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 |
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| Jazyk: | angličtina |
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IEEE
04.06.2023
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| ISSN: | 2379-190X |
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| 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. |
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| AbstractList | 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. |
| Author | Preti, Maria Giulia W. Bolton, Thomas A. De Ville, Dimitri Van Griffa, Alessandra |
| Author_xml | – sequence: 1 givenname: Maria Giulia surname: Preti fullname: Preti, Maria Giulia organization: CIBM Center for Biomedical Imaging,Switzerland – sequence: 2 givenname: Thomas A. surname: W. Bolton fullname: W. Bolton, Thomas A. organization: Centre Hospitalier Universitaire Vaudois,Connectomics Laboratory,Department of Radiology,Lausanne,Switzerland – sequence: 3 givenname: Alessandra surname: Griffa fullname: Griffa, Alessandra organization: Ecole Polytechnique Fédérale de Lausanne,Neuro-X Institute,Geneva,Switzerland – sequence: 4 givenname: Dimitri Van surname: De Ville fullname: De Ville, Dimitri Van organization: CIBM Center for Biomedical Imaging,Switzerland |
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| Snippet | Linking time-varying functional brain activity with underlying neural architecture remains a complex and challenging endeavor. A recent framework for this... |
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| SubjectTerms | Brain Couplings dynamic functional connectivity eigenconnectivities Filtering fMRI Functional magnetic resonance imaging Graph signal processing Image edge detection Measurement Signal processing structure-function coupling |
| Title | Graph Signal Processing For Neurogimaging to Reveal Dynamics of Brain Structure-Function Coupling |
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