Concurrent fNIRS-fMRI measurement to validate a method for separating deep and shallow fNIRS signals by using multidistance optodes

• Published in: Neurophotonics (Print) Vol. 2; no. 1; p. 015003
• Main Authors: Funane, Tsukasa, Sato, Hiroki, Yahata, Noriaki, Takizawa, Ryu, Nishimura, Yukika, Kinoshita, Akihide, Katura, Takusige, Atsumori, Hirokazu, Fukuda, Masato, Kasai, Kiyoto, Koizumi, Hideaki, Kiguchi, Masashi
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.01.2015
• Subjects: Research Papers; verbal-fluency task; finger tapping; laser Doppler flowmetry; working memory; functional near-infrared spectroscopy; functional magnetic resonance imaging
• ISSN: 2329-423X; 2329-4248; 2329-4248
• DOI: 10.1117/1.NPh.2.1.015003

It has been reported that a functional near-infrared spectroscopy (fNIRS) signal can be contaminated by extracerebral contributions. Many algorithms using multidistance separations to address this issue have been proposed, but their spatial separation performance has rarely been validated with simultaneous measurements of fNIRS and functional magnetic resonance imaging (fMRI). We previously proposed a method for discriminating between deep and shallow contributions in fNIRS signals, referred to as the multidistance independent component analysis (MD-ICA) method. In this study, to validate the MD-ICA method from the spatial aspect, multidistance fNIRS, fMRI, and laser-Doppler-flowmetry signals were simultaneously obtained for 12 healthy adult males during three tasks. The fNIRS signal was separated into deep and shallow signals by using the MD-ICA method, and the correlation between the waveforms of the separated fNIRS signals and the gray matter blood oxygenation level-dependent signals was analyzed. A three-way analysis of variance (signal depth×Hb kind×task) indicated that the main effect of fNIRS signal depth on the correlation is significant [F(1,1286)=5.34, p<0.05]. This result indicates that the MD-ICA method successfully separates fNIRS signals into spatially deep and shallow signals, and the accuracy and reliability of the fNIRS signal will be improved with the method.