Meta-analysis in human neuroimaging: computational modeling of large-scale databases.
Meta-analysis in human neuroimaging: computational modeling of large-scale databases.
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DOI：
10.1146/annurev-neuro-062012-170320
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发表时间：
2014
影响因子：
13.9
通讯作者：
Eickhoff SB
中科院分区：
文献类型：
--
作者：
Fox PT;Lancaster JL;Laird AR;Eickhoff SB
空间标准化应用标准化坐标作为参考空间内的解剖地址在近30年前被引入人类神经成像研究。在这三十年中，一系列令人印象深刻的方法学进步已经采用、扩展和推广了这一标准。总的来说，这项工作产生了前所未有的严谨性，规模和范围的方法论连贯的文献。大规模的在线数据库已经汇编了这些观察结果及其相关的元数据，刺激了元分析方法的发展，以利用这一不断扩大的语料库。基于坐标的元分析方法已经出现，并在严谨性和实用性方面不断发展。早期方法以与传统（非成像）荟萃分析大致相当的方式计算跨研究共识。最近的进展现在计算基于共激活的连接，基于连接的功能parcellation，和复杂的网络模型，从数据集代表数以万计的主题。对大规模数据库中的人类神经影像学数据进行荟萃分析现在处于计算神经生物学的最前沿。
Spatial normalization—applying standardized coordinates as anatomical addresses within a reference space—was introduced to human neuroimaging research nearly 30 years ago. Over these three decades, an impressive series of methodological advances have adopted, extended, and popularized this standard. Collectively, this work has generated a methodologically coherent literature of unprecedented rigor, size, and scope. Large-scale online databases have compiled these observations and their associated meta-data, stimulating the development of meta-analytic methods to exploit this expanding corpus. Coordinate-based meta-analytic methods have emerged and evolved in rigor and utility. Early methods computed cross-study consensus, in a manner roughly comparable to traditional (nonimaging) meta-analysis. Recent advances now compute coactivation-based connectivity, connectivity-based functional parcellation, and complex network models powered from data sets representing tens of thousands of subjects. Meta-analyses of human neuroimaging data in large-scale databases now stand at the forefront of computational neurobiology.