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<p>The brain network damage provoked by a neurological disease can be modelled as the result of the action of an operator, <i>K</i>, acting on the brain, inspired by physics. Here, we explore the matrix formulation of <i>K</i>, 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月27日

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  • Sofia Fazio,
  • Patrizia Ribino,
  • Francesca Gasparini,
  • Norbert Marwan,
  • Peppino Fazio,
  • Marco Gherardi,
  • Maria Mannone

摘要

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.