Accounting for motion in resting-state fMRI: What part of the spectrum are we characterizing in autism spectrum disorder?
Year: 2022
Type: article
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 in children without an intellectual disability 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 28.5% and 16.1% of autistic and typically developing children excluded, respectively, using a lenient criterion and 81.0% and 60.1% 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.
Source: NeuroImage
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,450
Funders: Autism Speaks, National Institutes of Health, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Biomedical Imaging and Bioengineering, Eunice Kennedy Shriver National Institute of Child Health and Human Development
Awards: Intellectual and Developmental Disabilities Research Centers 2020, State of the Art 3T Research Scanner, G2826372249, Somatosensory Inhibitory Dysfunction in Autism Spectrum Disorder., G3722706751, Resource for Quantitative Functional FMRI, Intellectual and Developmental Disabilities Research Centers 2013, Adolescent Changes in Brain and Behavior in Boys and Girls with ADHD, G561675859, G5855405599, G6193134970, GABAergic Sensorimotor Dysfunction in Tourette Syndrome, Statistical Methods for Incorporating Machine Learning Tools in Inference and Large-Scale Surveillance using Electronic Medical Records Data, 2/2 - Anomalous Motor System Physiology in ADHD: Biomarker Validation and Modeling Domains of Function, Visual-Motor Development in Infants at High Risk for Autism, Motor Skill Learning in Autism, Delay Discounting in Children with ADHD: Neuroimaging and Behavioral Correlates, G8287830055, G9000057464, G9000329170, Neurobehavioral Correlates of Frustration in Children with ADHD
OpenAlex ID: W4280499774