Transdiagnostic Symptom Domains Have Distinct Patterns of Association With Head Motion During Multimodal Imaging in Children

Abstract

Background

It is unclear how transdiagnostic symptoms including attention, disruptive behavior, and internalizing problems are linked to in-scanner motion in children across structural and functional magnetic resonance imaging (fMRI). In the current study, we examined whether transdiagnostic symptoms of attention, disruptive behavior, and internalizing problems were associated with scanner motion in children during multimodal imaging.

Methods

In 9045 children ages 9 to 10 years in the ABCD (Adolescent Brain Cognitive Development) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control (QC) metrics and mean framewise displacement for T1- and T2-weighted structural, resting-state, and diffusion MRI; stop-signal task; monetary incentive delay task; and emotional n-back task. The Child Behavior Checklist was used as a continuous measure of symptom severity.

Results

Greater severity of attention and disruptive behavior problems was associated with a lower likelihood of passing motion QC across imaging modalities, while increased internalizing severity was associated with a higher likelihood of passing. There was also an interaction between sex and attention-related problems in passing QC for T2-weighted and diffusion MRI scans. Increased attention and disruptive behavior problems were associated with increased mean motion, whereas increased internalizing problems were associated with decreased mean motion. Greater severity of attention problems was associated with worse performance across the fMRI tasks.

Conclusions

These findings have implications for advancing the development of computational and behavioral approaches for mitigating motion effects in youths, enhancing accessibility of imaging protocols and representativeness influences across child psychiatric disorders, and identifying brain-based biomarkers.