Sourty Marion, Thoraval Laurent, Roquet Daniel, Armspach Jean-Paul, Foucher Jack, Blanc Frédéric
Centre National de la Recherche Scientifique, FMTS, University of Strasbourg, ICube-UMR 7357 Strasbourg, France.
Centre National de la Recherche Scientifique, FMTS, University of Strasbourg, ICube-UMR 7357Strasbourg, France; CEMNIS (Noninvasive Neuromodulation Center), University Hospital of StrasbourgStrasbourg, France.
Front Comput Neurosci. 2016 Jun 23;10:60. doi: 10.3389/fncom.2016.00060. eCollection 2016.
Exploring time-varying connectivity networks in neurodegenerative disorders is a recent field of research in functional MRI. Dementia with Lewy bodies (DLB) represents 20% of the neurodegenerative forms of dementia. Fluctuations of cognition and vigilance are the key symptoms of DLB. To date, no dynamic functional connectivity (DFC) investigations of this disorder have been performed. In this paper, we refer to the concept of connectivity state as a piecewise stationary configuration of functional connectivity between brain networks. From this concept, we propose a new method for group-level as well as for subject-level studies to compare and characterize connectivity state changes between a set of resting-state networks (RSNs). Dynamic Bayesian networks, statistical and graph theory-based models, enable one to learn dependencies between interacting state-based processes. Product hidden Markov models (PHMM), an instance of dynamic Bayesian networks, are introduced here to capture both statistical and temporal aspects of DFC of a set of RSNs. This analysis was based on sliding-window cross-correlations between seven RSNs extracted from a group independent component analysis performed on 20 healthy elderly subjects and 16 patients with DLB. Statistical models of DFC differed in patients compared to healthy subjects for the occipito-parieto-frontal network, the medial occipital network and the right fronto-parietal network. In addition, pairwise comparisons of DFC of RSNs revealed a decrease of dependency between these two visual networks (occipito-parieto-frontal and medial occipital networks) and the right fronto-parietal control network. The analysis of DFC state changes thus pointed out networks related to the cognitive functions that are known to be impaired in DLB: visual processing as well as attentional and executive functions. Besides this context, product HMM applied to RSNs cross-correlations offers a promising new approach to investigate structural and temporal aspects of brain DFC.
探索神经退行性疾病中的时变连接网络是功能磁共振成像领域的一个新兴研究方向。路易体痴呆（DLB）占神经退行性痴呆形式的20%。认知和警觉性波动是DLB的关键症状。迄今为止，尚未对该疾病进行动态功能连接（DFC）研究。在本文中，我们将连接状态的概念定义为脑网络之间功能连接的分段平稳配置。基于这一概念，我们提出了一种新的方法，用于组水平和个体水平研究，以比较和表征一组静息态网络（RSN）之间的连接状态变化。动态贝叶斯网络，即基于统计和图论的模型，使人们能够了解基于状态的相互作用过程之间的依赖性。乘积隐马尔可夫模型（PHMM）作为动态贝叶斯网络的一个实例，在此被引入以捕捉一组RSN的DFC的统计和时间方面。该分析基于从对20名健康老年人和16名DLB患者进行的组独立成分分析中提取的七个RSN之间的滑动窗口互相关。与健康受试者相比，DLB患者在枕颞额网络、内侧枕叶网络和右侧额顶网络的DFC统计模型存在差异。此外，RSN的DFC成对比较显示，这两个视觉网络（枕颞额和内侧枕叶网络）与右侧额顶控制网络之间的依赖性降低。DFC状态变化分析因此指出了与DLB中已知受损的认知功能相关的网络：视觉处理以及注意力和执行功能。除此之外，应用于RSN互相关的乘积HMM为研究脑DFC的结构和时间方面提供了一种有前景的新方法。
