Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults.
Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults.
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DOI：
10.1016/j.neuroimage.2013.04.032
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发表时间：
2013-09
期刊：
影响因子：
5.7
通讯作者：
Kesler, Shelli R.
中科院分区：
文献类型：
--
作者：
Hosseini, S. M. Hadi;Kesler, Shelli R.
近年来，脑形态的协调变化（如体积、厚度、表面积）已被用作脑区域之间结构关联的衡量标准，以推断大规模结构相关网络（SCN）。然而，尚不清楚形态测量学的相关性如何与大脑区域之间的功能连接联系起来。静息状态网络（Resting-state networks， RSN）源于静息时神经活动的协调变化，反映了功能相关区域之间的连通性，在一定程度上也反映了脑区域之间的解剖连通性。因此，研究SCN和RSN之间的相似性，以帮助确定静息状态连接所定义的形态测量相关性是如何关联的，这是很有趣的。我们研究了36个健康个体的区域灰质体积与SCN之间的连接模式和小世界组织的相似性，这些相似性来源于个体之间的区域灰质体积的相关性。结果显示，SCN和RSN之间存在显著的相似性（60%为积极连接，40%为消极连接），这可能是由于SCN和RSN背后的共享经验相关功能连接所解释的。相反，网络的小世界参数有显著差异，这表明在静态网络中，SCN拓扑参数不能作为拓扑组织的替代品。虽然我们的数据表明，使用结构相关网络在理解各种脑部疾病的结构关联变化方面是有用的，但应该注意的是，部分观察到的变化可能是由反映静息状态连通性以外的因素解释的。
In recent years, coordinated variations in brain morphology (e.g. volume, thickness, surface area) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks (SCN). However, it remains unclear how morphometric correlations relate to functional connectivity between brain regions. Resting-state networks (RSN), derived from coordinated variations in neural activity at rest, have been shown to reflect connectivity between functionally related regions as well as, to some extent, anatomical connectivity between brain regions. Therefore, it is intriguing to investigate similarities between SCN and RSN to help identify how morphometric correlations relate to connections defined by resting-state connectivity. We investigated the similarities in connectivity patterns and small-world organization between SCN, derived from correlations of regional gray matter volume across individuals, and RSN in 36 healthy individuals. The results showed a significant similarity between SCN and RSN (60% for positive connections and 40% for negative connections) that might be explained by shared experience-related functional connectivity underlying both SCN and RSN. Conversely, the small-world parameters of the networks were significantly different, suggesting that SCN topological parameters cannot be regarded as a substitute for topological organization in resting-state networks. While our data suggest that using structural correlation networks can be useful in understanding alterations in structural associations in various brain disorders, it should be noted that a portion of the observed alterations might be explained by factors other than those reflecting resting-state connectivity.
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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：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
DOI：
10.1523/jneurosci.1929-08.2008
发表时间：
2008-09-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bassett DS;Bullmore E;Verchinski BA;Mattay VS;Weinberger DR;Meyer-Lindenberg A
通讯作者：
Meyer-Lindenberg A
影响因子：
5.7
作者：
Bassett, Danielle S.;Nelson, Brent G.;Mueller, Bryon A.;Camchong, Jazmin;Lim, Kelvin O.
通讯作者：
Lim, Kelvin O.
影响因子：
5.3
作者：
Achard, S;Salvador, R;Bullmore, ET
通讯作者：
Bullmore, ET