Thank you! This would be really interesting to see, but now we haven’t got the chance to do

🧵18/
Finally, thanks to the @biofinder.bsky.social team— Nicola Spotorno, Olof Strandberg, @aitchbi.bsky.social , @gsalvado.bsky.social, Erik Stomrud, Ruben Smith, Sebastian Palmqvist, @rikossenkoppele.bsky.social, Niklas Mattsson-Carlgren, and Oskar Hansson—for sharing the awesome data and support.

🧵17/
Thanks to Thomas Funck and Nicola Palomero-Gallagher for adding key receptor insights.

🧵16/
We’re deeply grateful to @misicbata.bsky.social, @alaindagher.bsky.social, Justine Hansen, @vincebaz.bsky.social, and @goloafs.bsky.social for providing essential brain connectome and biological datasets; and for their guidance with the SIR model.

🧵15/ Huge thanks to our amazing team and coauthors!

Endless thanks to @jwvogel.bsky.social for guiding and supporting this work from day one. To our amazing team DeMON lab, especially @anlijuncn.bsky.social for enormous support.

🧵14/ Explore our adapted computational model 👇
github.com/DeMONLab-Bio...

🧵13/ Why does this matter? 4⃣
Most importantly, we believe this distinction should shape how we study tau going forward: ⚪️presence and 🔴load likely need to be studied using different tools and at different scales—but both are essential to fully understand and stop tau progression.

🧵12/ Why does this matter? 3⃣
We also highlight 🎯neurotransmission marker dynamics as an underexplored yet promising direction for future research. Our findings point to the possibility of tau accumulating over a descending gradient of ionotropic excitatory potential.

🧵11/ Why does this matter? 2⃣
Our model shows they stem from distinct neural mechanisms: ⚪️presence is shaped by structural connectivity and neurotransmitter architecture, whereas 🔴load is driven by local biological vulnerability.

🧵10/ Why does this matter? 1️⃣
Today, Braak staging describes tau progression based on tau presence, while in vivo imaging and clinical trials typically assess tau load via PET. These are not equivalent measures.

🧵9/ (Cont.) We identified two (of the four subtypes) with especially interesting mechanisms:

🔷Posterior subtype: Linked strongly to acetylcholine—already a treatment target!
🔷MTL-sparing subtype: Spread better explained by specific *functional* brain networks.

🧵8/ We also explored why different Alzheimer's patients show unique tau patterns (subtypes)?
We tested patterns from the four Alzheimer’s subtypes @jwvogel.bsky.social (www.nature.com/articles/s41...), and identified two (of the four subtypes) with especially interesting mechanisms: (see next 🧵)

🧵7/ What drives 🔴Tau Load? — Determined by connectivity and local biological factors

Connectivity isn’t enough—local vulnerability is key. Factors such as regional amyloid-beta levels, MAPT expression, cerebral blood flow, and neurotransmission markers profiles all play a role.

🧵6/ Surprisingly, the balance of neurotransmission signals 🎯 (excitatory vs. inhibitory) and 5-HT receptors strongly overlap with tau presence. Regions with more ionotropic excitatory signaling are particularly vulnerable—supporting work focusing on E:I balance in AD.

🧵5/ What drives ⚪️Tau Presence? — Primarily brain connectivity

As expected, tau follows structural connectivity. But unexpectedly, we also found 🎯neurotransmitter marker distribution does an even better job explaining the pattern. We found this fascinating and explored further.

🧵4/ We next conducted an exploratory analysis using eight types of normative connectomes and 49 regional biological factors (thanks to @misicbata.bsky.social, his team, and the Allen Human Brain Atlas).