Open-source platforms to investigate analytical flexibility in neuroimaging
Bibliographic record
Abstract
Researchers in brain imaging have access to a multitude of analysis tools, many of which carry out the same or similar tasks but yield different results when applied to the same data. This analytical flexibility often undermines reproducibility and raises concerns about the robustness of neuroimaging studies. However, the array of software packages to investigate and address analytical flexibility is decentralized, scattered, and not well documented. Consequently, researchers often lack the necessary information and protocols to buttress the reliability of their findings across analytical tools. This review catalogs and describes software platforms (i.e., software or computational libraries) that can be used to address result variability arising from computational pipelines and environments and explores the use of computing platforms and neuroimaging pipeline frameworks in addressing this issue. This study offers guidance to the research community on accessing, understanding, and utilizing these platforms to address brain imaging analytical flexibility. Additionally, the article provides specific recommendations tailored to different user groups, considering the tools they intend to use with these platforms and their computational constraints.
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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 | 0.017 | 0.096 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".