Abdelnour Farras, Voss Henning U, Raj Ashish
Department of Radiology, Weill Cornell Medical College, New York, NY, USA.
Department of Radiology, Weill Cornell Medical College, New York, NY, USA.
Neuroimage. 2014 Apr 15;90:335-47. doi: 10.1016/j.neuroimage.2013.12.039. Epub 2013 Dec 30.
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.
大规模脑网络的解剖连接性与其功能连接性之间的关系极为重要，是一个活跃的研究领域。以往的尝试需要进行复杂的模拟，对每个皮质区域的动力学进行建模，并探索由解剖连接推导得出的区域间耦合。虽然从这些非线性模拟中获得了很多见解，但它们对于从解剖连接性预测功能来说可能是计算量很大的工具。线性模型很少受到关注。在这里，我们表明，一个经过适当设计的线性模型在捕捉支配解剖连接性与功能连接性之间关系的大脑长程二阶相关结构方面，似乎优于先前的非线性方法。我们基于图扩散推导了一个脑动力学线性网络，其中扩散量在图上进行随机游走。我们使用接受了扩散磁共振成像和静息态功能磁共振成像的受试者来测试我们的模型。应用于结构网络的网络扩散模型在很大程度上预测了从其功能磁共振成像数据得出的相关结构，比其他方法的预测程度更高。所提出方法的效用在于它可以常规地用于从解剖连接性推断功能相关性。而且由于它是线性的，也可以从功能数据推断解剖连接性。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通路中实施的纯粹机械过程在大脑中渗透。
