A Physics-Based View of Brain-Network Alteration in Neurological Disease

EasyChair Preprint 15483

Abstract

The brain network damage provoked by a neurological disease can be modeled as the result of the action of an operator, $K$, acting on the brain, inspired by physics. Here, we explore the matrix formulation of $K$, analyzing 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 endeavor 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 here presented to support the proposed modeling.

Keyphrases: Alzheimer-Perusini's disease progression, K-operator, functional network, predictive models

@booklet{EasyChair:15483,
  author    = {Sofia Fazio and Patrizia Ribino and Francesca Gasparini and Norbert Marwan and Peppino Fazio and Marco Gherardi and Maria Mannone},
  title     = {A Physics-Based View of Brain-Network Alteration in Neurological Disease},
  howpublished = {EasyChair Preprint 15483},
  year      = {EasyChair, 2024}}