Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep.
Hum Brain Mapp
; 45(12): e70003, 2024 Aug 15.
Article
en En
| MEDLINE
| ID: mdl-39185668
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Procesamiento de Imagen Asistido por Computador
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Encéfalo
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Imagen por Resonancia Magnética
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Huella de Carbono
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
Hum Brain Mapp
Asunto de la revista:
CEREBRO
Año:
2024
Tipo del documento:
Article
País de afiliación:
Reino Unido