RESUMEN
Alzheimer's Disease (AD) is the leading cause of dementia. It results in cortical thickness changes and is associated with a decline in cognition and behaviour. Such decline affects multiple important day-to-day functions, including memory, language, orientation, judgment and problem-solving. Recent research has made important progress in identifying brain regions associated with single outcomes, such as individual AD status and general cognitive decline. The complex projection from multiple brain areas to multiple AD outcomes, however, remains poorly understood. This makes the assessment and especially the prediction of multiple AD outcomes - each of which may unveil an integral yet different aspect of the disease - challenging, particularly when some are not strongly correlated. Here, uniting residual learning, partial least squares (PLS), and predictive modelling, we develop an explainable, generalisable, and reproducible method called the Residual Partial Least Squares Learning (the re-PLS Learning) to (1) chart the pathways between large-scale multivariate brain cortical thickness data (inputs) and multivariate disease and behaviour data (outcomes); (2) simultaneously predict multiple, non-pairwise-correlated outcomes; (3) control for confounding variables (e.g., age and gender) affecting both inputs and outcomes and the pathways in-between; (4) perform longitudinal AD disease status classification and disease severity prediction. We evaluate the performance of the proposed method against a variety of alternatives on data from AD patients, subjects with mild cognitive impairment (MCI), and cognitively normal individuals (n=1,196) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results unveil pockets of brain areas in the temporal, frontal, sensorimotor, and cingulate areas whose cortical thickness may be respectively associated with declines in different cognitive and behavioural subdomains in AD. Finally, we characterise re-PLS' geometric interpretation and mathematical support for delivering meaningful neurobiological insights and provide an open software package (re-PLS) available at https://github.com/thanhvd18/rePLS.
RESUMEN
Introduction: Public engagement is increasingly promoted in the scientific community. Although there are studies about researchers' perspectives on public engagement, these are predominantly from Global North settings and there is little data from the context of Southeast Asia. The Oxford University Clinical Research Unit (OUCRU) is a clinical and public health research programme with sites in Vietnam, Nepal and Indonesia. There is a dedicated public engagement team, and it is recognised as an important part of the research process. Methods: Through this study we explored the views and needs of local researchers with regards to practicing public engagement. We obtained opinions of 70 researchers through an online survey with both open-ended and closed-ended questions. Results: Most researchers perceived public engagement as improving public science literacy, rather than supporting public participation in science and research. While the participants largely see public engagement as a necessary practice, they experienced four main barriers to taking part in public engagement: time, lack of capacity, lack of support and personal perceptions. Most participants indicated they had somewhat to low confidence to communicate about science to the public. Experience, skill and knowledge, and personal preference emerged as factors that influence their perceived confidence for science communication. In our analysis, experience appeared to be the main factor contributing to researchers' high confidence. Recommendations: We recommended to support researchers by not only providing them with training for skills and knowledge, but also with opportunities to conduct public engagement, and a range of methods to suit their personal styles of communicating. It is also evident that more support is needed to build an enabling institutional environment that gives researchers professional recognition for their engagement work. This study, while modest in its scope, has informed our approach to supporting researcher-led engagement, and may guide other institutes wishing to improve this.
