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The application of graph theory to model the complex structure and functionof the brain has shed new light on its organization and function, prompting theemergence of network neuroscience. Despite the tremendous progress that hasbeen achieved in this field, still relatively few methods exploit the topologyof brain networks to analyze brain activity. Recent attempts in this directionhave leveraged on graph spectral analysis and graph signal processing todecompose brain activity in connectivity eigenmodes or gradients. If resultsare promising in terms of interpretability and functional relevance,methodologies and terminology are sometimes confusing. The goals of this paperare twofold. First, we summarize recent contributions related to connectivitygradients and graph signal processing, and attempt a clarification of theterminology and methods used in the field, while pointing out currentmethodological limitations. Second, we discuss the perspective that thefunctional relevance of connectivity gradients could be fruitfully exploited byconsidering them as graph Fourier bases of brain activity.

Gradients of Connectivity as Graph Fourier Bases of Brain Activity