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Peer-reviewedCC BY-NC-ND 4.0Communications Biology

High-resolution mri guided whole mouse brain neuronal cell type atlas using deep learning

  • Xinyue Han1
  • Rui Hu1
  • Zhuoheng Liu1
  • Jie Chen1
  • Mubashir Jafry2
  • Haoyu Song3
  • Yi Zhao4
  • Mingquan Lin5
  • Leonard E. White6
  • G. Allan Johnson7,8
  • Nian Wang1,9,10
10 affiliations
  1. 1Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, USA
  2. 2Missouri Southern State University, Joplin, USA
  3. 3Department of Computer Science, Purdue University, West Lafayette, USA
  4. 4Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, USA
  5. 5Department of Surgery, University of Minnesota, Minneapolis, USA
  6. 6Department of Neurology, Duke University Medical Center, Durham, USA
  7. 7Center for In Vivo Microscopy, Department of Radiology, Duke University, Durham, USA
  8. 8Department of Biomedical Engineering, Duke University, Durham, USA
  9. 9Department of Biomedical Engineering, UT Southwestern Medical Center, Dallas, USA
  10. 10Peter O’Donnell Brain Institute, UT Southwestern Medical Center, Dallas, USA
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Abstract

Cell types represent groups of cells with shared anatomical and functional properties. Traditional brain cell type atlases rely on single-cell sequencing, which provides molecular detail but lacks whole-brain, isotropic resolution. Diffusion magnetic resonance imaging (dMRI) offers a complementary approach for probing cytoarchitecture and myeloarchitecture, with quantitative metrics increasingly used as biomarkers of brain development and neurodegenerative disorders. However, the capacity of dMRI to predict cell types remains unclear. Here, we develop a multimodal framework by integrating high-resolution dMRI and three-dimensional light-sheet microscopy of adult mouse brains through registration to the Allen Mouse Brain Common Coordinate Framework. We investigate correlations between dMRI and spatial transcriptomics-derived cell types and generate a whole-brain neuronal cell type atlas at 10 µm isotropic resolution using deep learning. Together, these results establish an efficient, high-resolution strategy for brain neuron atlas generation and underscore the potential of advanced imaging techniques to illuminate cellular mechanisms of the brain.

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Cite

BibTeXRIS
BibTeX
@article{han2026highresolution,
  title = {{High-resolution mri guided whole mouse brain neuronal cell type atlas using deep learning}},
  author = {Xinyue Han and Rui Hu and Zhuoheng Liu and Jie Chen and Mubashir Jafry and Haoyu Song and Yi Zhao and Mingquan Lin and Leonard E. White and G. Allan Johnson and Nian Wang},
  journal = {Communications Biology},
  year = {2026},
  month = jul,
  doi = {10.1038/s42003-026-10608-y},
  url = {https://doi.org/10.1038/s42003-026-10608-y},
  note = {CC BY-NC-ND 4.0},
}
RIS
TY  - JOUR
TI  - High-resolution mri guided whole mouse brain neuronal cell type atlas using deep learning
AU  - Xinyue Han
AU  - Rui Hu
AU  - Zhuoheng Liu
AU  - Jie Chen
AU  - Mubashir Jafry
AU  - Haoyu Song
AU  - Yi Zhao
AU  - Mingquan Lin
AU  - Leonard E. White
AU  - G. Allan Johnson
AU  - Nian Wang
PY  - 2026
DA  - 2026/07/07
JO  - Communications Biology
DO  - 10.1038/s42003-026-10608-y
UR  - https://doi.org/10.1038/s42003-026-10608-y
AB  - Cell types represent groups of cells with shared anatomical and functional properties. Traditional brain cell type atlases rely on single-cell sequencing, which provides molecular detail but lacks whole-brain, isotropic resolution. Diffusion magnetic resonance imaging (dMRI) offers a complementary approach for probing cytoarchitecture and myeloarchitecture, with quantitative metrics increasingly used as biomarkers of brain development and neurodegenerative disorders. However, the capacity of dMRI to predict cell types remains unclear. Here, we develop a multimodal framework by integrating high-resolution dMRI and three-dimensional light-sheet microscopy of adult mouse brains through registration to the Allen Mouse Brain Common Coordinate Framework. We investigate correlations between dMRI and spatial transcriptomics-derived cell types and generate a whole-brain neuronal cell type atlas at 10 µm isotropic resolution using deep learning. Together, these results establish an efficient, high-resolution strategy for brain neuron atlas generation and underscore the potential of advanced imaging techniques to illuminate cellular mechanisms of the brain.
SN  - 2399-3642
ER  -