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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Vydáno v:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) s. 1 - 5
Hlavní autoři: Preti, Maria Giulia, W. Bolton, Thomas A., Griffa, Alessandra, De Ville, Dimitri Van
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 04.06.2023
Témata:
Brain
Couplings
dynamic functional connectivity
eigenconnectivities
Filtering
fMRI
Functional magnetic resonance imaging
Graph signal processing
Image edge detection
Measurement
Signal processing
structure-function coupling
ISSN:2379-190X
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Shrnutí: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.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095285