English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Journal Article

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

Authors

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;

External Resource

https://github.com/sofiafazio/K_operator_Jupyter_eigenavalues
(Supplementary material)

https://github.com/medusamedusa/K_operator_parkinson
(Supplementary material)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://publications.pik-potsdam.de/pubman/item/item_32645
Abstract
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.