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K-operator for Modelling Neurodegeneration: Simulations, fMRI Application, Eigenvalue Analysis and Recurrence Plots

Urheber*innen

Fazio,  Sofia
External Organizations;

Ribino,  Patrizia
External Organizations;

Gasparini,  Francesca
External Organizations;

/persons/resource/Marwan

Marwan,  Norbert       
Potsdam Institute for Climate Impact Research;

Fazio,  Peppino
External Organizations;

Gherardi,  Marco
External Organizations;

/persons/resource/maria.mannone

Mannone,  Maria
Potsdam Institute for Climate Impact Research;

Externe Ressourcen

https://github.com/sofiafazio/K_operator_Jupyter_eigenavalues
(Ergänzendes Material)

https://github.com/medusamedusa/K_operator_parkinson
(Ergänzendes Material)

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Ergänzendes Material (frei zugänglich)
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Zitation

Fazio, S., Ribino, P., Gasparini, F., Marwan, N., Fazio, P., Gherardi, M., Mannone, M. (in press): K-operator for Modelling Neurodegeneration: Simulations, fMRI Application, Eigenvalue Analysis and Recurrence Plots. - Journal of Medical Systems.


Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_32645
Zusammenfassung
The brain network damage provoked by a neurological disease can be modelled
as the result of the action of an operator, K, acting on the brain, inspired by physics. Here, we explore the matrix formulation of K, analysing eigenvalues and eigenvectors, with heuristic considerations on different techniques to approximate it. The primary objective of this paper is to lay the foundational groundwork for an innovative framework aimed at the development of predictive models regarding the progression of neurodegenerative diseases. This endeavour will leverage the potential of integrating these novel representations of brain damage with advanced machine-learning techniques. A case study based on real-world data is presented here to support the proposed modelling.