Representation in Brain Imaging Research: A Quebec Demographic Overview
Bibliographic record
Abstract
Neuroimaging research must reflect the diversity of the populations it aims to serve. This scoping review examines the demographic characteristics (age, sex, race and ethnicity, and geographic representation) of participants in brain MRI and positron-emission tomography studies conducted in Quebec, Canada, between 1992 and 2023. A total of 1,549 studies, representing 62,555 participants, were identified through searches of Medline, Embase and Google Scholar, following JBI methodology. The vast majority of studies (92.7%) were conducted in Montreal, with limited representation from other urban centers and almost none from rural areas. Reporting of demographic variables was inconsistent: 22.1% of studies failed to report participant age adequately, and 20.3% did not fully report sex. Race and ethnicity were the most poorly documented, with fewer than 4% of studies reporting this information. Among the 2,396 participants with recorded race and ethnicity, 94.2% were categorized as White, highlighting a significant mismatch with Quebec's population diversity. Healthy participant samples were largely concentrated in the 20-35 age range, while clinical populations generally aligned with the expected age of disease onset. These findings reveal major gaps in demographic representation and reporting in Quebec-based neuroimaging research. Improving diversity and transparency is essential to ensure that neuroimaging findings are generalizable, equitable and clinically meaningful. We recommend the adoption of standardized demographic reporting formats, such as the Brain Imaging Data Structure, and broader recruitment efforts to capture underrepresented groups, including rural residents and racial and ethnic minorities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads 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".