Differences in Head Motion During Functional Magnetic Resonance Imaging Across Pediatric Neuropsychiatric Disorders

IF 4 Q2 NEUROSCIENCES Biological psychiatry global open science Pub Date : 2024-12-27 DOI:10.1016/j.bpsgos.2024.100446

Tracey C. Shi , Katherine Durham , Rachel Marsh , David Pagliaccio

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Abstract

Background

Robust correction for head motion during functional magnetic resonance imaging is critical to avoid artifact-driven findings. Despite head motion differences across neuropsychiatric disorders, pediatric head motion across a range of diagnoses and covariates has not yet been evaluated. We tested 4 preregistered hypotheses: 1) externalizing disorder diagnoses will associate with more head motion during scanning; 2) internalizing disorder diagnoses will associate with less motion; 3) among children without attention-deficit/hyperactivity disorder, externalizing disorders will associate with more motion; and 4) among children with attention-deficit/hyperactivity disorder, comorbid internalizing disorders will associate with less motion.

Methods

Healthy Brain Network data releases 1.0–7.0 (n = 971) were analyzed in a discovery phase, and additional data released by February 29, 2024 (n = 437) were used in confirmatory analyses. Linear mixed-effects models were fitted with in-scanner head motion as the dependent variable. Binary independent variables of interest assessed for the presence or absence of externalizing or internalizing disorders.

Results

The confirmatory sample did not show significant associations between head motion and externalizing or internalizing disorders or support for the preregistered hypotheses. Across samples, there was a consistent interaction between age and neurodevelopmental diagnoses such that age-related decreases in head motion were attenuated in children with neurodevelopmental disorders.

Conclusions

Head motion remains an important confound in pediatric neuroimaging that may be associated with many factors, including neuropsychiatric symptoms, age, cognitive and physical attributes, and interactions among these variables. This work takes a step toward parsing these complex associations, focusing on neuropsychiatric diagnoses, age, and their interaction.

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