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High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization

Gregory D. Marquart, Kathryn M. Tabor, Eric J. Horstick, Mary Brown, View ORCID ProfileHarold A. Burgess
doi: https://doi.org/10.1101/081000
Gregory D. Marquart
Eunice Kennedy Shriver, Bethesda, MD 20892
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Kathryn M. Tabor
Eunice Kennedy Shriver, Bethesda, MD 20892
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Eric J. Horstick
Eunice Kennedy Shriver, Bethesda, MD 20892
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Mary Brown
Eunice Kennedy Shriver, Bethesda, MD 20892
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Harold A. Burgess
Eunice Kennedy Shriver, Bethesda, MD 20892
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  • ORCID record for Harold A. Burgess
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Abstract

Atlases provide a framework for information from diverse sources to be spatially mapped and integrated into a common reference space. In particular, brain atlases allow regional annotation of gene expression, cell morphology, connectivity and activity. In larval zebrafish, advances in genetics, imaging and computational methods have enabled the collection of large datasets providing such information on a whole-brain scale. However, datasets from different sources may not be aligned to the same spatial coordinate system, because technical considerations may necessitate use of different reference templates. Two recent brain atlases for larval zebrafish exemplify this problem. The Z-Brain atlas contains information on gene expression, neural activity and neuroanatomical segmentation acquired using immunohistochemical staining of fixed tissue. In contrast, the Zebrafish Brain Browser (ZBB) atlas was constructed from live scans of fluorescent reporter genes in transgenic larvae. Although different reference brains were used, the two atlases included several transgene patterns in common that provided potential 'bridges' for transforming each into the other’s coordinate space. We therefore tested multiple bridging channels and registration algorithms. The symmetric diffeomorphic normalization (SyN) algorithm in ANTs improved the precision of live brain registration while better preserving cell morphology than the previously used B-spline elastic registration algorithm. SyN could also be calibrated to correct for tissue distortion introduced during fixation and permeabilization. Finally, multi-reference channel optimization provided a transformation matrix that enabled Z-Brain and ZBB to be co-aligned with acceptable precision and minimal perturbation of cell and tissue morphology. This study demonstrates the feasibility of integrating whole brain datasets, despite disparate acquisition protocols and reference templates, when sufficient information is present for bridging.

ac
anterior commissure
DT
Thalamus
GT
Griseum tectale
Ha
Habenula
Hc
Hypothalamus caudal zone
Hi
Hypothalamus intermediate zone
MO
Medulla oblongata
NXm
Vagus motor neurons
OB
Olfactory bulb
OE
Olfactory epithelium
IO
Inferior olive
LC
Locus coeruleus
MN
Mauthner neuron
MO
Medulla oblongata
Pal
Pallium
pc
posterior commissure
Pr
Pretectum
SR
Superior raphe
Teg
Tegmentum
TeOn
Optic tectum neuropil
TG
Trigeminal ganglion
TL
Torus longitudinalis
Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 14, 2016.
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High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization
Gregory D. Marquart, Kathryn M. Tabor, Eric J. Horstick, Mary Brown, Harold A. Burgess
bioRxiv 081000; doi: https://doi.org/10.1101/081000
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High precision registration between zebrafish brain atlases using symmetric diffeomorphic normalization
Gregory D. Marquart, Kathryn M. Tabor, Eric J. Horstick, Mary Brown, Harold A. Burgess
bioRxiv 081000; doi: https://doi.org/10.1101/081000

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