Brain templates and atlases

被引:414
作者
Evans, Alan C. [1 ]
Janke, Andrew L. [2 ]
Collins, D. Louis [1 ]
Baillet, Sylvain [1 ]
机构
[1] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ H3A 2B4, Canada
[2] Australian Natl Univ, Eccles Inst Neurosci, Canberra, ACT 0200, Australia
关键词
Brain atlases; MRI templates; Spatial normalization; Databases; HUMAN CEREBRAL-CORTEX; AUTOMATED IMAGE REGISTRATION; POSITRON EMISSION TOMOGRAPHY; MULTIPLE-SCLEROSIS LESIONS; COMMON STEREOTACTIC SPACE; VOXEL-BASED MORPHOMETRY; SURFACE-BASED ANALYSIS; DEMENTED OLDER-ADULTS; WHITE-MATTER ANATOMY; OPEN ACCESS SERIES;
D O I
10.1016/j.neuroimage.2012.01.024
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease-specificity. Accounting, or not, for these various factors in defining stereotaxic space has created the specter of an ever-expanding set of atlases, customized for a particular experiment, that are mutually incompatible. These difficulties continue to plague the brain mapping field. This review article summarizes the evolution of stereotaxic space in term of the basic principles and associated conceptual challenges, the creation of population atlases and the future trends that can be expected in atlas evolution. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码:911 / 922
页数:12
相关论文
共 214 条
[1]   Infant brain probability templates for MRI segmentation and normalization [J].
Altaye, Mekibib ;
Holland, Scott K. ;
Wilke, Marko ;
Gaser, Christian .
NEUROIMAGE, 2008, 43 (04) :721-730
[2]  
Amunts K, 1999, J COMP NEUROL, V412, P319, DOI 10.1002/(SICI)1096-9861(19990920)412:2<319::AID-CNE10>3.0.CO
[3]  
2-7
[4]   Brodmann's areas 17 and 18 brought into stereotaxic space - Where and how variable? [J].
Amunts, K ;
Malikovic, A ;
Mohlberg, H ;
Schormann, T ;
Zilles, K .
NEUROIMAGE, 2000, 11 (01) :66-84
[5]  
[Anonymous], 1955, ATLAS TSITOARKHITEKT
[6]   Voxel-based morphometry - The methods [J].
Ashburner, J ;
Friston, KJ .
NEUROIMAGE, 2000, 11 (06) :805-821
[7]   Unified segmentation [J].
Ashburner, J ;
Friston, KJ .
NEUROIMAGE, 2005, 26 (03) :839-851
[8]  
Ashburner J, 1999, HUM BRAIN MAPP, V7, P254, DOI 10.1002/(SICI)1097-0193(1999)7:4<254::AID-HBM4>3.0.CO
[9]  
2-G
[10]   A fast diffeomorphic image registration algorithm [J].
Ashburner, John .
NEUROIMAGE, 2007, 38 (01) :95-113