Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore; Institute of Bioengineering, Center for Neuroprosthetics, Ecole Polytechnique Fédérale de Lausanne, Switzerland.
Department of Electrical and Computer Engineering, ASTAR-NUS Clinical Imaging Research Centre, Singapore Institute for Neurotechnology and Memory Networks Program, National University of Singapore, Singapore.
Neuroimage. 2019 Jul 1;194:42-54. doi: 10.1016/j.neuroimage.2019.03.019. Epub 2019 Mar 20.
Component analysis is a powerful tool to identify dominant patterns of interactions in multivariate datasets. In the context of fMRI data, methods such as principal component analysis or independent component analysis have been used to identify the brain networks shaping functional connectivity (FC). Importantly, these approaches are static in the sense that they ignore the temporal information contained in fMRI time series. Therefore, the corresponding components provide a static characterization of FC. Building upon recent findings suggesting that FC dynamics encode richer information about brain functional organization, we use a dynamic extension of component analysis to identify dynamic modes (DMs) of fMRI time series. We demonstrate the feasibility and relevance of this approach using resting-state and motor-task fMRI data of 730 healthy subjects of the Human Connectome Project (HCP). In resting-state, dominant DMs have strong resemblance with classical resting-state networks, with an additional temporal characterization of the networks in terms of oscillatory periods and damping times. In motor-task conditions, dominant DMs reveal interactions between several brain areas, including but not limited to the posterior parietal cortex and primary motor areas, that are not found with classical activation maps. Finally, we identify two canonical components linking the temporal properties of the resting-state DMs with 158 behavioral and demographic HCP measures. Altogether, these findings illustrate the benefits of the proposed dynamic component analysis framework, making it a promising tool to characterize the spatio-temporal organization of brain activity.
成分分析是一种强大的工具，可以识别多元数据集的主要交互模式。在 fMRI 数据的背景下，已经使用主成分分析或独立成分分析等方法来识别塑造功能连接（FC）的大脑网络。重要的是，这些方法是静态的，因为它们忽略了 fMRI 时间序列中包含的时间信息。因此，相应的成分提供了 FC 的静态特征描述。基于最近的研究结果表明，FC 动力学编码了关于大脑功能组织的更丰富信息，我们使用成分分析的动态扩展来识别 fMRI 时间序列的动态模式（DMs）。我们使用人类连接组计划（HCP）的 730 名健康受试者的静息态和运动任务 fMRI 数据证明了这种方法的可行性和相关性。在静息状态下，主导的 DMs 与经典静息态网络具有很强的相似性，并且网络在振荡周期和阻尼时间方面具有额外的时间特征描述。在运动任务条件下，主导的 DMs 揭示了几个大脑区域之间的相互作用，包括但不限于后顶叶皮层和初级运动区域，而这些区域在经典激活图中是找不到的。最后，我们确定了两个典型成分，将静息状态 DMs 的时间特性与 HCP 的 158 个行为和人口统计学测量联系起来。总之，这些发现说明了所提出的动态成分分析框架的好处，使其成为描述大脑活动时空组织的有前途的工具。
