Exploring resting-state functional connectivity with total interdependence.
Exploring resting-state functional connectivity with total interdependence.
复制标题
探索完全相互依赖的静息状态功能连接。
DOI：
10.1016/j.neuroimage.2012.01.079
复制
发表时间：
2012-04-02
期刊：
影响因子：
5.7
通讯作者：
Ding, Mingzhou
中科院分区：
文献类型：
--
作者：
Wen, Xiaotong;Mo, Jue;Ding, Mingzhou
静息态功能磁共振成像已成为研究正常脑功能网络机制以及神经和精神疾病对其损害的有力工具。分析上，独立成分分析和基于种子的互相关是评估静息态fMRI时间序列连通性的主要方法。这两种方法的一个共同点是，它们利用了同期（零滞后）测量数据的协变结构，但忽略了超越零滞后的时间关系。为了研究不同滞后的数据协变是否有助于我们对功能性大脑网络的理解，需要一种能够揭示两个静息状态BOLD信号之间整体时间关系的方法。在本文中，我们提出了这样一个措施称为总相互依存（TI）。比较TI与零滞后互相关（CC），我们报告三个结果。首先，当与随机置换过程相结合时，TI可以揭示CC未捕获的两个静止状态BOLD时间序列之间的时间关系的量。第二，比较静息态数据与任务状态数据记录在同一个扫描会话中，我们证明了与TI构建的静息态功能网络更精确地匹配的网络激活的任务。第三，TI被证明是更敏感的统计比CC，并提供更好的特征向量的网络聚类分析。
Resting-state fMRI has become a powerful tool for studying network mechanisms of normal brain functioning and its impairments by neurological and psychiatric disorders. Analytically, independent component analysis and seed-based cross correlation are the main methods for assessing the connectivity of resting-state fMRI time series. A feature common to both methods is that they exploit the covariation structures of contemporaneously (zero-lag) measured data but ignore temporal relations that extend beyond the zero-lag. To examine whether data covariations across different lags can contribute to our understanding of functional brain networks, a measure that can uncover the overall temporal relationship between two resting-state BOLD signals is needed. In this paper we propose such a measure referred as total interdependence (TI). Comparing TI with zero-lag cross correlation (CC) we report three results. First, when combined with a random permutation procedure, TI can reveal the amount of temporal relationship between two resting-state BOLD time series that is not captured by CC. Second, comparing resting-state data with task-state data recorded in the same scanning session, we demonstrate that the resting-state functional networks constructed with TI match more precisely the networks activated by the task. Third, TI is shown to be more statistically sensitive than CC and provides better feature vectors for network clustering analysis.
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DOI：
10.1073/pnas.0504136102
发表时间：
2005-07-05
影响因子：
11.1
作者：
Fox, MD;Snyder, AZ;Raichle, ME
通讯作者：
Raichle, ME
DOI：
10.1073/pnas.0308538101
发表时间：
2004-06-29
影响因子：
11.1
作者：
Brovelli, A;Ding, MZ;Bressler, SL
通讯作者：
Bressler, SL
DOI：
10.1098/rstb.2005.1634
发表时间：
2005-05-29
影响因子：
6.3
作者：
Beckmann, CF;DeLuca, M;Smith, SM
通讯作者：
Smith, SM
影响因子：
1.9
作者：
Ding, MZ;Bressler, SL;Liang, HL
通讯作者：
Liang, HL
影响因子：
14.5
作者：
Church, Jessica A.;Fair, Damien A.;Dosenbach, Nico U. F.;Cohen, Alexander L.;Miezin, Francis M.;Petersen, Steven E.;Schlaggar, Bradley L.
通讯作者：
Schlaggar, Bradley L.