Behavioral interpretations of intrinsic connectivity networks.

Behavioral interpretations of intrinsic connectivity networks.
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DOI:
10.1162/jocn_a_00077
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发表时间:
2011-12
影响因子:
3.2
通讯作者:
Fox PT
Fox PT
中科院分区:
医学3区
文献类型:
--
作者:
Laird AR;Fox PM;Eickhoff SB;Turner JA;Ray KL;McKay DR;Glahn DC;Beckmann CF;Smith SM;Fox PT

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越来越多的神经影像学研究对静息状态下的功能连接网络进行了调查,为人类大脑结构提供了深入了解。对静息态网络功能特性的评估受到与任务无关状态的限制,这导致无法将这些网络与特定的心理功能联系起来。然而,最近有研究表明,可以从静息态数据以及从BrainMap数据库中存档的数千个基于任务的神经影像学实验所提取的数据中提取出相似的大脑网络。在此,我们利用一种基于BrainMap行为分类法以及分层的、数据驱动的认知过程排序的神经信息学方法,在标准低阶分解下对这些内在连接网络进行了全面的功能阐释。我们的研究结果为静息态研究中大脑网络的功能解释以及未来对心理操作及其驱动任务的研究提供了资源。
An increasingly large number of neuroimaging studies have investigated functionally connected networks during rest, providing insight into human brain architecture. Assessment of the functional qualities of resting state networks has been limited by the task-independent state, which results in an inability to relate these networks to specific mental functions. However, it was recently demonstrated that similar brain networks can be extracted from resting state data and data extracted from thousands of task-based neuroimaging experiments archived in the BrainMap database. Here, we present a full functional explication of these intrinsic connectivity networks at a standard low order decomposition using a neuroinformatics approach based on the BrainMap behavioral taxonomy as well as a stratified, data-driven ordering of cognitive processes. Our results serve as a resource for functional interpretations of brain networks in resting state studies and future investigations into mental operations and the tasks that drive them.
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