Behavioral Interpretations of Intrinsic Connectivity Networks

被引:760
作者
Laird, Angela R. [1 ]
Fox, P. Mickle
Eickhoff, Simon B. [2 ,3 ]
Turner, Jessica A. [4 ]
Ray, Kimberly L.
McKay, D. Reese
Glahn, David C. [5 ,6 ]
Beckmann, Christian F. [7 ]
Smith, Stephen M. [7 ]
Fox, Peter T.
机构
[1] Univ Texas Hlth Sci Ctr San Antonio, Res Imaging Inst, San Antonio, TX 78229 USA
[2] Rhein Westfal TH Aachen, Aachen, Germany
[3] Res Ctr Julich, Julich, Germany
[4] Mind Res Network, Albuquerque, NM USA
[5] Inst Living, Hartford, CT USA
[6] Yale Univ, Hartford, CT USA
[7] Univ Oxford, Oxford OX1 2JD, England
关键词
RESTING-STATE NETWORKS; INDEPENDENT COMPONENT ANALYSIS; FUNCTIONAL CONNECTIVITY; NEUROIMAGING DATA; BRAIN NETWORKS; MOTOR CORTEX; LOCALIZATION; LOST; TALAIRACH; DISTINCT;
D O I
10.1162/jocn_a_00077
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
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.
引用
收藏
页码:4022 / 4037
页数:16
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