Journal
JOURNAL OF BIOMEDICAL OPTICS
Volume 20, Issue 12, Pages -Publisher
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
DOI: 10.1117/1.JBO.20.12.126003
Keywords
functional near-infrared spectroscopy; motion artifact correction; child brain imaging
Funding
- NIDCR NIH HHS [R56 DE022637, U01 DE025633] Funding Source: Medline
- NINDS NIH HHS [R01 NS094413, K23 NS062946] Funding Source: Medline
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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. (C) 2015 Society of Photo-Optical Instrumentation Engineers (SPIE)
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