Department of Bio and Brain Engineering, KAIST, Daejeon, Republic of Korea.
Brain Connect. 2012;2(4):218-24. doi: 10.1089/brain.2012.0079. Epub 2012 Aug 9.
Recent studies have shown that blood oxygen level-dependent low-frequency (<0.1 Hz) fluctuations (LFFs) during a resting-state exhibit a high degree of correlation with other regions that share cognitive function. Initial studies of resting-state network mapping have focused primarily on major networks such as the default mode network, primary motor, somatosensory, visual, and auditory networks. However, more specific or subnetworks, including those associated with specific motor functions, have yet to be properly addressed. We performed independent component analysis (ICA) in a specific target region of the brain, a process we name, "localized ICA." We demonstrated that when ICA is applied to localized fMRI data, it can be used to distinguish resting-state LFFs associated with specific motor functions (e.g., finger tapping, foot movement, or bilateral lip pulsing) in the primary motor cortex. These ICA components generated from localized data can then be used as functional regions of interest to map whole-brain connectivity. In addition, this method can be used to visualize inter-regional connectivity by expanding the localized region and identifying components that show connectivity between the two regions.
最近的研究表明，静息态下血氧水平依赖低频（<0.1 Hz）波动（LFF）与具有认知功能的其他区域具有高度相关性。静息态网络映射的初始研究主要集中在主要网络上，如默认模式网络、主要运动、躯体感觉、视觉和听觉网络。然而，更具体或子网，包括与特定运动功能相关的子网，尚未得到妥善解决。我们在大脑的特定目标区域进行独立成分分析（ICA），我们将这一过程命名为“局部 ICA”。我们证明，当 ICA 应用于局部 fMRI 数据时，它可用于区分与初级运动皮层中特定运动功能（例如，手指敲击、脚部运动或双侧唇部脉冲）相关的静息状态 LFF。然后，可将从局部数据生成的这些 ICA 分量用作功能感兴趣区以映射全脑连接。此外，通过扩展局部区域并识别显示两个区域之间连接的分量，此方法可用于可视化区域间连接。
