We compared the carbon emissions of preprocessing and statistical analysis of fMRI data in software packages FSL, SPM, and fMRIPrep using an existing open access dataset. Carbon emissions for fMRIPrep were 30x larger than those of FSL, and 23x those of SPM. We also compared the scientific performance of each package, reflected by sensitivity to statistical activation. Overall, fMRIPrep demonstrated slightly superior statistical sensitivity to both FSL and SPM, with FSL also outperforming SPM. However, this pattern varied by brain region. Researchers analysing fMRI data can use these findings to inform their choice of software package, considering the carbon footprint of data processing alongside usability and quality of derived output. Researchers should be conscious of how and when tools that elicit heavy compute are used, minimising energy usage and subsequent file size when possible. Researchers developing and using such tools should consider the extent to which computationally expensive steps are necessary to produce high quality results.

There is inconsistent evidence concerning whether physical pain and vicarious pain share neural resources. This may reflect different methodological approaches (e.g., univariate vs. multivariate fMRI analyses) and/or participant characteristics. Here we contrast people who report experiencing pain when seeing others in pain (vicarious pain responders) with non-responders (who do not report pain). Cues indicated the level and location of an electrical shock delivered to the participant (self) or experimenter (other), with behavioural ratings and neural responses (fMRI) obtained. Non-responders tend to rate their own pain as worse than others given identical cues, whereas responders show greater similarity between self and other ratings. Univariate neuroimaging analyses showed activity in regions of the pain matrix such as insula, mid-cingulate, and somatosensory cortices contrasting physical versus vicarious pain, and when regressing the level of self-pain. But these analyses did not differ by group. Multivariate analyses, by contrast, revealed several group differences. The ability to classify self versus other was less accurate in the vicarious pain responders (in the same regions implicated in the univariate analyses of physical pain). In conclusion, the degree of shared neural responses to physical and vicarious pain is increased in vicarious pain responders consistent with the notion of differences in the self–other boundary.

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 are stored.