Lioi Giulia, Gripon Vincent, Brahim Abdelbasset, Rousseau François, Farrugia Nicolas
IMT Atlantique, Brest, France.
INSERM, Laboratoire Traitement du Signal et de l'Image (LTSI) U1099, University of Rennes, Rennes, France.
Netw Neurosci. 2021 Apr 27;5(2):322-336. doi: 10.1162/netn_a_00183. eCollection 2021.
The application of graph theory to model the complex structure and function of the brain has shed new light on its organization, prompting the emergence of network neuroscience. Despite the tremendous progress that has been achieved in this field, still relatively few methods exploit the topology of brain networks to analyze brain activity. Recent attempts in this direction have leveraged on the one hand graph spectral analysis (to decompose brain connectivity into eigenmodes or gradients) and the other graph signal processing (to decompose brain activity "coupled to" an underlying network in graph Fourier modes). These studies have used a variety of imaging techniques (e.g., fMRI, electroencephalography, diffusion-weighted and myelin-sensitive imaging) and connectivity estimators to model brain networks. Results are promising in terms of interpretability and functional relevance, but methodologies and terminology are variable. The goals of this paper are twofold. First, we summarize recent contributions related to connectivity gradients and graph signal processing, and attempt a clarification of the terminology and methods used in the field, while pointing out current methodological limitations. Second, we discuss the perspective that the functional relevance of connectivity gradients could be fruitfully exploited by considering them as graph Fourier bases of brain activity.
将图论应用于对大脑复杂结构和功能进行建模，为大脑的组织方式带来了新的见解，促使网络神经科学的兴起。尽管该领域已取得了巨大进展，但利用脑网络拓扑结构来分析大脑活动的方法仍然相对较少。近期在这方面的尝试一方面利用了图谱分析（将大脑连通性分解为本征模式或梯度），另一方面利用了图信号处理（将与基础网络“耦合”的大脑活动分解为图傅里叶模式）。这些研究使用了多种成像技术（如功能磁共振成像、脑电图、扩散加权成像和髓鞘敏感成像）以及连通性估计器来对脑网络进行建模。在可解释性和功能相关性方面，结果很有前景，但方法和术语各不相同。本文的目标有两个。首先，我们总结与连通性梯度和图信号处理相关的近期贡献，并尝试澄清该领域使用的术语和方法，同时指出当前的方法局限性。其次，我们讨论这样一种观点，即通过将连通性梯度视为大脑活动的图傅里叶基，可以有效地利用其功能相关性。
