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  • 2021-2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aras Kayvanrad; Stephen R. Arnott; Nathan W. Churchill; Stefanie Hassel; +16 Authors

    Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scholarship@Westernarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Scholarship@Western
    Article . 2021
    License: CC BY
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2021
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2020
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    NeuroImage
    Article . 2021
    Data sources: DOAJ-Articles
    DOAJ
    Article . 2021
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scholarship@Westernarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Scholarship@Western
      Article . 2021
      License: CC BY
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2021
      License: CC BY
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2020
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      NeuroImage
      Article . 2021
      Data sources: DOAJ-Articles
      DOAJ
      Article . 2021
      Data sources: DOAJ
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Andrew S Moriarty; Nicholas Meader; Kym I E Snell; Richard D Riley; +7 Authors

    BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. \ud \ud OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. \ud \ud SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . \ud \ud SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. \ud \ud DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. \ud \ud MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model.\ud \ud AUTHORS' CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CORE (RIOXX-UK Aggre...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Article . 2021
    License: CC BY NC
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Europe PubMed Central
    Other literature type . 2021
    Data sources: PubMed Central
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ CORE (RIOXX-UK Aggre...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      Article . 2021
      License: CC BY NC
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Europe PubMed Central
      Other literature type . 2021
      Data sources: PubMed Central
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Daniella C. Terenzi; Subodh Verma; David A. Hess;
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scholarship@Westernarrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Scholarship@Westernarrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Shannon L. Stewart; Ashley Toohey; Angela Celebre; Jeff Poss;

    pandemic n = 3197) from mental health agencies across Ontario, Canada using the interRAI Child and Youth Mental Health assessment. Our findings revealed no increased incidence of witnessing domestic violence nor experiencing physical, sexual, or emotional abuse. Further, there were no increases in the risk of self-harm and suicide, anxiety, or depression among our sample of clinically referred youth. Finally, results demonstrated no increase in problematic videogaming/internet use, disordered eating, or alcohol intoxication, and a decrease in cannabis use. Our findings add to the growing body of knowledge as to the impact of the COVID-19 pandemic on children and youth. Further, findings underscore the importance of understanding the nuanced impact of the pandemic on various subgroups of children, youth, and families and highlight the need for continued monitoring of outcomes for these children and youth. Throughout the COVID-19 pandemic, population surveys revealed increased levels of anxiety and depression, while findings from large-scale population data analyses have revealed mixed findings with respect to the mental health consequences for children and youth. The purpose of this study was to examine the impact of the COVID-19 pandemic on the well-being and health-compromising behaviors of adolescents (12–18 years) previously referred for mental health services. Data were collected (pre-pandemic n = 3712

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ International Journa...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Europe PubMed Central
    Article . 2021
    Data sources: PubMed Central
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ International Journa...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Europe PubMed Central
      Article . 2021
      Data sources: PubMed Central
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Robert Smith; Sze Chai Hung; Joyce Goh; Hoi Lam Ip; +4 Authors

    IntroductionPerinatal depression is common and can often lead to adverse health outcomes for mother and child. Multiple pharmacological and non-pharmacological treatments have been evaluated against usual care or placebo controls in meta-analyses for preventing and treating perinatal depression compared. It is not yet established which of these candidate treatments might be the optimal approach for prevention or treatment.Methods and analysisA systematic review and Bayesian network meta-analyses will be conducted. Eight electronic databases shall be searched for randomised controlled trials that have evaluated the effectiveness of treatments for prevention and/or treatment of perinatal depression. Screening of articles shall be conducted by two reviewers independently. One network meta-analysis shall evaluate the effectiveness of interventions in preventing depression during the perinatal period. A second network meta-analysis shall compare the effectiveness of treatments for depression symptoms in women with perinatal depression. Bayesian 95% credible intervals shall be used to estimate the pooled mean effect size of each treatment, and surface under cumulative ranking area will be used to rank the treatments’ effectiveness.Ethics and disseminationWe shall report our findings so that healthcare providers can make informed decisions on what might be the optimal approach for addressing perinatal depression to prevent cases and improve outcomes in those suffering from depression through knowledge exchange workshops, international conference presentations and journal article publications.PROSPERO registration numberCRD42020200081.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Europe PubMed Centra...arrow_drop_down
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    Europe PubMed Central
    Article . 2021
    Data sources: PubMed Central
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    Authors: Lydia Kapiriri; Suzanne N Kiwanuka; Godfrey Biemba; Claudia Velez; +9 Authors

    Priority setting represents an even bigger challenge during public health emergencies than routine times. This is because such emergencies compete with routine programmes for the available health resources, strain health systems and shift health-care attention and resources towards containing the spread of the epidemic and treating those that fall seriously ill. This paper is part of a larger global study, the aim of which is to evaluate the degree to which national COVID-19 preparedness and response plans incorporated priority setting concepts. It provides important insights into what and how priority decisions were made in the context of a pandemic. Specifically, with a focus on a sample of 18 African countries pandemic plans, the paper aims to: (1) explore the degree to which the documented priority setting processes adhere to established quality indicators of effective priority setting and (2) examine if there is a relationship between the number of quality indicators present in the pandemic plans and the countrys economic context, health system and prior experiences with disease outbreaks. All the reviewed plans contained some aspects of expected priority setting processes but none of the national plans addressed all quality parameters. Most of the parameters were mentioned by less than 10 of the 18 country plans reviewed, and several plans identified one or two aspects of fair priority setting processes. Very few plans identified equity as a criterion for priority setting. Since the parameters are relevant to the quality of priority setting that is implemented during public health emergencies and most of the countries have pre-existing pandemic plans; it would be advisable that, for the future (if not already happening), countries consider priority setting as a critical part of their routine health emergency and disease outbreak plans. Such an approach would ensure that priority setting is integral to pandemic planning, response and recovery. Funding Agencies|McMaster University COVID-19 research fund

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    Europe PubMed Central
    Article . 2021
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    Health Policy and Planning
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      Health Policy and Planning
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    Authors: Shawn N. Whitehead; Askiel Bruno; Jeffrey M. Burns; S. Thomas Carmichael; +15 Authors

    White matter pathologies are critically involved in the etiology of vascular cognitive impairment–dementia (VCID), Alzheimer’s disease (AD), and Alzheimer’s disease and related diseases (ADRD), and therefore need to be considered a treatable target ( Roseborough A, Hachinski V, Whitehead S. White matter degeneration - a treatable target? Roseborough et al. JAMA Neurol [Internet]. 2020 Apr 27;77(7):793–4, [1] . To help address this often-missed area of research, several workshops have been sponsored by the Leo and Anne Albert Charitable Trust since 2015, resulting in the incorporation of “The Albert Research Institute for White Matter and Cognition” in 2020. The first annual “Institute” meeting was held virtually on March 3–4, 2021. The Institute provides a forum and workspace for communication and support of the advancement of white matter science and research to better understand the evolution and prevention of dementia. It serves as a platform for young investigator development, to introduce new data and debate biology mechanisms and new ideas, and to encourage and support new research collaborations and directions to clarify how white matter changes, with other genetic and health risk factors, contribute to cognitive impairment. Similar to previous Albert Trust–sponsored workshops (Barone et al. in J Transl Med 14:1–14, [2]; Sorond et al. in GeroScience 42:81–96, [3]), established expert investigators were identified and invited to present. Opportunities to attend and present were also extended by invitation to talented research fellows and younger scientists. Also, updates on institute-funded research collaborations were provided and discussed. The summary that follows is a synopsis of topics and discussion covered in the workshop.

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    Article . 2021
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    Authors: Sergi Borrego-Écija; Roser Sala-Llonch; John C. van Swieten; Barbara Borroni; +118 Authors

    The authors thank all the volunteers for their participation in this study. SBE is a recipient of the Rio-Hortega post-residency grant from the Instituto de Salud Carlos III, Spain. This study was partially funded by Fundació Marató de TV3, Spain (grant no. 20143810 to RSV). The GENFI study has been supported by the Medical Research Council UK, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, as well as other individual funding to investigators. KM has received funding from an Alzheimer’s Society PhD studentship. JDR acknowledges support from the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Unit and the University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre, the UK Dementia Research Institute, Alzheimer’s Research UK, the Brain Research Trust and the Wolfson Foundation. JCvS was supported by the Dioraphte Foundation grant 09-02-03-00, the Association for Frontotemporal Dementias Research Grant 2009, The Netherlands Organization for Scientific Research (NWO) grant HCMI 056-13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland and the Bluefield project. CG have received funding from JPND-Prefrontals VR Dnr 529-2014-7504, VR: 2015-02926, and 2018-02754, the Swedish FTD Initiative-Schörling Foundation, Alzheimer Foundation, Brain Foundation and Stockholm County Council ALF. DG has received support from the EU Joint Programme – Neurodegenerative Disease Research (JPND) and the Italian Ministry of Health (PreFrontALS) grant 733051042. JBR is funded by the Wellcome Trust (103838) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. MM has received funding from a Canadian Institutes of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. RV has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. EF has received funding from a CIHR grant #327387. JDR is an MRC Clinician Scientist (MR/M008525/1) and has received funding from the NIHR Rare Diseases Translational Research Collaboration (BRC149/NS/MH), the Bluefield Project and the Association for Frontotemporal Degeneration. MS was supported by a grant 779257 “Solve-RD” from the Horizon 2020 research and innovation programme. Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting an increased CTh loss associated with age and estimated proximity to symptoms onset in GRN carriers, even before the disease onset. © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.

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    Europe PubMed Central
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    NeuroImage: Clinical
    Article . 2021
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      NeuroImage: Clinical
      Article . 2021
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      Article . 2021
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    Authors: Shamini Selvarajah; Sophie Plante; Marsha Speevak; Andrea K. Vaags; +20 Authors

    Abstract Introduction Genotyping circulating tumor DNA (ctDNA) is a promising noninvasive clinical tool to identify the EGFR T790M resistance mutation in patients with advanced NSCLC with resistance to EGFR inhibitors. To facilitate standardization and clinical adoption of ctDNA testing across Canada, we developed a 2-phase multicenter study to standardize T790M mutation detection using plasma ctDNA testing. Methods In phase 1, commercial reference standards were distributed to participating clinical laboratories, to use their existing platforms for mutation detection. Baseline performance characteristics were established using known and blinded engineered plasma samples spiked with predetermined concentrations of T790M, L858R, and exon 19 deletion variants. In phase II, peripheral blood collected from local patients with known EGFR activating mutations and progressing on treatment were assayed for the presence of EGFR variants and concordance with a clinically validated test at the reference laboratory. Results All laboratories in phase 1 detected the variants at 0.5 % and 5.0 % allele frequencies, with no false positives. In phase 2, the concordance with the reference laboratory for detection of both the primary and resistance mutation was high, with next-generation sequencing and droplet digital polymerase chain reaction exhibiting the best overall concordance. Data also suggested that the ability to detect mutations at clinically relevant limits of detection is generally not platform-specific, but rather impacted by laboratory-specific practices. Conclusions Discrepancies among sending laboratories using the same assay suggest that laboratory-specific practices may impact performance. In addition, a negative or inconclusive ctDNA test should be followed by tumor testing when possible.

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    JTO Clinical and Research Reports
    Article . 2021
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    JTO Clinical and Research Reports
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      JTO Clinical and Research Reports
      Article . 2021
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      JTO Clinical and Research Reports
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    Authors: Jennifer Faber; Tamara Schaprian; Koyak Berkan; Kathrin Reetz; +27 Authors

    BACKGROUND: Given that new therapeutic options for spinocerebellar ataxias are on the horizon, there is a need for markers that reflect disease-related alterations, in particular, in the preataxic stage, in which clinical scales are lacking sensitivity. OBJECTIVE: The objective of this study was to quantify regional brain volumes and upper cervical spinal cord areas in spinocerebellar ataxia type 3 in vivo across the entire time course of the disease. METHODS: We applied a brain segmentation approach that included a lobular subsegmentation of the cerebellum to magnetic resonance images of 210 ataxic and 48 preataxic spinocerebellar ataxia type 3 mutation carriers and 63 healthy controls. In addition, cervical cord cross-sectional areas were determined at 2 levels. RESULTS: The metrics of cervical spinal cord segments C3 and C2, medulla oblongata, pons, and pallidum, and the cerebellar anterior lobe were reduced in preataxic mutation carriers compared with controls. Those of cervical spinal cord segments C2 and C3, medulla oblongata, pons, midbrain, cerebellar lobules crus II and X, cerebellar white matter, and pallidum were reduced in ataxic compared with nonataxic carriers. Of all metrics studied, pontine volume showed the steepest decline across the disease course. It covaried with ataxia severity, CAG repeat length, and age. The multivariate model derived from this analysis explained 46.33% of the variance of pontine volume. CONCLUSION: Regional brain and spinal cord tissue loss in spinocerebellar ataxia type 3 starts before ataxia onset. Pontine volume appears to be the most promising imaging biomarker candidate for interventional trials that aim at slowing the progression of spinocerebellar ataxia type 3. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Contains fulltext : 244289.pdf (Publisher’s version ) (Open Access)

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    Movement Disorders
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    Article . 2021
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    Scholarship@Western
    Article . 2021
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      Movement Disorders
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      DZNE Pub
      Article . 2021
      Data sources: DZNE Pub
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      Radboud Repository
      Article . 2021
      Data sources: Radboud Repository
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      UCrea
      Other literature type . 2021
      License: CC BY
      Data sources: UCrea
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      Scholarship@Western
      Article . 2021
      License: CC BY NC
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69 Research products
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Aras Kayvanrad; Stephen R. Arnott; Nathan W. Churchill; Stefanie Hassel; +16 Authors

    Quality assurance (QA) is crucial in longitudinal and/or multi-site studies, which involve the collection of data from a group of subjects over time and/or at different locations. It is important to regularly monitor the performance of the scanners over time and at different locations to detect and control for intrinsic differences (e.g., due to manufacturers) and changes in scanner performance (e.g., due to gradual component aging, software and/or hardware upgrades, etc.). As part of the Ontario Neurodegenerative Disease Research Initiative (ONDRI) and the Canadian Biomarker Integration Network in Depression (CAN-BIND), QA phantom scans were conducted approximately monthly for three to four years at 13 sites across Canada with 3T research MRI scanners. QA parameters were calculated for each scan using the functional Biomarker Imaging Research Network's (fBIRN) QA phantom and pipeline to capture between- and within-scanner variability. We also describe a QA protocol to measure the full-width-at-half-maximum (FWHM) of slice-wise point spread functions (PSF), used in conjunction with the fBIRN QA parameters. Variations in image resolution measured by the FWHM are a primary source of variance over time for many sites, as well as between sites and between manufacturers. We also identify an unexpected range of instabilities affecting individual slices in a number of scanners, which may amount to a substantial contribution of unexplained signal variance to their data. Finally, we identify a preliminary preprocessing approach to reduce this variance and/or alleviate the slice anomalies, and in a small human data set show that this change in preprocessing can have a significant impact on seed-based connectivity measurements for some individual subjects. We expect that other fMRI centres will find this approach to identifying and controlling scanner instabilities useful in similar studies.

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    Scholarship@Western
    Article . 2021
    License: CC BY
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    NeuroImage
    Article . 2021
    License: CC BY
    Data sources: Crossref
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    NeuroImage
    Article . 2020
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    NeuroImage
    Article . 2021
    Data sources: DOAJ-Articles
    DOAJ
    Article . 2021
    Data sources: DOAJ
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      Scholarship@Western
      Article . 2021
      License: CC BY
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      NeuroImage
      Article . 2021
      License: CC BY
      Data sources: Crossref
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      NeuroImage
      Article . 2020
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      NeuroImage
      Article . 2021
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      DOAJ
      Article . 2021
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Andrew S Moriarty; Nicholas Meader; Kym I E Snell; Richard D Riley; +7 Authors

    BACKGROUND: Relapse (the re-emergence of depressive symptoms after some level of improvement but preceding recovery) and recurrence (onset of a new depressive episode after recovery) are common in depression, lead to worse outcomes and quality of life for patients and exert a high economic cost on society. Outcomes can be predicted by using multivariable prognostic models, which use information about several predictors to produce an individualised risk estimate. The ability to accurately predict relapse or recurrence while patients are well (in remission) would allow the identification of high-risk individuals and may improve overall treatment outcomes for patients by enabling more efficient allocation of interventions to prevent relapse and recurrence. \ud \ud OBJECTIVES: To summarise the predictive performance of prognostic models developed to predict the risk of relapse, recurrence, sustained remission or recovery in adults with major depressive disorder who meet criteria for remission or recovery. \ud \ud SEARCH METHODS: We searched the Cochrane Library (current issue); Ovid MEDLINE (1946 onwards); Ovid Embase (1980 onwards); Ovid PsycINFO (1806 onwards); and Web of Science (1900 onwards) up to May 2020. We also searched sources of grey literature, screened the reference lists of included studies and performed a forward citation search. There were no restrictions applied to the searches by date, language or publication status . \ud \ud SELECTION CRITERIA: We included development and external validation (testing model performance in data separate from the development data) studies of any multivariable prognostic models (including two or more predictors) to predict relapse, recurrence, sustained remission, or recovery in adults (aged 18 years and over) with remitted depression, in any clinical setting. We included all study designs and accepted all definitions of relapse, recurrence and other related outcomes. We did not specify a comparator prognostic model. \ud \ud DATA COLLECTION AND ANALYSIS: Two review authors independently screened references; extracted data (using a template based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS)); and assessed risks of bias of included studies (using the Prediction model Risk Of Bias ASsessment Tool (PROBAST)). We referred any disagreements to a third independent review author. Where we found sufficient (10 or more) external validation studies of an individual model, we planned to perform a meta-analysis of its predictive performance, specifically with respect to its calibration (how well the predicted probabilities match the observed proportions of individuals that experience the outcome) and discrimination (the ability of the model to differentiate between those with and without the outcome). Recommendations could not be qualified using the GRADE system, as guidance is not yet available for prognostic model reviews. \ud \ud MAIN RESULTS: We identified 11 eligible prognostic model studies (10 unique prognostic models). Seven were model development studies; three were model development and external validation studies; and one was an external validation-only study. Multiple estimates of performance measures were not available for any of the models and, meta-analysis was therefore not possible. Ten out of the 11 included studies were assessed as being at high overall risk of bias. Common weaknesses included insufficient sample size, inappropriate handling of missing data and lack of information about discrimination and calibration. One paper (Klein 2018) was at low overall risk of bias and presented a prognostic model including the following predictors: number of previous depressive episodes, residual depressive symptoms and severity of the last depressive episode. The external predictive performance of this model was poor (C-statistic 0.59; calibration slope 0.56; confidence intervals not reported). None of the identified studies examined the clinical utility (net benefit) of the developed model.\ud \ud AUTHORS' CONCLUSIONS: Of the 10 prognostic models identified (across 11 studies), only four underwent external validation. Most of the studies (n = 10) were assessed as being at high overall risk of bias, and the one study that was at low risk of bias presented a model with poor predictive performance. There is a need for improved prognostic research in this clinical area, with future studies conforming to current best practice recommendations for prognostic model development/validation and reporting findings in line with the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement.

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    Article . 2021
    License: CC BY NC
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    Europe PubMed Central
    Other literature type . 2021
    Data sources: PubMed Central
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      Article . 2021
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      Europe PubMed Central
      Other literature type . 2021
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    Authors: Daniella C. Terenzi; Subodh Verma; David A. Hess;
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    Authors: Shannon L. Stewart; Ashley Toohey; Angela Celebre; Jeff Poss;

    pandemic n = 3197) from mental health agencies across Ontario, Canada using the interRAI Child and Youth Mental Health assessment. Our findings revealed no increased incidence of witnessing domestic violence nor experiencing physical, sexual, or emotional abuse. Further, there were no increases in the risk of self-harm and suicide, anxiety, or depression among our sample of clinically referred youth. Finally, results demonstrated no increase in problematic videogaming/internet use, disordered eating, or alcohol intoxication, and a decrease in cannabis use. Our findings add to the growing body of knowledge as to the impact of the COVID-19 pandemic on children and youth. Further, findings underscore the importance of understanding the nuanced impact of the pandemic on various subgroups of children, youth, and families and highlight the need for continued monitoring of outcomes for these children and youth. Throughout the COVID-19 pandemic, population surveys revealed increased levels of anxiety and depression, while findings from large-scale population data analyses have revealed mixed findings with respect to the mental health consequences for children and youth. The purpose of this study was to examine the impact of the COVID-19 pandemic on the well-being and health-compromising behaviors of adolescents (12–18 years) previously referred for mental health services. Data were collected (pre-pandemic n = 3712

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    Europe PubMed Central
    Article . 2021
    Data sources: PubMed Central
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      Europe PubMed Central
      Article . 2021
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    Authors: Robert Smith; Sze Chai Hung; Joyce Goh; Hoi Lam Ip; +4 Authors

    IntroductionPerinatal depression is common and can often lead to adverse health outcomes for mother and child. Multiple pharmacological and non-pharmacological treatments have been evaluated against usual care or placebo controls in meta-analyses for preventing and treating perinatal depression compared. It is not yet established which of these candidate treatments might be the optimal approach for prevention or treatment.Methods and analysisA systematic review and Bayesian network meta-analyses will be conducted. Eight electronic databases shall be searched for randomised controlled trials that have evaluated the effectiveness of treatments for prevention and/or treatment of perinatal depression. Screening of articles shall be conducted by two reviewers independently. One network meta-analysis shall evaluate the effectiveness of interventions in preventing depression during the perinatal period. A second network meta-analysis shall compare the effectiveness of treatments for depression symptoms in women with perinatal depression. Bayesian 95% credible intervals shall be used to estimate the pooled mean effect size of each treatment, and surface under cumulative ranking area will be used to rank the treatments’ effectiveness.Ethics and disseminationWe shall report our findings so that healthcare providers can make informed decisions on what might be the optimal approach for addressing perinatal depression to prevent cases and improve outcomes in those suffering from depression through knowledge exchange workshops, international conference presentations and journal article publications.PROSPERO registration numberCRD42020200081.

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    Authors: Lydia Kapiriri; Suzanne N Kiwanuka; Godfrey Biemba; Claudia Velez; +9 Authors

    Priority setting represents an even bigger challenge during public health emergencies than routine times. This is because such emergencies compete with routine programmes for the available health resources, strain health systems and shift health-care attention and resources towards containing the spread of the epidemic and treating those that fall seriously ill. This paper is part of a larger global study, the aim of which is to evaluate the degree to which national COVID-19 preparedness and response plans incorporated priority setting concepts. It provides important insights into what and how priority decisions were made in the context of a pandemic. Specifically, with a focus on a sample of 18 African countries pandemic plans, the paper aims to: (1) explore the degree to which the documented priority setting processes adhere to established quality indicators of effective priority setting and (2) examine if there is a relationship between the number of quality indicators present in the pandemic plans and the countrys economic context, health system and prior experiences with disease outbreaks. All the reviewed plans contained some aspects of expected priority setting processes but none of the national plans addressed all quality parameters. Most of the parameters were mentioned by less than 10 of the 18 country plans reviewed, and several plans identified one or two aspects of fair priority setting processes. Very few plans identified equity as a criterion for priority setting. Since the parameters are relevant to the quality of priority setting that is implemented during public health emergencies and most of the countries have pre-existing pandemic plans; it would be advisable that, for the future (if not already happening), countries consider priority setting as a critical part of their routine health emergency and disease outbreak plans. Such an approach would ensure that priority setting is integral to pandemic planning, response and recovery. Funding Agencies|McMaster University COVID-19 research fund

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    Health Policy and Planning
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    Authors: Shawn N. Whitehead; Askiel Bruno; Jeffrey M. Burns; S. Thomas Carmichael; +15 Authors

    White matter pathologies are critically involved in the etiology of vascular cognitive impairment–dementia (VCID), Alzheimer’s disease (AD), and Alzheimer’s disease and related diseases (ADRD), and therefore need to be considered a treatable target ( Roseborough A, Hachinski V, Whitehead S. White matter degeneration - a treatable target? Roseborough et al. JAMA Neurol [Internet]. 2020 Apr 27;77(7):793–4, [1] . To help address this often-missed area of research, several workshops have been sponsored by the Leo and Anne Albert Charitable Trust since 2015, resulting in the incorporation of “The Albert Research Institute for White Matter and Cognition” in 2020. The first annual “Institute” meeting was held virtually on March 3–4, 2021. The Institute provides a forum and workspace for communication and support of the advancement of white matter science and research to better understand the evolution and prevention of dementia. It serves as a platform for young investigator development, to introduce new data and debate biology mechanisms and new ideas, and to encourage and support new research collaborations and directions to clarify how white matter changes, with other genetic and health risk factors, contribute to cognitive impairment. Similar to previous Albert Trust–sponsored workshops (Barone et al. in J Transl Med 14:1–14, [2]; Sorond et al. in GeroScience 42:81–96, [3]), established expert investigators were identified and invited to present. Opportunities to attend and present were also extended by invitation to talented research fellows and younger scientists. Also, updates on institute-funded research collaborations were provided and discussed. The summary that follows is a synopsis of topics and discussion covered in the workshop.

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    Authors: Sergi Borrego-Écija; Roser Sala-Llonch; John C. van Swieten; Barbara Borroni; +118 Authors

    The authors thank all the volunteers for their participation in this study. SBE is a recipient of the Rio-Hortega post-residency grant from the Instituto de Salud Carlos III, Spain. This study was partially funded by Fundació Marató de TV3, Spain (grant no. 20143810 to RSV). The GENFI study has been supported by the Medical Research Council UK, the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, as well as other individual funding to investigators. KM has received funding from an Alzheimer’s Society PhD studentship. JDR acknowledges support from the National Institute for Health Research (NIHR) Queen Square Dementia Biomedical Research Unit and the University College London Hospitals Biomedical Research Centre, the Leonard Wolfson Experimental Neurology Centre, the UK Dementia Research Institute, Alzheimer’s Research UK, the Brain Research Trust and the Wolfson Foundation. JCvS was supported by the Dioraphte Foundation grant 09-02-03-00, the Association for Frontotemporal Dementias Research Grant 2009, The Netherlands Organization for Scientific Research (NWO) grant HCMI 056-13-018, ZonMw Memorabel (Deltaplan Dementie, project number 733 051 042), Alzheimer Nederland and the Bluefield project. CG have received funding from JPND-Prefrontals VR Dnr 529-2014-7504, VR: 2015-02926, and 2018-02754, the Swedish FTD Initiative-Schörling Foundation, Alzheimer Foundation, Brain Foundation and Stockholm County Council ALF. DG has received support from the EU Joint Programme – Neurodegenerative Disease Research (JPND) and the Italian Ministry of Health (PreFrontALS) grant 733051042. JBR is funded by the Wellcome Trust (103838) and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre. MM has received funding from a Canadian Institutes of Health Research operating grant and the Weston Brain Institute and Ontario Brain Institute. RV has received funding from the Mady Browaeys Fund for Research into Frontotemporal Dementia. EF has received funding from a CIHR grant #327387. JDR is an MRC Clinician Scientist (MR/M008525/1) and has received funding from the NIHR Rare Diseases Translational Research Collaboration (BRC149/NS/MH), the Bluefield Project and the Association for Frontotemporal Degeneration. MS was supported by a grant 779257 “Solve-RD” from the Horizon 2020 research and innovation programme. Mutations in the granulin gene (GRN) cause familial frontotemporal dementia. Understanding the structural brain changes in presymptomatic GRN carriers would enforce the use of neuroimaging biomarkers for early diagnosis and monitoring. We studied 100 presymptomatic GRN mutation carriers and 94 noncarriers from the Genetic Frontotemporal dementia initiative (GENFI), with MRI structural images. We analyzed 3T MRI structural images using the FreeSurfer pipeline to calculate the whole brain cortical thickness (CTh) for each subject. We also perform a vertex-wise general linear model to assess differences between groups in the relationship between CTh and diverse covariables as gender, age, the estimated years to onset and education. We also explored differences according to TMEM106B genotype, a possible disease modifier. Whole brain CTh did not differ between carriers and noncarriers. Both groups showed age-related cortical thinning. The group-by-age interaction analysis showed that this age-related cortical thinning was significantly greater in GRN carriers in the left superior frontal cortex. TMEM106B did not significantly influence the age-related cortical thinning. Our results validate and expand previous findings suggesting an increased CTh loss associated with age and estimated proximity to symptoms onset in GRN carriers, even before the disease onset. © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.

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      NeuroImage: Clinical
      Article . 2021
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      Article . 2021
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    Authors: Shamini Selvarajah; Sophie Plante; Marsha Speevak; Andrea K. Vaags; +20 Authors

    Abstract Introduction Genotyping circulating tumor DNA (ctDNA) is a promising noninvasive clinical tool to identify the EGFR T790M resistance mutation in patients with advanced NSCLC with resistance to EGFR inhibitors. To facilitate standardization and clinical adoption of ctDNA testing across Canada, we developed a 2-phase multicenter study to standardize T790M mutation detection using plasma ctDNA testing. Methods In phase 1, commercial reference standards were distributed to participating clinical laboratories, to use their existing platforms for mutation detection. Baseline performance characteristics were established using known and blinded engineered plasma samples spiked with predetermined concentrations of T790M, L858R, and exon 19 deletion variants. In phase II, peripheral blood collected from local patients with known EGFR activating mutations and progressing on treatment were assayed for the presence of EGFR variants and concordance with a clinically validated test at the reference laboratory. Results All laboratories in phase 1 detected the variants at 0.5 % and 5.0 % allele frequencies, with no false positives. In phase 2, the concordance with the reference laboratory for detection of both the primary and resistance mutation was high, with next-generation sequencing and droplet digital polymerase chain reaction exhibiting the best overall concordance. Data also suggested that the ability to detect mutations at clinically relevant limits of detection is generally not platform-specific, but rather impacted by laboratory-specific practices. Conclusions Discrepancies among sending laboratories using the same assay suggest that laboratory-specific practices may impact performance. In addition, a negative or inconclusive ctDNA test should be followed by tumor testing when possible.

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    JTO Clinical and Research Reports
    Article . 2021
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    JTO Clinical and Research Reports
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      JTO Clinical and Research Reports
      Article . 2021
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      JTO Clinical and Research Reports
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    Authors: Jennifer Faber; Tamara Schaprian; Koyak Berkan; Kathrin Reetz; +27 Authors

    BACKGROUND: Given that new therapeutic options for spinocerebellar ataxias are on the horizon, there is a need for markers that reflect disease-related alterations, in particular, in the preataxic stage, in which clinical scales are lacking sensitivity. OBJECTIVE: The objective of this study was to quantify regional brain volumes and upper cervical spinal cord areas in spinocerebellar ataxia type 3 in vivo across the entire time course of the disease. METHODS: We applied a brain segmentation approach that included a lobular subsegmentation of the cerebellum to magnetic resonance images of 210 ataxic and 48 preataxic spinocerebellar ataxia type 3 mutation carriers and 63 healthy controls. In addition, cervical cord cross-sectional areas were determined at 2 levels. RESULTS: The metrics of cervical spinal cord segments C3 and C2, medulla oblongata, pons, and pallidum, and the cerebellar anterior lobe were reduced in preataxic mutation carriers compared with controls. Those of cervical spinal cord segments C2 and C3, medulla oblongata, pons, midbrain, cerebellar lobules crus II and X, cerebellar white matter, and pallidum were reduced in ataxic compared with nonataxic carriers. Of all metrics studied, pontine volume showed the steepest decline across the disease course. It covaried with ataxia severity, CAG repeat length, and age. The multivariate model derived from this analysis explained 46.33% of the variance of pontine volume. CONCLUSION: Regional brain and spinal cord tissue loss in spinocerebellar ataxia type 3 starts before ataxia onset. Pontine volume appears to be the most promising imaging biomarker candidate for interventional trials that aim at slowing the progression of spinocerebellar ataxia type 3. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. Contains fulltext : 244289.pdf (Publisher’s version ) (Open Access)

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    Movement Disorders
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    Article . 2021
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    Article . 2021
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    Scholarship@Western
    Article . 2021
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      Article . 2021
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