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  1. Article ; Online: Values and preferences related to workplace mental health programs and interventions

    Jill K. Murphy / Jasmine M. Noble / Promit Ananyo Chakraborty / Georgia Michlig / Erin E. Michalak / Andrew J. Greenshaw / Raymond W. Lam

    PLoS ONE, Vol 18, Iss

    An international survey

    2023  Volume 9

    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2023-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Personalized relapse prediction in patients with major depressive disorder using digital biomarkers

    Srinivasan Vairavan / Homa Rashidisabet / Qingqin S. Li / Seth Ness / Randall L. Morrison / Claudio N. Soares / Rudolf Uher / Benicio N. Frey / Raymond W. Lam / Sidney H. Kennedy / Madhukar Trivedi / Wayne C. Drevets / Vaibhav A. Narayan

    Scientific Reports, Vol 13, Iss 1, Pp 1-

    2023  Volume 14

    Abstract: 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 ‘ ... ...

    Abstract 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.
    Keywords Medicine ; R ; Science ; Q
    Subject code 150
    Language English
    Publishing date 2023-10-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Resting-state EEG delta and alpha power predict response to cognitive behavioral therapy in depression

    Benjamin Schwartzmann / Lena C. Quilty / Prabhjot Dhami / Rudolf Uher / Timothy A. Allen / Stefan Kloiber / Raymond W. Lam / Benicio N. Frey / Roumen Milev / Daniel J. Müller / Claudio N. Soares / Jane A. Foster / Susan Rotzinger / Sidney H. Kennedy / Faranak Farzan

    Scientific Reports, Vol 13, Iss 1, Pp 1-

    a Canadian biomarker integration network for depression study

    2023  Volume 12

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Medicine ; R ; Science ; Q
    Subject code 150
    Language English
    Publishing date 2023-05-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Association of intranasal esketamine, a novel ‘standard of care’ treatment and outcomes in the management of patients with treatment-resistant depression

    Peter Giacobbe / Ayal Schaffer / Raymond W Lam / Roumen Milev / Gustavo Vazquez / Gilmar Gutierrez / Joshua Rosenblat / Jennifer Swainson / Ganapathy Karthikeyan / Nisha Ravindran / André Do / Emily Hawken

    BMJ Open, Vol 12, Iss

    protocol of a prospective cohort observational study of naturalistic clinical practice

    2022  Volume 9

    Abstract: Introduction Esketamine is the S-enantiomer of racemic ketamine and has been approved by the Food and Drug Administration for the management of treatment resistant depression, demonstrating effective and long-lasting benefits. The objective of this ... ...

    Abstract Introduction Esketamine is the S-enantiomer of racemic ketamine and has been approved by the Food and Drug Administration for the management of treatment resistant depression, demonstrating effective and long-lasting benefits. The objective of this observational study is to elucidate the association of intranasal (IN) esketamine with beneficial and negative outcomes in the management of treatment resistant major depressive disorder.Methods and analysis This is a multicentre prospective cohort observational study of naturalistic clinical practice. We expect to recruit 10 patients per research centre (6 centres, total 60 subjects). After approval to receive IN esketamine as part of their standard of care management of moderate to severe treatment resistant depression, patients will be invited to participate in this study. Association of esketamine treatment with outcomes in the management of depression will be assessed by measuring the severity of depression symptoms using the Montgomery-Åsberg Depression Rating Scale (MADRS), and tolerability by systematically tracking common side effects of ketamine treatment, dissociation using the simplified 6-Item Clinician Administered Dissociative Symptom Scale and potential for abuse using the Likeability and Craving Questionnaire (LCQ). Change in depressive symptoms (MADRS total scores) over time will be evaluated by within-subject repeated measures analysis of variance. We will calculate the relative risk associated with the beneficial (reduction in total scores for depression) outcomes, and the side effect and dropout rates (tolerability) of adding IN esketamine to patients’ current pharmacological treatments. Covariate analysis will assess the impact of site and demographic variables on treatment outcomes.Ethics and dissemination Approval to perform this study was obtained through the Health Sciences Research Ethics Board at Queen’s University. Findings will be shared among collaborators, through departmental meetings, presented on different academic venues and ...
    Keywords Medicine ; R
    Subject code 310
    Language English
    Publishing date 2022-09-01T00:00:00Z
    Publisher BMJ Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: A detailed manual segmentation procedure for the hypothalamus for 3T T1-weighted MRI

    Mohammad Ali / Jee Su Suh / Milita Ramonas / Stefanie Hassel / Stephen R. Arnott / Stephen C. Strother / Luciano Minuzzi / Roberto B. Sassi / Raymond W. Lam / Roumen Milev / Daniel J. Müller / Valerie H. Taylor / Sidney H. Kennedy / Benicio N. Frey

    MethodsX, Vol 9, Iss , Pp 101864- (2022)

    2022  

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Manual Segmentation of the Hypothalamus using standard 3T anatomical T1w images ; Science ; Q
    Subject code 000
    Language English
    Publishing date 2022-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Metabolomic signatures associated with depression and predictors of antidepressant response in humans

    Giorgia Caspani / Gustavo Turecki / Raymond W. Lam / Roumen V. Milev / Benicio N. Frey / Glenda M. MacQueen / Daniel J. Müller / Susan Rotzinger / Sidney H. Kennedy / Jane A. Foster / Jonathan R. Swann

    Communications Biology, Vol 4, Iss 1, Pp 1-

    A CAN-BIND-1 report

    2021  Volume 11

    Abstract: 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 ... ...

    Abstract 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.
    Keywords Biology (General) ; QH301-705.5
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher Nature Portfolio
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Comparative efficacy of escitalopram in the treatment of major depressive disorder

    Mazen K Ali / Raymond W Lam

    Neuropsychiatric Disease and Treatment, Vol 2011, Iss default, Pp 39-

    2011  Volume 49

    Abstract: Mazen K Ali, Raymond W LamDepartment of Psychiatry, University of British Columbia, and Mood Disorders Centre, University of British Columbia Hospital, Vancouver, CanadaBackground: Escitalopram is an allosteric selective serotonin reuptake inhibitor ( ... ...

    Abstract Mazen K Ali, Raymond W LamDepartment of Psychiatry, University of British Columbia, and Mood Disorders Centre, University of British Columbia Hospital, Vancouver, CanadaBackground: Escitalopram is an allosteric selective serotonin reuptake inhibitor (SSRI) with some indication of superior efficacy in the treatment of major depressive disorder. In this systematic review, we critically evaluate the evidence for comparative efficacy and tolerability of escitalopram, focusing on pooled and meta-analysis studies.Methods: A literature search was conducted for escitalopram studies that quantitatively synthesized data from comparative randomized controlled trials in MDD. Studies were excluded if they did not focus on efficacy, involved primarily subgroups of patients, or synthesized data included in subsequent studies. Outcomes extracted from the included studies were weighted mean difference or standard mean difference, response and remission rates, and withdrawal rate owing to adverse events.Results: The search initially identified 24 eligible studies, of which 12 (six pooled analysis and six meta-analysis studies) met the criteria for review. The pooled and meta-analysis studies with citalopram showed significant but modest differences in favor of escitalopram, with weighted mean differences ranging from 1.13 to 1.73 points on the Montgomery Asberg Depression Rating Scale, response rate differences of 7.0%–8.3%, and remission rate differences of 5.1%–17.6%. Pooled analysis studies showed efficacy differences compared with duloxetine and with serotonin noradrenaline reuptake inhibitors combined, but meta-analysis studies did not. The effect sizes of the efficacy differences increased in the severely depressed patient subgroups.Conclusion: Based on pooled and meta-analysis studies, escitalopram demonstrates superior efficacy compared with citalopram and with SSRIs combined. Escitalopram shows similar efficacy to serotonin noradrenaline reuptake inhibitors but the number of trials in these comparisons is limited. Efficacy differences are modest but clinically relevant, especially in more severely depressed patients.Keywords: escitalopram, depressive disorders, meta-analysis, pooled analysis, efficacy, antidepressants
    Keywords Neurosciences. Biological psychiatry. Neuropsychiatry ; RC321-571 ; Internal medicine ; RC31-1245 ; Medicine ; R ; DOAJ:Neurology ; DOAJ:Medicine (General) ; DOAJ:Health Sciences
    Subject code 150
    Language English
    Publishing date 2011-02-01T00:00:00Z
    Publisher Dove Press
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Accelerated brain aging in major depressive disorder and antidepressant treatment response

    Pedro L. Ballester / Jee Su Suh / Nikita Nogovitsyn / Stefanie Hassel / Stephen C. Strother / Stephen R. Arnott / Luciano Minuzzi / Roberto B. Sassi / Raymond W. Lam / Roumen Milev / Daniel J. Müller / Valerie H. Taylor / Sidney H. Kennedy / Benicio N. Frey

    NeuroImage: Clinical, Vol 32, Iss , Pp 102864- (2021)

    A CAN-BIND report

    2021  

    Abstract: 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 ... ...

    Abstract Objectives: Previous studies suggest that major depressive disorder (MDD) may be associated with volumetric indications of accelerated brain aging. This study investigated neuroanatomical signs of accelerated aging in MDD and evaluated whether a brain age gap is associated with antidepressant response. Methods: Individuals in a major depressive episode received escitalopram treatment (10–20 mg/d) for 8 weeks. Depression severity was assessed at baseline and at weeks 8 and 16 using the Montgomery-Asberg Depression Rating Scale (MADRS). Response to treatment was characterized by a significant reduction in the MADRS (≥50%). Nonresponders received adjunctive aripiprazole treatment (2–10 mg/d) for a further 8 weeks. The brain-predicted age difference (brain-PAD) at baseline was determined using machine learning methods trained on 3377 healthy individuals from seven publicly available datasets. The model used features from all brain regions extracted from structural magnetic resonance imaging data. Results: Brain-PAD was significantly higher in older MDD participants compared to younger MDD participants [t(147.35) = -2.35, p < 0.03]. BMI was significantly associated with brain-PAD in the MDD group [r(155) = 0.19, p < 0.03]. Response to treatment was not significantly associated with brain-PAD. Conclusion: We found an elevated brain age gap in older individuals with MDD. Brain-PAD was not associated with overall treatment response to escitalopram monotherapy or escitalopram plus adjunctive aripiprazole.
    Keywords Treatment response ; Major depressive disorder ; Brain age ; Machine learning ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Neurology. Diseases of the nervous system ; RC346-429
    Subject code 150 ; 616
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Replication of machine learning methods to predict treatment outcome with antidepressant medications in patients with major depressive disorder from STAR*D and CAN-BIND-1.

    John-Jose Nunez / Teyden T Nguyen / Yihan Zhou / Bo Cao / Raymond T Ng / Jun Chen / Benicio N Frey / Roumen Milev / Daniel J Müller / Susan Rotzinger / Claudio N Soares / Rudolf Uher / Sidney H Kennedy / Raymond W Lam

    PLoS ONE, Vol 16, Iss 6, p e

    2021  Volume 0253023

    Abstract: Objectives Antidepressants 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 ...

    Abstract Objectives Antidepressants 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. Methods We 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. Results Our 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. Conclusion We 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 ...
    Keywords Medicine ; R ; Science ; Q
    Subject code 006
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Needs, gaps and opportunities for standard and e-mental health care among at-risk populations in the Asia Pacific in the context of COVID-19

    Jill K. Murphy / Amna Khan / Qiumeng Sun / Harry Minas / Simon Hatcher / Chee H. Ng / Mellissa Withers / Andrew Greenshaw / Erin E. Michalak / Promit Ananyo Chakraborty / Karen Sharmini Sandanasamy / Nurashikin Ibrahim / Arun Ravindran / Jun Chen / Vu Cong Nguyen / Raymond W. Lam

    International Journal for Equity in Health, Vol 20, Iss 1, Pp 1-

    a rapid scoping review

    2021  Volume 22

    Abstract: Abstract Background The COVID-19 pandemic is expected to have profound mental health impact, including in the Asia Pacific Economic Cooperation (APEC) region. Some populations might be at higher risk of experiencing negative mental health impacts and may ...

    Abstract Abstract Background The COVID-19 pandemic is expected to have profound mental health impact, including in the Asia Pacific Economic Cooperation (APEC) region. Some populations might be at higher risk of experiencing negative mental health impacts and may encounter increased barriers to accessing mental health care. The pandemic and related restrictions have led to changes in care delivery, including a rapid shift to the use of e-mental health and digital technologies. It is therefore essential to consider needs and opportunities for equitable mental health care delivery to the most at-risk populations. This rapid scoping review: 1) identifies populations in the APEC region that are at higher risk of the negative mental health impacts of COVID-19, 2) identifies needs and gaps in access to standard and e-mental health care among these populations, and 3) explores the potential of e-mental health to address these needs. Methods We conducted a rapid scoping review following the PRISMA Extension for Scoping Reviews (PRISMA-ScR). We searched Medline, Embase and PsychInfo databases and Google Scholar using a search strategy developed in consultation with a biomedical librarian. We included records related to mental health or psychosocial risk factors and COVID-19 among at-risk groups; that referred to one or more APEC member economies or had a global, thus generalizable, scope; English language papers, and papers with full text available. Results A total of 132 records published between December 2019 and August 2020 were included in the final analysis. Several priority at-risk populations, risk factors, challenges and recommendations for standard and e-mental health care were identified. Results demonstrate that e-mental health care can be a viable option for care delivery but that specific accessibility and acceptability considerations must be considered. Options for in-person, hybrid or “low-tech” care must also remain available. Conclusions The COVID-19 pandemic has highlighted the urgent need for equitable standard ...
    Keywords COVID-19 ; Mental health ; Equity ; Asia Pacific ; E-mental health ; At-risk populations ; Public aspects of medicine ; RA1-1270
    Subject code 360
    Language English
    Publishing date 2021-07-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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