Three-dimensional Talairach-Tournoux brain atlas

Author(s):  
Anthony Fang ◽  
Wieslaw L. Nowinski ◽  
Bonnie T. Nguyen ◽  
R. Nick Bryan
2018 ◽  
Vol 21 (4) ◽  
pp. 625-637 ◽  
Author(s):  
Tatsuya C. Murakami ◽  
Tomoyuki Mano ◽  
Shu Saikawa ◽  
Shuhei A. Horiguchi ◽  
Daichi Shigeta ◽  
...  

PLoS ONE ◽  
2012 ◽  
Vol 7 (9) ◽  
pp. e44086 ◽  
Author(s):  
José M. Simões ◽  
Magda C. Teles ◽  
Rui F. Oliveira ◽  
Annemie Van der Linden ◽  
Marleen Verhoye

2018 ◽  
Author(s):  
Susan J. Tappan ◽  
Brian S. Eastwood ◽  
Nathan O’Connor ◽  
Quanxin Wang ◽  
Lydia Ng ◽  
...  

Identification and delineation of brain regions in histologic mouse brain sections is especially pivotal for many neurogenomics, transcriptomics, proteomics and connectomics studies, yet this process is prone to observer error and bias. Here we present a novel brain navigation system, named NeuroInfo, whose general principle is similar to that of a global positioning system (GPS) in a car. NeuroInfo automatically navigates an investigator through the complex microscopic anatomy of histologic sections of mouse brains (thereafter: “experimental mouse brain sections”). This is achieved by automatically registering a digital image of an experimental mouse brain section with a three-dimensional (3D) digital mouse brain atlas that is essentially based on the third version of the Allen Mouse Brain Common Coordinate Framework (CCF v3), retrieving graphical region delineations and annotations from the 3D digital mouse brain atlas, and superimposing this information onto the digital image of the experimental mouse brain section on a computer screen. By doing so, NeuroInfo helps in solving the long-standing problem faced by researchers investigating experimental mouse brain sections under a light microscope—that of correctly identifying the distinct brain regions contained within the experimental mouse brain sections. Specifically, NeuroInfo provides an intuitive, readily-available computer microscopy tool to enhance researchers’ ability to correctly identify specific brain regions in experimental mouse brain sections. Extensive validation studies of NeuroInfo demonstrated that this novel technology performs remarkably well in accurately delineating regions that are large and/or located in the dorsal parts of mouse brains, independent on whether the sections were imaged with fluorescence or brightfield microscopy. This novel navigation system provides a highly efficient way for registering a digital image of an experimental mouse brain section with the 3D digital mouse brain atlas in a minute and accurate delineation of the image in real-time.


2011 ◽  
Vol 10 (1) ◽  
pp. 33-55 ◽  
Author(s):  
Wieslaw L. Nowinski ◽  
Beng Choon Chua ◽  
Guo Liang Yang ◽  
Guo Yu Qian

1988 ◽  
Author(s):  
Robert Dann ◽  
John Hoford ◽  
Stane Kovacic ◽  
Martin Reivich ◽  
Ruzena Bajcsy

2021 ◽  
Author(s):  
Kadharbatcha S Saleem ◽  
Alexandru V Avram ◽  
Daniel Glen ◽  
Cecil Chern-Chyi Yen ◽  
Frank Q Ye ◽  
...  

Subcortical nuclei and other deep brain structures are known to play an important role in the regulation of the central and peripheral nervous systems. It can be difficult to identify and delineate many of these nuclei and their finer subdivisions in conventional MRI due to their small size, buried location, and often subtle contrast compared to neighboring tissue. To address this problem, we applied a multi-modal approach in ex vivo non-human primate (NHP) brain that includes high-resolution mean apparent propagator (MAP)-MRI and five different histological stains imaged with high-resolution microscopy in the brain of the same subject. By registering these high-dimensional MRI data to high-resolution histology data, we can map the location, boundaries, subdivisions, and micro-architectural features of subcortical gray matter regions in the macaque monkey brain. At high spatial resolution, diffusion MRI in general, and MAP-MRI in particular, can distinguish a large number of deep brain structures, including the larger and smaller white matter fiber tracts as well as architectonic features within various nuclei. Correlation with histology from the same brain enables a thorough validation of the structures identified with MAP-MRI. Moreover, anatomical details that are evident in images of MAP-MRI parameters are not visible in conventional T1-weighted images. We also derived subcortical template SC21 from segmented MRI slices in three-dimensions and registered this volume to a previously published anatomical template with cortical parcellation (Reveley et al., 2017; Saleem and Logothetis, 2012), thereby integrating the 3D segmentation of both cortical and subcortical regions into the same volume. This newly updated three-dimensional D99 digital brain atlas (V2.0) is intended for use as a reference standard for macaque neuroanatomical, functional, and connectional imaging studies, involving both cortical and subcortical targets. The SC21 and D99 digital templates are available as volumes and surfaces in standard NIFTI and GIFTI formats.


2018 ◽  
Author(s):  
Hong Ni ◽  
Chaozhen Tan ◽  
Zhao Feng ◽  
Shangbin Chen ◽  
Zoutao Zhang ◽  
...  

AbstractMapping the brain structures in three-dimensional accurately is critical for an in-depth understanding of the brain functions. By using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficiently use of various datasets. However, because of the heterogeneous and non-uniform characteristics of the brain structures at cellular level brought with the recently developed high-resolution whole-brain microscopes, traditional registration methods are difficult to apply to the robust mapping of various large volume datasets. Here, we proposed a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large volume datasets at cellular level by introducing the extract regional features of the anatomically invariant method and a strategy of parameter acquisition and large volume transformation. By performing validation on model data and biological images, BrainsMapi can not only achieve robust registration on sample tearing and streak image datasets, different individual and modality datasets accurately, but also are able to complete the registration of large volume dataset at cellular level which dataset size reaches 20 TB. Besides, it can also complete the registration of historical vectorized dataset. BrainsMapi would facilitate the comparison, reuse and integration of a variety of brain datasets.


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