Paper
Spreading of pathological proteins through brain networks: a case study for Alzheimers disease
Authors
G. Landi, A. Scaravelli, M. C. Tesi, C. Testa
Abstract
Mathematical modeling offers a valuable approach to understanding Alzheimers disease (AD) given its complexity, unknown causes, and lack of effective treatments. Models, once validated, offer a powerful tool to test medical hypotheses that are otherwise difficult to verify directly. Our focus here is on elucidating the spread of misfolded tau protein, a critical hallmark of AD alongside Abeta protein, taking also into account the synergistic interaction between the two proteins. We consider distinct modelling choices, all employing network frameworks for protein evolution, differentiated by their network architecture and diffusion operators. By carefully comparing these models against clinical tau concentration data, gathered through advanced multimodal analysis techniques, we show that certain models replicate better the proteins dynamics. This investigation underscores a crucial insight: in modeling complex pathologies, the precision with which the mathematical framework is chosen is crucial, especially when validation against clinical data is considered decisive.
Metadata
Related papers
Fractal universe and quantum gravity made simple
Fabio Briscese, Gianluca Calcagni • 2026-03-25
POLY-SIM: Polyglot Speaker Identification with Missing Modality Grand Challenge 2026 Evaluation Plan
Marta Moscati, Muhammad Saad Saeed, Marina Zanoni, Mubashir Noman, Rohan Kuma... • 2026-03-25
LensWalk: Agentic Video Understanding by Planning How You See in Videos
Keliang Li, Yansong Li, Hongze Shen, Mengdi Liu, Hong Chang, Shiguang Shan • 2026-03-25
Orientation Reconstruction of Proteins using Coulomb Explosions
Tomas André, Alfredo Bellisario, Nicusor Timneanu, Carl Caleman • 2026-03-25
The role of spatial context and multitask learning in the detection of organic and conventional farming systems based on Sentinel-2 time series
Jan Hemmerling, Marcel Schwieder, Philippe Rufin, Leon-Friedrich Thomas, Mire... • 2026-03-25
Raw Data (Debug)
{
"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.18755v1</id>\n <title>Spreading of pathological proteins through brain networks: a case study for Alzheimers disease</title>\n <updated>2026-03-19T11:04:05Z</updated>\n <link href='https://arxiv.org/abs/2603.18755v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.18755v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Mathematical modeling offers a valuable approach to understanding Alzheimers disease (AD) given its complexity, unknown causes, and lack of effective treatments. Models, once validated, offer a powerful tool to test medical hypotheses that are otherwise difficult to verify directly. Our focus here is on elucidating the spread of misfolded tau protein, a critical hallmark of AD alongside Abeta protein, taking also into account the synergistic interaction between the two proteins. We consider distinct modelling choices, all employing network frameworks for protein evolution, differentiated by their network architecture and diffusion operators. By carefully comparing these models against clinical tau concentration data, gathered through advanced multimodal analysis techniques, we show that certain models replicate better the proteins dynamics. This investigation underscores a crucial insight: in modeling complex pathologies, the precision with which the mathematical framework is chosen is crucial, especially when validation against clinical data is considered decisive.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='math.AP'/>\n <published>2026-03-19T11:04:05Z</published>\n <arxiv:comment>23 pages, 1 figure, 4 tables</arxiv:comment>\n <arxiv:primary_category term='math.AP'/>\n <author>\n <name>G. Landi</name>\n </author>\n <author>\n <name>A. Scaravelli</name>\n </author>\n <author>\n <name>M. C. Tesi</name>\n </author>\n <author>\n <name>C. Testa</name>\n </author>\n <arxiv:doi>10.3934/mbe.2026024</arxiv:doi>\n <link href='https://doi.org/10.3934/mbe.2026024' rel='related' title='doi'/>\n </entry>"
}