Using, misusing, and improving online machine learning-based meta-analysis of neuroimaging published data: A perspective on NeuroQuery
Bibliographic record
Abstract
Online, text-based meta-analysis tools for large databases represent a new digital advance for medical, health, and neuroscience research, among other fields. NeuroQuery is an instance of such a tool for neuroimaging research; it employs supervised machine learning to draw from over 13,000 publications and perform a meta-synthesis, generating predictive fMRI scans based on keyword combinations. Although NeuroQuery is a sophisticated tool, a lack of understanding of how it practically works and its limitations may lead to flawed results and conclusions, undermining its potential value. We review potential risks and limitations, including algorithm limitations, potential biases in the database, and user misinterpretation. Simulating the perspective of an end user, we present an example of unreliable but possible metanalysis results on autistic spectrum disorder (ASD). We then report an analysis of the underlying query from a sophisticated user perspective. Using the same examples, we illustrate possible improvements for the use of NeuroQuery and identify how this tool may be valuable in the context of emerging machine-learning meta-analytical approaches. Although a thorough understanding of NeuroQuery is helpful, we conclude that understanding its limitations plays a more critical role in ensuring validity and reliability of its use. While NeuroQuery is currently not appropriate for rigorous scientific analysis, it could be useful for hypothesis development, preliminary fMRI data mining, exploratory and supplemental analysis as well as literature survey.
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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 | 0.530 | 0.800 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.014 | 0.022 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.023 | 0.017 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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; 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".