P245 Computational modeling of neural signal disruptions to predict multiple sclerosis progression
Vishnu Prathapan*1, Peter Eipert1, Markus Kipp2, Revathi Appali3,4, Oliver Schmitt1,2
1Medical School Hamburg University of Applied Sciences and Medical University, Am Kaiserkai 1, 20457, Hamburg, Germany
2Department of Anatomy, University of Rostock Gertrudenstr 9, 18057, Rostock, Germany
3Institute of General Electrical Engineering, University of Rostock, Albert-Einstein-Straße 2, 18059, Rostock, Germany
4Department of Aging of Individuals and Society, Interdisciplinary Faculty, University of Rostock, Universitätsplatz 1, 18055, Rostock, Germany
*vishnupratapan@gmail.com
Introduction
A computational approach is proposed to overcome the limitations of existing methods in predicting Multiple Sclerosis (MS) progression. MS is marked by myelin sheath disruption, impairing neuronal signal transmission and leading to neurodegeneration and functional decline. Predicting MS progression is challenging due to disease heterogeneity, limited longitudinal data, small sample sizes, and data inconsistencies. Current models rely on static biomarkers, failing to capture dynamic interactions between immune responses, neurodegeneration, and remyelination. Furthermore, the absence of personalized models and challenges in integrating multimodal data hinder early intervention and treatment optimization [1].
Methods
This study analyzes dynamic network changes, in response to localized disturbances, offering deeper insights into MS disease progression. The Izhikevich neuron model [2] is used for its computational efficiency, scalability, and ability to simulate diverse neuronal firing patterns relevant to specific brain regions. A myelin-based delay quotient adapted based on prior research [3, 4], models demyelination and remyelination effects observed in MS. The model is validated using varied conduction values, connection weights, and nodal lengths in a three-node configuration before extending to complex networks. Finally, interconnected neuronal modules representing distinct brain regions are simulated to replicate MS conditions.
Results
Signal propagation patterns are analyzed by altering myelin-based conduction delay parameters at specific nodes, with results compared against a control model. As expected, conduction deficits significantly impact network dynamics, illustrating how neuronal signaling adapts to disease-induced disruptions.
Discussion
This model could provide insights into MS progression by capturing evolving network disruptions when applied to a connectome. This computational approach holds promise as a foundation for predictive clinical tools, supporting early diagnosis and treatment strategies. This study offers a novel perspective on MS progression and potential therapeutic interventions by integrating dynamic network modelling with biological mechanisms.
Acknowledgements
The authors thank the University of Rostock, and the Medical School Hamburg University of Applied Sciences and Medical University for institutional support.
References
1. Prathapan, V., Eipert, P., Wigger, N., Kipp, M., Appali, R., & Schmitt, O. (2024). Modeling and simulation for prediction of multiple sclerosis progression: A review and perspective. Computers in Biology and Medicine, 108416.
2. Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on neural networks, 14(6), 1569-1572.
3. Kannan, V., Kiani, N. A., Piehl, F., & Tegner, J. (2017). A minimal unified model of disease trajectories captures hallmarks of multiple sclerosis. Mathematical Biosciences, 289, 1-8.
4. https://doi.org/10.1371/journal.pcbi.1010507
