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Cross-modal coherent registration of whole mouse brains

Nature Methods volume 19, pages 111–118 (2022)Cite this article

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Abstract

Recent whole-brain mapping projects are collecting large-scale three-dimensional images using modalities such as serial two-photon tomography, fluorescence micro-optical sectioning tomography, light-sheet fluorescence microscopy, volumetric imaging with synchronous on-the-fly scan and readout or magnetic resonance imaging. Registration of these multi-dimensional whole-brain images onto a standard atlas is essential for characterizing neuron types and constructing brain wiring diagrams. However, cross-modal image registration is challenging due to intrinsic variations of brain anatomy and artifacts resulting from different sample preparation methods and imaging modalities. We introduce a cross-modal registration method, mBrainAligner, which uses coherent landmark mapping and deep neural networks to align whole mouse brain images to the standard Allen Common Coordinate Framework atlas. We build a brain atlas for the fluorescence micro-optical sectioning tomography modality to facilitate single-cell mapping, and used our method to generate a whole-brain map of three-dimensional single-neuron morphology and neuron cell types.

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Fig. 1: Overview of mBrainAligner.
Fig. 2: Accuracy of mBrainAligner in brain delineation.
Fig. 3: Accuracy of mBrainAligner in computational soma localization and axon projection prediction.
Fig. 4: fMOST-domain atlas of mouse brain.
Fig. 5: Topography of thalamic neuronal projections by integrating Common Coordinate Framework-registered brains.

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Data availability

The full resolution fMOST image datasets (https://download.brainimagelibrary.org/biccn/zeng/luo/fMOST/) of all mouse brains used in this study, as well as the original and CCFv3 registered single neuron reconstructions (https://doi.org/10.35077/g.25), are available at BICCN’s Brain Image Library (BIL) at Pittsburgh Supercomputing Center (www.brainimagelibrary.org). The single-neuron reconstructions, the CCFv3 registered version of these reconstructions, as well as 3D navigation movie-gallery of these data are also available at SEU-ALLEN Joint Center, Institute for Brain and Intelligence (https://braintell.org/projects/fullmorpho/). The Allen CCFv3 is available at (http://atlas.brain-map.org/), and MRI mouse brian used in this study can be obtained through (https://www.nitrc.org/projects/incfwhsmouse). We also provided downsampled fMOST, LSFM, VISoR and MRI whole mouse brain data and LSFM partially imaged brain data along with code and testing scripts at Github page (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/mBrainAligner). The full resolution LSFM and VISoR brain images are available on request. Source data are provided with this paper.

Code availability

The source code of all mBrainAligner modules, including stripe artifacts removal, automatic global registration, automatic local registration, semiautomatic refinement along with the binary executable files and sample data can be found at mBrainAligner’s GitHub page (https://github.com/Vaa3D/vaa3d_tools/tree/master/hackathon/mBrainAligner). The automatic global, local registration and semiautomatic registration modules were written in C++ (v.11) with Qt (v.5.6), and the stripe artifacts removal module was implemented using Matlab 2016.

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Acknowledgements

This work was initialized and funded by Southeast University to support Open Science collaboration through the SEU-ALLEN Joint Center, Institute of Brain and Intelligence. We thank Y. Wang and Q. Wang (Allen Institute for Brain Science) for assistance with manual annotation of some somas, Y. Yu (Allen Institute for Brain Science) for downsampling several fMOST datasets, S. Jiang (Southeast University) for assistance in data storage and transmission, D.H. Lu (Tencent) for building mBrainAligner’s web portal interface, M. Dierssen (Center for Genomic Regulation) and L. Manubens-Gil (Southeast University) for providing partial brain LSFM data, W. Luo, H. Wang, J. Yang, M. Wang, Y. Tang and R. Zhang (Anhui University) for help with semiautomatic registration, the team at SEU-ALLEN Joint Center for neuron reconstruction, and the supercomputing platform of Anhui University for computing support. This work was also partially funded by the National Science Foundation of China (NSFC) grant 61871411 and the University Synergy Innovation Program of Anhui Province GXXT-2019-008 and GXXT-2021-001 to L.Q., the NSFC grant 32071367 to Y.W., the NSFC-Guangdong Joint Fund U20A6005 to G.B. and the Fundamental Research Funds for the Central Universities 2242021k30046.

Author information

Author notes

  1. These authors contributed equally: Lei Qu, Yuanyuan Li.

Authors and Affiliations

  1. Ministry of Education Key Laboratory of Intelligent Computation & Signal Processing, Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Electronics and Information Engineering, Anhui University, Hefei, China

    Lei Qu, Yuanyuan Li, Jun Wu, Yu Liu, Tao Wang, Longfei Li, Kaixuan Guo, Wan Wan & Lei Ouyang

  2. SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China

    Lei Qu, Peng Xie, Lijuan Liu, Yimin Wang, Feng Xiong & Hanchuan Peng

  3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China

    Lei Qu & Guoqiang Bi

  4. Ministry of Education Key Laboratory of Developmental Genes and Human Disease, School of Life Science and Technology, Southeast University, Nanjing, China

    Lijuan Liu

  5. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Yimin Wang

  6. Department of Cell, Developmental & Regenerative Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Anna C. Kolstad & Zhuhao Wu

  7. Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Zhuhao Wu

  8. CAS Key Laboratory of Brain Connectome and Manipulation, Interdisciplinary Center for Brain Information, The Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China

    Fang Xu & Guoqiang Bi

  9. Tencent Jarvis Lab, Shenzhen, Guangdong, China

    Yefeng Zheng

  10. Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China

    Hui Gong & Qingming Luo

  11. HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China

    Hui Gong & Qingming Luo

  12. CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Science, Shanghai, China

    Hui Gong & Qingming Luo

  13. School of Biomedical Engineering, Hainan University, Haikou, China

    Qingming Luo

  14. Center for Integrative Imaging, Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, China

    Guoqiang Bi

  15. Center for Integrative Connectomics, Department of Neurobiology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA

    Hongwei Dong

  16. Allen Institute for Brain Science, Seattle, WA, USA

    Michael Hawrylycz, Hongkui Zeng & Hanchuan Peng

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  1. Lei Qu
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Contributions

H.P. conceptualized the study and provided guidance for this study. L.Q. led the development of mBrainAligner. H.P., L.Q., P.X., Li L. and Y.L. designed all experiments. L.Q. and Y.L. designed and implemented the CLM. Y.L. conducted validation analyses of mBrainAligner, generated the fMOST atlas and created the figures. Y.L. designed and Long L. developed the semiautomatic registration module. Long L. designed and implemented the 2.5D corner detection algorithm. L.Q. and W.W. designed and implemented the stripe artifacts removal tool. L.Q. and T.W. codeveloped tools for image and various neuronal structure warping, and T.W. assisted in building the fMOST atlas. K.G. and Y.Z. collaborated on deep-learning models. L.O. developed the global registration module. F.X. developed registration scripts and helped in aligning neurons to CCF. J.W. and Y.L. collaborated with L.Q. to set up and manage the software-developing team and the data-analyzing team. P.X. conducted the cell type analysis and provided tools for soma localization and axon projection analyses. Li L. provided and validated soma localization and axon projection data. Y.W. contributed to neuronal reconstruction. H.G. and Q.L. provided the fMOST imaging data. A.K. and Z.W. provided the LSFM imaging data. F.X. and G.B. provided the VISoR imaging data. H.D. assisted in anatomy analysis. H.Z. provided the fMOST dataset. H.P., L.Q. and Y.L. wrote the manuscript with input from all coauthors.

Corresponding author

Correspondence to Hanchuan Peng.

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The authors declare no competing interests.

Additional information

Peer review Information Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Nina Vogt was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Semi-automatic registration.

a, Interface of semi-automatic registration module. b, Three levels of sub-region granularity (coarse, medium, and fine) are provided for each main brain region. c-h, Comparison of registration accuracy on 31 fMOST brains when semi-automatic registration is applied after automatic registration. Box plot: center line, median; box limits, upper and lower quartiles; whiskers, 1.5 x interquartile range; points, outliers.

Source data

Extended Data Fig. 2 Architecture and performance of deep learning-based segmentation network.

a, Architecture of segmentation network. b, 3D visualization of segmentation results of six brain regions. c-d, Comparison of registration accuracy of mBrainAligner on 31 fMOST brains with deep learning features toggled on and off. Box plot: center line, median; box limits, upper and lower quartiles; whiskers, 1.5 x interquartile range; points, outliers.

Source data

Extended Data Fig. 3 Comparison of different methods in registering images of different modalities to CCFv3.

Four green dashed boxes show the brain registration results of four different modalities (LSFM, VISoR, MRI, fMOST) generated by different methods (MIRACL, ClearMap, aMAP and mBrainAligner,). The first and second rows of each box show the coronal and sagittal view of registered brains respectively with zoom-in view of two subregions. Registered brains of different modalities with brain region boundaries (orange) defined in CCFv3 overlaid. In each box, arrows of the same color point to the same spatial coordinates.

Extended Data Fig. 4 Comparison of soma localization between manual annotation and computational prediction results generated by different methods.

The left side of diagram shows manually annotated results, and the right side shows their computationally predicted results.

Extended Data Fig. 5 Comparison of neuron mapping results generated by different methods.

a-f, 63 CCFv3 mapped neurons of six brains (ID 17302, 17545, 18454, 18455, 18457 and 18464) with somas residing in VPM or VPL. The first row of a-f shows CCFv3 aligned brains generated by four different methods (mBrainAligner, MIRACL, ClearMap and aMAP). The cross-lines overlaid on the aligned brains point to the same coordinate on the inner boundary of cortex. The left-most image of second row of a-f shows corresponding slice of CCFv3 average template, and the right three images show the maximum intensity projection view of CCFv3 with mapped neurons overlaid, with red for mBrainAligner, green for MIRACL, yellow for ClearMap and purple for aMAP.

Extended Data Fig. 6 fMOST-domain atlas of mouse brain (2D views).

Coronal sections of fMOST-domain atlas of mouse brain, with average template shown on left and annotation template on right.

Extended Data Fig. 7 Topography of thalamic neuron projections by integrating CCF-registered brains.

a, Location of axonal arbors of cortical projection neurons grouped by thalamic nuclei. Dots represent center of axonal arbors in the flattened 2D cortical map. Locations of corresponding somas are color-coded in three separate columns: (left to right) the anterior-posterior (soma-x), dorsal-ventral (soma-y) and lateral-medial (soma-z) axes. b, Examples of single-neuron projections (VPL, LGd, MG). Colors represent individual neurons.

Extended Data Fig. 8 Stripe artifacts removal.

a, One coronal slice of fMOST mouse brain image. b, The Fourier spectrum of image shown in a. c, Constructed Gaussian notch filter. d, Log transformed image of a. e, Filtered spectrum of log-transformed image using filter as shown in c. f, Image after stripe removal.

Extended Data Fig. 9 Partial image alignment.

a, Partially imaged brain region of hippocampus (in LSFM modality). b, Global alignment result of partial image to CCFv3. The left column shows the maximum intensity projection of globally aligned partial brain (top) and its blended view with CCFv3 average template (bottom), with their three-view shown on the right. c, Result after semi-automatic refinement.

Supplementary information

Reporting Summary

Supplementary Video 1

Stripe artifacts removal.

Supplementary Video 2

Global registration of mouse brain.

Supplementary Video 3

Coherent landmark mapping.

Supplementary Video 4

Semiautomatic registration of mouse brain.

Source data

Source Data Fig. 2

Statistical source data of Fig.2b.

Source Data Fig. 3

Statistical source data of Fig.3c,i.

Source Data Fig. 4

Statistical source data of Fig.4d.

Source Data Extended Data Fig. 1

Statistical source data of Extended Data Fig.1c,d.

Source Data Extended Data Fig. 2

Statistical source data of Extended Data Fig.2c,d.

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Qu, L., Li, Y., Xie, P. et al. Cross-modal coherent registration of whole mouse brains. Nat Methods 19, 111–118 (2022). https://doi.org/10.1038/s41592-021-01334-w

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