Reporting Checklists in Neuroimaging: Promoting Transparency, Replicability, and Reproducibility
Bibliographic record
Abstract
Neuroimaging plays a crucial role in understanding brain structure and function. Nevertheless, the lack of transparency, reproducibility, and reliability of the findings is a significant obstacle for the field. To address these challenges, there is an ongoing effort to develop reporting checklists for neuroimaging studies to increase the likelihood that fundamental aspects of study design and execution are reported. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with the currently available imaging modality-specific neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieved the intended objectives. We propose a development cycle for neuroimaging reporting checklists and describe each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can strengthen checklist development and adherence. We also identify roles that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones. Ultimately, the aim of this effort is to increase the quality, transparency, and reproducibility of neuroimaging research.
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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 | 0.779 | 0.899 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".