Neuroinformatics of the Allen Mouse Brain Connectivity Atlas

被引：141

作者：

Kuan, Leonard ^{[1
]}

Li, Yang ^{[1
]}

Lau, Chris ^{[1
]}

Feng, David ^{[1
]}

Bernard, Amy ^{[1
]}

Sunkin, Susan M. ^{[1
]}

Zeng, Hongkui ^{[1
]}

Dang, Chinh ^{[1
]}

Hawrylycz, Michael ^{[1
]}

Ng, Lydia ^{[1
]}

机构：

[1] Allen Inst Brain Sci, Seattle, WA 98103 USA

来源：

METHODS | 2015年 / 73卷

关键词：

Mouse connectivity atlas; Image registration; Signal detection; Digital atlas; Neuronal projection; IMAGE REGISTRATION; NEOCORTEX;

D O I：

10.1016/j.ymeth.2014.12.013

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

The Allen Mouse Brain Connectivity Atlas is a mesoscale whole brain axonal projection atlas of the C57B1/6J mouse brain. Anatomical trajectories throughout the brain were mapped into a common 3D space using a standardized platform to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. This connectivity atlas has several desirable features, including brain-wide coverage, validated and versatile experimental techniques, a single standardized data format, a quantifiable and integrated neuroinformatics resource, and an open-access public online database (http://connectivity.brain-map.org/). Meaningful informatics data quantification and comparison is key to effective use and interpretation of connectome data. This relies on successful definition of a high fidelity atlas template and framework, mapping precision of raw data sets into the 3D reference framework, accurate signal detection and quantitative connection strength algorithms, and effective presentation in an integrated online application. Here we describe key informatics pipeline steps in the creation of the Allen Mouse Brain Connectivity Atlas and include basic application use cases. (C) 2014 Elsevier Inc. All rights reserved.

引用

页码：4 / 17

页数：14

共 20 条

[1] Interoperable atlases of the human brain [J].

Amunts, K. ;

Hawrylycz, M. J. ;

Van Essen, D. C. ;

Van Horn, J. D. ;

Harel, N. ;

Poline, J. -B. ;

De Martino, F. ;

Bjaalie, J. G. ;

Dehaene-Lambertz, G. ;

Dehaene, S. ;

Valdes-Sosa, P. ;

Thirion, B. ;

Zilles, K. ;

Hill, S. L. ;

Abrams, M. B. ;

Tass, P. A. ;

Vanduffel, W. ;

Evans, A. C. ;

Eickhoff, S. B. .

NEUROIMAGE, 2014, 99 :525-532

[2]

[Anonymous], 1999, Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision and Materials Science

[3] Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain [J].