{"id":13,"date":"2017-09-06T09:33:00","date_gmt":"2017-09-06T00:33:00","guid":{"rendered":"http:\/\/www.cns.atr.jp\/~oyamashi\/?page_id=13"},"modified":"2025-11-26T09:56:39","modified_gmt":"2025-11-26T00:56:39","slug":"papers","status":"publish","type":"page","link":"https:\/\/bicr.atr.jp\/~oyamashi\/papers\/","title":{"rendered":"\u767a\u8868\u8ad6\u6587"},"content":{"rendered":"
\n

List of Peer-Reviewed Papers<\/strong><\/p>\n

Kashiwagi, Y., Tokuda, T., Takahara, Y., Masaki, Y., Sakai, Y., Yoshimoto, J., … & Yamashita, O.<\/strong> (2025). Generalizable stratification based on thalamo\u2013somatomotor functional connectivity predicts responses to antidepressants in patients with depression.<\/a>\u00a0Molecular Psychiatry<\/i>, 1-12.<\/p>\n

Yamashita, O<\/strong>., Yamashita, A., Takahara, Y., Sakai, Y., Okamoto, Y., Okada, G., … & Kawato, M. (2025). Computational mechanisms of neuroimaging biomarkers uncovered by multicenter resting-state fMRI connectivity variation profile.<\/a>\u00a0Molecular Psychiatry<\/i>, 1-12.<\/p>\n

Ogawa, T., Aihara, T., & Yamashita, O.<\/strong> (2025). Neural correlates and dynamical brain states of creative insight in a spatial problem task.<\/a>\u00a0Scientific Reports<\/i>,\u00a015<\/i>(1), 28216.<\/p>\n

Li, Y., Chen, B., Hu, Z., Suzuki, K., Bai, W., Koike, Y., & Yamashita, O.<\/strong> (2025). Correntropy-Based Improper Likelihood Model for Robust Electrophysiological Source Imaging.<\/a>\u00a0IEEE Transactions on Medical Imaging<\/i>.<\/p>\n

Takahara, Y., Kashiwagi, Y., Tokuda, T., Yoshimoto, J., Sakai, Y., Yamashita, A., … & Yamashita, O.<\/strong> (2025). Comprehensive evaluation of pipelines for classification of psychiatric disorders using multi-site resting-state fMRI datasets.<\/a>\u00a0Neural Networks<\/i>,\u00a0187<\/i>, 107335.<\/p>\n

Li, Y., Chen, B., Yoshimura, Koike, Y.,\u00a0 Yamashita, O.<\/strong> (2025)\u00a0 Sparse Bayesian correntropy learning for robust muscle activity reconstruction from noisy brain recordings<\/a>, Neural Networks<\/em>, 182, 106899<\/p>\n

Bai, W., Yamashita, O<\/strong>., & Yoshimoto, J. (2025). Functionally specialized spectral organization of the resting human cortex.\u00a0<\/a>Neural Networks<\/i>, 107195.<\/p>\n

Itahashi, T., Yamashita, A., Takahara, Y., Yahata, N., Aoki, Y. Y., Fujino, J., …, Yamashita O.<\/strong> & Hashimoto, R. I. (2024). Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism.<\/a> Molecular Psychiatry<\/em>, 1-13.<\/p>\n

Tanaka, S. C., Kasai, K., Okamoto, Y., Koike, S., Hayashi, T., Yamashita, A., Yamashita O.<\/strong> … & Hanakawa, T. (2024). The status of MRI databases across the world focused on psychiatric and neurological disorders.<\/a> Psychiatry and Clinical Neurosciences<\/em>, 78(10):563-57.<\/p>\n

Fukuma, R., Majima, K., Kawahara, Y., Yamashita, O<\/strong>., Shiraishi, Y., Kishima, H., & Yanagisawa, T. (2024). Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition.<\/a>\u00a0Communications Biology<\/i>,\u00a07<\/i>(1), 595.<\/p>\n

Endo, H., Ikeda, S., Harada, K., Yamagata, H., Matsubara, T., Matsuo, K., … & Yamashita, O.<\/strong> (2024). Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data<\/a>.\u00a0Frontiers in Psychiatry<\/i>,\u00a015<\/i>, 1288808.<\/p>\n

Ajioka, T, Nakai, N, Yamashita, O<\/strong>, Takumi, T (2024) End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging.<\/a> PLoS Comput Biol 20(3): e1011074.<\/p>\n

Takeda, Y., Gomi, T., Umebayashi, R., Tomita, S., Suzuki, K., Hiroe, N., … & Yamashita, O.<\/strong> (2023). Sensor array design of optically pumped magnetometers for accurately estimating source currents.<\/a>\u00a0NeuroImage<\/i>,\u00a0277<\/i>, 120257.<\/p>\n

Bai, W., Yamashita, O.<\/strong>, & Yoshimoto, J. (2023). Learning task-agnostic and interpretable subsequence-based representation of time series and its applications in fMRI analysis.\u00a0<\/a>Neural Networks<\/i>,\u00a0163<\/i>, 327-340.<\/p>\n

Nakai, N., Sato, M., Yamashita, O.<\/strong>, Sekine, Y., Fu, X., Nakai, J., … & Takumi, T. (2023). Virtual reality-based real-time imaging reveals abnormal cortical dynamics during behavioral transitions in a mouse model of autism.<\/a>\u00a0Cell Reports<\/i>.<\/p>\n

Nakamura, Y., Ishida, T., Tanaka, S.C., Mitsuyama, Y., Yokoyama, S., … Yamashita, O.<\/strong>, Imamizu, H., Morimoto, J., Okamoto, Y., Murai, T., Hashimoto, R.-I., Kasai, K., Kawato, M. and Koike, S. (2023), Distinctive alterations in the mesocorticolimbic circuits in various psychiatric disorders<\/a>. Psychiatry Clinical Neuroscience.<\/em><\/p>\n

Ishida, T., Nakamura, Y.,…. ,Yamashita, O.<\/strong>,…. and Koike, S. (2023), Aberrant Large-Scale Network Interactions Across Psychiatric Disorders Revealed by Large-Sample Multi-Site Resting-State Functional Magnetic Resonance Imaging Datasets<\/a>, Schizophrenia Bulletin, <\/em>sbad022<\/p>\n

Okada, G., Yoshioka, T., Yamashita, A., Itai, E., Yokoyama, S., Kamishikiryo, T., … Yamashita O<\/strong>. ,… Okamoto, Y. (2023). Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data.<\/a> Journal of Affective Disorders,<\/i> 326, 262-266.<\/p>\n

Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Yamashita, O<\/strong>., Hosomi, K., … & Saitoh, Y. (2022). Neurofeedback training without explicit phantom hand movements and hand-like visual feedback to modulate pain: A randomized crossover feasibility trial.\u00a0<\/a>The Journal of Pain,<\/i>\u00a023<\/i>(12), 2080-2091.<\/p>\n

Ikeda, S., Kawano, K., Watanabe,\u00a0 S., Yamashita, O.<\/strong>, & Kawahara, Y. (2022). Predicting behavior through dynamic modes in resting-state fMRI data.<\/a> NeuroImage<\/em>, Vol.247, p.118801<\/p>\n

Takeda, Y., Hiroe, N., & Yamashita, O.<\/strong> (2021). Whole-brain propagating patterns in human resting-state brain activities.<\/a>\u00a0NeuroImage<\/i>, Vol.245, p.118711<\/p>\n

Hayashi, R., Yamashita, O.*<\/strong>, Yamada, T., Kawaguchi, H., & Higo, N. (2021). Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey.<\/a>\u00a0Cerebral Cortex Communications<\/i>.\u00a0 (*equal contribution)<\/p>\n

Tanaka, S.C., Yamashita, A., Yahata, N.,Yamashita, O<\/strong>., Kawato, M., & Imamizu, H. (2021). <\/i>\u00a0A multi-site, multi-disorder resting-state magnetic resonance image database.<\/a>\u00a0Scientific Data<\/i>\u00a08,\u00a0<\/b>227.<\/p>\n

Maikusa, N., Zhu, Y., Uematsu, A., Yamashita, A., Saotome, K., Okada, N., …, Yamashita, O.<\/strong>, Tanaka C. S. & Koike, S. (2021). Comparison of traveling\u2010subject and ComBat harmonization methods for assessing structural brain characteristics<\/a>. Human Brain Mapping<\/em>.<\/p>\n<\/div>\n

Tokuda, T., Yamashita, O<\/strong>., Sakai, Y., & Yoshimoto, J. (2021). Clustering of multiple psychiatric disorders using functional connectivity in the data-driven brain subnetwork<\/a>.\u00a0Frontiers in Psychiatry<\/i>, 1428.<\/p>\n

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Tokuda, T., Yamashita, O<\/strong>., & Yoshimoto, J. (2021). Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization.<\/a>\u00a0Neural Networks<\/i>,\u00a0142<\/i>, 269-287.<\/p>\n

Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., … Yamashita, O<\/strong> & Imamizu, H. (2021). Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts.<\/a> Frontiers in Psychiatry<\/i>,\u00a012<\/i>, 888.<\/p>\n

Suzuki, K., & Yamashita, O.<\/strong> (2021). MEG current source reconstruction using a meta-analysis fMRI prior.<\/a>\u00a0NeuroImage<\/i>, 118034.<\/p>\n

Koike, S., Tanaka, S. C., Okada, T., Aso, T., Yamashita, A., Yamashita, O.<\/strong>, … & Hayashi, T. (2021). Brain\/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan.<\/a>\u00a0NeuroImage: Clinical<\/i>, 102600.<\/p>\n

Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., … Yamashita, O<\/strong> & Imamizu, H. (2020). Generalizable brain network markers of major depressive disorder across multiple imaging sites.<\/a> PLoS biology<\/i>,\u00a018<\/i>(12), e3000966.<\/p>\n

Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O.<\/strong>, … & Saitoh, Y. (2020). BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial.\u00a0<\/a>Neurology<\/i>,\u00a095<\/i>(4), e417-e426.<\/p>\n

Shiraishi, Y., Kawahara, Y., Yamashita, O<\/strong>., Fukuma, R., Yamamoto, S., Saitoh, Y., … & Yanagisawa, T. (2020). Neural decoding of electrocorticographic signals using dynamic mode decomposition.<\/a>\u00a0Journal of Neural Engineering<\/i>. 17, 3.<\/p>\n

Aihara, T., Shimokawa, T., Ogawa, T., Okada, Y., Ishikawa, A., Inoue, Y.,\u00a0 Yamashita, O<\/strong>. (2020). Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography.<\/a> Frontiers in neuroscience<\/i>,\u00a014<\/i>, 32.<\/p>\n

Sanger, T. D., Yamashita, O.<\/strong>, Kawato, M. (2020). Expansion coding and computation in the cerebellum: 50 years after the Marr\u2013Albus codon theory.<\/a> The Journal of Physiology<\/i>,\u00a0598<\/i>(5), 913-928.<\/p>\n

Endo, H., Hiroe, N., Yamashita, O. <\/strong> (2020). Evaluation of resting spatio-temporal dynamics of a neural mass model using resting fMRI connectivity and EEG microstates.<\/a> Frontiers in Computational Neuroscience<\/em>, 13, 91.<\/p>\n

Takeda Y., Itahashi T., Sato M., Yamashita O.<\/strong>\u00a0(2019), Estimating repetitive spatiotemporal patterns from many subjects\u2019 resting-state fMRIs<\/a>, NeuroImage<\/em>,\u00a0 Vol 2-3,\u00a0 p. 116182,<\/p>\n

Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, …, Yamashita O<\/strong>., Imamizu H. (2019) Harmonization of resting-state functional MRI data across multiple imaging sites via the separation of site differences into sampling bias and measurement bias.<\/a> PLoS Biol<\/em> 17(4): e3000042<\/p>\n<\/div>\n

Takeda Y., Suzuki K., Kawato M., Yamashita O.<\/strong>\u00a0(2019), MEG Source Imaging and Group Analysis Using VBMEG<\/a>, Frontiers in Neuroscience<\/em>, 13, 241<\/p>\n

Shimokawa, T., Ishii, T., Takahashi, Y., Mitani, Y., Mifune, H., Chubashi, S., Satoh, M., Oba, Y., Adachi, K., Sugawara, S., Yamashita, O<\/strong>. (2019), Development of multi-directional functional near-infrared spectroscopy system for human neuroimaging studies<\/a>,\u00a0Biomedical Optics Express<\/em>, Vol.\u00a011<\/strong>, No. 3, pp.1393-1404<\/p>\n

Filatova, O., Yang, Y., Dewald, J., Tian, R., Maceira-Elvira, P., Takeda, Y., Kwakkel, G., Yamashita, O.<\/strong>, van der Helm FCT (2018), Dynamic information flow based on EEG and diffusion MRI in stroke: a proof-of-principle study<\/a>, Frontiers in Neural Circuits<\/em>, Vol.12<\/strong>, Article 79<\/p>\n

Sato M, Yamashita O<\/strong>, Sato Ma, Miyawaki Y (2018) Information spreading by a combination of MEG source estimation and multivariate pattern classification.<\/a> PLoS ONE<\/em> 13(6): e0198806.<\/p>\n

Ogawa T, Aihara T, Shimokawa T, and Yamashita O<\/strong>\u00a0(2018), Large-scale brain network associated with creative insight: combined voxel-based morphometry and resting-state functional connectivity analyses<\/a>, Scientific Reports<\/em>, 8:6477<\/p>\n

Aihara T, Ogawa T, Shimokawa T, and Yamashita O <\/strong>(2017), Anodal transcranial direct current stimulation of the right anterior temporal lobe did not significantly affect verbal insight.<\/a> PLoS One<\/em>,\u00a012(9): e0184749<\/p>\n

Hirayama J, Hyv\u00e4rinen A, Kiviniemi V, Kawanabe M, Yamashita O<\/strong>\u00a0(2016), Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis.<\/a>\u00a0 PLoS One<\/em>, 21;11(12):e0168180<\/p>\n

Sato T, Nambu I, Takeda K, Aihara T, Yamashita O<\/strong>, 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.<\/a> Neuroimage<\/em>, 141(1), 120-132<\/p>\n

Shimokawa T., Ishii T., Takahashi Y., Sugawara S., Sato M., and Yamashita O.<\/strong> (2016), Diffuse optical tomography using multi-directional sources and detectors.<\/a> Biomedical Optics Express<\/em>\u00a07<\/strong>, 2623-2640<\/p>\n

Yamashita O<\/strong>.,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.<\/a> \u00a0NeuroImage<\/em>,\u00a0135<\/strong>, 287-299<\/p>\n

Takeda, Y., Hiroe, N., Yamashita, O<\/strong>., Sato, M., (2016), Estimating repetitive spatiotemporal patterns from resting-state brain activity data.<\/a> \u00a0NeuroImage<\/em>,\u00a0133<\/strong>, 251-265<\/p>\n

Hoang H, Yamashita O<\/strong>, 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,<\/a>\u00a0Front. Comput. Neurosci<\/em>.\u00a09<\/strong>:56.<\/p>\n

Fukushima M, Yamashita O<\/strong>, Knosche TR, Sato M (2015), “MEG source reconstruction based on identification of directed source interactions on whole-brain anatomical networks”<\/a>, NeuroImage<\/em>, Volume 105, 408-427<\/p>\n

Yamashita O<\/strong>, 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)<\/a>,Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.18, No.6 pp. 1026-1033<\/p>\n

Shimokawa T, Kosaka T, Yamashita O<\/strong>, Hiroe N, Amita T, Inoue Y, and Sato M (2013), Extended hierarchical Bayesian diffuse optical tomography for removing sclap artifact<\/a>, Biomedical Optics Express<\/em> Vol.4, 2411-2432<\/p>\n

Shimokawa T, Kosaka T, Yamashita O<\/strong>, Hiroe N, Amita T, Inoue Y, and Sato M (2012),
\n“Hierarchical Bayesian estimation improves depth accuracy and spatial resolution of diffuse optical tomography”<\/a>, Optics Express<\/em>, Vol.20, 20427-20446<\/p>\n

Yanagisawa T*, Yamashita O*<\/strong>, Hirata M, Kishima H, Saitoh Y, Goto T, Yoshimine T, and Kamitani Y (2012),Regulation of motor representation by phase\u2013amplitude coupling in the sensorimotor cortex. <\/a>Journal of\u00a0 Neuroscience<\/em>, vol.32(44):15467-75 \u00a0(*equal contribution)<\/strong><\/p>\n

Fukushima M, Yamashita O<\/strong>, 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.<\/em>, Vol.59,1561-1571<\/p>\n

Bosch J, Riera J, Biscay R, Wong K, Galka G, Yamashita O<\/strong>, Sadatao N, Kawashima R, Aubert E, Rodriguez R, Valdes-Sosa P, Miwakeichi F, Ozaki T (2010),\u00a0 Spatio-temporal correlations from fMRI time series based on the NN-ARx model, Journal of Integrative Neuroscience<\/em>, Vol.9, pp.381-406<\/p>\n

Fujiwara Y, Yamashita O<\/strong>, 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<\/em>, Vol.45, pp.393-409<\/p>\n

Miyawaki Y, Uchida H, Yamashita O<\/strong>, 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<\/em>, Vol.60(5):915-29.<\/p>\n

Yoshioka T, Toyama K, Kawato M, Yamashita O<\/strong>, Nishina S, Yamagishi N, Sato M (2008), Evaluation of hierarchical Bayesian method through retinotopic brain activities reconstruction from fMRI and MEG signals. Neuroimage<\/em>. Vol.42(4):1397-413.<\/p>\n

Yamashita O<\/strong>, Sato M, Yoshioka T, Tong F, Kamitani Y (2008). Sparse estimation automatically selects voxels relevant for the decoding of fMRI activity patterns. Neuroimage<\/em>. Vol.42(4):1414-29.<\/p>\n

Shibata K, Yamagishi N, Goda N, Yoshioka T, Yamashita O<\/strong>, Sato M and Kawato M (2008), The Effects of Feature Attention on Prestimulus Cortical Activity in the Human Visual System, Cerebral Cortex<\/em>, Vol.18, pp.1664-75<\/p>\n

Wong K, Galka A, Yamashita O<\/strong> and Ozaki T (2006), Modelling non-stationary variance in EEG time series by state space GARCH model, Computers in Biology and Medicine<\/em>, Vol. 36, Issue 12, pp.1327-1335<\/p>\n

Yamashita O<\/strong>, 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<\/em>, Vol.25, Issue 2, pp.478-490<\/p>\n

Galka A, Yamashita O<\/strong> and Ozaki T (2004), GARCH modelling of covariance in dynamical estimation of inverse solutions, Physics Letters A<\/em>, Vol. 333, Issues 3-4, 6, pp.261-268<\/p>\n

Riera J, Bosch J, Yamashita O<\/strong>, Kawashima R, Sadato N, Okada T and Ozaki T (2004), fMRI activation maps based on the NNARx model, NeuroImage<\/em>, Vol. 23, Issue 2, pp.680-697<\/p>\n

Galka A, Yamashita O<\/strong>, Ozaki T, Biscay R and Valdes-Sosa P (2004), A solution to the dynamical inverse problem of EEG generation using spatiotemporal Kalman filtering, NeuroImage<\/em>, Vol.23, Issue 2, pp.435-453<\/p>\n

Yamashita O<\/strong>, 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<\/em>, Vol.21, Issue 4, pp.221-235<\/p>\n

——<\/p>\n

List of Peer-reviewed Conference papers<\/strong><\/p>\n

Li, Y., Chen, B., Yamashita, O.<\/strong>, Yoshimura, N., & Koike, Y. (2023, June). Adaptive sparseness for correntropy-based robust regression via automatic relevance determination. In 2023 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.<\/p>\n

Bai, W., Tokuda, T., Yamashita, O.,<\/strong> & Yoshimoto, J. (2020, December). Reconstructing Temporal Dynamics of fMRI Time Series via Encoded Contextual Information. In\u00a02020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)<\/i>\u00a0(pp. 968-971). IEEE.<\/p>\n","protected":false},"excerpt":{"rendered":"

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