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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年12月29日

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  • 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

摘要

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.