Differing mechanisms drive regional tau distribution and load in Alzheimer's disease
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 7:00 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
96
Submission Type:
Abstract Submission
Authors:
Yu Xiao1, Nicola Spotorno2, Lijun An1, Vincent Bazinet3, Olof Strandberg2, Justine Hansen3, Golia Shafiei4, Hamid Behjat1, Erik Stomrud2, Ruben Smith1, Sebastian Palmqvist2, Rik Ossenkoppele2, Niklas Mattsson-Carlgren2, Alain Dagher3, Bratislav Misic5, Oskar Hansson6, Jacob Vogel1
Institutions:
1Lund University, Lund, Sweden, 2Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ, Lund, Scania, 3McGill University, Montreal, QC, 4University of Pennsylvania, Philadelphia, PA, 5Montreal Neurological Institute, Montreal, QC, 6Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ, Lund, Scania
First Author:
Co-Author(s):
Nicola Spotorno
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Olof Strandberg
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Erik Stomrud
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Sebastian Palmqvist
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Rik Ossenkoppele
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Niklas Mattsson-Carlgren
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Oskar Hansson
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Faculty of Medicine, Lund Univ
Lund, Scania
Introduction:
Tau pathology is a hallmark of Alzheimer's disease (AD), spreading through the brain in specific patterns likely driven by brain connectivity (Liu et al., 2012; Walsh et al., 2016). Accumulating evidence suggests that certain brain regions are more susceptible to tau accumulation due to cellular composition, gene expression, receptor profiles, developmental patterns, or pathological conditions (Brettschneider et al., 2015; Mrdjen et al., 2019). However, most computational models simulating tau propagation focus on connectome-based spreading only, often constrained to specific connectivity types (Vogel et al., 2020; Yang et al., 2021), with limited exploration of regional vulnerability (Anand et al., 2022). We used the Susceptible-Infected-Removed (SIR) agent-based model (Zheng et al., 2019), a connectome-based spreading model integrating regional biological properties to modulate tau spread. Using diverse brain connectomes and regional biological measures, we aimed to investigate whether tau propagation is driven by connectome-based spreading, regional vulnerability, or their interplay.
Methods:
We analyzed 646 [18F]RO948-PET scans of amyloid-β (Aβ)-positive participants (219 unimpaired, 212 mild cognitive impairment, 215 AD dementia) from the Swedish BioFINDER-2 study. Regional standardized uptake value ratios (SUVR) from 66 Desikan-Killiany regions were extracted to represent tau load (Fig.1b). SUVRs were converted to "tau-positive probabilities" (TPP) using Gaussian mixture models, representing tau distribution (Fig.1c). The SIR model simulated group-level tau spread through brain networks while allowing a priori regional biological information to influence tau synthesis, clearance, spreading, or misfolding (Fig.1a). Model performance was evaluated using Pearson correlation (R) between simulated and observed tau patterns. The entorhinal cortex was used as the primary epicenter. Overall, 1,576 models were tested: 8 connectome-only and 1,568 combining connectomes with regional biological factors (Fig. 2a, 2b). Based on our results, we evaluated correlations between receptor profiles and tau patterns using Pearson correlation (R). Analysis of 19 individual receptors was included, and a priori receptor ratios based on prior research (Lauterborn et al., 2021) and a posteriori ratios based on our findings.
Results:
Simulation over structural connectivity (SC) explained tau distribution better than tau load (Fig.1d,1c). Models incorporating regional factors improved tau load simulation more than tau distribution (Fig.1f,1g,2c). Top models for tau load included SC and MAPT expression influencing misfolding, Aβ deposition influencing spread, or cerebral blood flow influencing clearance (Fig.2d). For tau distribution, receptor similarity (RS) combined with MAPT expression or cortical expansion provided the best fit (Fig.2e). Compared to SC, RS showed stronger connections in the inferior parietal, medial orbital frontal, superior temporal, and precuneus regions, with enhanced contralateral connectivity (Fig.2f). Interestingly, histamine, serotonin, and nicotinic acetylcholine receptors were also frequent regional factors in top tau simulations. Following these findings, tau distribution correlated strongly with serotonin receptor ratios (5HT1a/5HT1b), dopamine-to-norepinephrine transporter ratios (DAT/NET), and the ratio of excitatory to inhibitory neurons (Fig.2g). Conversely, tau load (SUVR) was most strongly associated with the distribution of histamine receptor, acetylcholine receptors and norepinephrine transporter (Fig.2g).
Conclusions:
Tau distribution and load are mediated by distinct factors. Braak-like tau distribution aligns with SC and MAPT bioavailability, while tau load is shaped by a combination of connectivity and regional properties. Receptor distributions, particularly monoamine receptor and transporter ratios, may influence tau patterns. Our work complements previous findings while uncovering under-investigated factors affecting tau propagation.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
PET Modeling and Analysis 2
Other Methods
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Keywords:
Computational Neuroscience
Computing
Modeling
MRI
Multivariate
Positron Emission Tomography (PET)
RECEPTORS
1|2Indicates the priority used for review
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