Original title: Activity-dependent epidemic spreading on multiscale brain networks predicts Alzheimer's disease progression
Christoffer G. Alexandersen, Suman S. Kulkarni, Jessica T. Davis, Sebastian N. Roemer-Cassiano +2 · August 2026
Alzheimer's disease spreads through the brain as harmful proteins travel between connected areas. Standard mathematical models look at these brain wiring routes. However, they ignore how active brain cells are, even though firing cells make proteins spread faster. To fix this, researchers built a model that combines brain activity with disease transmission. In this system, a tipping point called an epidemic threshold decides if tiny seeds of disease grow, and specific wiring patterns choose where it starts. Brain activity changes this tipping point and redirects the route of the disease. The researchers tested their framework using human brain scans over time, tracking cell activity and protein buildup. They found that factoring in cell activity explains where the disease travels better than looking at physical wiring alone. Furthermore, a person's predicted threshold matches how widely the disease spreads, suggesting that altering brain activity could help slow Alzheimer's.
Imagine you are trying to stop a fire from spreading through a row of houses. You can see which houses share walls, so you plan to protect the ones with the most connections. But the fire jumps more easily when people are awake and moving around, and that activity differs from house to house, so your wall-based plan misses where the next spark lands. In this paper, the houses are brain regions, the shared walls are the brain's wiring, and the activity is how often nerve cells fire; the model shows that ignoring this activity makes you misjudge where disease will spread.
If neuronal activity helps drive Alzheimer's spread, then the brain's wiring is only half the story. That makes activity-modulating therapies a plausible way to slow or prevent disease, and gives researchers a new way to predict how broadly pathology will spread in a specific person.
The abstract reports associations, not proven causation, and does not give sample sizes or effect sizes. Simulations validate the theory on synthetic networks, while the human test relies on glucose metabolism as a proxy for activity and tau as a marker of progression. The therapy idea is motivated, not tested.
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Activity-dependent epidemic spreading on multiscale brain networks predicts Alzheimer's disease progression — Christoffer G. Alexandersen, Suman S. Kulkarni, Jessica T. Davis et al.
Abstract licence: CC0
Explanation generated by Synthesis from the abstract — it is not the original paper. This is a preprint, so it hasn't necessarily been peer reviewed.