Connectivity as a universal predictor of tau spreading in typical and atypical Alzheimer’s disease
Authors: de Bruin Hannah , Groot Colin ,   ADNI, Barthel Henryk , Bischof Gérard N , Boellaard Ronald , Brendel Matthias , Cash David M , Coath William , Day Gregory S , Dickerson Brad C , Doering Elena , Drzezga Alexander , van Dyck Christopher H. , van Eimeren Thilo , van der Flier Wiesje M. , Fredericks Carolyn , Fryer Tim D , van de Giessen Elsmarieke , Gordon Brian A. , Graff‐Radford Jonathan , Hobbs Diana A , Höglinger Günter , Hönig Merle C , Irwin David J , Jones P Simon , Josephs Keith A. , Katsumi Yuta , Lee Eddie B , Levin Johannes , Malpetti Maura , McGinnis Scott M , Mecca Adam P , Nasrallah Ilya M. , O'Brien John T , O'Dell Ryan S , Palleis Carla , Perneczky Robert , Phillips Jeffrey S , Pijnenburg Yolande A.L. , Putcha Deepti , Rahmouni Nesrine , Rosa‐Neto Pedro , Rowe James B , Rullmann Michael , Sabri Osama , Saur Dorothee , Schildan Andreas , Schott Jonathan M , Schroeter Matthias L , Servaes Stijn , Sintini Irene , Stevenson Jenna , Therriault Joseph , Touroutoglou Alexandra , Trainer Anne E , Visser Denise , Weston Philip SJ , Whitwell Jennifer L. , Wolk David A , Franzmeier Nicolai , Ossenkoppele Rik 
Published: January 2025

Background

There is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.

Method

We included 269 amyloid‐β‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐β‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.

Result

Tau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3).

Conclusion

This multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.