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JACIII Vol.18 No.6 pp. 1026-1033
doi: 10.20965/jaciii.2014.p1026
(2014)

Paper:

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Hierarchical Bayesian Model for Diffuse Optical Tomography of the Human Brain: Human Experimental Study

Okito Yamashita*,**, Takeaki Shimokawa*, Takashi Kosaka*,
Takashi Amita***, Yoshihiro Inoue***, and Masa-aki Sato*

*Neural Information Analysis Laboratories, ATR, Soraku-gun, Kyoto 619-0288, Japan

**Brain Functional Imaging Technologies Group, CiNet, 1-4 Yamadaoka, Suita City, Osaka 565-0871, Japan

***Medical Systems Division Research and Development Department, Shimadzu Corporation, Nakagyo-ku, Kyoto 604-8511, Japan

Received:
September 30, 2013
Accepted:
May 15, 2014
Published:
November 20, 2014
Creative Commons License
Keywords:
near infrared spectroscopy, diffuse optical tomography, hierarchical Bayesian model, automatic relevance determination prior, scalp blood flow
Abstract
Diffuse optical tomography (DOT) is an emerging technology for improving the spatial resolution of conventional multi-channel near infrared spectroscopy (NIRS). The hemodynamics changes in two distinct anatomical layers, the scalp and the cortex, are known as the main contributor of NIRS measurement. Although any DOT algorithm has the ability to reconstruct scalp and cortical hemodynamics changes in their respective layers, no DOT algorithm has used a model characterizing the distinct nature of scalp and cortical hemodynamics changes to achieve accurate separation. Previously, we have proposed a hierarchical Bayesian model for DOT in which distinct prior distributions for the scalp and the cortical hemodynamics changes are assumed and then verified the reconstruction performance with a phantom experiment and a computer simulation of a real human head model (Shimokawa et al. 2013, Biomedical Optical Express). Here, we investigate the reconstruction accuracy of the proposed algorithm using human experimental data for the first time. We measured the brain activities of a single subject during a finger extension task with NIRS and fMRI. Our DOT reconstruction was compared with the fMRI localization results. Consequently, a remarkable consistency between fMRI and our DOT reconstruction was observed both in the spatial and temporal patterns. By extending the advantages of NIRS such as low running cost and portability with our DOT method, it might be possible to advance brain research in a real environment, which cannot be done with fMRI.
Cite this article as:
O. Yamashita, T. Shimokawa, T. Kosaka, T. Amita, Y. Inoue, and M. Sato, “Hierarchical Bayesian Model for Diffuse Optical Tomography of the Human Brain: Human Experimental Study,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.6, pp. 1026-1033, 2014.
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