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.ResultsThe 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.ConclusionThe 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.

Background: Cognitive behavioral therapy (CBT) is an efficacious treatment for depression. CBT may in part exert its therapeutic effect by strengthening inhibitory control (IC). However, the relationship between CBT and IC has yet to be thoroughly explored; in particular, it is not clear whether IC is modulated following a course of CBT. Methods: Forty-one adults with depression were recruited to undergo sixteen weeks of CBT; data from twenty-five healthy controls were used for baseline comparisons. Participants completed an affective Go/No-go task, a measure of IC, while undergoing electroencephalography recording. Electroencephalography measures of interest were the event-related potential (ERP) N2 and P3 components. Results: At baseline, individuals with depression exhibited greater ERP amplitudes during the N2 regardless of affective condition; source reconstruction attributed this to patients exhibiting greater activity in the superior frontal gyrus, right temporal lobe, and anterior cingulate cortex (ACC). Following treatment, the ERP amplitude during the N2 component decreased for angry stimuli but increased for happy stimuli. Source reconstruction attributed ERP changes in the angry-condition to the left occipital and fusiform gyrus, and changes in the happy-condition to regions including the superior frontal gyrus and ACC. Limitations: There was no placebo-treatment group. Conclusions: Neural correlates of the affective Go/No-go task were altered in depression and were found to be selectively modulated over the course of CBT in a condition- and region-specific manner. These findings support the notion that the modulation of IC may be one of the potential therapeutic mechanisms of CBT in the treatment of depression.

Background: Intermittent theta burst stimulation (iTBS) is a newer form of repetitive transcranial magnetic stimulation (rTMS) for patients with treatment resistant depression (TRD). Applying multiple daily iTBS sessions may enable patients to achieve remission more rapidly. Objective: We compared the efficacy and tolerability of a twice-daily versus once-daily iTBS protocol in patients with TRD. We hypothesized that twice-daily iTBS would result in a greater improvement in depression scores compared to once-daily iTBS. Methods: 208 participants (131 females) with TRD were randomized to receive either iTBS (600 pulses) delivered twice-daily with a 54-min interval between treatments or once-daily (1200 pulses) with 1 sham treatment with the same interval between treatments, to ensure equal levels of daily therapeutic contact and blinding of patients and raters. The primary outcome measure was change in depression scores on the Hamilton Rating Scale for Depression (HRSD-17) after 10 days of treatment and 30 days of treatments. Results: HRSD-17 scores improved in both the twice-daily and once-daily iTBS groups; however, these improvements did not significantly differ between the two groups at either the 10-day or 30-day timepoints. Response and remission rates were low (

Abstract The pathophysiology of major depressive disorder (MDD) encompasses an array of changes at molecular and neurobiological levels. As chronic stress promotes neurotoxicity there are alterations in the expression of genes and gene-regulatory molecules. The hippocampus is particularly sensitive to the effects of stress and its posterior volumes can deliver clinically valuable information about the outcomes of antidepressant treatment. In the present work, we analyzed individuals with MDD (N = 201) and healthy controls (HC = 104), as part of the CAN-BIND-1 study. We used magnetic resonance imaging (MRI) to measure hippocampal volumes, evaluated gene expression with RNA sequencing, and assessed DNA methylation with the (Infinium MethylationEpic Beadchip), in order to investigate the association between hippocampal volume and both RNA expression and DNA methylation. We identified 60 RNAs which were differentially expressed between groups. Of these, 21 displayed differential methylation, and seven displayed a correlation between methylation and expression. We found a negative association between expression of Brain Abundant Membrane Attached Signal Protein 1 antisense 1 RNA (BASP1-AS1) and right hippocampal tail volume in the MDD group (β = −0.218, p = 0.021). There was a moderating effect of the duration of the current episode on the association between the expression of BASP1-AS1 and right hippocampal tail volume in the MDD group (β = −0.48, 95% C.I. [−0.80, −0.16]. t = −2.95 p = 0.004). In conclusion, we found that overexpression of BASP1-AS1 was correlated with DNA methylation, and was negatively associated with right tail hippocampal volume in MDD.

Background: Numerous neuromodulatory therapies are currently under investigation or in clinical use for the treatment of psychiatric conditions. Objective/hypothesis: We sought to catalogue past and present human research studies on psychiatric neuromodulation and identify relevant trends in this field. Methods: ClinicalTrials.gov (https://www.clinicaltrials.gov/) and the International Clinical Trials Registry Platform (https://www.who.int/ictrp/en/) were queried in March 2020 for trials assessing the outcome of neuromodulation for psychiatric disorders. Relevant trials were categorized by variables such as neuromodulation modality, country, brain target, publication status, design, and funding source. Results: From 72,086 initial search results, 1252 unique trials were identified. The number of trials registered annually has consistently increased. Half of all trials were active and a quarter have translated to publications. The largest proportion of trials involved depression (45%), schizophrenia (18%), and substance use disorders (14%). Trials spanned 37 countries; China, the second largest contributor (13%) after the United States (28%), has increased its output substantially in recent years. Over 75% of trials involved non-convulsive non-invasive modalities (e.g., transcranial magnetic stimulation), while convulsive (e.g., electroconvulsive therapy) and invasive modalities (e.g., deep brain stimulation) were less represented. 72% of trials featured approved or cleared interventions. Characteristic inter-modality differences were observed with respect to enrollment size, trial design/phase, and funding. Dorsolateral prefrontal cortex accounted for over half of focal neuromodulation trial targets. The proportion of trials examining biological correlates of neuromodulation has increased. Conclusion(s): These results provide a comprehensive overview of the state of psychiatric neuromodulation research, revealing the growing scope and internationalism of this field.

Abstract Identifying biomarkers of antidepressant response may advance personalized treatment of major depressive disorder (MDD). We aimed to identify longitudinal changes in gene expression associated with response to antidepressants in a sample of MDD patients treated with escitalopram. Patients (N = 153) from the CAN-BIND-1 cohort were treated for 8 weeks, and depressive symptoms were assessed using the Montgomery-Åsberg Depression Rating Scale at 0, 2, 4, 6, and 8 weeks. We identified three groups of patients according to response status: early responders (22.9%), later responders (32.0%), and nonresponders (45.1%). RNA sequencing was performed in blood obtained at weeks 0, 2, and 8. RNA expression was modeled using growth models, and differences in the longitudinal changes in expression according to response were investigated using multiple regression models. The expression of RNAs related to response was investigated in the brains of depressed individuals, as well as in neuronal cells in vitro. We identified four RNAs (CERCAM, DARS-AS1, FAM228B, HBEGF) whose change over time was independently associated with a response status. For all except HBEGF, responders showed higher expression over time, compared to nonresponders. While the change in all RNAs differentiated early responders from nonresponders, changes in DARS-AS1 and HBEGF also differentiated later responders from nonresponders. Additionally, HBEGF was downregulated in the brains of depressed individuals, and increased in response to escitalopram treatment in vitro. In conclusion, using longitudinal assessments of gene expression, we provide insights into biological processes involved in the intermediate stages of escitalopram response, highlighting several genes with potential utility as biomarkers of antidepressant response.

Caspani et al. report on sex-specific plasma lipoproteins predictive of response to two commonly used antidepressants. The authors show that lipoproteins belonging to the same main class but differing in size and density exhibit distinct associations with depression and with response to pharmacotherapy, highlighting the importance of evaluating lipoprotein subclass in depression research.

Background: Although there is substantial research demonstrating the effects of duration and quality of sleep on outcomes in major depressive disorder (MDD), there is little research examining whether rhythmicity of sleep can also affect outcomes in MDD. The objective of our study was to investigate the relationship between rhythmicity of sleep and clinical outcomes in MDD Methods: The prospective study (N = 208) included MDD patients treated with escitalopram for 8-weeks. Rhythmicity of sleep was assessed with Biological Rhythms Interview for Assessment in Neuropsychiatry (BRIAN) at baseline and after 8-weeks. Depression was assessed with Montgomery-Asberg Depression Rating Scale (MADRS) and Quick Inventory of Depressive Symptomatology Self-Report (QIDS-SR) throughout 8-weeks. Outcomes were depression severity over 8-weeks and remission of depression after 8-weeks. Mixed effect models (MMRMs) and logistic regression models were conducted Results: Baseline BRIAN sleep score predicted MADRS (Adjusted B = 0.34;95%CI:0.01–0.66,p = 0.04) and QIDS-SR (Adjusted B = 0.19;95%CI:0.02–0.36,p = 0.03) scores over time in MMRMs. BRIAN falling asleep (Adjusted B = 0.44;95%CI:0.01–0.86,p = 0.04) and switching off (Adjusted B = 0.50;95%CI:0.03–0.97,p = 0.04) items predicted QIDS-SR scores over time. BRIAN sleep score predicted remission with MADRS (Adjusted OR:0.87;95%CI:0.76–0.99) and QIDS-SR (Adjusted OR:0.76;95%CI:0.64–0.90) in logistic regression models. BRIAN falling asleep item predicted remission with MADRS (Adjusted OR:0.71;95%CI:0.64–0.90) and QIDS-SR (Adjusted OR:0.62;95%CI:0.42–0.92). BRIAN switching off item predicted remission with QIDS-SR (Adjusted OR:0.62;95%CI:0.40–0.96) Limitations: Did not examine for circadian rhythm sleep-wake disorders or social conditions that causes circadian disturbances (e.g., shiftwork) Conclusions: A disturbed rhythmicity of sleep, particularly in the ability to switch off when resting and falling asleep, may increase the risk of poor clinical outcomes in MDD.

Abstract Antidepressant outcomes in older adults with depression is poor, possibly because of comorbidities such as cerebrovascular disease. Therefore, we leveraged multiple genome-wide approaches to understand the genetic architecture of antidepressant response. Our sample included 307 older adults (≥60 years) with current major depression, treated with venlafaxine extended-release for 12 weeks. A standard genome-wide association study (GWAS) was conducted for post-treatment remission status, followed by in silico biological characterization of associated genes, as well as polygenic risk scoring for depression, neurodegenerative and cerebrovascular disease. The top-associated variants for remission status and percentage symptom improvement were PIEZO1 rs12597726 (OR = 0.33 [0.21, 0.51], p = 1.42 × 10−6) and intergenic rs6916777 (Beta = 14.03 [8.47, 19.59], p = 1.25 × 10−6), respectively. Pathway analysis revealed significant contributions from genes involved in the ubiquitin-proteasome system, which regulates intracellular protein degradation with has implications for inflammation, as well as atherosclerotic cardiovascular disease (n = 25 of 190 genes, p = 8.03 × 10−6, FDR-corrected p = 0.01). Given the polygenicity of complex outcomes such as antidepressant response, we also explored 11 polygenic risk scores associated with risk for Alzheimer’s disease and stroke. Of the 11 scores, risk for cardioembolic stroke was the second-best predictor of non-remission, after being male (Accuracy = 0.70 [0.59, 0.79], Sensitivity = 0.72, Specificity = 0.67; p = 2.45 × 10−4). Although our findings did not reach genome-wide significance, they point to previously-implicated mechanisms and provide support for the roles of vascular and inflammatory pathways in LLD. Overall, significant enrichment of genes involved in protein degradation pathways that may be impaired, as well as the predictive capacity of risk for cardioembolic stroke, support a link between late-life depression remission and risk for vascular dysfunction.

Conventional monoamine-based pharmacotherapy, considered the first-line treatment for major depressive disorder (MDD), has several challenges, including high rates of non-response. To address these challenges, preclinical and clinical studies have sought to characterize antidepressant response through monoamine-independent mechanisms. One striking example is glutamate, the brain's foremost excitatory neurotransmitter: since the 1990s, studies have consistently reported altered levels of glutamate in MDD, as well as antidepressant effects following molecular targeting of glutamatergic receptors. Therapeutically, this has led to advances in the discovery, testing, and clinical application of a wide array of glutamatergic agents, particularly ketamine. Notably, ketamine has been demonstrated to rapidly improve mood symptoms, unlike monoamine-based interventions, and the neurobiological basis behind this rapid antidepressant response is under active investigation. Advances in brain imaging techniques, including functional magnetic resonance imaging, magnetic resonance spectroscopy, and positron emission tomography, enable the identification of the brain network-based characteristics distinguishing rapid glutamatergic modulation from the effect of slow-acting conventional monoamine-based pharmacology. Here, we review brain imaging studies that examine brain connectivity features associated with rapid antidepressant response in MDD patients treated with glutamatergic pharmacotherapies in contrast with patients treated with slow-acting monoamine-based treatments. Trends in recent brain imaging literature suggest that the activity of brain regions is organized into coherent functionally distinct networks, termed intrinsic connectivity networks (ICNs). We provide an overview of major ICNs implicated in depression and explore how treatment response following glutamatergic modulation alters functional connectivity of limbic, cognitive, and executive nodes within ICNs, with well-characterized anti-anhedonic effects and the enhancement of “top-down” executive control. Alterations within and between the core ICNs could potentially exert downstream effects on the nodes within other brain networks of relevance to MDD that are structurally and functionally interconnected through glutamatergic synapses. Understanding similarities and differences in brain ICNs features underlying treatment response will positively impact the trajectory and outcomes for adults suffering from MDD and will facilitate the development of biomarkers to enable glutamate-based precision therapeutics.

Abstract Objective To report side effect frequency and severity in patients with major depressive disorder (MDD) receiving escitalopram and aripiprazole adjunctive therapy and to examine whether pretreatment anxious depression is associated with the number and presence of specific side effects. Methods 188 of the 211 trial participants provided information on side effects during treatment with escitalopram (10–20 mg) for 8 weeks, and nonresponders received further augmentation on aripiprazole (2–10 mg) adjunctive therapy for another 8 weeks, whereas responders remained on escitalopram. Participants completed the Toronto Side Effects Scale at weeks 2, 4, 10, and 12. Covariate‐adjusted negative binomial regression and Wilcoxon tests examined the association between anxious depression (GAD‐7 ≥ 10) and number of side effects. Covariate‐adjusted logistic regression and chi‐square tests explored the association between anxious depression and specific side effects. Results For both therapies, the most frequent side effects were also the most severe. They mostly related to the central nervous system (CNS) (i.e., drowsiness and nervousness). Between baseline and week 2, the number of side effects participants experienced (incidence rate ratio [IRR] = 1.38, p = .010) or had trouble with (IRR = 1.34, p = .026) was significantly higher among those with anxious depression for escitalopram but not adjunctive aripiprazole. Further, odds of experiencing and having trouble with nervousness and agitation were also significantly higher in anxious depression for escitalopram only (p

Abstract Background Patients who present to emergency departments after intentional self-harm are at an increased risk of dying by suicide. This applies particularly to men, who represent nearly two-thirds of those who die by suicide in Ontario. One way of potentially addressing this gap is to offer a course of blended problem-solving therapy, comprised of a brief course of evidence-based psychotherapy for individuals at risk for suicide, facilitated by the use of a patient-facing smartphone application and a clinician-facing “dashboard.” This approach has the potential to combine the benefits of face-to-face therapy and technology to create a novel intervention. Methods This is a cohort study nested within a larger pragmatic multicentre pre- and post-design cluster randomised trial. Suicidal ideation assessed by the Beck Scale for Suicide Ideation is the primary outcome variable. Secondary outcome measures include depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder 7-item scale), post-traumatic stress disorder (Primary Care PTSD Screen), health-related quality of life (EuroQol 5-dimension 5-level questionnaire), meaning in life (Experienced Meaning in Life Scale), perceived social supports (Multidimensional Scale of Perceived Social Support), alcohol use (Alcohol Use Disorders Identification Test), drug use (Drug Abuse Screening Test Short Form 10), problem-solving skills (Social Problem-Solving Inventory–Revised Short Form), and self-reported healthcare costs, as well as health service use measured using Ontario administrative health data. A process evaluation will also be conducted following study completion. Discussion The cohort study will test whether better adherence to the intervention results in better outcomes. The value of the cohort study design is that we can examine in more detail certain subgroups or other variables that are not available in the larger cluster randomised trial. This trial will aim to improve standards by informing best practice in management of men who self-harm and present to hospitals in Ontario. Trial registration ClinicalTrials.gov , NCT03473535 . Registered on March 22, 2018.

Abstract Background Recent evidence underscores the utility of rapid-acting antidepressant interventions, such as ketamine, in alleviating symptoms of major depressive episodes (MDE). However, to date, there have been limited head-to-head comparisons of intravenous (IV) ketamine infusions with other antidepressant treatment strategies in large randomized trials. This study protocol describes an ongoing multi-centre, prospective, randomized, crossover, non-inferiority trial comparing acute treatment of individuals meeting diagnostic criteria for a major depressive episode (MDE) with ketamine and electroconvulsive therapy (ECT) on efficacy, speed of therapeutic effects, side effects, and health care resource utilization. A secondary aim is to compare a 6-month maintenance strategy for ketamine responders to standard of care ECT maintenance. Finally, through the measurement of clinical, cognitive, neuroimaging, and molecular markers we aim to establish predictors and moderators of treatment response as well as treatment-elicited effects on these outcomes. Methods Across four participating Canadian institutions, 240 patients with major depressive disorder or bipolar disorder experiencing a MDE are randomized (1:1) to a course of ECT or racemic IV ketamine (0.5 mg/kg) administered 3 times/week for 3 or 4 weeks. Non-responders (

It is not fully understood why some patients respond or do not respond to antidepressant treatment. Here the authors show that in the blood of individuals with depression, GPR56 expression increases in responders to antidepressant treatment, but not in non-responders.

Background: Dorsomedial prefrontal cortex (DMPFC) repetitive transcranial magnetic stimulation (rTMS) is a novel intervention for treatment-refractory depression (TRD). To date, many open-label case series and one randomized controlled trial of modest sample size have provided preliminary evidence that DMPFC-rTMS is an effective treatment for TRD. Here, we report the results of a large, double-blinded, sham-controlled trial of DMPFC-rTMS for TRD. Objective: The primary aim of this study was to determine the efficacy of DMPFC-rTMS for TRD under sham-controlled conditions. Methods: 120 TRD patients were randomized to receive 30 twice-daily sessions of either active high-frequency, active low-frequency, or sham DMPFC-rTMS using a novel bent active/sham double-cone coil. Placebo stimulation also involved the use of surface electrodes placed above the eyebrows. The 17-item Hamilton Rating Scale for Depression served as the primary outcome measure. Results: Although there was a significant main effect of treatment across all arms, active DMPFC-rTMS was not superior to sham. Both participants and assessors were unable to accuracy determine whether patients received active or placebo stimulation. However, technicians’ treatment arm guesses were significantly above chance. Conclusion: DMPFC rTMS did not result in improvement of depressive symptoms greater than sham stimulation. We cannot rule out that the sham apparatus may also have elicited an antidepressant effect via electrical trigeminal stimulation; future DMPFC-rTMS trials are therefore warranted.

The habenula (Hb) is a small, evolutionarily conserved epithalamic structure implicated in functions such as reward and mood regulation. Prior imaging work suggests that Hb's structural and functional properties may relate to treatment response in depression and other mood disorders. We used multimodal MRI techniques to investigate the potential involvement of Hb in response to subcallosal cingulate area deep brain stimulation (SCC-DBS) for treatment-resistant mood disorders. Using an automated segmentation technique, we compared Hb volume at baseline and at a subsequent post-operative timepoint (4.4 ± 3.0 years after surgery) in a cohort of 32 patients who received SCC-DBS. Clinical response to treatment (≥50% decrease in HAMD-17 from baseline to 12 months post-operation) was significantly associated with longitudinal Hb volume change: responders tended to have increased Hb volume over time, while non-responders showed decreased Hb volume (t = 2.4, p = 0.021). We additionally used functional MRI (fMRI) in a subcohort of SCC-DBS patients (n = 12) to investigate immediate within-patient changes in Hb functional connectivity associated with SCC-DBS stimulation. Active DBS was significantly associated with increased Hb connectivity to several prefrontal and corticolimbic regions (TFCE-adjusted pBonferroni < 0.0001), many of which have been previously implicated in the neurocircuitry of depression. Taken together, our results suggest that Hb may play an important role in the antidepressant effect of SCC-DBS.

The Ontario Brain Institute's “Brain-CODE” is a large-scale informatics platform designed to support the collection, storage and integration of diverse types of data across several brain disorders as a means to understand underlying causes of brain dysfunction and developing novel approaches to treatment. By providing access to aggregated datasets on participants with and without different brain disorders, Brain-CODE will facilitate analyses both within and across diseases and cover multiple brain disorders and a wide array of data, including clinical, neuroimaging, and molecular. To help achieve these goals, consensus methodology was used to identify a set of core demographic and clinical variables that should be routinely collected across all participating programs. Establishment of Common Data Elements within Brain-CODE is critical to enable a high degree of consistency in data collection across studies and thus optimize the ability of investigators to analyze pooled participant-level data within and across brain disorders. Results are also presented using selected common data elements pooled across three studies to better understand psychiatric comorbidity in neurological disease (Alzheimer's disease/amnesic mild cognitive impairment, amyotrophic lateral sclerosis, cerebrovascular disease, frontotemporal dementia, and Parkinson's disease).

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.

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.

Stellate ganglion block (SGB) is a procedure involving the injection of a local anesthetic surrounding the stellate ganglion to inhibit sympathetic outflow. The objective of this review was to summarize existing evidence on the use of SGB in adults with psychiatric disorders. A systematic search identified 17 published studies and 4 registered clinical trials. Eighty-eight percent of published studies, including 2 randomized controlled trials (RCTs), used SGB for posttraumatic stress disorder (PTSD), although its use for schizophrenia spectrum disorders was also explored. Administration of 1 to 2 SGBs using right-sided laterality with 0.5% ropivacaine was most common. Preliminary evidence from clinical trials and case studies supports the feasibility of SGB for treating psychiatric disorders involving dysregulation of the sympathetic nervous system, although effectiveness evidence from RCTs is mixed. One RCT concluded that improvement in PTSD symptoms was significant, while the other concluded that it was nonsignificant. Improvements were noted within 5 minutes of SGB and lasted 1 month or longer. Registered clinical trials are exploring the use of SGB in new psychiatric disorders, including major depressive disorder and borderline personality disorder. More studies with larger sample sizes and alternate protocols are needed to further explore therapeutic potential of SGB for psychiatric disorders.

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.

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

ObjectivesAntidepressants are first-line treatments for major depressive disorder (MDD), but 40-60% of patients will not respond, hence, predicting response would be a major clinical advance. Machine learning algorithms hold promise to predict treatment outcomes based on clinical symptoms and episode features. We sought to independently replicate recent machine learning methodology predicting antidepressant outcomes using the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, and then externally validate these methods to train models using data from the Canadian Biomarker Integration Network in Depression (CAN-BIND-1) dataset.MethodsWe replicated methodology from Nie et al (2018) using common algorithms based on linear regressions and decision trees to predict treatment-resistant depression (TRD, defined as failing to respond to 2 or more antidepressants) in the STAR*D dataset. We then trained and externally validated models using the clinical features found in both datasets to predict response (≥50% reduction on the Quick Inventory for Depressive Symptomatology, Self-Rated [QIDS-SR]) and remission (endpoint QIDS-SR score ≤5) in the CAN-BIND-1 dataset. We evaluated additional models to investigate how different outcomes and features may affect prediction performance.ResultsOur replicated models predicted TRD in the STAR*D dataset with slightly better balanced accuracy than Nie et al (70%-73% versus 64%-71%, respectively). Prediction performance on our external methodology validation on the CAN-BIND-1 dataset varied depending on outcome; performance was worse for response (best balanced accuracy 65%) compared to remission (77%). Using the smaller set of features found in both datasets generally improved prediction performance when evaluated on the STAR*D dataset.ConclusionWe successfully replicated prior work predicting antidepressant treatment outcomes using machine learning methods and clinical data. We found similar prediction performance using these methods on an external database, although prediction of remission was better than prediction of response. Future work is needed to improve prediction performance to be clinically useful.

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

(1) Objective: Considering that current knowledge of mechanisms involved in the molecular pathogenesis of Social Anxiety Disorder (SAD) is limited, we conducted a systematic review to evaluate cumulative data obtained by Proton Magnetic Resonance Spectroscopic (1H MRS) studies. (2) Methods: A computer-based literature search of Medline, EMBASE, PsycInfo, and ProQuest was performed. Only cross-sectional studies using 1H MRS techniques in participants with SAD and healthy controls (HCs) were selected. (3) Results: The search generated eight studies. The results indicated regional abnormalities in the ‘fear neurocircuitry’ in patients with SAD. The implicated regions included the anterior cingulate cortex (ACC), dorsomedial prefrontal cortex (dmPFC), dorsolateral prefrontal cortex (dlPFC), insula, occipital cortex (OC), as well as the subcortical regions, including the thalamus, caudate, and the putamen. (4) Conclusions: The evidence derived from eight studies suggests that possible pathophysiological mechanisms of SAD include impairments in the integrity and function of neurons and glial cells, including disturbances in energy metabolism, maintenance of phospholipid membranes, dysregulations of second messenger systems, and excitatory/inhibitory neurocircuitry. Conducting more cross-sectional studies with larger sample sizes is warranted given the limited evidence in this area of research.

Major depressive disorder (MDD) is considered a highly heterogeneous clinical and neurobiological mental disorder. We employed a novel layered treatment design to investigate whether cortical thickness features at baseline differentiated treatment responders from non-responders after 8 and 16 weeks of a standardized sequential antidepressant treatment. Secondary analyses examined baseline differences between MDD and controls as a replication analysis and longitudinal changes in thickness after 8 weeks of escitalopram treatment. 181 MDD and 95 healthy comparison (HC) participants were studied. After 8 weeks of escitalopram treatment (10–20 mg/d, flexible dosage), responders (>50% decrease in Montgomery-Åsberg Depression Scale score) were continued on escitalopram; non-responders received adjunctive aripiprazole (2–10 mg/d, flexible dosage). MDD participants were classified into subgroups according to their response profiles at weeks 8 and 16. Baseline group differences in cortical thickness were analyzed with FreeSurfer between HC and MDD groups as well as between response groups. Two-stage longitudinal processing was used to investigate 8-week escitalopram treatment-related changes in cortical thickness. Compared to HC, the MDD group exhibited thinner cortex in the left rostral middle frontal cortex [MNI(X,Y,Z=−29,9,54.5,−7.7); CWP=0.0002]. No baseline differences in cortical thickness were observed between responders and non-responders based on week-8 or week-16 response profile. No changes in cortical thickness was observed after 8 weeks of escitalopram monotherapy. In a two-step 16-week sequential clinical trial we found that baseline cortical thickness does not appear to be associated to clinical response to pharmacotherapy at 8 or 16 weeks. Keywords: Major depressive disorder, Cortical thickness, Structural neuroimaging, Antidepressant response, Clinical trial

Abstract Background Most mental disorders begin in adolescence; however, there are gaps in our understanding of youth mental health. Clinical and policy gaps arise from our current inability to predict, from amongst all youth who experience mild behavioural disturbances, who will go on to develop a mental illness, what that illness will be, and what can be done to change its course and prevent its worsening to a serious mental illness (SMI). There are also gaps in our understanding of how known risk factors set off neurobiological changes that may play a role in determining who will develop a SMI. Project goals are (i) to identify youth at different stages of risk of SMI so that intervention can begin as soon as possible and (ii) to understand the triggers of these mental illnesses. Method This 2-site longitudinal study will recruit 240 youth, ages 12–25, who are at different stages of risk for developing a SMI. The sample includes (a) healthy individuals, (b) symptom-free individuals who have a first-degree relative with a SMI, (c) youth who are experiencing distress and may have mild symptoms of anxiety or depression, and (d) youth who are already demonstrating attenuated symptoms of SMI such as bipolar disorder or psychosis. We will assess, every 6 months for one year, a wide range of clinical and psychosocial factors to determine which factors can be used to predict key outcomes. We will also assess neuroimaging and peripheral markers. We will develop and validate a prediction algorithm that includes demographic, clinical and psychosocial predictors. We will also determine if adding biological markers to our algorithm improves prediction. Discussion Outcomes from this study include an improved clinical staging model for SMI and prediction algorithms that can be used by health care providers as decision-support tools in their practices. Secondly, we may have a greater understanding of clinical, social and cognitive factors associated with the clinical stages of development of a SMI, as well as new insights from neuroimaging and later neurochemical biomarker studies regarding predisposition to SMI development and progression through the clinical stages of illness.