ORCID Profile
0000-0002-6507-7063
Current Organisation
Trinity College Dublin
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Publisher: Center for Open Science
Date: 14-03-2022
Abstract: Training in robust research practices is becoming increasingly common. However, many course participants may encounter challenges in implementation of what they learned after returning to their research groups. In this piece, we summarize insights and "lessons learned" from a group of former course participants. We offer practical tips on implementation and cultural change that may be useful for researchers at any career stage. In addition, we provide a list of considerations for course instructors to help them support course attendees after training is over.
Publisher: Springer Science and Business Media LLC
Date: 09-02-2016
DOI: 10.1038/MP.2015.227
Publisher: Public Library of Science (PLoS)
Date: 05-01-2023
DOI: 10.1371/JOURNAL.PCBI.1010750
Abstract: Open, reproducible, and replicable research practices are a fundamental part of science. Training is often organized on a grassroots level, offered by early career researchers, for early career researchers. Buffet style courses that cover many topics can inspire participants to try new things however, they can also be overwhelming. Participants who want to implement new practices may not know where to start once they return to their research team. We describe ten simple rules to guide participants of relevant training courses in implementing robust research practices in their own projects, once they return to their research group. This includes (1) prioritizing and planning which practices to implement, which involves obtaining support and convincing others involved in the research project of the added value of implementing new practices (2) managing problems that arise during implementation and (3) making reproducible research and open science practices an integral part of a future research career. We also outline strategies that course organizers can use to prepare participants for implementation and support them during this process.
Publisher: Springer Science and Business Media LLC
Date: 31-08-2018
DOI: 10.1038/S41380-018-0228-9
Abstract: Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to in idual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. In idual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of in idual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site s le of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different s les was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
Publisher: Elsevier BV
Date: 09-2015
DOI: 10.1016/J.PSCYCHRESNS.2015.05.012
Abstract: Previous structural magnetic resonance imaging (S-MRI) studies of bipolar disorder have reported variable morphological changes in subcortical brain structures and ventricles. This study aimed to establish trait-related subcortical volumetric and shape abnormalities in a large, homogeneous s le of prospectively confirmed euthymic bipolar I disorder (BD-I) patients (n=60), compared with healthy volunteers (n=60). Participants were in idually matched for age and gender. Volume and shape metrics were derived from manually segmented S-MR images for the hippoc us, amygdala, caudate nucleus, and lateral ventricles. Group differences were analysed, controlling for age, gender and intracranial volume. BD-I patients displayed significantly smaller left hippoc al volumes and significantly larger left lateral ventricle volumes compared with controls. Shape analysis revealed an area of contraction in the anterior head and medial border of the left hippoc us, as well as expansion in the right hippoc al tail medially, in patients compared with controls. There were no significant associations between volume or shape variation and lithium status or duration of use. A reduction in the head of the left hippoc us in BD-I patients is interesting, given this region's link to verbal memory. Shape analysis of lateral ventricular changes in patients indicated that these are not regionally specific.
No related grants have been discovered for Joanne Kenney.