Children’s head motion during fMRI tasks is heritable and stable over time

Abstract

Head motion during fMRI scans negatively impacts data quality, and as post-acquisition techniques for addressing motion become increasingly stringent, data retention decreases. Studies conducted with adult participants suggest that movement acts as a relatively stable, heritable phenotype that serves as a marker for other genetically influenced phenotypes. Whether these patterns extend downward to childhood has critical implications for the interpretation and generalizability of fMRI data acquired from children. We examined factors affecting scanner motion in two samples: a population-based twin sample of 73 participants (ages 7-12 years) and a case-control sample of 32 non-struggling and 78 struggling readers (ages 8-11 years), 30 of whom were scanned multiple times. Age, but not ADHD symptoms, was significantly related to scanner movement. Movement also varied as a function of task type, run length, and session length. Twin pair concordance for head motion was high for monozygotic twins and moderate for dizygotic twins. Cross-session test-retest reliability was high. Together, these findings suggest that children's head motion is a genetically influenced trait that has the potential to systematically affect individual differences in BOLD changes within and across groups. We discuss recommendations for future work and best practices for pediatric neuroimaging.

Developmental Changes in Task‐Induced Brain Deactivation in Humans Revealed by a Motor Task

“…The average FD of all frames across all participants was 0.087 ± 0.041 mm, 0.062 ± 0.031 mm, and 0.061 ± 0.026 mm for the CH, ADO, and AD groups, respectively. These values were much smaller when compared to those reported in a previous study (Engelhardt et al , ), perhaps owing to our special care to prevent head motions. Although a one‐way ANOVA showed a significant group effect [ F (2, 57) = 3.84, p < 0.05], post‐hoc Bonferroni t ‐tests showed no significant group differences in any combinations.…”

Age differences in head motion and estimates of cortical morphology

“…Less head motion occurs when watching a movie than during rest (replicating Vanderwal et al, 2015; Huijbers et al, 2017). Head motion in different scans from the same individuals is correlated and indexes reliable inter-individual differences (replicating Zeng et al, 2014; Engelhardt et al, 2017; Hodgson et al, 2017). Cortical thickness decreases with age (replicating Fjell et al, 2009; Salat et al, 2004).…”

“…Frame-wise displacement was substantially higher between volumes at the beginning of each scan run, so the first five volumes were excluded. This is the same approach to estimating head motion that is commonly used (e.g., Alexander-Bloch et al, 2016; Engelhardt et al, 2017; Power et al, 2012; Savalia et al, 2017). Estimated directly from the T1-weighted volume as ‘average edge strength’ (AES) (Aksoy et al, 2012; Zacà et al, in press).…”

“…Findings of relationships between obesity and cortical thickness have been mixed (Shaw et al, 2017, 2018; Veit et al, 2014). More generally, head motion has been suggested to be a neurobiological trait–being both stable over time and heritable (Engelhardt et al, 2017; Hodgson et al, 2017; Zeng et al, 2014).…”

Typicality of Functional Connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases and preprocessing pipelines

“…Other methods entail identification and exclusion of time points for which head movement exceeds a certain threshold (Power et al, 2014;Patel et al, 2014). Such threshold becomes increasingly stringent as the effects of motion have received greater recognition (Engelhardt et al, 2017). We did not investigate such measures (e.g.…”