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


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

September 30, 2013
May 15, 2014
November 20, 2014
near infrared spectroscopy, diffuse optical tomography, hierarchical Bayesian model, automatic relevance determination prior, scalp blood flow
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.
Data files:
  1. [1] Y. Hoshi andM. Tamura, “Detection of dynamic changes in cerebral oxygenation coupled to neuronal function during mental work in man,” Neurosci. Lett., Vol.150, No.1, pp. 5-8, Feb. 1993.
  2. [2] M. Ferrari and V. Quaresima, “A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application,” Neuroimage, Vol.63, No.2, pp. 921-935, Mar. 2012.
  3. [3] T. Takahashi, Y. Takikawa, R. Kawagoe, S. Shibuya, T. Iwano, and S. Kitazawa, “Influence of skin blood flow on near-infrared spectroscopy signals measured on the forehead during a verbal fluency task,” Neuroimage, Vol.57, No.3, pp. 991-1002, Aug. 2011.
  4. [4] Y. Zhang, D. H. Brooks, M. A. Franceschini, and D. A. Boas, “Eigenvector-based spatial filtering for reduction of physiological interference in diffuse optical imaging,” J. Biomed. Opt., Vol.10, No.1, p. 011014, 2005.
  5. [5] S. Kohno, I. Miyai, A. Seiyama, I. Oda, A. Ishikawa, S. Tsuneishi, T. Amita, and K. Shimizu, “Removal of the skin blood flow artifact in functional near-infrared spectroscopic imaging data through independent component analysis,” J. Biomed. Opt., Vol.12, No.6, p. 062111, 2007.
  6. [6] Q. Zhang, G. E. Strangman, and G. Ganis, “Adaptive filtering to reduce global interference in non-invasive NIRS measures of brain activation: how well and when does it work?,” Neuroimage, Vol.45, No.3, pp. 788-94, Apr. 2009.
  7. [7] N. M. Gregg, B.R.White, B. W. Zeff,A. J.Berger, and J. P.Culver, “Brain specificity of diffuse optical imaging: improvements from superficial signal regression and tomography,” Front. Neuroenergetics, Vol.2, pp. 1-8, Jan. 2010.
  8. [8] A. R. Laird, K. M. Mcmillan, J. L. Lancaster, P. Kochunov, P. E. Turkeltaub, J. V Pardo, and P. T. Fox, “A Comparison of Label-Based Review and ALE Meta-Analysis in the Stroop Task,” Hum. Brain Mapp., Vol.21, No.February, pp. 6-21, 2005.
  9. [9] A. P. Gibson, J. C. Hebden, and S. R. Arridge, “Recent advances in diffuse optical imaging,” Phys.Med. Biol., Vol.50, No.4, pp. R1-43, Feb. 2005.
  10. [10] T. Durduran, R. Choe,W. B. Baker, and A. G. Yodh, “Diffuse optics for tissue monitoring and tomography,” Reports Prog. Phys., Vol.73, No.7, p. 076701, Jul. 2010.
  11. [11] J. P. Culver, T. Durduran, D. Furuya, C. Cheung, J. H. Greenberg, and A. G. Yodh, “Diffuse optical tomography of cerebral blood flow, oxygenation, and metabolism in rat during focal ischemia,” J. Cereb. Blood Flow Metab., Vol.23, No.8, pp. 911-924, Aug. 2003.
  12. [12] M. Guven, B. Yazici, X. Intes, and B. Chance, “Diffuse optical tomography with a priori anatomical information,” Phys. Med. Biol., Vol.50, No.12, pp. 2837-2858, Jun. 2005.
  13. [13] N. Cao, A. Nehorai, and M. Jacobs, “Image reconstruction for diffuse optical tomography using sparsity regularization and expectation-maximization algorithm,” Opt. Express, Vol.15, No.21, pp. 13695-13708, Oct. 2007.
  14. [14] H. Niu, F. Tian, Z.-J. Lin, and H. Liu, “Development of a compensation algorithm for accurate depth localization in diffuse optical tomography,” Opt. Lett., Vol.35, No.3, pp. 429-431, Feb. 2010.
  15. [15] T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. Sato, “Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography,” Opt. Express, Vol.20, No.18, pp. 20427-20446, Aug. 2012.
  16. [16] T. Shimokawa, T. Kosaka, O. Yamashita, N. Hiroe, T. Amita, Y. Inoue, and M. Sato, “Extended hierarchical Bayesian diffuse optical tomography for removing scalp artifact,” Biomed. Opt. Express, Vol.4, No.11, pp. 2411-2432, Jan. 2013.
  17. [17] T. Sato, K. Takeda, I. Nambu, T. Aihara, O. Yamashita, Y. Inoue, Y. Otaka, Y. Wada, M. Kawato, M. Sato, and R. Osu, “Reduction of global interference of scalp hemodynamics in functional nearinfrared spectroscopy using short distance probes,” Neuroimage (in revision).
  18. [18] B. R. White and J. P. Culver, “Phase-encoded retinotopy as an evaluation of diffuse optical neuroimaging,” Neuroimage, Vol.49, No.1, pp. 568-577, Jan. 2010.
  19. [19] B. R. White, A. Z. Snyder, A. L. Cohen, S. E. Petersen, M. E. Raichle, B. L. Schlaggar, and J. P. Culver, “Resting-state functional connectivity in the human brain revealed with diffuse optical tomography,” Neuroimage, Vol.47, No.1, pp. 148-156, Aug. 2009.
  20. [20] A. C. Faul and M. E. Tipping, “Analysis of sparse Bayesian learning,” Adv. Neural Inf. Process. Syst., pp. 383-390, 2002.
  21. [21] D. MacKay, “Bayesian nonlinear modeling for the prediction competition,” ASHRAE Trans., Vol.100, Part 2, pp. 1053-1062, 1994.
  22. [22] H. Attias, “Inferring parameters and structure of latent variable models by variational Bayes,” in Proc. 15th Conf. on Uncertainty in Artificial Intelligence, pp. 21-30, 1999.
  23. [23] B. R. Fischl, “FreeSurfer,” Neuroimage, Vol.62, No.2, pp. 774-781, Aug. 2012.
  24. [24] Q. Fang and D. A. Boas, “Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units,” Opt. Express, Vol.17, No.22, pp. 20178-20190, Oct. 2009.
  25. [25] Q. Fang, “Mesh-based Monte Carlo method using fast ray-tracing in Plüker coordinates,” Biomed. Opt. Express, Vol.1, No.1, pp. 165-175, Jul. 2010.
  26. [26] A. Li, G. Boverman, Y. Zhang, D. Brooks, E. L. Miller, M. E. Kilmer, Q. Zhang, E. M. C. Hillman, and D. A. Boas, “Optimal linear inverse solution with multiple priors in diffuse optical tomography,” Appl. Opt., Vol.44, No.10, pp. 1948-1956, Apr. 2005.
  27. [27] E. Kirilina, A. Jelzow, A. Heine, M. Niessing, H.Wabnitz, R. Brühl, B. Ittermann, A. M. Jacobs, and I. Tachtsidis, “The physiological origin of task-evoked systemic artefacts in functional near infrared spectroscopy,” Neuroimage, Vol.61, No.1, pp. 70-81, May 2012.

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