School of Physics, University of Sydney, Sydney, NSW, Australia.
Center for Integrative Brain Function, University of Sydney, Sydney, NSW, Australia.
PLoS One. 2020 Oct 1;15(10):e0239717. doi: 10.1371/journal.pone.0239717. eCollection 2020.
It is shown that the statistical properties of connections between regions of the brain and their dependence on coarse-graining and thresholding in published data can be reproduced by a simple distance-based physical connectivity model. This allows studies with differing parcellation and thresholding to be interrelated objectively, and for the results of future studies on more finely grained or differently thresholded networks to be predicted. As examples of the implications, it is shown that the dependences of network measures on thresholding and parcellation imply that chosen brain regions can appear to form a small world network, even though the network at finer scales, or ultimately of individual neurons, may not be small world networks themselves.
研究表明，通过简单的基于距离的物理连通性模型，可以再现大脑区域之间连接的统计特性及其对已发表数据的粗粒化和阈值处理的依赖性。这使得具有不同分割和阈值处理的研究能够客观地相互关联，并可以预测未来对更精细分割或不同阈值处理的网络的研究结果。作为影响的示例，研究表明网络度量对阈值处理和分割的依赖性意味着所选脑区即使在更精细的尺度上，或者最终是单个神经元的网络，也可能不是小世界网络，而这些脑区可能看起来形成了小世界网络。
