Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.
Distinguishing between different percolation regimes in noisy dynamic networks with an application to epileptic seizures.
复制标题
DOI：
10.1371/journal.pcbi.1011188
复制
发表时间：
2023-06
影响因子：
4.3
通讯作者：
中科院分区：
文献类型：
--
作者：
在临床神经科学中，癫痫发作与大脑中突然出现的耦合活动有关。由此产生的功能网络边缘表明大脑区域之间足够强的耦合与渗透的概念是一致的，渗透是复杂网络中的一种现象，对应于一个巨大的连接组件的突然出现。传统上，工作集中在无噪声渗流与单调的网络增长过程，但现实世界的网络更复杂。我们开发了一类随机图隐马尔可夫模型（RG-Hyndrome）的特征逾渗制度在嘈杂的，动态发展的网络中存在的边缘出生和边缘死亡。这门课用来了解癫痫发作时所经历的相变类型，特别是区分癫痫发作中不同的渗透机制。我们开发了一个假设检验框架推断假定的渗流机制。作为一个必要的先驱，我们提出了一个EM算法估计参数的噪声网络序列只观察到在一个纵向的时间点的子采样。我们的研究结果表明，不同类型的渗滤可以发生在人类癫痫发作。推断的类型可能建议量身定制的治疗策略，并为癫痫的基础科学提供新的见解。先前的工作表明，爆炸密度增加（即，在癫痫发作期间癫痫患者的脑功能连接网络中的更多边缘）与重叠的概念相一致，重叠是复杂网络中对应于巨大连接成分的突然出现的现象。我们的工作深入研究，提供统计方法来揭示密度增加背后的底层网络演化行为。我们的目标是回答这个问题：我们如何在实践中区分不同的渗流制度？我们开发了一类随机图隐马尔可夫模型（RG-HMM）和适用于现实世界背景的必要推理方法，用于表征存在边出生、死亡和噪声的动态演化网络中的渗透机制。我们提出了一个EM算法与粒子滤波和数据增强估计参数，和一个假设检验框架，使用贝叶斯因子推断之间的Erdos-Renyi（经典类型）和产品规则（爆炸型）的渗流制度。我们对真实的癫痫发作数据的应用表明，根据临床癫痫发作类型，不同类型的渗流可能发生在人类癫痫发作的不同阶段。
In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks—in which edges indicate strong enough coupling between brain regions—are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy. Prior work has shown that an explosive density increase (i.e., more edges) in the brain functional connectivity networks in epilepsy patients during seizure onset aligns with the notion of percolation—a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Our work delves deeper to provide statistical methods to uncover the underlying network evolution behavior behind the density increase. We aim to answer the question: How can we distinguish between different percolation regimes in practice? We develop a class of random graph hidden Markov models (RG-HMMs) and the necessary inferential methodologies applicable to real-world context, for characterizing percolation regimes in dynamically evolving networks in the presence of edge birth, death and noise. We present an EM algorithm with particle filtering and data augmentation for estimating parameters, and a hypothesis testing framework using Bayes factor for inferring between the Erdos-Renyi (a classical type) and the product-rule (an explosive type) percolation regimes. Our application to real seizure data suggests that different types of percolation can occur at different stages of human seizures depending on the clinical seizure types.
登录
查看更多内容
爆炸同步是动态相空间中爆炸渗透的过程
影响因子：
4.6
作者：
Zhang X;Zou Y;Boccaletti S;Liu Z
通讯作者：
Liu Z
影响因子：
14.5
作者：
Guye, Maxime;Regis, Jean;Bartolomei, Fabrice
通讯作者：
Bartolomei, Fabrice
影响因子：
56.9
作者：
Riordan, Oliver;Warnke, Lutz
通讯作者：
Warnke, Lutz
影响因子：
2.2
作者：
Schindler, Kaspar;Amor, Frederique;Rummel, Christian
通讯作者：
Rummel, Christian
DOI：
10.1214/09-aoas313
发表时间：
2010-06-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Snijders TA;Koskinen J;Schweinberger M
通讯作者：
Schweinberger M