Head motion during fMRI tasks is reduced in children and adults if participants take breaks

Abstract

Head motion remains a challenging confound in functional magnetic resonance imaging (fMRI) studies of both children and adults. Most pediatric neuroimaging labs have developed experience-based, child-friendly standards concerning e.g. the maximum length of a session or the time between mock scanner training and actual scanning. However, it is unclear which factors of child-friendly neuroimaging approaches are effective in reducing head motion. Here, we investigate three main factors including (i) time lag of mock scanner training to the actual scan, (ii) prior scan time, and (iii) task engagement in a dataset of 77 children (aged 6-13) and 64 adults (aged 18-35) using a multilevel modeling approach. In children, distributing fMRI data acquisition across multiple same-day sessions reduces head motion. In adults, motion is reduced after inside-scanner breaks. Despite these positive effects of splitting up data acquisition, motion increases over the course of a study as well as over the course of a run in both children and adults. Our results suggest that splitting up fMRI data acquisition is an effective tool to reduce head motion in general. At the same time, different ways of splitting up data acquisition benefit children and adults.HighlightsIn children, fMRI data acquisition split into multiple sessions reduces head motionIn adults, fMRI data acquisition split by inside-scanner breaks reduces head motionIn both children and adults, motion increases over the duration of a studyIn both children and adults, motion increases over the duration of a run

Impact of concatenating fMRI data on reliability for functional connectomics

“…Another possibility is that the participants might have low head motion at the beginning of each scan (i.e., an onset effect). A recent study found that head motion events occur more frequently with longer scan length and suggested a scanner break inside the scanner to improve head motion in both children and adults samples ( Meissner et al, 2020 ). The present work tested for the presence of an onset effect (i.e., whether concatenating time-series data from the onset of each scan could achieve higher reliability) but did not find any clear evidence to support this hypothesis.…”

“…While the need for more data per individual appears to be straight-forward, how to achieve the desired amount of data is not. It is not always feasible to obtain more data due to practical limitations of a crowded scan protocol, or tolerability of the scanning environment ( Meissner et al, 2020 ; Menon et al, 1997 ; Krause et al, 2019 ). Additionally, this solution does little to address questions about how to improve the amount of data available for analysis in existing data acquisitions.…”

Functional and morphological correlates of developmental dyslexia: A multimodal investigation of the ventral occipitotemporal cortex

“…It is well known that head motion presents a challenge in functional MRI (fMRI) studies, especially in pediatric populations. For this reason, we allowed our participants to take a break every 20 minutes, a procedure that has proved to reduce head motion during functional data acquisition 40 . Metrics about head motion were calculated from the translational and rotational parameters of the rigid correction of the head motion: (1) the maximum absolute translation of each brain volume as compared to the previous volume (maximum motion), (2) the mean absolute displacement of each brain volume as compared to the previous volume (mean motion), (3) the average of the absolute value of the Euler angle of the rotation of each brain volume as compared to the previous volume (rotation), and (4) the framewise displacement (FD) that measures the movement of any given frame relative to the previous frame 41 .…”

Impact of Concatenating fMRI Data on Reliability for Functional Connectomics

“…While the need for more data per individual appears to be straightforward, how to achieve the desired amount of data is not. It is not always feasible to obtain more data due to practical limitations of a crowded scan protocol, or tolerability of the scanning environment (Krause et al, 2019;Meissner et al, 2020;Menon et al, 1997). Additionally, this solution does little to address questions about how to improve the amount of data available for analysis in existing data acquisitions.…”