Stillman Paul E, Wilson James D, Denny Matthew J, Desmarais Bruce A, Bhamidi Shankar, Cranmer Skyler J, Lu Zhong-Lin
The Ohio State University, Department of Psychology, Columbus, OH, 43210, USA.
University of San Francisco, Department of Mathematics and Statistics, San Francisco, CA, 94117, USA.
Sci Rep. 2017 Sep 15;7(1):11694. doi: 10.1038/s41598-017-09896-6.
We investigate the functional organization of the Default Mode Network (DMN) - an important subnetwork within the brain associated with a wide range of higher-order cognitive functions. While past work has shown the whole-brain network of functional connectivity follows small-world organizational principles, subnetwork structure is less well understood. Current statistical tools, however, are not suited to quantifying the operating characteristics of functional networks as they often require threshold censoring of information and do not allow for inferential testing of the role that local processes play in determining network structure. Here, we develop the correlation Generalized Exponential Random Graph Model (cGERGM) - a statistical network model that uses local processes to capture the emergent structural properties of correlation networks without loss of information. Examining the DMN with the cGERGM, we show that, rather than demonstrating small-world properties, the DMN appears to be organized according to principles of a segregated highway - suggesting it is optimized for function-specific coordination between brain regions as opposed to information integration across the DMN. We further validate our findings through assessing the power and accuracy of the cGERGM on a testbed of simulated networks representing various commonly observed brain architectures.
我们研究了默认模式网络（DMN）的功能组织，DMN是大脑中一个重要的子网，与广泛的高阶认知功能相关。虽然过去的研究表明，全脑功能连接网络遵循小世界组织原则，但子网结构的了解较少。然而，当前的统计工具并不适合量化功能网络的运行特征，因为它们通常需要对信息进行阈值审查，并且不允许对局部过程在确定网络结构中所起的作用进行推断性测试。在这里，我们开发了相关广义指数随机图模型（cGERGM），这是一种统计网络模型，它使用局部过程来捕捉相关网络的涌现结构属性而不会丢失信息。用cGERGM检查DMN，我们发现，DMN似乎不是按照小世界属性组织的，而是按照隔离高速公路的原则组织的——这表明它是为大脑区域之间特定功能的协调而优化的，而不是为跨DMN的信息整合而优化的。我们通过在代表各种常见观察到的脑结构的模拟网络测试平台上评估cGERGM的能力和准确性，进一步验证了我们的发现。
