Healthcare professionals’ and patients’ perspectives on consent to clinical genetic testing: moving towards a more relational approach

Background: This paper proposes a refocusing of consent for clinical genetic testing, moving away from an emphasis on autonomy and information provision, towards an emphasis on the virtues of healthcare professionals seeking consent, and the relationships they construct with their patients. Methods: We draw on focus groups with UK healthcare professionals working in the field of clinical genetics, as well as in-depth interviews with patients who have sought genetic testing in the UK’s National Health Service (data collected 2013–2015). We explore two aspects of consent: first, how healthcare professionals consider the act of ‘consenting’patients; and second how these professional accounts, along with the accounts of patients, deepen our understanding of the consent process. Results: Our findings suggest that while healthcare professionals working in genetic medicine put much effort into ensuring patients’understanding about their impending genetic test, they acknowledge, and we show, that patients can still leave genetic consultations relatively uninformed. Moreover, we show how placing emphasis on the informational aspect of genetic testing is not always reflective of, or valuable to, patients’decision-making. Rather, decision-making is socially contextualises–also based on factors outside of information provision. Conclusions: A more collaborative on-going consent process, grounded in virtue ethics and values of honesty,openness and trustworthiness, is proposed

Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging

Given that scientific practices contribute to the climate crisis, scientists should reflect on the planetary impact of their work. Research computing can have a substantial carbon footprint in cases where researchers employ computationally expensive processes with large amounts of data. Analysis of human neuroimaging data, such as Magnetic Resonance Imaging brain scans, is one such case. Here, we consider ten ways in which those who conduct human neuroimaging research can reduce the carbon footprint of their research computing, by making adjustments to the ways in which studies are planned, executed, and analysed; as well as where and how data is stored.</p

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep

Computationally expensive data processing in neuroimaging research places demands on energy consumption – and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the fMRI preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data. Eight variants of fMRIPrep were run with 257 participants who had completed an fMRI stop signal task (the same data also used in the original validation of fMRIPrep). Some variants led to substantial reductions in carbon emissions without sacrificing data quality: for instance, disabling FreeSurfer surface reconstruction reduced carbon emissions by 48%. We provide six recommendations for minimising emissions without compromising performance. By varying parameters and computational resources, neuroimagers can substantially reduce the carbon footprint of their preprocessing. This is one aspect of our research carbon footprint over which neuroimagers have control and agency to act upon.</p