List of Peer-Reviewed Papers
Itahashi, T., Yamashita, A., Takahara, Y., Yahata, N., Aoki, Y. Y., Fujino, J., …, Yamashita O. & Hashimoto, R. I. (2024). Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism. Molecular Psychiatry, 1-13.
Tanaka, S. C., Kasai, K., Okamoto, Y., Koike, S., Hayashi, T., Yamashita, A., Yamashita O. … & Hanakawa, T. (2024). The status of MRI databases across the world focused on psychiatric and neurological disorders. Psychiatry and Clinical Neurosciences, 78(10):563-57.
Fukuma, R., Majima, K., Kawahara, Y., Yamashita, O., Shiraishi, Y., Kishima, H., & Yanagisawa, T. (2024). Fast, accurate, and interpretable decoding of electrocorticographic signals using dynamic mode decomposition. Communications Biology, 7(1), 595.
Endo, H., Ikeda, S., Harada, K., Yamagata, H., Matsubara, T., Matsuo, K., … & Yamashita, O. (2024). Manifold alteration between major depressive disorder and healthy control subjects using dynamic mode decomposition in resting-state fMRI data. Frontiers in Psychiatry, 15, 1288808.
Ajioka, T, Nakai, N, Yamashita, O, Takumi, T (2024) End-to-end deep learning approach to mouse behavior classification from cortex-wide calcium imaging. PLoS Comput Biol 20(3): e1011074.
Takeda, Y., Gomi, T., Umebayashi, R., Tomita, S., Suzuki, K., Hiroe, N., … & Yamashita, O. (2023). Sensor array design of optically pumped magnetometers for accurately estimating source currents. NeuroImage, 277, 120257.
Bai, W., Yamashita, O., & Yoshimoto, J. (2023). Learning task-agnostic and interpretable subsequence-based representation of time series and its applications in fMRI analysis. Neural Networks, 163, 327-340.
Nakai, N., Sato, M., Yamashita, O., 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. Cell Reports.
Nakamura, Y., Ishida, T., Tanaka, S.C., Mitsuyama, Y., Yokoyama, S., … Yamashita, O., 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. Psychiatry Clinical Neuroscience.
Ishida, T., Nakamura, Y.,…. ,Yamashita, O.,…. 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, Schizophrenia Bulletin, sbad022
Okada, G., Yoshioka, T., Yamashita, A., Itai, E., Yokoyama, S., Kamishikiryo, T., … Yamashita O. ,… Okamoto, Y. (2023). Verification of the brain network marker of major depressive disorder: Test-retest reliability and anterograde generalization performance for newly acquired data. Journal of Affective Disorders, 326, 262-266.
Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Yamashita, O., 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. The Journal of Pain, 23(12), 2080-2091.
Ikeda, S., Kawano, K., Watanabe, S., Yamashita, O., & Kawahara, Y. (2022). Predicting behavior through dynamic modes in resting-state fMRI data. NeuroImage, Vol.247, p.118801
Takeda, Y., Hiroe, N., & Yamashita, O. (2021). Whole-brain propagating patterns in human resting-state brain activities. NeuroImage, Vol.245, p.118711
Hayashi, R., Yamashita, O.*, Yamada, T., Kawaguchi, H., & Higo, N. (2021). Diffuse Optical Tomography Using fNIRS Signals Measured from the Skull Surface of the Macaque Monkey. Cerebral Cortex Communications. (*equal contribution)
Tanaka, S.C., Yamashita, A., Yahata, N.,Yamashita, O., Kawato, M., & Imamizu, H. (2021). A multi-site, multi-disorder resting-state magnetic resonance image database. Scientific Data 8, 227.
Maikusa, N., Zhu, Y., Uematsu, A., Yamashita, A., Saotome, K., Okada, N., …, Yamashita, O., Tanaka C. S. & Koike, S. (2021). Comparison of traveling‐subject and ComBat harmonization methods for assessing structural brain characteristics. Human Brain Mapping.
Tokuda, T., Yamashita, O., Sakai, Y., & Yoshimoto, J. (2021). Clustering of multiple psychiatric disorders using functional connectivity in the data-driven brain subnetwork. Frontiers in Psychiatry, 1428.
Tokuda, T., Yamashita, O., & Yoshimoto, J. (2021). Multiple clustering for identifying subject clusters and brain sub-networks using functional connectivity matrices without vectorization. Neural Networks, 142, 269-287.
Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., … Yamashita, O & Imamizu, H. (2021). Common Brain Networks Between Major Depressive-Disorder Diagnosis and Symptoms of Depression That Are Validated for Independent Cohorts. Frontiers in Psychiatry, 12, 888.
Suzuki, K., & Yamashita, O. (2021). MEG current source reconstruction using a meta-analysis fMRI prior. NeuroImage, 118034.
Koike, S., Tanaka, S. C., Okada, T., Aso, T., Yamashita, A., Yamashita, O., … & Hayashi, T. (2021). Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. NeuroImage: Clinical, 102600.
Yamashita, A., Sakai, Y., Yamada, T., Yahata, N., Kunimatsu, A., Okada, N., … Yamashita, O & Imamizu, H. (2020). Generalizable brain network markers of major depressive disorder across multiple imaging sites. PLoS biology, 18(12), e3000966.
Yanagisawa, T., Fukuma, R., Seymour, B., Tanaka, M., Hosomi, K., Yamashita, O., … & Saitoh, Y. (2020). BCI training to move a virtual hand reduces phantom limb pain: A randomized crossover trial. Neurology, 95(4), e417-e426.
Shiraishi, Y., Kawahara, Y., Yamashita, O., Fukuma, R., Yamamoto, S., Saitoh, Y., … & Yanagisawa, T. (2020). Neural decoding of electrocorticographic signals using dynamic mode decomposition. Journal of Neural Engineering. 17, 3.
Aihara, T., Shimokawa, T., Ogawa, T., Okada, Y., Ishikawa, A., Inoue, Y., Yamashita, O. (2020). Resting-State Functional Connectivity Estimated With Hierarchical Bayesian Diffuse Optical Tomography. Frontiers in neuroscience, 14, 32.
Sanger, T. D., Yamashita, O., Kawato, M. (2020). Expansion coding and computation in the cerebellum: 50 years after the Marr–Albus codon theory. The Journal of Physiology, 598(5), 913-928.
Endo, H., Hiroe, N., Yamashita, O. (2020). Evaluation of resting spatio-temporal dynamics of a neural mass model using resting fMRI connectivity and EEG microstates. Frontiers in Computational Neuroscience, 13, 91.
Takeda Y., Itahashi T., Sato M., Yamashita O. (2019), Estimating repetitive spatiotemporal patterns from many subjects’ resting-state fMRIs, NeuroImage, Vol 2-3, p. 116182,
Yamashita A, Yahata N, Itahashi T, Lisi G, Yamada T, Ichikawa N, …, Yamashita O., 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. PLoS Biol 17(4): e3000042
Takeda Y., Suzuki K., Kawato M., Yamashita O. (2019), MEG Source Imaging and Group Analysis Using VBMEG, Frontiers in Neuroscience, 13, 241
Shimokawa, T., Ishii, T., Takahashi, Y., Mitani, Y., Mifune, H., Chubashi, S., Satoh, M., Oba, Y., Adachi, K., Sugawara, S., Yamashita, O. (2019), Development of multi-directional functional near-infrared spectroscopy system for human neuroimaging studies, Biomedical Optics Express, Vol. 11, No. 3, pp.1393-1404
Filatova, O., Yang, Y., Dewald, J., Tian, R., Maceira-Elvira, P., Takeda, Y., Kwakkel, G., Yamashita, O., van der Helm FCT (2018), Dynamic information flow based on EEG and diffusion MRI in stroke: a proof-of-principle study, Frontiers in Neural Circuits, Vol.12, Article 79
Sato M, Yamashita O, Sato Ma, 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. Biomedical Optics 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. Journal of Neuroscience, 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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List of Peer-reviewed Conference papers
Li, Y., Chen, B., Yamashita, O., 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.
Bai, W., Tokuda, T., Yamashita, O., & Yoshimoto, J. (2020, December). Reconstructing Temporal Dynamics of fMRI Time Series via Encoded Contextual Information. In 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 968-971). IEEE.
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