List of Peer-Reviewed Papers
Sato M, Yamashita O, Sato M-a, Miyawaki Y (2018) Information spreading by a combination of MEG source estimation and multivariate pattern classification. PLoS ONE 13(6): e0198806.
Ogawa T, Aihara T, Shimokawa T, and Yamashita O, (2018), “Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses”, Scientific Reports, 8:6477
Aihara T, Ogawa T, Shimokawa T, and Yamashita O, (2017), “Anodal transcranial direct current stimulation of the right anterior temporal lobe did not significantly affect verbal insight.” PLoS One, 12(9): e0184749
Hirayama J, Hyvärinen A, Kiviniemi V, Kawanabe M, Yamashita O, (2016), “Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis.” PLoS One, 21;11(12):e0168180
Sato T, Nambu I, Takeda K, Aihara T, Yamashita O, Isogaya Y, Inoue Y, Otaka Y, Wada Y, Kawato M, Sato MA, Osu R., (2016), “Reduction of global interference of scalp-hemodynamics in functional near-infrared spectroscopy using short distance probes.” Neuroimage, 141(1), 120-132
Shimokawa T., Ishii T., Takahashi Y., Sugawara S., Sato M., and Yamashita O., (2016), “Diffuse optical tomography using multi-directional sources and detectors.” Biomed. Opt. Express 7, 2623-2640
Yamashita O.,Shimokawa T., Aisu R., Amita T., Inoue Y., Sato M. (2016), “Multi-subject and multi-task experimental validation of the hierarchical Bayesian diffuse optical tomography algorithm.” NeuroImage, 135, 287-299
Takeda, Y., Hiroe, N., Yamashita, O., Sato, M., (2016), “Estimating repetitive spatiotemporal patterns from resting-state brain activity data.” NeuroImage, 133, 251-265
Hoang H, Yamashita O, Tokuda IT, Sato M, Kawato M and Toyama K (2015), “Segmental Bayesian estimation of gap-junctional and inhibitory conductance of inferior olive neurons from spike trains with complicated dynamics”, Front. Comput. Neurosci. 9:56.
Fukushima M, Yamashita O, Knosche TR, Sato M (2015), “MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks”, NeuroImage, Volume 105, 408-427
Yamashita O, Shimokawa T, Kosaka T, Amita T, Inoue Y, and Sato M (2014), “Hierarchical Bayesian Model for Diffuse Optical Tomography of the Human Brain: Human Experimental Study” (open access, need registration to the journal),Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.18, No.6 pp. 1026-1033
Shimokawa T, Kosaka T, Yamashita O, Hiroe N, Amita T, Inoue Y, and Sato M (2013), “Extended hierarchical Bayesian diffuse optical tomography for removing sclap artifact”, Biomedical Optics Express Vol.4, 2411-2432
Shimokawa T, Kosaka T, Yamashita O, Hiroe N, Amita T, Inoue Y, and Sato M (2012),
“Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography”, Optics Express, Vol.20, 20427-20446
Yanagisawa T*, Yamashita O*, Hirata M, Kishima H, Saitoh Y, Goto T, Yoshimine T, and Kamitani Y (2012),“Regulation of motor representation by phase–amplitude coupling in the sensorimotor cortex.” J Neurosci, vol.32(44):15467-75 (*equal contribution)
Fukushima M, Yamashita O, Kanemura A, Ishii S, Kawato M, and Sato M (2012),”A State-Space Modeling Approach for Localization of Focal Current Sources From MEG”. IEEE Transaction on Biomed. Eng., Vol.59,1561-1571
Bosch J, Riera J, Biscay R, Wong K, Galka G, Yamashita O, Sadatao N, Kawashima R, Aubert E, Rodriguez R, Valdes-Sosa P, Miwakeichi F, Ozaki T (2010), “Spatio-temporal correlations from fMRI time series based on the NN-ARx model”, Journal of Integrative Neuroscience, Vol.9, pp.381-406
Fujiwara Y, Yamashita O, Kawawaki D, Doya K, Kawato M, Toyama K and Sato M (2009), “A hierarchical Bayesian method to resolve an inverse problem of MEG contaminated with eye movement artifacts”. NeuroImage, Vol.45, pp.393-409
Miyawaki Y, Uchida H, Yamashita O, Sato M, Morito Y, Tanabe H, Sadato N, Kamitani Y (2008), “Visual image reconstruction from human brain activity using a combination of multiscale local image decoders”. Neuron, Vol.60(5):915-29.
Yoshioka T, Toyama K, Kawato M, Yamashita O, Nishina S, Yamagishi N, Sato M (2008), “Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals”. Neuroimage. Vol.42(4):1397-413.
Yamashita O, Sato M, Yoshioka T, Tong F, Kamitani Y (2008). “Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns”. Neuroimage. Vol.42(4):1414-29.
Shibata K, Yamagishi N, Goda N, Yoshioka T, Yamashita O, Sato M and Kawato M (2008), “The Effects of Feature Attention on Prestimulus Cortical Activity in the Human Visual System”, Cerebral Cortex, Vol.18, pp.1664-75
Wong K, Galka A, Yamashita O and Ozaki T (2006), “Modelling non-stationary variance in EEG time series by state space GARCH model”,Computers in Biology and Medicine, Vol. 36, Issue 12, pp.1327-1335
Yamashita O, Sadato N, Okada T and Ozaki T (2005), “Evaluating frequency-wise directed connectivity of BOLD signals applying relative power contribution with the linear multivariate time-series models”, NeuroImage, Vol.25, Issue 2, pp.478-490
Galka A, Yamashita O and Ozaki T (2004), “GARCH modelling of covariance in dynamical estimation of inverse solutions”, Physics Letters A, Vol. 333, Issues 3-4, 6, pp.261-268
Riera J, Bosch J, Yamashita O, Kawashima R, Sadato N, Okada T and Ozaki T (2004), “fMRI activation maps based on the NNARx model”, NeuroImage, Vol. 23, Issue 2, pp.680-697
Galka A, Yamashita O, Ozaki T, Biscay R and Valdes-Sosa P (2004), “A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering”, NeuroImage, Vol.23, Issue 2, pp.435-453
Yamashita O, Galka A, Ozaki T, Biscay R and Valdes-Sosa P (2004), “Recursive Penalized Least Squares Solution for Dynamical Inverse Problems of EEG Generation”, Human Brain Mapping, Vol.21, Issue 4, pp.221-235
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