Graph Signal Processing For Neurogimaging to Reveal Dynamics of Brain Structure-Function Coupling
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 b...
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| Published in: | Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
04.06.2023
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| Subjects: | |
| ISSN: | 2379-190X |
| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 2379-190X |
| DOI: | 10.1109/ICASSP49357.2023.10095285 |