Connectome Analysis Unit

RIKEN Center for Brain Science

Spatially extended Fitzhugh-Nagumo dynamics.

Demo of our 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: http://dataportal.brainminds.jp

Our research focuses on mapping and analyzing the neuronal connectivity (connectome) of the brain. The challenge is how to automatically process and understand diverse experimental data of ever-increasing size and complexity. We are therefore developing digital brain atlases, connectome analysis and brain simulation, 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 healthy vs. diseased brains will help to create new disease treatments and cures and lead the way to new artificial intelligence and autonomous agents.

Research Topics

Segmentation using AI

We use U-net to segment tracer signals from NanoZoomer images.

Machine Learning

Simulation

Marmoset brain simulation using TVB (The Virtual Brain).

Simulation

Image Processing Pipeline

Processing pipeline for Brain/MINDS experiment image data.

Data Processing Pipeline

Databasing

Database to manage and analysis Brain/MINDS experiment data

Databasing

Unit Leader

Alexander woodward

Unit Leader

Unit Members

Rui Gong

Research Scientist

Itsuko Ishii

Technical Staff

Masahide Maeda

Technical Staff

Takuto Okuno

Research Scientist

Frederic Papazian

Technical Staff

Publications

Talks & Posters

  • Gong, R. Improving Multi-Modal Brain Atlasing by using Generative Adversarial Networks. The 44th Annual Meeting of the Japan Neuroscience Society 2021. Poster.
  • 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. Brain Atlasing and Databasing in the Brain/MINDS Project. Neuroinformatics 2019. Talk.
  • Woodward, A. Computational techniques for 3D brain atlas construction of the Common Marmoset. 5th Eu-Japan Workshop on Neurorobotics/Cognitive Systems. Talk.

Journal Publications

  • 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.
  • Woodward, A., Gong, R., Abe, H. et al. 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
  • Gutierrez, C.E., Skibbe, H., Nakae, K. et al. 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

Abstracts

  • 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.