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Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

ABSTRACT: 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 functional magnetic resonance imaging (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.

SUBMITTER: Souter NE

PROVIDER: S-EPMC11345634 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

Souter Nicholas E NE Bhagwat Nikhil N Racey Chris C Wilkinson Reese R Duncan Niall W NW Samuel Gabrielle G Lannelongue Loïc L Selvan Raghavendra R Rae Charlotte L CL

Human brain mapping 20240801 12

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 functional magnetic resonance imaging (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 ...[more]

PMID: 39185668

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Project description:Energy and carbon (C) footprints of agricultural production practices have garnered high attention due to rising energy costs and increasing global warming. However, the contribution of conservation and regenerative farming practices, including cover cropping, on energy and C footprints have not yet been documented for cropping systems in arid and semi-arid regions. This study evaluated the energy and C footprint of cover crop integrated silage maize (Zea mays L.) and sorghum (Sorghum bicolor L. Moench) production systems in the semi-arid region of the southwestern US. The treatments were mixtures of winter cover crops: i) grasses and legumes (GL), ii) grasses, brassicas, and legumes (GBL), iii) grasses and brassicas (GB), and iv) no cover crops (NCC) control for each crop production system. Results showed cover crops had 24.1-24.5% greater energy input than NCC. In silage maize rotation, energy output was 17-22% greater in GBL and GL than in NCC. In silage sorghum rotation, the energy output was 15-24% greater in all cover crops than in NCC. The resulting net energy was 16-21% greater in GBL and GL than in NCC under silage maize, while it was 18-24% greater in GBL and GB than in NCC under silage sorghum. In the silage maize system, the C-footprint per kg yield was not different among treatments, whereas in silage sorghum, it was 58% greater in GBL than in NCC. The benefit-to-cost ratio was greater than one for all treatments, but the additional revenue through C credit programs could make cover cropping a more feasible and beneficial approach, improving economic and environmental sustainability while producing silage crops. While the C footprint was crop rotation specific, cover cropping should be encouraged over crop-fallow systems to producers in semi-arid environments to reduce energy usage and increase C-credit benefits. Clear national and state policy on the C credit program will also enhance economic and environmental benefits by adopting cover cropping and other regenerative farming practices.

Dataset Information

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

Publications

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.

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