Ex Vivo Primate Brain Imaging

MRI experiments, histological methods, and data registration were performed on an ex vivo squirrel monkey brain following the procedures described in [18 (link), 19 (link)]. Briefly, ex vivo imaging was performed a Varian 9.4 T magnet, with diffusion weighted scans acquired using a PGSE multi-shot spin-warp imaging sequence (TR = 4.6 s, TE = 42 ms, 32 gradient directions, b ≈ 1000s/mm², 300 μm voxel, 192×128×115 matrix). Diffusion processing was performed in “histology” space after registration (see below) using constrained spherical deconvolution [20 (link)] for voxel-wise reconstruction, resulting in diffusion FODs reconstructed in histology space.

Partial Protocol Preview
This section provides a glimpse into the protocol.
The remaining content is hidden due to licensing restrictions, but the full text is available at the following link: Access Free Full Text.

Nath V., Schilling K.G., Remedios S., Bayrak R.G., Gao Y., Blaber J.A., Huo Y., Landman B.A, & Anderson A.W. (2019). LEARNING 3D WHITE MATTER MICROSTRUCTURE FROM 2D HISTOLOGY. Proceedings. IEEE International Symposium on Biomedical Imaging, 2019, 186-190.

Publication 2019

9.4 T magnet

Brain Diffusion Histological methods Reconstruction Scans Squirrel monkey

Corresponding Organization :

Other organizations : Vanderbilt University

Top 5 similar protocols

Variable analysis

independent variables

MRI experiments
Histological methods
Data registration

dependent variables

Diffusion weighted scans
Diffusion FODs reconstructed in histology space

control variables

Ex vivo squirrel monkey brain
Varian 9.4 T magnet
PGSE multi-shot spin-warp imaging sequence (TR = 4.6 s, TE = 42 ms, 32 gradient directions, b ≈ 1000s/mm^2, 300 μm voxel, 192×128×115 matrix)
Constrained spherical deconvolution for voxel-wise reconstruction

controls

The procedures described in [18] and [19] were followed, which likely include relevant positive and negative controls, but these are not explicitly mentioned in the provided information.

Annotations

Based on most similar protocols

Etiam vel ipsum. Morbi facilisis vestibulum nisl. Praesent cursus laoreet felis. Integer adipiscing pretium orci. Nulla facilisi. Quisque posuere bibendum purus. Nulla quam mauris, cursus eget, convallis ac, molestie non, enim. Aliquam congue. Quisque sagittis nonummy sapien. Proin molestie sem vitae urna. Maecenas lorem.

As authors may omit details in methods from publication, our AI will look for missing critical information across the 5 most similar protocols.

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!