How to best train children and adolescents for fMRI? Meta-analysis of the training methods in developmental neuroimaging
Bibliographic record
Abstract
Neuroeducation aims to improve pedagogical approaches by adding neuroimaging data. Practical and technical challenges emerge when children undergo magnetic resonance imaging (MRI), thereby raising several problems. We performed a meta-analysis of functional MRI datasets that were published during 1995 to 2011 according to the type of training of 4001 typically developing children and adolescents. The meta-analysis investigated whether different types of training (standard, mock, coaching trainings) improved the success rate of functional MRI inclusion rate and decreased the exclusion rate for excessive motion. We wondered if these specific trainings have differential developmental effects. Additionally, we examined if certain factors, such as age, the type of the cognitive tasks, the sex ratio, the financial compensation, the session order with structural MRI and the duration of the functional runs would influence the functional MRI success rate (more inclusion and less exclusion). The results indicated that coaching training for all of the children is the most relevant type of training to reduce motion and include more data. The type of task also took part in the success rate for fMRI. We propose guidelines to optimize the inclusion rate of functional MRI studies with typically developing children. Finally, we offer clinical and educational implications.
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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 | 0.037 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.027 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".