Comparing the carbon footprint of fMRI data processing and analysis approaches
Bibliographic record
Abstract
We compared the carbon emissions of preprocessing and statistical analysis of fMRI data in software packages FSL, SPM, and fMRIPrep using an existing open access dataset. Carbon emissions for fMRIPrep were 30x larger than those of FSL, and 23x those of SPM. We also compared the scientific performance of each package, reflected by sensitivity to statistical activation. Overall, fMRIPrep demonstrated slightly superior statistical sensitivity to both FSL and SPM, with FSL also outperforming SPM. However, this pattern varied by brain region. Researchers analysing fMRI data can use these findings to inform their choice of software package, considering the carbon footprint of data processing alongside usability and quality of derived output. Researchers should be conscious of how and when tools that elicit heavy compute are used, minimising energy usage and subsequent file size when possible. Researchers developing and using such tools should consider the extent to which computationally expensive steps are necessary to produce high-quality results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".