Type Last update Details
CaImaging 2021-05-20
Version : 1
Authors : Takahiro Kondo1,2, Risa Saito3, Masaki Otaka3, Kimika Yoshino-Saito1,4, Akihiro Yamanaka5, Tetsuo Yamamori6, Akiya Watakabe6, Hiroaki Mizukami7, Mark J. Schnitzer8,9,10, Kenji F. Tanaka2,11, Junichi Ushiba12,13*, Hideyuki Okano1,2*
1. Department of Physiology (Okano Lab), Keio University School of Medicine, Keio University School of Medicine
2. Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science
3. Graduate School of Science and Technology, Keio University, Kanagawa, Japan
4. Japan Society for the Promotion of Science, Tokyo, Japan
5. Research Institute of Environmental Medicine, Nagoya University, Nagoya, Japan
6. Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science
7. Division of Genetic Therapeutics, Center for Molecular Medicine, Jichi Medical University, Tochigi, Japan
8. James H. Clark Center for Biomedical Engineering and Sciences, Stanford University, Stanford, CA, USA
9. CNC Program, Stanford University, Stanford, CA, USA
10. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
11. Department of Neuropsychiatry, Keio University School of Medicine, Tokyo, Japan
12. Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan
13. Keio Institute of Pure and Applied Sciences (KiPAS), Kanagawa, Japan
*corresponding authors: Junichi Ushiba (ushiba@brain.bio.keio.ac.jp), Hideyuki Okano (hidokano@a2.keio.jp)
Description :

This dataset contains neuronal population activity data recorded during naturalistic behavior of common marmosets. Calcium imaging data was collected with a miniature microscope in the motor cortex of two common marmosets (identified by letters M and W).

3 types of data are available for download :
1. raw microendoscope data (multi-tiff files, 1440x1080 pixels resolution at 20Hz sampling rate. Files are divided in 4GB segments)
2. individual cell activity time series, extracted from raw data (CSV files)
3. regions of interest (ROI) of the extracted cells (png image files)

Notes :
* we analyzed all imaging data using Mosaic provided by Inscopix (Inscopix Data Processing Software, IDPS) following the "Recommended workflow" (https://support.inscopix.com/mosaic-workflow) except for acquisition-specific artifacts.
* PCA-ICA was used as the cell identification algorithm, and a serial number was affected to each identified cell (starting from 0). Before cell identification, spatial downsampling, motion correction, and df/F were applied.
* In traces.csv file, the first column indicates the time (in seconds), from the second column onward, each column represents the time series signal of each cells sorted by their serial numbers (i.e. the second column corresponds to cell #0, the third column corresponds to cell #1, and so on).
* A low-pass filter was applied to each time series signal.
* For the ROI image files (contained in th0.9.zip archive), the cell serial number is indicated in the file name (between th0.9 prefix and .png extension), but note that 0 is omitted for the first file (i.e. th0.9.png corresponds to cell #0, th0.91.png to cell #1, and so on)

ECoG 2021-01-14
Version : 1
Authors : Misako Komatsu1,2*, Noritaka Ichinohe1,2
1. Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Center for Brain Science
2. Department of Ultrastructural Research, National Center of Neurology and Psychiatry
*corresponding author: Misako Komatsu (mkomatsu@brain.riken.jp)
Description :

We used auditory stimuli of different durations (AD) and frequencies (AF). In AD, 10 types of pure sinusoidal tones (1 ms rise/fall) with different durations (10, 25, 50, 75, 100, 125, 150, 175, 200, and 225 ms; 1000 Hz; 2000 stimuli in total) were randomly presented with an equal probability of 10%. In AF, 10 types of tones with different frequencies (700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, and 1600 Hz; 50 ms; 1000 stimuli in total) were presented with an equal probability of 10%.
Epidural ECoG recordings were taken in the passive listening condition while monkeys were in awake and ketamine (30 mg/kg i.m.) administrated conditions. ECoG data were sampled at 1KHz.
Data format information can be found on TychoWiki.

Atlas 2021-06-17
Version : 1.1
Unique Identifier : https://doi.org/10.24475/bma.4520
Authors : Alexander Woodward1*, Rui Gong1, Ken Nakae2, Junichi Hata3,4,5, Hideyuki Okano3,4, Shin Ishii2, Yoko Yamaguchi6,7,8
1. Connectome Analysis Unit, Integrative Computational Brain Science Collaboration Division, RIKEN Center for Brain Science
2. Integrated Systems Biology Laboratory (Ishii Laboratory), Division of Systems Informatics, Department of Systems Science, Graduate School of Informatics, Kyoto University
3. Laboratory for Marmoset Neural Architecture, RIKEN Center for Brain Science
4. Department of Physiology (Okano Lab), Keio University School of Medicine, Keio University School of Medicine
5. Division of Regenerative Medicine, Jikei University School of Medicine
6. Applied Electronics Laboratory, Kanazawa Institute of Technology
7. Graduate School of Information Science and Technology, The University Tokyo
8. Laboratory for Cognitive Brain Mapping, RIKEN Center for Brain Science
*corresponding author: Alexander Woodward (alexander.woodward@riken.jp)
Description :

This atlas is composed of a population average ex-vivo MRI T2WI contrast mapped with the BMA 2017 Ex Vivo (published by Woodward et al. The Brain/MINDS 3D digital marmoset brain atlas).
The population average MRI was constructed based on scans of 25 individual brains. The 25 brains were aligned with one another by iteratively applying linear and non-linear registration and averaging the transformation files until convergence. Data of individual brains were then resampled with an isotropic spatial resolution of 100×100×100µm3 and averaged across brain. The registration procedure gave a brain shape with a high signal-to-noise ratio compared to an individual MRI scan. The average MRI was then AC-PC aligned within an RAS (Right-Anterior-Superior) coordinate system.
Cortical flat map and mid-thickness surfaces for the left and right hemispheres are now provided in the common GIFTI format (*. gii) for surface data. These are compatible with Connectome Workbench (version 1.5.0 and higher) and a Scene file is also provided for easy viewing of the data.

MRI 2017-11-02
Version : 1
Unique Identifier : https://doi.org/10.24475/bminds.hbi.3775
Authors : Kiyoto Kasai1, Naohiro Okada1, Akira Yasumura1
1. Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo
Description :

Human brain images obtained with 3T MRI. The dataset includes T1-weighted images of patients with schizophrenia, those with major depressive disorder, and those with bipolar disorder, as well as of healthy controls. This dataset will contribute to promoting research on brain mapping in human psychiatric disorders.
See detailed dataset description at https://dataportal.brainminds.jp/human-brain-images-about

ECoG 2018-06-30
Version : 1
Authors : Misako Komatsu1*, Eriko Sugano2, Hiroshi Tomita2, Naotaka Fujii3*
1. Laboratory for Molecular Analysis of Higher Brain Function, RIKEN Brain Science Institute
2. Department of Chemistry and Biological Sciences, Iwate University
3. Laboratory for Adaptive Intelligence, RIKEN Brain Science Institute
*corresponding authors: Misako Komatsu (mkomatsu@brain.riken.jp), Naotaka Fujii (na@brain.riken.jp)
Description :

Eight weeks after the virus injections, we applied 3.5 V to each LED for 200 ms to monitor the development of neural responses to photostimuli. In each session, eight LEDs were pseudo-randomly illuminated and 50 stimulation trials were performed for every LED. Inter stimulus intervals were fixed at 2 s. ECoG data were sampled at 1KHz.
Further information can be found on TychoWiki

MRI 2017-07-12
Version : 1
Unique Identifier : https://doi.org/10.24475/bminds.mri.kfs.3405
Authors : Fumiko Seki1,2,3, Keigo Hikishima1,2,4, Yuji Komaki1,2, Junichi Hata1,2,3, Akiko Uematsu1,2,3, Norio Okahara2, Masafumi Yamamoto2, Haruka Shinohara2, Erika Sasaki1,2, Hideyuki Okano1,3*
1. Department of Physiology (Okano Lab), Keio University School of Medicine, Keio University School of Medicine
2. Central Institute for Experimental Animals
3. Laboratory for Marmoset Neural Architecture, RIKEN Brain Science Institute
4. Okinawa Institute of Science and Technology Graduate University
*corresponding author: Hideyuki Okano (hidokano@a2.keio.jp)
Description :

The dataset includes NifTI files of MRI T1-weighted images data and T2-weighted images at the age of 1 month, 3 months, 6 months, 12 months, 18 months, and 24 months. The templates at the age of 18 and 24 months were registered to the stereotaxic coordinates defined in Paxinos[^1] and Woodward et al[^2]. For the template at the age of 1, 3, 6, 12 months, we newly defined the stereotaxic coordinates. For details regarding the procedure, please download Additional_information_about_the_dataset.pdf

[^1]: Paxinos G. The marmoset brain in stereotaxic coordinates. Academic Press. 2012. [^2]: Woodward A, Hashikawa T, Maeda M, Kaneko T, Hikishima K, Iriki A, Okano H, Yamaguchi Y. The Brain / MINDS 3D digital marmoset brain atlas. DOI: https://doi.org/10.24475/bma.2799

MRI 2017-06-15
Version : 1
Unique Identifier : https://doi.org/10.24475/bminds.mri.kau.3236
Authors : Akiko Uematsu1, Junichi Hata2, Yuji Komaki3, Fumiko Seki1, Chihoko Yamada3, Norio Okahara3, Yoko Kurotaki3, Erika Sasaki1,2,3, Hideyuki Okano1,2,3*
1. Department of Physiology (Okano Lab), Keio University School of Medicine, Keio University School of Medicine
2. Laboratory for Marmoset Neural Architecture, RIKEN Brain Science Institute
3. Central Institute for Experimental Animals
*corresponding author: Hideyuki Okano (hidokano@a2.keio.jp)
Description :

The dataset includes gziped NIfTI files (.nii.gz) of MRI T2WI and DTI metrics (FA, MD, RD, and AD) in-vivo data; the ages of the data are 1,3,6,9,12,15,18 months old.
The creation process of these averaged images is referred to the script of FSL software (https://fsl.fmrib.ox.ac.uk/), fslvm_2_template, partially modified; the non-linear registration process was replaced to the script of ANTs (http://stnava.github.io/ANTs/), antsRegsitrationSyN.sh. The details are described in the text “Dataset Description and Image Processing”.
In addition, age-specific DWI templates (.mif files) are provided, which are created using the script of Mrtrix3 (http://www.mrtrix.org/), population_template. You may convert .mif file to NIFTI, extracting b-vector and b-value information by conducting the following Mrtrix3’s command: # mrconvert dwi_input.mif dwi_output.nii -export_grad_fsl bvec bval
Files can be downloaded each image type or as one zip file.

Atlas 2020-04-22
Version : 1.1
Unique Identifier : https://doi.org/10.24475/bma.2799
Authors : Alexander Woodward1*, Tsutomu Hashikawa1,2, Masahide Maeda1, Takaaki Kaneko3, Keigo Hikishima4, Atsushi Iriki2, Hideyuki Okano3, Yoko Yamaguchi1*
1. Neuroinformatics Japan Center, RIKEN Brain Science Institute
2. Laboratory for Symbolic Cognitive Development, RIKEN Brain Science Institute
3. Laboratory for Marmoset Neural Architecture, RIKEN Brain Science Institute
4. Animal Resources Section, Okinawa Institute of Science and Technology Graduate University
*corresponding authors: Alexander Woodward (alexander.woodward@riken.jp), Yoko Yamaguchi (yokoy@brain.riken.jp)
Description :

The dataset includes NIfTI files of MRI T2 ex-vivo data; reconstructed Nissl stained images of the same brain, registered to the shape of the MRI; brain region segmentation (with separate color lookup table); and gray, mid-cortical and white matter boundary segmentation. In addition, a 3D Slicer scene file is provided that can be used for testing the dataset within the freely downloadable 3D Slicer software (https://www.slicer.org/). The scene file can be dragged directly into 3D Slicer and the atlas can be used immediately. Files can be downloaded individually or as one zip file.
The atlas can be viewed online via the Zooming Atlas Viewer (ZAV) by clicking here.