Clinical Imaging Sciences Centre (CISC), Brighton & Sussex Medical School, Falmer, UK.
Med Eng Phys. 2013 Oct;35(10):1525-31. doi: 10.1016/j.medengphy.2013.04.013. Epub 2013 Jun 3.
An outstanding issue in graph-theoretical studies of brain functional connectivity is the lack of formal criteria for choosing parcellation granularity and correlation threshold. Here, we propose detectability of scale-freeness as a benchmark to evaluate time-series extraction settings. Scale-freeness, i.e., power-law distribution of node connections, is a fundamental topological property that is highly conserved across biological networks, and as such needs to be manifest within plausible reconstructions of brain connectivity. We demonstrate that scale-free network topology only emerges when adequately fine cortical parcellations are adopted alongside an appropriate correlation threshold, and provide the full design of the first open-source hardware platform to accelerate the calculation of large linear regression arrays.
图论研究脑功能连接的一个突出问题是缺乏选择分割粒度和相关阈值的正式标准。在这里，我们提出可检测性作为评估时间序列提取设置的基准。无标度性，即节点连接的幂律分布，是一种基本的拓扑性质，在生物网络中高度保守，因此需要在大脑连接的合理重建中表现出来。我们证明，只有采用足够精细的皮质分割和适当的相关阈值，才会出现无标度网络拓扑，并且提供了第一个开源硬件平台的完整设计，以加速大型线性回归数组的计算。
