🚨Thrilled to share our latest work just published in @nature.com where we looked into the optimal fMRI scan time for brain-wide association studies (BWAS) 🧠⏱️! Full thread below👇:

How could we predict multiple neurodegenerative disease using just blood sample and a single model? Check out this incredible work by @anlijuncn.bsky.social ⬇️

How does brain change functionally in AD and aging? Check out this cool work from @jorittmo.bsky.social

Preprint 🥳

"A unified imaging-histology framework for superficial white matter architecture studies in the human brain"

🧠 SMW key for brain structure-function links
🤔 understudied because of its complexity
💡 SMW mapping for histology & MRI
🌟 OA

📖 lnkd.in/ef8bGGrF

by Youngeun Hwang et al

Check out Xiaoyu’s fantastic work!!

Really important and cool work by @xiaoyucaly.bsky.social! Congratulations!

‼️New preprint!
We are happy to share our latest work led by @teanijarv.bsky.social investigating why tau pathology in AD often accumulates more in one hemisphere of the brain than the other.

Check out 🔗https://biorxiv.org/content/10.1101/2025.04.15.648728v1 or dive into the details below👇

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

🧠Exciting new findings from a great collaboration between the BioFINDER study and the talented
@xiaoyucaly.bsky.social from @jwvogel.bsky.social group!! In their preprint, they show that tau presence and tau load are guided by unique brain mechanisms. Dive into the details here 👇

I am so very proud announce my lab's first preprint!!

@xiaoyucaly.bsky.social delves into the mechanisms driving tau distribution:
* Tau sequence driven by connectivity dynamics
* Tau load driven by local factors

Check out her thread below

🧵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.

GitHub - DeMONLab-BioFINDER/Xiao_Tau_simulation_SIR Contribute to DeMONLab-BioFINDER/Xiao_Tau_simulation_SIR development by creating an account on GitHub.

🧵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.

Four distinct trajectories of tau deposition identified in Alzheimer’s disease - Nature Medicine Systematic characterization of longitudinal tau variability in human Alzheimer’s disease using an unbiased subtyping algorithm reveals four trajectories of tau deposition with distinct clinical featur...

🧵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).

🧵3/ We first tested well-known factors that influence tau in AD. Found:

⚪️ Tau presence is largely explained by synaptic spread, measured via structural brain connectivity.
🔴 Tau load is shaped by both synaptic spread and regional MAPT gene expression / beta-amyloid deposition

🧵2/ Why? We adapted a mechanistic computational model simulating tau accumulation in the human brain (SIR, journals.plos.org/plosbiology/...). Besides connectome-based spread, the model simulates regional tau synthesis, misfolding and clearance, allowing empirical regional data to affect.

🧵1/ Using tau-PET imaging data from
@biofinder.bsky.social, we distinguish between:

⚪️Tau Presence: Whether and when tau appears in a brain region?
🔴Tau Load: How much tau builds up there?

We show that ⚪️tau presence is highly consistent across people, but 🔴tau load varies widely.

🧠New preprint! What drives tau, the pathological protein in AD, to spread?
We found that WHERE tau appears and HOW MUCH accumulates are governed by different mechanisms. Check it out:
www.biorxiv.org/content/10.1...


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