Group specific optimisation of fMRI processing steps for child and adult data

Abstract

Motion is a major issue in functional magnetic resonance imaging (fMRI) dataseries and causes artifacts or increased overall noise obscuring signals of interest. It is particularly important to be able to control for and correct these artifacts when dealing with child data. We analysed the data from 35 children (4–8 years old) and 13 adults (18–30 years old) during an emotional face paradigm. The children were split into low and high motion groups on the basis of having less or more than an estimated maximal movement of one voxel (3.75 mm) and one degree of rotation in any motion direction between any pair of scans in the run. Several different preprocessing steps were evaluated for their ability to correct for the excess motion using agnostic canonical variates analysis (aCVA) in the NPAIRS (Nonparametric, Prediction, Activation, Influence, Reproducibility, re-Sampling) framework. The adult dataset was reasonably stable whereas the motion-prone child datasets benefited greatly from motion parameter regression (MPR). Motion parameter regression had a strong beneficial impact on all datasets, a result that was largely unaffected by other preprocessing choices; however, motion correction on its own did not have as much impact. The low motion child group subjected to MPR had reproducibility values at par with those of the adult group, but needed almost twice as many subjects to achieve this result, indicating weaker responses in young children. The aCVA showed greater sensitivity to the task response pattern than the mixed effects general linear model (mGLM) in the expected face processing regions, although the mGLM showed more responses in some other areas. This work illustrates that preprocessing choices must be made in a group-specific fashion to optimise fMRI results.