Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep

Nicholas E. Souter, Nikhil Bhagwat, Chris Racey, Reese Wilkinson, Niall W. Duncan, Gabrielle Samuel, Loïc Lannelongue, Raghavendra Selvan, Charlotte L. Rae

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article numbere70003
JournalHuman Brain Mapping
Volume45
Issue number12
DOIs
Publication statusPublished - Aug 15 2024

Keywords

  • Carbon
  • Computing
  • fMRI
  • fMRIPrep
  • footprint
  • neuroimaging
  • preprocessing

ASJC Scopus subject areas

  • Anatomy
  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology

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