Network diffusion accurately models the relationship between structural and functional brain connectivity networks.
Network diffusion accurately models the relationship between structural and functional brain connectivity networks.
复制标题
DOI：
10.1016/j.neuroimage.2013.12.039
复制
发表时间：
2014-04-15
期刊：
影响因子：
5.7
通讯作者：
Raj A
中科院分区：
文献类型：
--
作者：
Abdelnour F;Voss HU;Raj A
大规模脑网络的解剖连接与其功能连接之间的关系非常重要，也是一个活跃的研究领域。之前的尝试需要复杂的模拟，对每个皮质区域的动态进行建模，并探索由解剖连接导出的区域之间的耦合。虽然从这些非线性模拟中获得了很多见解，但它们可能是从解剖连接预测功能的计算负担工具。很少有人关注线性模型。在这里，我们表明，一个适当设计的线性模型似乎是上级到以前的非线性方法在捕捉大脑的长程二阶相关结构，管理解剖和功能连接之间的关系。我们推导出一个线性网络的大脑动力学的基础上图扩散，扩散量经历了一个图上的随机游走。我们测试我们的模型，使用受试者进行扩散MRI和静息状态fMRI。应用于结构网络的网络扩散模型在很大程度上预测了从它们的fMRI数据中得到的相关结构，比其他方法更大程度上。所提出的方法的效用是，它可以经常被用来推断功能相关性的解剖连接。由于它是线性的，解剖连接也可以从功能数据中推断出来。我们模型的成功证实了大脑中总体平均信号的线性，并意味着它们的长程相关结构可能通过在其结构连接途径上制定的纯粹机械过程渗透到大脑中。
The relationship between anatomic connectivity of large-scale brain networks and their functional connectivity is of immense importance and an area of active research. Previous attempts have required complex simulations which model the dynamics of each cortical region, and explore the coupling between regions as derived by anatomic connections. While much insight is gained from these non-linear simulations, they can be computationally taxing tools for predicting functional from anatomic connectivities. Little attention has been paid to linear models. Here we show that a properly designed linear model appears to be superior to previous non-linear approaches in capturing the brain’s long-range second order correlation structure that governs the relationship between anatomic and functional connectivities. We derive a linear network of brain dynamics based on graph diffusion, whereby the diffusing quantity undergoes a random walk on a graph. We test our model using subjects who underwent diffusion MRI and resting state fMRI. The network diffusion model applied to the structural networks largely predicts the correlation structures derived from their fMRI data, to a greater extent than other approaches. The utility of the proposed approach is that it can routinely be used to infer functional correlation from anatomic connectivity. And since it is linear, anatomic connectivity can also be inferred from functional data. The success of our model confirms the linearity of ensemble average signals in the brain, and implies that their long-range correlation structure may percolate within the brain via purely mechanistic processes enacted on its structural connectivity pathways.
登录
查看更多内容
影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
影响因子：
5.7
作者：
Dawson, Debra Ann;Cha, Kuwook;Shmuel, Amir
通讯作者：
Shmuel, Amir
DOI：
10.1073/pnas.0901831106
发表时间：
2009-06-23
影响因子：
11.1
作者：
Deco, Gustavo;Jirsa, Viktor;Koetter, Rolf
通讯作者：
Koetter, Rolf
影响因子：
5.7
作者：
Cabral, Joana;Hugues, Etienne;Deco, Gustavo
通讯作者：
Deco, Gustavo
DOI：
10.1146/annurev-clinpsy-040510-143934
发表时间：
2011-01-01
影响因子：
18.4
作者：
Bullmore, Edward T.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.