Research Papers: Imaging

Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children

[+] Author Affiliations
Xiao-Su Hu, Megan Gomba, Nicole Confer, Mark Shalinsky

University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104

Maria M. Arredondo

University of Michigan, Department of Psychology, 530 Church Street, Ann Arbor, Michigan 48109

Alexandre F. DaSilva

University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104

University of Michigan, Biologic & Materials Sciences Department, School of Dentistry, Headache & Orofacial Pain Effort Lab, 1011 North University Avenue, Ann Arbor, Michigan 48109

Timothy D. Johnson

University of Michigan, Department of Biostatistics, School of Public Health, 1415 Washington Heights, Ann Arbor, Michigan 48109

Ioulia Kovelman

University of Michigan, Center for Human Growth and Development, 300 North Ingalls Street, Ann Arbor, MI 48104

University of Michigan, Department of Psychology, 530 Church Street, Ann Arbor, Michigan 48109

J. Biomed. Opt. 20(12), 126003 (Dec 11, 2015). doi:10.1117/1.JBO.20.12.126003
History: Received October 26, 2015; Accepted November 5, 2015
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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.

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© 2015 Society of Photo-Optical Instrumentation Engineers

Citation

Xiao-Su Hu ; Maria M. Arredondo ; Megan Gomba ; Nicole Confer ; Alexandre F. DaSilva, et al.
"Comparison of motion correction techniques applied to functional near-infrared spectroscopy data from children", J. Biomed. Opt. 20(12), 126003 (Dec 11, 2015). ; http://dx.doi.org/10.1117/1.JBO.20.12.126003


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