Holistic 3D modeling of molecularly defined brain structures is crucial for understanding complex brain functions. Using emerging tissue profiling technologies, researchers charted comprehensive atlases of mammalian brain with sub-cellular resolution and spatially resolved transcriptomic data. However, these tera-scale volumetric atlases pose computational challenges for modeling intricate brain structures within the native spatial context. We propose \textbf{Tera-MIND}, a novel generative framework capable of simulating \textbf{Tera}-scale \textbf{M}ouse bra\textbf{IN}s in 3D using a patch-based and boundary-aware \textbf{D}iffusion model. Taking spatial gene expression as conditional input, we generate virtual mouse brains with comprehensive cellular morphological detail at teravoxel scale. Through the lens of 3D \textit{gene}-\textit{gene} self-attention, we identify spatial molecular interactions for key transcriptomic pathways, including glutamatergic and dopaminergic neuronal systems. Lastly, we showcase the translational applicability of Tera-MIND on previously unseen human brain samples. Tera-MIND offers an efficient generative modeling of whole virtual organisms, paving the way for integrative applications in biomedical research. Project website: https://musikisomorphie.github.io/Tera-MIND.html
@article{arxiv.2503.01220,
title = {Tera-MIND: Tera-scale mouse brain simulation via spatial mRNA-guided diffusion},
author = {Jiqing Wu and Ingrid Berg and Yawei Li and Ender Konukoglu and Viktor H. Koelzer},
journal= {arXiv preprint arXiv:2503.01220},
year = {2025}
}