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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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Modeling and simulation for prediction of multiple sclerosis progression.

Vishnu Prathapan¹, Peter Eipert¹, Nicole Wigger²

¹Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany.

Computers in Biology and Medicine

|April 24, 2024

Summary

This study proposes using computational models and connectomes to predict multiple sclerosis (MS) progression. The Hodgkin-Huxley model simulates changes in neuronal signaling, offering a new tool for understanding MS disease trajectories.

Keywords:

Computational model Disease progression Multiple sclerosis Network model Neuronal patterns Progression prediction

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Area of Science:

Neuroscience
Computational Biology
Biomedical Engineering

Background:

Multiple sclerosis (MS) has numerous predictive techniques, including clinical, radiological, biological, and computational markers.
Existing methods have limitations in directly addressing diseased neural connectivity.
A novel approach is needed to accurately predict MS disease progression.

Purpose of the Study:

To propose an alternative method for predicting multiple sclerosis (MS) disease progression.
To leverage computational models and connectomes for enhanced disease trajectory prediction.
To explore the potential of the Hodgkin-Huxley model in simulating MS-related neural changes.

Main Methods:

Categorized existing MS prediction methods into clinical, radiological, biological, and computational markers.
Proposed using computational models combined with connectomes as a predictive tool.
Employed the Hodgkin-Huxley (HH) model to simulate signal propagation in a network, altering conduction parameters to mimic MS-related myelin changes.

Main Results:

The Hodgkin-Huxley model was validated for signal propagation dynamics under varying parameters.
Simulations demonstrated that altering conduction parameters in the network mimicked MS conditions.
Observed variations in signal propagation patterns provided insights into potential disease progression.

Conclusions:

Computational models, particularly the Hodgkin-Huxley model, offer a promising approach for predicting MS disease progression.
Integrating these models with connectomes can provide a deeper understanding of MS-related neural alterations.
This preliminary work opens a new avenue for developing advanced clinical tools for MS management.