scholarly journals Multiscale Exploration of Mouse Brain Microstructures Using the Knife-Edge Scanning Microscope Brain Atlas

2011 ◽  
Vol 5 ◽  
Author(s):  
Ji Ryang Chung ◽  
Chul Sung ◽  
David Mayerich ◽  
Jaerock Kwon ◽  
Daniel E. Miller ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Brain Atlas ◽  
Knife Edge ◽  
Scanning Microscope

Registration of a 3D mouse brain atlas with brain microstructure data

2003 ◽  
Vol 52-54 ◽  
pp. 307-312 ◽  
Author(s):  
Wonryull Koh ◽  
Bruce H. McCormick
Keyword(s):  
Mouse Brain ◽  
Brain Atlas ◽  
Brain Microstructure

scholarly journals Flexible annotation atlas of the mouse brain: combining and dividing brain structures of the Allen Brain Atlas while maintaining anatomical hierarchy

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Norio Takata ◽  
Nobuhiko Sato ◽  
Yuji Komaki ◽  
Hideyuki Okano ◽  
Kenji F. Tanaka
Keyword(s):  
Mouse Brain ◽  
Brain Structures ◽  
Brain Atlas ◽  
Resting State Functional Connectivity ◽  
Brain Science ◽  
Step Procedure ◽  
Public Resources ◽  
Large Brain ◽  
Magnetic Resonance Imaging Mri ◽  
And Function

AbstractA brain atlas is necessary for analyzing structure and function in neuroimaging research. Although various annotation volumes (AVs) for the mouse brain have been proposed, it is common in magnetic resonance imaging (MRI) of the mouse brain that regions-of-interest (ROIs) for brain structures (nodes) are created arbitrarily according to each researcher’s necessity, leading to inconsistent ROIs among studies. One reason for such a situation is the fact that earlier AVs were fixed, i.e. combination and division of nodes were not implemented. This report presents a pipeline for constructing a flexible annotation atlas (FAA) of the mouse brain by leveraging public resources of the Allen Institute for Brain Science on brain structure, gene expression, and axonal projection. A mere two-step procedure with user-specified, text-based information and Python codes constructs FAA with nodes which can be combined or divided objectively while maintaining anatomical hierarchy of brain structures. Four FAAs with total node count of 4, 101, 866, and 1381 were demonstrated. Unique characteristics of FAA realized analysis of resting-state functional connectivity (FC) across the anatomical hierarchy and among cortical layers, which were thin but large brain structures. FAA can improve the consistency of whole brain ROI definition among laboratories by fulfilling various requests from researchers with its flexibility and reproducibility.

scholarly journals Comparing the Expression of Genes Related to Serotonin (5-HT) in C57BL/6J Mice and Humans Based on Data Available at the Allen Mouse Brain Atlas and Allen Human Brain Atlas

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
C. A. Acevedo-Triana ◽  
L. A. León ◽  
F. P. Cardenas
Keyword(s):  
Human Brain ◽  
Mouse Brain ◽  
Expression Patterns ◽  
Brain Atlas ◽  
Expression Data ◽  
Expression Of Genes ◽  
High Degree ◽  
The Relationship ◽  
Allen Mouse Brain Atlas

Brain atlases are tools based on comprehensive studies used to locate biological characteristics (structures, connections, proteins, and gene expression) in different regions of the brain. These atlases have been disseminated to the point where tools have been created to store, manage, and share the information they contain. This study used the data published by the Allen Mouse Brain Atlas (2004) for mice (C57BL/6J) and Allen Human Brain Atlas (2010) for humans (6 donors) to compare the expression of serotonin-related genes. Genes of interest were searched for manually in each case (in situ hybridization for mice and microarrays for humans), normalized expression data (z-scores) were extracted, and the results were graphed. Despite the differences in methodology, quantification, and subjects used in the process, a high degree of similarity was found between expression data. Here we compare expression in a way that allows the use of translational research methods to infer and validate knowledge. This type of study allows part of the relationship between structures and functions to be identified, by examining expression patterns and comparing levels of expression in different states, anatomical correlations, and phenotypes between different species. The study concludes by discussing the importance of knowing, managing, and disseminating comprehensive, open-access studies in neuroscience.

scholarly journals PubAnatomy 3D: Integrating Medline Exploration with the Allen Mouse Brain Atlas

2013 ◽  
Vol 7 ◽  
Author(s):  
Yang Gang ◽  
Dai Manhong ◽  
Song Jean ◽  
Mirel BarBara ◽  
Meng Fan
Keyword(s):  
Mouse Brain ◽  
Brain Atlas ◽  
Allen Mouse Brain Atlas

scholarly journals Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

2021 ◽  
Vol 15 ◽  
Author(s):  
Jan Krepl ◽  
Francesco Casalegno ◽  
Emilie Delattre ◽  
Csaba Erö ◽  
Huanxiang Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Supervised Learning ◽  
Mouse Brain ◽  
Brain Structures ◽  
Brain Atlas ◽  
Perceptual Similarity ◽  
Gene Expressions ◽  
Individual Gene ◽  
Staining Techniques ◽  
Quality Maps

The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.

scholarly journals Automatic navigation system for the mouse brain

2018 ◽  
Author(s):  
Susan J. Tappan ◽  
Brian S. Eastwood ◽  
Nathan O’Connor ◽  
Quanxin Wang ◽  
Lydia Ng ◽  
...  
Keyword(s):  
Mouse Brain ◽  
Digital Image ◽  
Navigation System ◽  
Three Dimensional ◽  
Brain Regions ◽  
Brain Atlas ◽  
Brain Section ◽  
Observer Error ◽  
Novel Technology ◽  
Experimental Mouse

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.

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