Validation of brain‐derived signals in near‐infrared spectroscopy through multivoxel analysis of concurrent functional magnetic resonance imaging. Issue 10 (19th July 2017)

• Record Type: Journal Article
• Authors: Moriguchi, Yoshiya; Noda, Takamasa; Nakayashiki, Kosei; Takata, Yohei; Setoyama, Shiori; Kawasaki, Shingo; Kunisato, Yoshihiko; Mishima, Kazuo; Nakagome, Kazuyuki; Hanakawa, Takashi
• Is Part Of: Human brain mapping. Volume 38:Issue 10(2017)
• Page Start: 5274
• Page End: 5291
• Publication Date: 2017-07-19
• Subjects: fMRI -- NIRS -- multivoxel pattern analysis -- PLSR; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.23734
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Near‐infrared spectroscopy (NIRS) is a convenient and safe brain‐mapping tool. However, its inevitable confounding with hemodynamic responses outside the brain, especially in the frontotemporal head, has questioned its validity. Some researchers attempted to validate NIRS signals through concurrent measurements with functional magnetic resonance imaging (fMRI), but, counterintuitively, NIRS signals rarely correlate with local fMRI signals in NIRS channels, although both mapping techniques should measure the same hemoglobin concentration. Here, we tested a novel hypothesis that different voxel s within the scalp and the brain tissues might have substantially different hemoglobin absorption rates of near‐infrared light, which might differentially contribute to NIRS signals across channels. Therefore, we newly applied a multivariate approach, a partial least squares regression, to explain NIRS signals with multivoxel information from fMRI within the brain and soft tissues in the head. We concurrently obtained fMRI and NIRS signals in 9 healthy human subjects engaging in an n ‐back task. The multivariate fMRI model was quite successfully able to predict the NIRS signals by cross‐validation (interclass correlation coefficient = ∼0.85). This result confirmed that fMRI and NIRS surely measure the same hemoglobin concentration. Additional application of Monte‐Carlo permutation tests confirmed that the model surely reflects temporal and spatial hemodynamic information, not random noise. After this thorough validation, we calculated the ratios of the contributions of the brain and soft‐tissue hemodynamics to the NIRS signals, and found that the contribution ratios were quite different across different NIRS channels in reality, presumably because of the structural complexity of the frontotemporal regions. Hum Brain Mapp 38:5274–5291, 2017 . © 2017 Wiley Periodicals, Inc.