Network theory insights lead to a mathematical representation of Parkinson’s disease

Network theory insights lead to a mathematical representation of Parkinson’s disease


Connectivity matrix and brain network of a healthy control and of a Parkinson’s Disease patient, and the K-operator ideally transforming the healthy network into the diseased one. Credit: The European Physical Journal Special Topics (2024). DOI: 10.1140/epjs/s11734-024-01345-6

Neurodegenerative diseases, such as Parkinson’s disease, can be thought of as arising from malfunctions in the network of neuronal agglomerates in the brain. It is therefore often useful to apply insights from a branch of mathematics called network theory when studying the development of these diseases.

A group of European physicists and engineers led by Maria Mannone of the National Research Council of Italy, the University of Potsdam, Germany, and the Potsdam Institute for Climate Impact Research (PIK), Germany, has now taken this further by defining a matrix transforming the brain network of a healthy individual into one affected by Parkinson’s disease.

This has been published in The European Physical Journal Special Topics,

“Our work derives from two historic ideas: that brain functions can be mapped to specific areas, and that connections between them can be mapped non-invasively,” explains Mannone. “These ideas are behind the technique of functional magnetic resonance imaging (fMRI), and we used fMRI images to define our matrices.”

The researchers borrow an idea from theoretical physics, that the brain network can be described as a matrix and any change in that matrix, such as that occurring when an illness develops, can be modeled as a mathematical operator—represented as a matrix—acting on it.

“In classical times, illnesses were seen as demons or deities acting on patients,” says Mannone. “This concept is not dissimilar; we chose to name our ‘demon operator’ K, for the German ‘Krankheit,’ disease.”

They computed K for Parkinson’s disease from analysis of the brains of patients using fMRI data from the Parkinson’s Progression Markers Initiative at the University of Southern California and a healthy volunteer, and observed the parts of the network where most changes occurred.

This essentially theoretical approach can have many applications. It is possible to monitor the course of disease by seeing how K changes over time, and perhaps also to compute an ‘inverse’ operator, simulate disease reversal, and see which parts of the Parkinson’s brain would benefit most from further intervention.

“And, of course, this idea does not only apply to Parkinson’s disease,” concludes Mannone. “We are already looking at Ks for Alzheimer’s disease and schizophrenia.”

More information:
Maria Mannone et al, A brain-network operator for modeling disease: a first data-based application for Parkinson’s disease, The European Physical Journal Special Topics (2024). DOI: 10.1140/epjs/s11734-024-01345-6

Citation: Network theory insights lead to a mathematical representation of Parkinson’s disease (2024, November 22) retrieved 22 November 2024 from

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

Leave a Comment

Your email address will not be published. Required fields are marked *