Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time.
Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time.
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DOI：
10.1002/hbm.22599
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发表时间：
2014-12
影响因子：
4.8
通讯作者：
Van De Ville D
中科院分区：
文献类型：
--
作者：
Leonardi N;Shirer WR;Greicius MD;Van De Ville D
静息状态功能连接性（FC）在扫描期间变化很大。通过应用无监督学习技术，在波动的FC中发现了一组共同进化的连接，或动态FC（dFC）的可重复模式。基于k均值聚类和滑动窗口相关性的结果，最近有人假设dFC可能在几个离散的FC状态中循环。或者，已经提出使用主成分分析将dFC表示为多个FC模式的线性组合。由于目前尚不清楚FC模式的稀疏或非稀疏组合是否最合适，并且由于这影响了它们的解释和作为认知处理标记的使用，因此我们研究的目标是通过对模拟的，基于任务的和静息状态的dFC进行实证评估来评估稀疏性的影响。为了这个目的，我们应用矩阵分解的变量约束在时域和研究的再现性随后表示的dFC和FC模式的表达随着时间的推移。在受试者驱动的任务中，根据数据的性质，通过交替FC状态很好地描述了dFC。估计的FC模式显示出丰富的结构与已知的功能网络的组合，使三个不同的任务的准确识别。在休息期间，多个FC模式重叠更好地描述了dFC。执行控制网络，这是关键的工作记忆，出现分组交替与外部或内部导向的网络。这些结果表明，FC模式的组合可以提供一种有意义的方式来解开静息状态的dFC。《脑地图》35：5984-5995，2014年。© 2014作者。《人脑图谱》（Human Brain Mapping）由Wiley Periodicals，Inc.出版。
Resting‐state functional connectivity (FC) is highly variable across the duration of a scan. Groups of coevolving connections, or reproducible patterns of dynamic FC (dFC), have been revealed in fluctuating FC by applying unsupervised learning techniques. Based on results from k‐means clustering and sliding‐window correlations, it has recently been hypothesized that dFC may cycle through several discrete FC states. Alternatively, it has been proposed to represent dFC as a linear combination of multiple FC patterns using principal component analysis. As it is unclear whether sparse or nonsparse combinations of FC patterns are most appropriate, and as this affects their interpretation and use as markers of cognitive processing, the goal of our study was to evaluate the impact of sparsity by performing an empirical evaluation of simulated, task‐based, and resting‐state dFC. To this aim, we applied matrix factorizations subject to variable constraints in the temporal domain and studied both the reproducibility of ensuing representations of dFC and the expression of FC patterns over time. During subject‐driven tasks, dFC was well described by alternating FC states in accordance with the nature of the data. The estimated FC patterns showed a rich structure with combinations of known functional networks enabling accurate identification of three different tasks. During rest, dFC was better described by multiple FC patterns that overlap. The executive control networks, which are critical for working memory, appeared grouped alternately with externally or internally oriented networks. These results suggest that combinations of FC patterns can provide a meaningful way to disentangle resting‐state dFC. Hum Brain Mapp 35:5984–5995, 2014. © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.
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DOI：
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发表时间：
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影响因子：
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作者：
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