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Gigascience. 2017 Aug 1;6(8):1-15. doi: 10.1093/gigascience/gix056.

High-precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization.

Author information

Division of Developmental Biology, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Building 6B, Room: 3B-308, 6 Center Dr., Bethesda, MD 20892-0002.
Neuroscience and Cognitive Science Program, University of Maryland, College Park, MD 20742.
Advanced Research Computing, Department of Computer Science, Virginia Polytechnic Institute and State University, 3050 Torgersen Hall, Blacksburg, VA 24061-0531.


Atlases provide a framework for spatially mapping information from diverse sources into a common reference space. Specifically, brain atlases allow annotation of gene expression, cell morphology, connectivity, and activity. In larval zebrafish, advances in genetics, imaging, and computational methods now allow the collection of such information brain-wide. However, due to technical considerations, disparate datasets may use different references and may not be aligned to the same coordinate space. Two recent larval zebrafish atlases exemplify this problem: Z-Brain, containing gene expression, neural activity, and neuroanatomical segmentations, was acquired using immunohistochemical stains, while the Zebrafish Brain Browser (ZBB) was constructed from live scans of fluorescent reporters in transgenic larvae. Although different references were used, the atlases included several common transgenic patterns that provide potential "bridges" for transforming each into the other's coordinate space. We tested multiple bridging channels and registration algorithms and found that the symmetric diffeomorphic normalization algorithm improved live brain registration precision while better preserving cell morphology than B-spline-based registrations. Symmetric diffeomorphic normalization also corrected for tissue distortion introduced during fixation. Multi-reference channel optimization provided a transformation that enabled Z-Brain and ZBB to be co-aligned with precision of approximately a single cell diameter and minimal perturbation of cell and tissue morphology. Finally, we developed software to visualize brain regions in 3 dimensions, including a virtual reality neuroanatomy explorer. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate reference templates and acquisition protocols, when sufficient information is present for bridging. Increased accuracy and interoperability of zebrafish digital brain atlases will facilitate neurobiological studies.


ANTs; SyN; Unity; X3D; atlas; brain imaging; diffeomorphism; normalization; registration; transgenic; virtual reality; zebrafish

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