Kunert-Graf James M, Eschenburg Kristian M, Galas David J, Kutz J Nathan, Rane Swati D, Brunton Bingni W
Pacific Northwest Research Institute, Seattle, WA, United States.
Department of Bioengineering, University of Washington, Seattle, WA, United States.
Front Comput Neurosci. 2019 Oct 31;13:75. doi: 10.3389/fncom.2019.00075. eCollection 2019.
Resting state networks (RSNs) extracted from functional magnetic resonance imaging (fMRI) scans are believed to reflect the intrinsic organization and network structure of brain regions. Most traditional methods for computing RSNs typically assume these functional networks are static throughout the duration of a scan lasting 5-15 min. However, they are known to vary on timescales ranging from seconds to years; in addition, the dynamic properties of RSNs are affected in a wide variety of neurological disorders. Recently, there has been a proliferation of methods for characterizing RSN dynamics, yet it remains a challenge to extract reproducible time-resolved networks. In this paper, we develop a novel method based on dynamic mode decomposition (DMD) to extract networks from short windows of noisy, high-dimensional fMRI data, allowing RSNs from single scans to be resolved robustly at a temporal resolution of seconds. After validating the method on a synthetic dataset, we analyze data from 120 individuals from the Human Connectome Project and show that unsupervised clustering of DMD modes discovers RSNs at both the group (gDMD) and the single subject (sDMD) levels. The gDMD modes closely resemble canonical RSNs. Compared to established methods, sDMD modes capture individualized RSN structure that both better resembles the population RSN and better captures subject-level variation. We further leverage this time-resolved sDMD analysis to infer occupancy and transitions among RSNs with high reproducibility. This automated DMD-based method is a powerful tool to characterize spatial and temporal structures of RSNs in individual subjects.
从功能磁共振成像（fMRI）扫描中提取的静息态网络（RSNs）被认为反映了脑区的内在组织和网络结构。大多数计算RSNs的传统方法通常假定这些功能网络在持续5 - 15分钟的扫描过程中是静态的。然而，已知它们在从秒到年的时间尺度上会发生变化；此外，RSNs的动态特性在多种神经系统疾病中会受到影响。最近，用于表征RSN动态的方法激增，但提取可重复的时间分辨网络仍然是一个挑战。在本文中，我们开发了一种基于动态模式分解（DMD）的新方法，用于从有噪声的高维fMRI数据的短窗口中提取网络，从而能够以秒级的时间分辨率稳健地解析单次扫描中的RSNs。在合成数据集上验证该方法后，我们分析了来自人类连接组计划的120名个体的数据，并表明DMD模式的无监督聚类在组（gDMD）和单受试者（sDMD）水平上都发现了RSNs。gDMD模式与典型的RSNs非常相似。与现有方法相比，sDMD模式捕获的个性化RSN结构既更类似于总体RSN，又能更好地捕获受试者水平的变异。我们进一步利用这种时间分辨的sDMD分析来推断RSNs之间具有高重现性的占用情况和转换。这种基于DMD的自动化方法是表征个体受试者中RSNs的空间和时间结构的强大工具。
