Connectome Analysis Unit

RIKEN Center for Brain Science

Spatially extended Fitzhugh-Nagumo dynamics.

Video of our online 3D Brain Atlas Viewer.

About Us

We have been developing the Brain/MINDS Data Portal, a growing resource of neuroimaging data for the common marmoset (Callithrix jacchus). Please take a look at https://dataportal.brainminds.jp


Our research focuses on mapping and analyzing the neuronal connectivity (connectome) of the brain from structural and functional neuroimaging data. The challenge is how to deal with diverse experimental data of ever-increasing size and complexity. We are therefore developing digital brain atlases, new connectome analysis techniques, brain simulations, and a comprehensive neuroimaging database platform. As a member of the Brain/MINDS project (https://brainminds.jp/en/), our group applies these solutions to study the brain of the common marmoset monkey. Analyzing connectomes across species and between healthy and diseased brains will help to create new disease treatments and cures and pave the way to new artificial intelligence and autonomous agents.


Check out our YouTube channel.

Visit our GitHub page.

Contact us at: caucontact [at] ml.riken.jp

Unit Members

Alexander woodward

Unit Leader

Rui Gong

Research Scientist

Itsuko Ishii

Technical Staff

Masahide Maeda

Technical Staff

Takuto Okuno

Research Scientist

Frederic Papazian

Technical Staff

Patrice Delmas

Visiting Research Fellow

Federico Da Rold

Visiting Research Fellow

Junichi Chikazoe

Visiting Research Fellow

Publications

Journals

  • Takuto Okuno, Noritaka Ichinohe, Alexander Woodward. A reappraisal of the default mode and frontoparietal networks in the common marmoset brain, Front. Neuroimaging 2:1345643. (2024). doi: https://doi.org/10.3389/fnimg.2023.1345643
  • Takuto Okuno, Junichi Hata, Yawara Haga, Kanako Muta, Hiromichi Tsukada, Ken Nakae, Hideyuki Okano, Alexander Woodward. Group Surrogate Data Generating Models and Similarity Quantifiation of Multivariate Time-Series: A Resting-State fMRI Study, NeuroImage, 120329 (2023). doi: https://doi.org/10.1016/j.neuroimage.2023.120329
  • Akiya Watakabe, Henrik Skibbe, Ken Nakae, Hiroshi Abe, Noritaka Ichinohe, Muhammad Febrian Rachmadi, Jian Wang, Masafumi Takaji, Hiroaki Mizukami, Alexander Woodward, Rui Gong, Junichi Hata, David C. Van Essen, Hideyuki Okano, Shin Ishii, Tetsuo Yamamori. Local and long-distance organization of prefrontal cortex circuits in the marmoset brain. Neuron, Volume 111, ISSUE 14, P2258-2273.e10, July 19, 2023; doi: https://doi.org/10.1016/j.neuron.2023.04.028
  • Junichi Hata, Ken Nakae, Hiromichi Tsukada, Alexander Woodward, Yawara Haga, Mayu Iida, Akiko Uematsu, Fumiko Seki, Noritaka Ichinohe, Rui Gong, Takaaki Kaneko, Daisuke Yoshimaru, Akiya Watakabe, Hiroshi Abe, Toshiki Tani, Hiro Taiyo Hamada, Carlos Enrique Gutierrez, Henrik Skibbe, Masahide Maeda, Frederic Papazian, Kei Hagiya, Noriyuki Kishi, Shin Ishii, Kenji Doya, Tomomi Shimogori, Tetsuo Yamamori, Keiji Tanaka, Hirotaka James Okano & Hideyuki Okano.
    Multi-modal brain magnetic resonance imaging database covering marmosets with a wide age range. Scientific Data 10, 221 (2023).
    doi: https://doi.org/10.1038/s41597-023-02121-2
  • Shi, L., Woodward, A., Igarashi, J. Quantitative measures of topographic and divergent/convergent connectivity in diffusion MRI of the human cerebral cortex. bioRxiv 2022.12.25.521904; doi: https://doi.org/10.1101/2022.12.25.521904
  • Okuno, T., Woodward, A. Vector Auto-Regressive Deep Neural Network: A Data-Driven Deep Learning-Based Directed Functional Connectivity Estimation Toolbox. Frontiers in Neuroscience 15, 1518 (2021). doi: https://doi.org/10.3389/fnins.2021.764796
  • Gutierrez, C.E., Skibbe, H., Nakae, K., Tsukada, H., Lienard, J., Watakabe, A., Hata, J., Reisert, M., Woodward, A., Yamaguchi, Y., Yamamori, Y., Okano, H., Ishii, S., & Doya, K. Optimization and validation of diffusion MRI-based fiber tracking with neural tracer data as a reference. Sci Rep 10, 21285 (2020). doi: https://doi.org/10.1038/s41598-020-78284-4
  • Woodward, A., Gong, R., Abe, H., Nakae, K., Hata, J., Skibbe, H., Yamaguchi, Y., Ishii, S., Okano, H., Yamamori, T., & Ichinohe, N. The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain. Brain Struct Funct 225, 1225–1243 (2020). doi: https://doi.org/10.1007/s00429-020-02073-y
  • Woodward, A., Hashikawa, T., Maeda, M., Kaneko, T., Hikishima, K., Iriki, A., et al. The Brain/MINDS 3D Digital Marmoset Brain Atlas. Scientific Data, 5, 180009, (2018). doi: https://doi.org/10.1038/sdata.2018.9.
     
 

Conferences & Abstracts

  • Takuto Okuno, Alexander Woodward (2024). The evolution of the default mode network in mouse, rat, marmoset, macaque and human. The Mechanism of Brain and Mind Winter Workshop 2024. Poster.
  • Takuto Okuno, Junichi Hata, Hideyuki Okano, Alexander Woodward (2023). Directional and non-directional seed-based connectivity analysis of resting-state fMRI voxels in human and marmoset. The 46th Annual Meeting of the Japan Neuroscience Society. (Abstract)
  • Rui Gong, Hiroshi Abe, Toshiki Tani, Noritaka Ichinohe, Alexander Woodward. Towards a Population-Based Anatomical Marmoset Brain Atlas
    (2023). The 12th Annual Meeting of Japan Society for Marmoset Research
  • Takuto Okuno, Junichi Hata, Hiromichi Tsukada, Ken Nakae, Hideyuki Okano, Alexander Woodward (2022). Similarity quantification and group surrogate data generating models of multivariate time series: a resting-state fMRI study.  The 45th Annual Meeting of the Japan Neuroscience Society. Abstract. Poster.
  • Rui Gong, Frederic Papazian, Masahide Maeda, Jonathan Lai, Hiroshi Abe, Toshiki Tani, Noritaka Ichinohe, Alexander Woodward (2022). ZAViewer: An Online High-Resolution Zooming Brain Image Viewer with Artificial Intelligence Capability. The 45th Annual Meeting of the Japan Neuroscience Society. Abstract. Poster.
  • Akiya Watakabe, Henrik Skibbe, Ken Nakae, Hiroshi Abe, Noritaka Ichinohe, Jian Wang, Masafumi Takaji, Hiroaki Mizukami, Alexander Woodward, Rui Gong, Junichi Hata, Hideyuki Okano, Shin Ishii. (2022). Two contrasting features of corticocortical and corticostriatal projections of the marmoset prefrontal cortex. The 45th Annual Meeting of the Japan Neuroscience Society. Abstract. Poster. Abstract. Poster.
  • Okuno, T., Woodward, A. (2021). Extending linear auto-regression to a vector auto-regressive deep neural network for functional connectome analysis. The 44th Annual Meeting of the Japan Neuroscience Society. (Abstract)
  • Gong, R., Woodward, A. (2021). Improving Multi-Modal Brain Atlasing by using Generative Adversarial Networks. The 44th Annual Meeting of the Japan Neuroscience Society. (Abstract).
  • Gong, R. Improving Brain Image Registration using Artificial intelligence. 10th Japan Marmoset Research Society Meeting 2021. (Poster.)
  • Gee, T., Gimel’farb, G., Woodward, A., Ababou, R., Strozzi, A. G., & Delmas, P. (2020). Guided stereo to improve depth resolution of a small baseline stereo camera using an image sequence. In Advanced Concepts for Intelligent Vision Systems: 20th International Conference, ACIVS
    2020, Auckland, New Zealand, February 10–14, 2020, Proceedings 20
    (pp. 480-491). Springer International Publishing.
  • Okuno, T. Investigating a Data-Driven Deep-Learning Approach to Simulate Whole Brain Dynamics. International Symposium on Artificial Intelligence and Brain Science 2020. Poster.
  • Woodward, A., Rogers, M., Abe, H., Tani, T., Gong, R., Ichinohe, N., Gee, T., Delmas, P., Yamamori, Y. (2020). Towards Quantitative Brain Architecture Analysis for Delineating the Cortex of the Common Marmoset Brain. The 43th Annual Meeting of the Japan Neuroscience Society. (Abstract).
  • Woodward, A., Gong, R., Abe, H., Skibbe, H., Nakae, K., Gutierrez, C. E., Tsukada, H., Maeda, M., Ichinohe, N, Yamaguchi, Y. (2019). Large-Scale Automatic Tracer Segmentation in Brain Section Fluorescence Images Using Artificial Intelligence. The 42th Annual Meeting of the Japan Neuroscience Society. (Abstract).
  • Woodward, A, Gong, R., Abe, H., Skibbe, H., Nakae, K., Gutierrez, C. E., Tsukada, H., Maeda, M., Ichinohe, N, Yamaguchi, Y, (2019). Large-Scale Automatic Tracer Segmentation in Brain Section Fluorescence Images Using Artificial Intelligence. NEURO2019.
  • Woodward, A, Gong R, Yamaguchi, Y, (2018). Marmoset Brain Atlasing Techniques in the Brain/MINDS Project, Japan Society for Marmoset Research Meeting 2019.
  • Gong, R, Woodward, A, Nakae, K, Ichinohe, N, Watakabe, A, Yamamori, T, Yamaguchi, Y, (2018). The Brain/MINDS Cortical Flap Map for Visualize Neuroimaging Data. AINI2018.

Talks

  • Okuno, T. Directional and non-directional seed-based connectivity analysis of resting-state fMRI voxels in human and marmoset. The 46th Annual Meeting of the Japan Neuroscience Society 2023
  • Okuno, T. Group Surrogate Data Generating Models and Similarity Quantification of Multivariate Time-Series: A Resting-State fMRI Study. JHBI 6th (Nov 16th 2022). https://www.nips.ac.jp/fmritms/en/kenkyukai/information/2022/04/6thJHBI.html
  • Okuno, T. Extending linear auto-regression to a vector auto-regressive deep neural network for functional connectome analysis. The 13th Workshop on multitrack event-trains in neural, social, seismological, and financial data 2021.
  • Okuno, T. Extending linear auto-regression to a vector auto-regressive deep neural network for functional connectome analysis. The 44th Annual Meeting of the Japan Neuroscience Society 2021.
  • Woodward, A. Brain Atlasing and Databasing in the Brain/MINDS Project. Neuroinformatics 2019, Warsaw Poland. Keynote.
  • Woodward, A. Computational techniques for 3D brain atlas construction of the Common Marmoset. 5th Eu-Japan Workshop on Neurorobotics/Cognitive Systems.
  • Woodward, A. The Brain/MINDS project: Image processing techniques for 3D brain reconstruction and atlasing of the common marmoset. IVCNZ 2018, Auckland, New Zealand. Keynote.