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 qual

SUBMITTER: Souter NE 

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

REPOSITORIES: biostudies-literature