Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep

• Published in: Human brain mapping Vol. 45; no. 12; pp. e70003 - n/a
• Main Authors: Souter, Nicholas E., Bhagwat, Nikhil, Racey, Chris, Wilkinson, Reese, Duncan, Niall W., Samuel, Gabrielle, Lannelongue, Loïc, Selvan, Raghavendra, Rae, Charlotte L.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.08.2024
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Brain Mapping - standards; Carbon; Carbon Footprint; Computational neuroscience; Computing; Data processing; Emission measurements; Emissions; Emissions control; Energy consumption; Female; Flags; fMRI; fMRIPrep; footprint; Footprint analysis; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Image Processing, Computer-Assisted - standards; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Medical imaging; Neuroimaging; Parameter estimation; Preprocessing; Signal quality; Smoothness; Young Adult; fMRI; footprint; fMRIPrep; preprocessing; Computing; Carbon; neuroimaging
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.70003

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. Computing needed for data processing has a carbon footprint. We estimated the carbon emissions arising from eight variants of functional magnetic resonance imaging (fMRI) preprocessing pipeline fMRIPrep. Emissions can be reduced substantially without comprising preprocessing performance, measured using statistical activation and data smoothness. For instance, disabling FreeSurfer surface reconstruction reduced average emissions by 48%.