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

Jonathan Lai

Part-time Researcher

Patrice Delmas

Visiting Research Fellow

Publications

Journals

  • 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). 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). 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). 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).  https://doi.org/10.1038/sdata.2018.9.
     
 

Conferences & Abstracts

  • 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.)
  • 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. 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. Talk.
  • Woodward, A. Brain Atlasing and Databasing in the Brain/MINDS Project. Neuroinformatics 2019, Warsaw Poland. Keynote talk.
  • Woodward, A. Computational techniques for 3D brain atlas construction of the Common Marmoset. 5th Eu-Japan Workshop on Neurorobotics/Cognitive Systems. Talk.
  • 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 talk.