Nature Methods, 29 December, 2025, DOI：https://doi.org/10.1038/s41592-025-02961-3

TransBrain: a computational framework for translating brain-wide phenotypes between humans and mice

Shangzheng Huang, Tongyu Zhang, Changsheng Dong, Yingchao Shi, Yingjie Peng, Xiya Liu, Kaixin Li, Luqi Cheng, Qi Wang, Yini He, Yitong Guo, Fengqian Xiao, Xiaohan Tian, Junxing Xian, Changjiang Zhang, Qian Wu, Yijuan Zou, Long Li, Bing Liu, Xiaoqun Wang & Ang Li

Abstract

Despite advances in whole-brain imaging technologies, the lack of quantitative approaches to bridge rodent preclinical and human studies remains a critical challenge. Here we present TransBrain, a computational framework enabling bidirectional translation of brain-wide phenotypes between humans and mice. TransBrain improves human–mouse homology mapping accuracy through (1) a cortical and subcortical detached region-specific deep neural network trained on integrated multimodal human transcriptomics to improve cortical correspondence (89.5% improvement over the original transcriptome), which revealed 2 evolutionarily conserved gradients, and (2) a graph-based approach to construct a unified cross-species representational space incorporating anatomical hierarchies and structural connectivity. We demonstrate TransBrain’s utility through three cross-species applications: quantitative assessment of resting-state brain organizational features, inferring human cognitive functions from mouse optogenetic circuits and translating molecular insights from mouse models to individual-level mechanisms in autism. TransBrain enables quantitative cross-species comparison and mechanistic investigation of both normal and pathological brain functions.

文章链接：https://www.nature.com/articles/s41592-025-02961-3

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