Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?

Abstract

The exclusion of high-motion participants can reduce the impact of motion in functional Magnetic Resonance Imaging (fMRI) data. However, the exclusion of high-motion participants may change the distribution of clinically relevant variables in the study sample, and the resulting sample may not be representative of the population. Our goals are two-fold: 1) to document the biases introduced by common motion exclusion practices in functional connectivity research and 2) to introduce a framework to address these biases by treating excluded scans as a missing data problem. We use a study of autism spectrum disorder to illustrate the problem and the potential solution. We aggregated data from 545 children (8-13 years old) who participated in resting-state fMRI studies at Kennedy Krieger Institute (173 autistic and 372 typically developing) between 2007 and 2020. We found that autistic children were more likely to be excluded than typically developing children, with 29.1% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 80.8% and 59.8% with a stricter criterion. The resulting sample of autistic children with usable data tended to be older, have milder social deficits, better motor control, and higher intellectual ability than the original sample. These measures were also related to functional connectivity strength among children with usable data. This suggests that the generalizability of previous studies reporting naïve analyses (i.e., based only on participants with usable data) may be limited by the selection of older children with less severe clinical profiles because these children are better able to remain still during an rs-fMRI scan. We adapt doubly robust targeted minimum loss based estimation with an ensemble of machine learning algorithms to address these data losses and the resulting biases. The proposed approach selects more edges that differ in functional connectivity between autistic and typically developing children than the naïve approach, supporting this as a promising solution to improve the study of heterogeneous populations in which motion is common.

Increasing the representation of minoritized youth for inclusive and reproducible brain-behavior associations

“…Still, we achieved comparable brain-behavior associations from both single and repeated fMRI scans. The focus should be on obtaining a set of least motion-corrupted timepoints from which data can be resampled, which is of particular importance for developmental and clinical cohorts, as those participants who are excluded for motion artifacts are usually those who are the youngest 13,21,22 or who exhibit more severe symptoms/difficulties (e.g., ADHD, ASD) 43,44 . Retaining their data would not only maximize inclusivity but also enable a wider spectrum of behaviors to be investigated.…”

Systematic evaluation of head motion on resting‐state functional connectivity MRI in the neonate

“…A more stringent cut‐off would expectedly yield a shorter time series. Thus, to prevent the possible confounding effect of different signal‐to‐noise levels due to differences in residual volumes after censoring (Nebel et al, 2022), all data sets were capped at 4 min scan length. For each censored data, mean FD was measured by averaging FD over survived volumes.…”