Fan Zhang1, Daniel Cheong1, Ali F Khan1, Yuxuan Chen2, Lei Ding3, Han Yuan4. 1. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA. 2. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA. 3. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Institute for Biomedical Engineering, Science and Technology, University of Oklahoma, Norman, OK, USA. 4. Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, OK, USA; Institute for Biomedical Engineering, Science and Technology, University of Oklahoma, Norman, OK, USA. Electronic address: hanyuan@ou.edu.
Abstract
BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage. NEW METHOD: We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated. RESULTS: After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas. COMPARISON WITH EXISTING METHODS: Analysis showed that our method improves upon the four established preprocessing methods in the literature. CONCLUSIONS: An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.
BACKGROUND: Functional near-infrared spectroscopy (fNIRS) has been increasingly employed to monitor cerebral hemodynamics in normal and diseased conditions. However, fNIRS suffers from its susceptibility to superficial activity and systemic physiological noise. The objective of the study was to establish a noise reduction method for fNIRS in a whole-head montage. NEW METHOD: We have developed an automated denoising method for whole-head fNIRS. A high-density montage consisting of 109 long-separation channels and 8 short-separation channels was used for recording. Auxiliary sensors were also used to measure motion, respiration and pulse simultaneously. The method incorporates principal component analysis and general linear model to identify and remove a globally uniform superficial component. Our denoising method was evaluated in experimental data acquired from a group of healthy human subjects during a visually cued motor task and further compared with a minimal preprocessing method and three established denoising methods in the literature. Quantitative metrics including contrast-to-noise ratio, within-subject standard deviation and adjusted coefficient of determination were evaluated. RESULTS: After denoising, whole-head topography of fNIRS revealed focal activations concurrently in the primary motor and visual areas. COMPARISON WITH EXISTING METHODS: Analysis showed that our method improves upon the four established preprocessing methods in the literature. CONCLUSIONS: An automatic, effective and robust preprocessing pipeline was established for removing physiological noise in whole-head fNIRS recordings. Our method can enable fNIRS as a reliable tool in monitoring large-scale, network-level brain activities for clinical uses.