title: Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children.
abstract: Motion artifacts are the most significant sources of noise in the context of pediatric brain imaging designs and data analyses, especially in applications of functional near-infrared spectroscopy (fNIRS), in which it can completely affect the quality of the data acquired. Different methods have been developed to correct motion artifacts in fNIRS data, but the relative effectiveness of these methods for data from child and infant subjects (which is often found to be significantly noisier than adult data) remains largely unexplored. The issue is further complicated by the heterogeneity of fNIRS data artifacts. We compared the efficacy of the six most prevalent motion artifact correction techniques with fNIRS data acquired from children participating in a language acquisition task, including wavelet, spline interpolation, principal component analysis, moving average (MA), correlation-based signal improvement, and combination of wavelet and MA. The evaluation of five predefined metrics suggests that the MA and wavelet methods yield the best outcomes. These findings elucidate the varied nature of fNIRS data artifacts and the efficacy of artifact correction methods with pediatric populations, as well as help inform both the theory and practice of optical brain imaging analysis.
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104.
authors.forename: Xiao-Su
authors.initials: XS
authors.lastname: Hu
authors.affiliation: University of Michigan, Department of Psychology, 530 Church Street, Ann Arbor, Michigan 48109.
authors.forename: Maria M
authors.initials: MM
authors.lastname: Arredondo
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104.
authors.forename: Megan
authors.initials: M
authors.lastname: Gomba
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104.
authors.forename: Nicole
authors.initials: N
authors.lastname: Confer
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104cUniversity of Michigan, Biologic & Materials Sciences Department, School of Dentistry, Headache & Orofacial Pain Effort Lab, 1011 North Universi.
authors.forename: Alexandre F
authors.initials: AF
authors.lastname: DaSilva
authors.affiliation: University of Michigan, Department of Biostatistics, School of Public Health, 1415 Washington Heights, Ann Arbor, Michigan 48109.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Johnson
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104.
authors.forename: Mark
authors.initials: M
authors.lastname: Shalinsky
authors.affiliation: University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104bUniversity of Michigan, Department of Psychology, 530 Church Street, Ann Arbor, Michigan 48109.
authors.forename: Ioulia
authors.initials: I
authors.lastname: Kovelman
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001921:Brain / Q000502:physiology; D002648:Child; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007802:Language; D008297:Male; D015233:Models, Statistical; D009038:Motion; D059906:Neuroimaging / Q000379:methods*; D025341:Principal Component Analysis; D012044:Regression Analysis; D019265:Spectroscopy, Near-Infrared / Q000379:methods*