Tau-BNO: Brain Neural Operator for Tau Transport Model
Nuutti Barron, Heng Rao, Urmi Saha, Yu Gu, Zhenghao Liu, Ge Yu, Defu Yang, Ashish Raj, Minghan Chen

TL;DR
Tau-BNO is a neural operator framework that efficiently approximates complex tau transport models in the brain, enabling rapid simulations and new insights into tau pathology spread in neurodegenerative diseases.
Contribution
It introduces a novel neural operator surrogate that captures microscale biophysical processes and network transport, significantly reducing computation time for tau propagation models.
Findings
Achieves high predictive accuracy with R^2≈0.98
Reduces simulation time from hours to seconds
Outperforms state-of-the-art sequence models by 89%
Abstract
Mechanistic modeling provides a biophysically grounded framework for studying the spread of pathological tau protein in tauopathies like Alzheimer's disease. Existing approaches typically model tau propagation as a diffusive process on the brain's structural connectome, reproducing macroscopic patterns but neglecting microscale cellular transport and reaction mechanisms. The Network Transport Model (NTM) was introduced to fill this gap, explaining how region-level progression of tau emerges from microscale biophysical processes. However, the NTM faces a common challenge for complex models defined by large systems of partial differential equations: the inability to perform parameter inference and mechanistic discovery due to high computational burden and slow model simulations. To overcome this barrier, we propose Tau-BNO, a Brain Neural Operator surrogate framework for rapidly…
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Taxonomy
TopicsFunctional Brain Connectivity Studies · Neural dynamics and brain function · Alzheimer's disease research and treatments
