A seed-based cross-modal comparison of brain connectivity measures.
A seed-based cross-modal comparison of brain connectivity measures.
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DOI：
10.1007/s00429-016-1264-3
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发表时间：
2017-04
影响因子：
3.1
通讯作者：
Eickhoff SB
中科院分区：
文献类型：
--
作者：
Reid AT;Hoffstaedter F;Gong G;Laird AR;Fox P;Evans AC;Amunts K;Eickhoff SB
人类神经成像方法提供了许多可以推断人脑连接结构的手段。例如，血氧水平依赖（BOLD）信号时间序列中的相关性通常用于推断“功能连接”。在结构形态测量中，例如基于体素的形态测量（VBM）或皮质厚度（CT），样本之间的相关性也被用于估计连接性，通过对连接的大脑区域的相互营养效应来估计。在本研究中，我们比较了从四种常见相关方法获得的基于种子的连接性估计：静息状态功能连接性（RS-fMRI）、元分析连接性建模（MACM）、VBM相关性和CT相关性。我们发现，两种功能的方法（RS-fMRI和MACM）有最好的协议。虽然两种结构方法（CT和VBM）具有优于随机的收敛性，但它们彼此之间的相似性并不比功能方法更大。模态之间的对应程度在种子区域之间变化很大，并且还取决于应用于连接分布的阈值。这些结果证明了从结构和功能协方差推断的连接性之间的某种程度的相似性，特别是对于最鲁棒的功能连接区域（例如，默认模式网络）。然而，他们也警告说，这些措施可能会捕捉大脑结构和功能的非常不同的方面。
Human neuroimaging methods have provided a number of means by which the connectivity structure of the human brain can be inferred. For instance, correlations in blood oxygen level dependent (BOLD) signal time series are commonly used to make inferences about “functional connectivity”. Correlations across samples in structural morphometric measures, such as voxel-based morphometry (VBM) or cortical thickness (CT), have also been used to estimate connectivity, putatively through mutually trophic effects on connected brain areas. In this study, we have compared seed-based connectivity estimates obtained from four common correlational approaches: resting-state functional connectivity (RS-fMRI), meta-analytic connectivity modelling (MACM), VBM correlations, and CT correlations. We found that the two functional approaches (RS-fMRI and MACM) had the best agreement. While the two structural approaches (CT and VBM) had better-than-random convergence, they were no more similar to each other than to the functional approaches. The degree of correspondence between modalities varied considerably across seed regions, and also depended on the threshold applied to the connectivity distribution. These results demonstrate some degree of similarity between connectivity inferred from structural and functional covariance, particularly for the most robust functionally connected regions (e.g., the default mode network). However, they also caution that these measures likely capture very different aspects of brain structure and function.
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影响因子：
13.9
作者：
Fox PT;Lancaster JL;Laird AR;Eickhoff SB
通讯作者：
Eickhoff SB
影响因子：
4.8
作者：
Eickhoff, Simon B.;Laird, Angela R.;Grefkes, Christian;Wang, Ling E.;Zilles, Karl;Fox, Peter T.
通讯作者：
Fox, Peter T.
DOI：
10.1523/jneurosci.3554-12.2013
发表时间：
2013-02-13
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Alexander-Bloch A;Raznahan A;Bullmore E;Giedd J
通讯作者：
Giedd J
影响因子：
5.7
作者：
Evans, Alan C.
通讯作者：
Evans, Alan C.
影响因子：
14.5
作者：
ABOITIZ, F;SCHEIBEL, AB;ZAIDEL, E
通讯作者：
ZAIDEL, E