Individualised MRI training for paediatric neuroimaging in autism spectrum disorders: A child-focused approach
Bibliographic record
Abstract
Abstract Magnetic Resonance Imaging (MRI) in paediatric cohorts is often complicated by reluctance to enter the scanner and head motion-related imaging artefacts. The MRI scanner environment is highly unusual and may distress younger cohorts, especially in children with sensory sensitivity and separation anxiety. The issue is particularly challenging in children with Autism Spectrum Disorders (ASD), where delivery of instructions for novel task demands in an unfamiliar setting may be limited or less effective due to communication disorder or intellectual disability. These issues together often give rise to excessive head motion that can significantly reduce the quality of images acquired, or render data unusable. Here we report an individualised MRI training procedure that enables young children with ASD to better tolerate the MRI scanner environment based on a child-focused approach and an individualised familiarisation strategy. The training procedure was implemented in a locally recruited study of monozygotic twins (n=12; 6 twin pairs; age range 7.1 to 12.85 years) concordant or discordant for ASD. MRI image quality indices were better or comparable to images acquired from an independent multi-centre ASD cohort. The success of these findings is promising and may be useful to improve the quality of paediatric neuroimaging in similar clinical populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".