AUTHORS: Preti M. G., Bolton T. A. W., Griffa A., Van De Ville D.

International Conference on Acoustics, Speech, and Signal Processing (ICASSP), : , Rhodes Island, Greece, June 2023


ABSTRACT

Linking time-varying functional brain activity with underlying neural architecture remains a complex and challenging endeavor. A recent framework for this undertaking is graph signal processing (GSP), where functional activity patterns are treated as signals living on a graph that is characterized by structural connectivity. Then graph spectral filtering can be used to obtain the parts of functional activity that are more or less smooth on the graph; i.e., more coupled or decoupled from brain structure, respectively. Given the time-varying behavior of functional magnetic resonance imaging (fMRI) networks, structure- function coupling may also change over time. Here, we leverage the GSP framework in a sliding-window setting to investigate the dynamics of brain structure-function coupling during resting-state at the node- and edge-wise levels. We conclude that dynamics are captured by both node- and edge-wise metrics of structure-function coupling and we identify principal patterns of dynamic functional connectivity respectively coupled and decoupled from structure.


BibTex

https://doi.org/10.1109/ICASSP49357.2023.10095285


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