Neuroimaging research pipelines group

OHBM SEA-SIG > Neuroimaging research pipelines group

Neuroimaging research pipelines group

Chair: Nick Souter

Neuroimaging research often relies on compute-intensive data processing and analysis in order to study brain structure and function. In order to process and analyse data, computers require energy, which in turn has a carbon footprint. Storing and working with large datasets can also place demands on energy, by requiring regular backups and because novel servers may need to be manufactured and transported. The size of this footprint can be substantial when relying on high-performance computing (HPC). Our work in this area so far has focused on magnetic resonance imaging (MRI). However, other imaging modalities, including MEG, EEG, and PET, frequently also require energy intensive computing – in future we hope to also focus attention on these modalities.

The overall objective of this subgroup is to contribute towards efforts for zero-carbon data processing and storage. This will involve making use of low carbon energy sources, and shifting toward environmentally sustainable computing cluster infrastructure. However, making this change will also rely on changes in user behaviour. That’s why our current work focuses largely on better understanding how the environmental impacts of research computing can be measured and reduced. Specific aims include:

  • Empirically measuring and comparing the carbon footprint of neuroimaging analysis tools, such as FSL, SPM, and fMRIPrep
  • Striving to create and promote green computing tools, including carbon trackers and climate-aware task schedulers
  • Providing education on the impact of computing on the climate crisis, within and beyond neuroimaging research, through workshops, tutorials, and presentations
  • Generating general recommendations for green computing in neuroimaging research
  • Considering and discussing the overlaps and tensions between environmental sustainability and open science practices, such as data sharing and preregistration

SEA-SIG has a separate site hosted on GitHub, with specific examples of our work (Updates coming soon!).

Green computing tools

See our green computing tools page for a list of tools that can be used to measure or reduce the carbon footprint of your computing, including some developed by SEA-SIG members.

To access our toolboxes, click here.

Papers

For publications and preprints focused specifically on green computing from SEA-SIG members, see:

  • Souter, N. E., Racey, C., Bhagwat, N., Wilkinson, R., Duncan, N. W., Samuel, G., Lannelongue, L., Selvan, R., & Rae, C. (2024). Comparing the carbon footprint of fMRI data processing and analysis approaches. OSF Preprints. https://doi.org/10.31219/osf.io/k8gte
  • Souter, N. E., Bhagwat, N., Racey, C., Wilkinson, R., Duncan, N. W., Samuel, G., Lannelongue, L., Selvan, R., & Rae, C. (2024). Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep. Human Brain Mapping, 45(12), e70003. https://doi.org/10.1002/hbm.70003
  • Souter, N. E., Lannelongue, L., Samuel, G., Racey, C., Colling, L., Bhagwat, N., Selvan, R., & Rae, C. (2023). Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging. Imaging Neuroscience, 1, 1-15. https://doi.org/10.1162/imag_a_00043

For other papers focused more generally on the carbon footprint of research computing, see:

  • Lannelongue, L., Aronson, H-E. G., Bateman, A., et al. (2023). GREENER principles for environmentally sustainable computational science. Nature Computational Science, 3, 514-521. https://doi.org/10.1038/s43588-023-00461-y
  • Lannelongue, L., & Inouye, M. (2023). Carbon footprint estimation for computational research.  Nature Reviews Methods Primers, 3, 9. https://doi.org/10.1038/s43586-023-00202-5
  • Li, Y., & Chao, X. (2021). Toward sustainability: Trade-off between data quality and quantity in crop pest recognition. Frontiers in Plant Science, 12, 811241. https://doi.org/10.3389/fpls.2021.811241
  • Malmodin, J., Lövehagen, N., Bergmark, P., & Lundén, D. (2024). ICT sector electricity consumption and greenhouse gas emissions – 2020 outcome. Telecommunications Policy, 48(3), 102701. https://doi.org/10.1016/j.telpol.2023.102701
  • Wilkinson, R., Mleczko, M. M., Brewin, R. J. W., et al. (2024). Environmental impacts of earth observation data in the constellation and cloud computing era. Science of The Total Environment, 909, 168584. https://doi.org/10.1016/j.scitotenv.2023.168584

Resources

To learn more about green computing, check out: