Predicting multiscan MRI outcomes in children with neurodevelopmental conditions following MRI simulator training

Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet...

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Bibliographic Details
Published in:Developmental cognitive neuroscience 2021-12, Vol.52, p.101009-101009, Article 101009
Main Authors: Simhal, Anish K., Filho, José O.A., Segura, Patricia, Cloud, Jessica, Petkova, Eva, Gallagher, Richard, Castellanos, F. Xavier, Colcombe, Stan, Milham, Michael P., Di Martino, Adriana
Format: Article
Language:English
Subjects:
Attention Deficit Disorder with Hyperactivity
Attention deficit hyperactivity disorder
Autism Spectrum Disorder
Brain - diagnostic imaging
Child
Children
Humans
Magnetic Resonance Imaging - methods
Male
Motion
MRI simulator
MRI training
Neurodevelopmental disorders
Neuroimaging
Original Research
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Summary:Pediatric brain imaging holds significant promise for understanding neurodevelopment. However, the requirement to remain still inside a noisy, enclosed scanner remains a challenge. Verbal or visual descriptions of the process, and/or practice in MRI simulators are the norm in preparing children. Yet, the factors predictive of successfully obtaining neuroimaging data remain unclear. We examined data from 250 children (6–12 years, 197 males) with autism and/or attention-deficit/hyperactivity disorder. Children completed systematic MRI simulator training aimed to habituate to the scanner environment and minimize head motion. An MRI session comprised multiple structural, resting-state, task and diffusion scans. Of the 201 children passing simulator training and attempting scanning, nearly all (94%) successfully completed the first structural scan in the sequence, and 88% also completed the following functional scan. The number of successful scans decreased as the sequence progressed. Multivariate analyses revealed that age was the strongest predictor of successful scans in the session, with younger children having lower success rates. After age, sensorimotor atypicalities contributed most to prediction. Results provide insights on factors to consider in designing pediatric brain imaging protocols.
ISSN:1878-9293
1878-9307
DOI:10.1016/j.dcn.2021.101009