Measuring and reducing the carbon footprint of <scp>fMRI</scp> preprocessing in <scp>fMRIPrep</scp>
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.
Authors
- Gabrielle Samuel (ORCID: https://orcid.org/0000-0001-8111-2730)
- R. D. Wilkinson (ORCID: https://orcid.org/0000-0002-3908-7313)
- Charlotte L. Rae (ORCID: https://orcid.org/0000-0002-4503-4971)
- Nikhil Bhagwat (ORCID: https://orcid.org/0000-0001-6073-7141)
- Loïc Lannelongue (ORCID: https://orcid.org/0000-0002-9135-1345)
- Raghavendra Selvan (ORCID: https://orcid.org/0000-0003-4302-0207)
- Chris Racey (ORCID: https://orcid.org/0000-0001-7081-8260)
- Niall W. Duncan (ORCID: https://orcid.org/0000-0002-3225-9119)
- Nicholas E. Souter (ORCID: https://orcid.org/0000-0002-0999-1811)
Institutions
- University of Copenhagen (DK)
- Montreal Neurological Institute and Hospital (CA)
- University of Sussex (GB)
- King's College London (GB)
- University of Cambridge (GB)
- British Heart Foundation (GB)
- Bridge University (SS)
- King's College School (GB)
- Health Data Research UK (GB)
- Taipei Medical University (TW)
- McGill University (CA)
Publication Details
- Journal
- Human Brain Mapping
- Published
- 2024-08-15
- DOI
- https://doi.org/10.1002/hbm.70003
- Citations
- 9
- Primary Topic
- Functional Brain Connectivity Studies
- Type
- article
- Field-Weighted Citation Impact
- 2.74
Funders
- Scottish Government
- National Institute for Health and Care Research
- British Heart Foundation
- Department of Health and Social Care
- University of Sussex
- Public Health Agency
- Chief Scientist Office, Scottish Government Health and Social Care Directorate
- Medical Research Council
- Engineering and Physical Sciences Research Council
- Economic and Social Research Council
- Health and Social Care Research and Development Division
- NIHR Cambridge Biomedical Research Centre