Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition
Extracting Reproducible Time-Resolved Resting State Networks Using Dynamic Mode Decomposition
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DOI：
10.3389/fncom.2019.00075
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发表时间：
2019-10-31
影响因子：
3.2
通讯作者：
Brunton, Bingni W.
中科院分区：
文献类型：
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作者：
Kunert-Graf, James M.;Eschenburg, Kristian M.;Brunton, Bingni W.
从功能磁共振成像（fMRI）扫描中提取的静息态网络（RSN）被认为反映了大脑区域的内在组织和网络结构。大多数计算 RSN 的传统方法通常假设这些功能网络在持续 5-15 分钟的扫描期间是静态的。然而，众所周知，它们的时间尺度从几秒到几年不等。此外，RSN 的动态特性受到多种神经系统疾病的影响。最近，表征 RSN 动力学的方法不断涌现，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）的新颖方法，从噪声高维 fMRI 数据的短窗口中提取网络，从而能够以秒的时间分辨率稳健地解析单次扫描的 RSN。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项目的 120 名个体的数据，并表明 DMD 模式的无监督聚类可以在群体 (gDMD) 和单个受试者 (sDMD) 水平上发现 RSN。 gDMD 模式与规范 RSN 非常相似。与已建立的方法相比，sDMD 模式捕获个性化的 RSN 结构，该结构既更好地类似于群体 RSN，又更好地捕获受试者水平的变化。我们进一步利用这种时间分辨 sDMD 分析来推断 RSN 之间的占用率和转换，具有高重现性。这种基于 DMD 的自动化方法是表征个体受试者 RSN 空间和时间结构的强大工具。
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.