Department of Biomedical Engineering, Hanyang University, Seoul, Korea.
Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Sci Rep. 2019 Mar 11;9(1):4072. doi: 10.1038/s41598-019-40699-z.
Brain networks are integrated and segregated into several intrinsic connectivity networks (ICNs). Frequency specificity of ICNs have been studied to show that different ICNs have a unqiue contribution to brain network integration along frequencies. The purpose of this study was to evaluate the contribution of individual ICN to brain network integration along their frequency. We used 14 ICNs and determined 2 frequency bands (LF1, 0.030.08 Hz and LF2, 0.0090.012 Hz) from the hierarchical clustering of 101 frequency bins. We proposed a novel measure, called ICN efficiency, representing the difference between the global efficiencies of the whole brain network with and without the ICN to evaluate the contribution of the ICN to brain network integration. We found that each ICN had a different ICN efficiency at 2 frequency bands. We also found that the distinct subregions of the same ICN had a frequency specific contribution to brain network integration. Futhermore, the integration with other ICNs of the distinct subregions of the same ICN were different at 2 frequency bands. In conclusion, the contribution of each ICN to brain network integration is frequency specific and distinct subregions of the same ICN have functionally distinct roles with other ICNs at 2 frequency bands.
脑网络被整合和分割成几个内在连接网络（ICN）。已经研究了 ICN 的频率特异性，以表明不同的 ICN 对大脑网络沿着频率的整合具有独特的贡献。本研究旨在评估单个 ICN 对其频率下脑网络整合的贡献。我们使用了 14 个 ICN，并从 101 个频率区间的层次聚类中确定了 2 个频带（LF1，0.03-0.08 Hz 和 LF2，0.009-0.012 Hz）。我们提出了一种新的度量方法，称为 ICN 效率，它代表了在包含和不包含 ICN 的情况下整个脑网络全局效率之间的差异，用于评估 ICN 对脑网络整合的贡献。我们发现，每个 ICN 在 2 个频带下都有不同的 ICN 效率。我们还发现，同一 ICN 的不同子区域具有频率特异性的脑网络整合贡献。此外，同一 ICN 的不同子区域与其他 ICN 的整合在 2 个频带下也不同。总之，每个 ICN 对脑网络整合的贡献是频率特异性的，同一 ICN 的不同子区域在 2 个频带下与其他 ICN 具有功能不同的作用。
