- Email: [email protected]
A three-dimensional digital atlas of the zebrafish brain
NeuroImage 51 (2010) 76–82
Contents lists available at ScienceDirect
NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g
A three-dimensional digital atlas of the zebrafish brain Jeremy F.P. Ullmann a,⁎, Gary Cowin b, Nyoman D. Kurniawan b, Shaun P. Collin a,c a b c
Sensory Neurobiology Group, School of Biomedical Sciences, University of Queensland, St Lucia Qld 4072, Australia Centre for Magnetic Resonance, University of Queensland, Brisbane Qld, Australia School of Animal Biology, The University of Western Australia, Crawley 6009, Western Australia, Australia
a r t i c l e
i n f o
Article history: Received 31 August 2009 Revised 21 January 2010 Accepted 25 January 2010 Available online 6 February 2010 Keywords: Zebrafish Brain Magnetic resonance histology (MRH) Three-dimensional (3D) Atlas Map
a b s t r a c t In the past three decades, the zebrafish has become a vital animal model in a range of biological sciences. To augment current neurobiological research, we have developed the first three-dimensional digital atlas of the zebrafish brain from T2⁎-weighted magnetic resonance histology (MRH) images acquired on a 16.4-T superconducting magnet. We achieved an isotropic resolution of 10 μm, which is the highest resolution achieved in a vertebrate brain and, for the first time, is comparable in slice thickness to conventional histology. By using manual segmentation, 53 anatomical structures, including fiber tracts as small as 40 μm, were delineated. Using Amira software, structures were also individually segmented and reconstructed to create three-dimensional animations. Additional quantitative information including, volume, surface areas, and mean gray scale intensities were also determined. Finally, we established a stereotaxic coordinate system as a framework in which maps created from other modalities can be incorporated into the atlas. © 2010 Elsevier Inc. All rights reserved.
Introduction Over the past 30 years, the zebrafish has become a preeminent model for biology (Grunwald and Eisen, 2002). Initially developed as an inexpensive vertebrate model system that permits forward genetic analyses, it has since grown to be used in a wide range of research fields including developmental biology, cancer, pharmacology and toxicology, disease modeling, and neuroscience. To date, the majority of experiments utilizing zebrafish have been performed with embryonic and juvenile fish, which have the advantage of being almost completely transparent, yet recent research in adult zebrafish has been burgeoning, particularly in studies of cognitive aging (Keller and Murtha, 2004; Yu et al., 2006) and central nervous system regeneration (Becker and Becker, 2008; Zupanc, 2009). However, an insufficient comprehension of the neuroanatomy and physiology of the adult zebrafish brain has been identified as a limiting factor in many studies (Lieschke and Currie, 2007). High-resolution magnetic resonance imaging or magnetic resonance histology (MRH) is an ideal method in which to acquire detailed neuroanatomical information of an intact brain. To maximize resolution MRH scans are typically performed on fixed in situ or ex vivo samples. Although other forms of MRI such as in vivo imaging are preferable as they limit the possible effects of fixation, they require larger radiofrequency coils, larger acquisition matrices, and shorter scan times (Kabli et al., 2006). Furthermore, the use of contrast agents
such as Magnevist (Bayer) are not possible for imaging the brain as the gadolinium–DTPA complex is an extracellular contrast agent that cannot penetrate the blood–brain barrier (Weinmann et al., 1984). In contrast, by fixing and ‘staining’ a sample for MRH (Ullmann et al., in press), longer scan times can be used, resulting in increased contrast and signal-to-noise ratios, and a resolution of comparable slice thickness to conventional histology. Moreover, when combined with image post-processing such as structure segmentation, spatial analysis, and three-dimensional manipulation, the size, stereotaxic location, and morphology of a large number of anatomical structures can be identified, thereby providing greater visual and computational power than offered by a conventional two-dimensional brain atlas. In order to augment current research on adult zebrafish, we have constructed a wild-type adult zebrafish brain atlas from T2⁎-weighted three-dimensional MRH images acquired on a 16.4-T magnet. This atlas is the first of its kind for the zebrafish brain, boasting an isotropic resolution of 10 μm, which is the highest resolution MRH atlas in any species thus far. Moreover, with a total of 53 segmented structures identified, it is one of the most detailed MRH atlases currently available. Finally, this 3D atlas can serve as a framework for the creation of additional maps using other modalities. Materials and methods Specimen preparation and imaging
⁎ Corresponding author. Fax: +617 3409 9839. E-mail address: [email protected] (J.F.P. Ullmann). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.01.086
Specimen preparation and imaging followed the protocol of Ullmann et al. (in press) and all animal experiments were performed
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
according to procedures approved by the University of Queensland Animal Ethics Committee (AEC No. SBMS/074/08). Briefly, one adult wild-type zebrafish (4 months old and 33.16-mm total length), whose F1s were purchased commercially, was obtained from the Australian Zebrafish Phenomics Facility, Brisbane, Queensland, Australia. The wild type was used in order to better reflect the natural condition but no comparison with standard mutant strains such as AB was
77
performed. The individual was immersion-fixed and stained for 12 h in a 5 ml solution of 4% paraformaldehyde and 0.5% Magnevist (Bayer). The intact, fixed, and stained fish brain was prepared for imaging by first allowing the brain to come to room temperature (24 °C) and was then placed in Fomblin (perfluoropolyether, Ausimont, Morristown, NJ, USA) for 2 h prior to imaging to allow for any potential out-gassing.
Table 1 List of brain stuctures, abbreviations, color, and their location in the major and minor brain divisions.
78
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
Fig. 1. (A) Axial MRH image and schematic drawing of structures. (B) Sagittal MRH image and schematic drawing of structures. All scale bars are 250 μm. All abbreviations can be found in Table 1.
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
All images were acquired on a Bruker (Ettlingen, Germany) AV II spectrometer running ParaVision 4, interfaced to a 16.4-T vertical magnet with a bore diameter of 89 mm, and a Bruker micro2.5 imaging gradient (150 G/cm, gradient stabilization time b150 μs). A custom-made horizontal, solenoid radiofrequency (rf) coil was used without cryocooling and with an inner diameter and length of 5 × 4 mm. We chose to use a 3D FLASH (Frahm et al., 1986) gradient echo T2⁎-weighted sequence instead of a spin-echo sequence as it provided us with two advantages. First, the use of a small flip angle in gradient echo sequences permits the acquisition of highresolution images with small repetition times. Consequently, a greater number of averages could be acquired thereby increasing the signal to noise per unit time. Second, as gradient-echo sequences are more sensitive to the effects of Magnevist® than spin-echo sequences, it allowed us to delineate a greater number of structures. The acquisition parameters for the gradient-echo scan were the following: TR = 80 ms, TE = 12 ms, flip angle = 30°, field of view = 5.2 × 3.0 × 3.0 mm. The number of averages was 16, and the total imaging time for the 3D data set was just over 13 h. The acquisition matrix was 512 × 202 × 202, which was symmetrically zero-filled to 512 × 300 × 300 prior to Fourier transformation resulting in an isotropic image resolution of 10 μm. Additionally, an 80% trapezoid windowing was applied. This filters out the first and last 10% of the refocusing echo thereby enhancing the signal-to-noise ratio.
Image post-processing Manual segmentation was performed by a single person (JFPU) using Amira (Visage Imaging, Inc.) software and verified by an expert in teleost neuroanatomy. Structures were initially segmented slice-byslice in the sagittal perspective. Boundaries were then confirmed by careful inspection in the two other orthogonal views (axial and coronal). The most distinct structures such as the bulbus olfactorius, tectum opticum, and crista cerebellaris were segmented first as their boundaries established the major divisions in the zebrafish central nervous system and made it relatively easier to identify smaller structures. Only structures with clear boundaries were segmented,
79
while structures without clear borders were grouped together (i.e., dorsal telencephalon). A total of approximately 1000 slices were manually traced and verified. In most cases, the tracing and correction process had to be repeated iteratively in order to obtain an acceptable result from a neuroanatomical perspective. A color code scheme was selected that allowed all structures to be easily viewed when reconstructed into three-dimensional surface renderings. Additionally, all ventricles were labeled white in color. The majority of the nomenclature used in the atlas was based on that of Northcutt and Davis (1983) and Wullimann et al. (1996), and the guidelines for the location and boundaries of structures were obtained from Wullimann et al. (1996).
Results and discussion In this study, we present the first three-dimensional atlas of the wild-type zebrafish brain. Using magnetic resonance histology (MRH), we were able to clearly identify 53 structures at an isotropic resolution of 10 μm, making it the highest resolution magnetic resonance (MR) atlas in any species examined thus far. The high resolution achieved allowed us to not only visualize large structures but also fiber tracts, e.g., fasciculus retroflexus, as small as 40 μm in diameter. Table 1 lists all structures, ventricles, and fiber tracts including their abbreviations in order of their location in the major and minor brain divisions of the teleost central nervous system (CNS). Although we obtained a slice thickness (10 μm) comparable or thinner to many histological atlases (Franklin and Paxinos, 2007; Wullimann et al., 1996), smaller structures, such as groups of adjacent nuclei, could still not be demarcated and were instead left as aggregates. For example, in the histological atlas by Wullimann et al. (1996), cytoarchitectonic differentiation allowed for the subdivision of the dorsal and ventral telencephalic areas into distinct groups of cell masses (e.g., the medial, dorsal, lateral, posterior, and central zones of the dorsal telencephalon). However, in our MRH atlas, only a boundary between the dorsal and ventral areas could be delineated. Two major technical challenges hinder anatomists from identifying structures in MR-based atlases: the optimization of the signal-tonoise ratio (SNR) and the contrast-to-noise ratio (CNR). To maximize
Fig. 2. Labeling of the zebrafish brain atlas. (A) Three-dimensional representation of the atlas from the dorsal view. (B) Labeled axial slice. (C) Labeled horizontal slice. (D) Labeled sagittal slice. All scale bars are 0.5 mm. Small inserts indicate orientation of slices.
80
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
Table 2 List of brain structures with the volumes, surface areas, x, y, and z coordinates and mean gray scale values. Structure names
Volume (mm3)
Surface area (mm2)
Center x (mm)
Center y (mm)
Center z (mm)
Mean gray scale values
Bulbus olfactorius Area ventralis telencephali Area dorsalis Telencephali Nuclues praeopticus parvocellaris, pars anterior Nuclues praeopticus parvocellaris, pars posterior Habenula Saccus dorsalis Thalamus Corpus mamillare Torus lateralis Hypothalamus Nucleus diffuses lobi inferioris hypothalami Glandula pituitaria Nucleus praetectalis Superficialis, pars parvocellularis Diencephalon Stratum marginale Stratum centrale Stratum periventriculare tecti optici Torus longitudinalis Nucleus centralis tori semicircularis Nucleus ventrolateralis tori semicircularis Nervus oculomotorius Nucleus interpeduncularis Mesencephalon Vestibulolateralis lobe of Ce Eminentia granularis Corpus cerebelli Valvula cerebelli, pars medialis Valvula cerebelli, pars medialis, granular layer Valvula cerebelli, pars lateralis Nucleus gustatorius secundarius Nucleus octavolateralis Lobus vagi Lobus facialis Crista cerebellaris Griseum central Formatio reticularis Rhombencephalon Tractus pretectomamillaris Tractus opticus Commissura horizontalis Tractus tectobulbaris Fasciculus longitudinalis lateralis Fasciculus longitudinalis medialis Comissura ansulata Tractus cerebellaris posterior Comissura anterior Tractus gustatorius secundarius Fasciculus retroflexus Ventriculus mesencephali Recessus Lateralis Ventriculus diencephali Ventriculus rhombencephali
0.0596 0.1474 0.4926 0.0129 0.0038 0.0106 0.0046 0.0191 0.0117 0.0275 0.2575 0.1524 0.0132 0.0131 0.0395 0.1416 0.5203 0.1675 0.0298 0.0142 0.0692 0.0010 0.0159 0.3759 0.1834 0.0569 0.0753 0.0165 0.0293 0.0168 0.0170 0.0081 0.0514 0.0082 0.0991 0.0267 0.5044 0.0404 0.0010 0.0316 0.0021 0.0639 0.0112 0.0365 0.0040 0.0018 0.0026 0.0027 0.0012 0.0647 0.0037 0.0018 0.0055
0.9110 2.1654 3.7980 0.4670 0.1746 0.2867 0.1800 0.5022 0.3714 0.6968 2.7125 2.2889 0.3398 0.4453 0.9830 9.4675 8.1651 5.1869 0.6901 0.5145 1.5533 0.1266 0.3670 5.5730 2.9664 1.1686 1.5002 0.4514 0.8791 0.5588 0.5509 0.2736 1.0051 0.2363 1.5124 0.7790 6.2307 1.1282 0.0854 1.0853 0.1897 1.5168 0.5688 1.2380 0.2555 0.1730 0.1248 0.2667 0.1211 2.7190 0.2314 0.1449 0.3266
0.6067 1.3708 1.1779 1.5791 1.7799 1.7816 1.6677 2.0490 2.6943 2.4059 2.2428 2.7210 2.4366 1.8642 1.8142 2.3174 2.3078 2.3789 2.2793 2.4497 2.5108 2.6157 2.7985 2.4074 3.0566 3.0514 3.4512 2.5671 2.5879 2.4627 2.9012 3.5321 4.0138 3.7495 2.9449 3.1238 3.6885 2.8780 2.2087 1.8274 2.1780 2.5995 2.9039 3.5167 2.6111 2.9459 1.3995 3.5166 2.2615 2.3106 2.5589 2.2233 3.4319
1.4106 1.4519 1.4175 1.4719 1.5019 1.4904 1.4332 1.5071 1.5098 1.6012 1.5083 1.5381 1.5547 1.5039 1.4958 1.5654 1.5276 1.4885 1.5120 1.5171 1.4764 1.4791 1.4759 1.4959 1.5453 1.5588 1.5381 1.5159 1.5146 1.5209 1.4390 1.4614 1.4575 1.4939 1.5550 1.4782 1.4321 1.4975 1.4960 1.4984 1.5104 1.5240 1.4685 1.4377 1.4939 1.5160 1.4302 1.4044 1.4899 1.5458 1.5333 1.5190 1.4937
1.4756 1.5000 1.8430 1.4176 1.3344 1.8496 2.0324 1.6173 1.2261 1.2536 1.1515 1.1472 0.7582 1.5271 1.5326 1.8683 1.8633 1.8860 1.9730 1.9649 1.8968 1.3906 1.4717 1.6080 2.1775 1.9634 2.0257 1.9462 1.8967 2.0045 1.7946 1.8940 1.9333 1.9143 2.1055 1.7704 1.6629 1.4551 1.4960 1.4184 1.3797 1.6395 1.6434 1.6514 1.3831 1.8690 1.6586 1.7196 1.6572 1.8306 1.1094 1.2041 1.8789
11,179.81 14,517.94 13,999.8 16,201.91 17,023.11 18,400.52 13,192.57 17,738.04 14,167.2 18,561.84 18,982.37 19,811.75 13,917.94 13,946.65 15,814.4 17,689.1 15,563.77 19,743.44 22,152.12 18,895.62 17,117.6 5819.18 17,464.46 14,756.75 18,837.36 19,774.68 20,571.72 21,747.34 17,049.34 19,575.36 16,636.52 10,191.28 13,701.33 15,736.64 19,927.87 13,548.52 10,274.04 12,726.64 8108.54 8043.56 10,096.41 14,878.36 5722.91 4707.6 7093.33 8310.79 13,095.57 5257.85 16,114.88 11,825.66 10,599.22 13,105.44 8637.85
the SNR, samples are frequently fixed to prevent cellular degradation during long scan times. However, studies have shown that the production of methylene bridges during the fixation process reduces the MR signal (Petiet et al., 2007; Ullmann et al., in press). Accordingly, the signal-to-noise ratio and hence image quality is diminished. To increase the CNR, numerous studies have successfully employed gadolinium-based contrast agents (Kim et al., 2008; Petiet et al., 2007; Spencer et al., 2006). Gadolinium alters the relaxation times of the water protons, thereby allowing for the identification of a greater number of anatomical structures. Despite the ability of gadolinium-based contrast agents to increase the CNR in ex vivo imaging, these agents were initially developed as non-specific, nontoxic, extracellular contrast agents for in vivo pathological imaging. Other paramagnetic elements such as, manganese and cromium (Lauffer, 2002), have the potential to target different anatomical
structures (Huang et al., 2009), which will further enhance the ability to differentiate nuclear structures using MRH. Despite these difficulties, three-dimensional MRH atlases exhibit several significant advantages. In the majority of conventional atlases, structures in the CNS are labeled by being overlaid with text or the structures are illustrated with a schematic drawing (e.g., Fig. 1). A three-dimensional MRH atlas allows easier visualization and labeling of each structure/voxel with a name and color. For example, Fig. 2 demonstrates a typical segmentation performed on the T2⁎-weighted images in each of the three orthogonal planes. By assigning each voxel to a brain structure, borders are no longer just boundaries between one type of tissue and another but now define entire structures, thereby allowing for the creation of volumetric and surface renderings (see Supplementary Material 1, 2, and 3 for surface renderings of the entire brain and the color coding for the different neuroanatomical
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
structures). Moreover, quantitative measurements including volume, surface area, and mean gray scale values can also be calculated (see Table 2). This could allow for future phenotypic comparisons of normal and diseased brains as previously demonstrated in mice (Bock et al., 2006; Chen et al., 2005). It is expected that the measurements of neuroanatomical structures in wildtype zebrafish will mirror those in other strains, i.e., AB, but care must be taken when comparing the absolute size of neurological features, where age and size must be matched. A second advantage of our approach is the ability to image an intact brain. Most conventional atlases, including the zebrafish brain atlas, are created using standard histological techniques including dehydration, sectioning, rehydration of tissue, and subsequent staining. This protocol makes three-dimensional calculations of distances and three-dimensional reconstruction difficult as each step in the protocol creates significant tissue distortion. Although we cannot completely discount the possible effects of fixation, the non-destructive nature of MRH allows for the visualization of the spatial relationships of a large number of neuroanatomical structures in situ and without the distortion usually associated with histological processing. A good illustration of this technical advantage can be seen when examining the horizontal commissure. In Fig. 3B, one can clearly visualize the horizontal commissure crossing the midline of the brain in the ventral hypothalamus. In Fig. 3A, one can then follow the tracts as they run caudally along each side of the brain, before turning dorsally and rostrally towards the anterior optic tectum (see Supplementary Material 4 for a three-dimensional animation). Finally, using the intrinsic three-axis nature of MRI-based atlases, we have established a stereotaxic coordinate system (Fig. 4) with x coordinates running from rostral to caudal, the y coordinates running medial to lateral, and the z coordinates running ventral to dorsal. The center x, y, and z coordinates for each structure can be found in Table 2. The mid-sagittal plane that divides the brain into the left and right hemispheres was defined as the coordinate plane y = 0. This system provides immense new opportunities as it provides a standard framework into which data obtained from other modalities can be incorporated. For example, by using our stereotaxic coordinate system, gene-expression data can be accurately plotted onto regions of the three-dimensional brain. Furthermore, as our atlas has a slice thickness comparable to histological atlases, higher resolution histological sections can also be easily overlaid onto the MR data. Additionally, this high-resolution atlas can serve as a reference system
81
Fig. 4. Stereotaxic coordinate system. (A) Three-dimensional view of the coordinate axis. (B) Axial slice at x = 2.55 overlaid with coordinate grid. (C) Sagittal slice at y = 0 overlaid with the coordinate grid. All coordinates are given in millimeters.
from which other maps can be created. For example, the atlas can be used to delineate boundaries in a lower resolution in vivo atlas or to locate nuclei in which to place seeds for fiber tracking using diffusion tensor imaging. Conclusion This atlas is a vital reference for the use of the zebrafish as an animal model for neuroscience. It not only provides the user with important information on the spatial relationships of neuronal structures but also provides quantitative information and an accurate stereotaxic coordinate system. The resolution obtained is significantly higher than other MR atlases. Although the slice thickness is comparable to conventional histology, the in-plane resolution and the number of structures obtained are still less than the zebrafish histological atlas. Nevertheless, new techniques to enhance resolution (Flint et al., 2009) and improve staining (Blackwell et al., 2009) could eventually enable a similar number of structures to be identified as is now possible with conventional histology. Acknowledgments All MRH work presented in this study was performed at the Centre for Magnetic Resonance at the University of Queensland. The 16.4-T scanner is supported by the Queensland Government through the Queensland NMR Network. This project was funded by the Australian Research Council (Grant LP0776985 to SPC) and Ridley Aquafeed. The authors would like to thank Angela Lawton and the Australian Zebrafish Phenomics Facility (NHMRC Grant 455871) for providing all the animals and Kylie O'Grady for critical comments and support. J.F.P. U. was supported by an Endeavour International Postgraduate Research Scholarship and a University of Queensland International Living Allowance Scholarship, and N.D.K. would like to thank the Eijkman Institute Jakarta for their support. Appendix A. Supplementary data
Fig. 3. Lateral (A) and dorsal (B) views of tectum opticum in blue, hypothalamus in yellow, and comrnissura horizontalis in purple.
Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.01.086.
82
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
References Becker, C.G., Becker, T., 2008. Adult zebrafish as a model for successful central nervous system regeneration. Restor. Neurol. Neurosci. 26, 71–80. Blackwell, M.L., Farrar, C.T., Fischl, B., Rosen, B.R., 2009. Target-specific contrast agents for magnetic resonance microscopy. NeuroImage 46, 382–393. Bock, N.A., Kovacevic, N., Lipina, T.V., Roder, J.C., Ackerman, S.L., Henkelman, R.M., 2006. In vivo magnetic resonance imaging and semiautomated image analysis extend the brain phenotye for cdf/cdf mice. J. Neurosci. 26, 4455–4459. Chen, J.X., Kovacevic, N., Lobaugh, N.J., Sled, J.G., Henkelman, R.M., Henderson, J.T., 2005. Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI. NeuroImage 29, 99–105. Flint, J.J., Lee, C.H., Hansen, B., Fey, M., Schmidig, D., Bui, J.D., King, M.A., VestergaardPoulsen, P., Blackband, S.J., 2009. Magnetic resonance microscopy of mammalian neurons. NeuroImage 46, 1037–1040. Frahm, J., Haase, A., Matthaei, D., 1986. Rapid three-dimensional MR imaging using the FLASH technique. J. Comp. Assist. Tomogr. 10, 363–368. Franklin, K., Paxinos, G., 2007. The Mouse Brain in Stereotaxic Coordinates, 3 ed. Academic Press, San Diego. Grunwald, D.J., Eisen, J.S., 2002. Headwaters of the zebrafish—emergence of a new model vertebrate. Nat. Rev. Genet. 3, 717–724. Huang, S., Liu, C., Diai, G., Kim, Y.R., Rosen, B.R., 2009. Manipulation of tissue contrast using contrast agents for enhanced MR microscopy ex vivo mouse brain. NeuroImage 46, 589–599. Kabli, S., Alia, A., Spaink, H.P., Verbeek, F.J., de Groot, H.J.M., 2006. Magnetic resonance microscopy of adult zebrafish. Zebrafish 3, 2006.
Keller, E.T., Murtha, J.M., 2004. The use of mature zebrafish (Danio rerio) as a model for human aging and disease. Comp. Biochem. Physiol., C 138, 335–341. Kim, S., Pickup, S., Hsu, O., Poptani, H., 2008. Enhanced delineation of white matter structures of the fixed mouse brain using Gd-DTPA in microscopic MRI. NMR Biomed. 22, 303–309. Lauffer, R.B., 2002. Paramagnetic metal complexes as water proton relaxation agents for NMR imaging: theory and design. Chem. Rev. 87, 901–927. Lieschke, G.J., Currie, P.D., 2007. Animal models of human disease: zebrafish swim into view. Nat. Rev. 8, 353–367. Northcutt, R.G., Davis, R.E., 1983. Telencephalic Organization in Ray-Finned Fishes. University of Michigan Press, Ann Arbor. Petiet, A., Hedlund, L., Johnson, G.A., 2007. Staining methods for magnetic resonance microscopy of the rat fetus. J. Magn. Reson. Imaging 25, 1192–1198. Spencer, R.G., Fishbein, K.W., Cheng, A., Mattson, M.P., 2006. Compatibility of Gd-DTPA perfusion and histologic studies of the brain. Magn. Reson. Imaging 24, 27–31. Ullmann, J.F.P., Cowin, G., Kurniawan, N.D., Collin, S.P., in press. Magnetic resonance histology of the adult zebrafish brain: optimization of fixation and gadolinium enhancement. NMR in Biomedicine. Weinmann, H.-J., Brasch, R.C., Press, W.-R., Wesbey, G.E., 1984. Characteristics of gadolinium–DTPA complex: a potential NMR contrast agent. Am. J. Roentgenol. 142, 619–624. Wullimann, M.F., Rupp, B., Reichert, H., 1996. Neuroanatomy of the Zebrafish Brain: A Topological Atlas. Birkhäuser Verlag, Basel. Yu, L., Tucci, V., Kishi, S., Zhdanova, I.V., 2006. Cognitive aging in zebrafish. PLOS One 1, 1–13. Zupanc, G.K.H., 2009. Towards brain repair: insights from teleost fish. Semin. Cell Dev. Biol. 20, 683–690.
Contents lists available at ScienceDirect
NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g
A three-dimensional digital atlas of the zebrafish brain Jeremy F.P. Ullmann a,⁎, Gary Cowin b, Nyoman D. Kurniawan b, Shaun P. Collin a,c a b c
Sensory Neurobiology Group, School of Biomedical Sciences, University of Queensland, St Lucia Qld 4072, Australia Centre for Magnetic Resonance, University of Queensland, Brisbane Qld, Australia School of Animal Biology, The University of Western Australia, Crawley 6009, Western Australia, Australia
a r t i c l e
i n f o
Article history: Received 31 August 2009 Revised 21 January 2010 Accepted 25 January 2010 Available online 6 February 2010 Keywords: Zebrafish Brain Magnetic resonance histology (MRH) Three-dimensional (3D) Atlas Map
a b s t r a c t In the past three decades, the zebrafish has become a vital animal model in a range of biological sciences. To augment current neurobiological research, we have developed the first three-dimensional digital atlas of the zebrafish brain from T2⁎-weighted magnetic resonance histology (MRH) images acquired on a 16.4-T superconducting magnet. We achieved an isotropic resolution of 10 μm, which is the highest resolution achieved in a vertebrate brain and, for the first time, is comparable in slice thickness to conventional histology. By using manual segmentation, 53 anatomical structures, including fiber tracts as small as 40 μm, were delineated. Using Amira software, structures were also individually segmented and reconstructed to create three-dimensional animations. Additional quantitative information including, volume, surface areas, and mean gray scale intensities were also determined. Finally, we established a stereotaxic coordinate system as a framework in which maps created from other modalities can be incorporated into the atlas. © 2010 Elsevier Inc. All rights reserved.
Introduction Over the past 30 years, the zebrafish has become a preeminent model for biology (Grunwald and Eisen, 2002). Initially developed as an inexpensive vertebrate model system that permits forward genetic analyses, it has since grown to be used in a wide range of research fields including developmental biology, cancer, pharmacology and toxicology, disease modeling, and neuroscience. To date, the majority of experiments utilizing zebrafish have been performed with embryonic and juvenile fish, which have the advantage of being almost completely transparent, yet recent research in adult zebrafish has been burgeoning, particularly in studies of cognitive aging (Keller and Murtha, 2004; Yu et al., 2006) and central nervous system regeneration (Becker and Becker, 2008; Zupanc, 2009). However, an insufficient comprehension of the neuroanatomy and physiology of the adult zebrafish brain has been identified as a limiting factor in many studies (Lieschke and Currie, 2007). High-resolution magnetic resonance imaging or magnetic resonance histology (MRH) is an ideal method in which to acquire detailed neuroanatomical information of an intact brain. To maximize resolution MRH scans are typically performed on fixed in situ or ex vivo samples. Although other forms of MRI such as in vivo imaging are preferable as they limit the possible effects of fixation, they require larger radiofrequency coils, larger acquisition matrices, and shorter scan times (Kabli et al., 2006). Furthermore, the use of contrast agents
such as Magnevist (Bayer) are not possible for imaging the brain as the gadolinium–DTPA complex is an extracellular contrast agent that cannot penetrate the blood–brain barrier (Weinmann et al., 1984). In contrast, by fixing and ‘staining’ a sample for MRH (Ullmann et al., in press), longer scan times can be used, resulting in increased contrast and signal-to-noise ratios, and a resolution of comparable slice thickness to conventional histology. Moreover, when combined with image post-processing such as structure segmentation, spatial analysis, and three-dimensional manipulation, the size, stereotaxic location, and morphology of a large number of anatomical structures can be identified, thereby providing greater visual and computational power than offered by a conventional two-dimensional brain atlas. In order to augment current research on adult zebrafish, we have constructed a wild-type adult zebrafish brain atlas from T2⁎-weighted three-dimensional MRH images acquired on a 16.4-T magnet. This atlas is the first of its kind for the zebrafish brain, boasting an isotropic resolution of 10 μm, which is the highest resolution MRH atlas in any species thus far. Moreover, with a total of 53 segmented structures identified, it is one of the most detailed MRH atlases currently available. Finally, this 3D atlas can serve as a framework for the creation of additional maps using other modalities. Materials and methods Specimen preparation and imaging
⁎ Corresponding author. Fax: +617 3409 9839. E-mail address: [email protected] (J.F.P. Ullmann). 1053-8119/$ – see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2010.01.086
Specimen preparation and imaging followed the protocol of Ullmann et al. (in press) and all animal experiments were performed
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
according to procedures approved by the University of Queensland Animal Ethics Committee (AEC No. SBMS/074/08). Briefly, one adult wild-type zebrafish (4 months old and 33.16-mm total length), whose F1s were purchased commercially, was obtained from the Australian Zebrafish Phenomics Facility, Brisbane, Queensland, Australia. The wild type was used in order to better reflect the natural condition but no comparison with standard mutant strains such as AB was
77
performed. The individual was immersion-fixed and stained for 12 h in a 5 ml solution of 4% paraformaldehyde and 0.5% Magnevist (Bayer). The intact, fixed, and stained fish brain was prepared for imaging by first allowing the brain to come to room temperature (24 °C) and was then placed in Fomblin (perfluoropolyether, Ausimont, Morristown, NJ, USA) for 2 h prior to imaging to allow for any potential out-gassing.
Table 1 List of brain stuctures, abbreviations, color, and their location in the major and minor brain divisions.
78
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
Fig. 1. (A) Axial MRH image and schematic drawing of structures. (B) Sagittal MRH image and schematic drawing of structures. All scale bars are 250 μm. All abbreviations can be found in Table 1.
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
All images were acquired on a Bruker (Ettlingen, Germany) AV II spectrometer running ParaVision 4, interfaced to a 16.4-T vertical magnet with a bore diameter of 89 mm, and a Bruker micro2.5 imaging gradient (150 G/cm, gradient stabilization time b150 μs). A custom-made horizontal, solenoid radiofrequency (rf) coil was used without cryocooling and with an inner diameter and length of 5 × 4 mm. We chose to use a 3D FLASH (Frahm et al., 1986) gradient echo T2⁎-weighted sequence instead of a spin-echo sequence as it provided us with two advantages. First, the use of a small flip angle in gradient echo sequences permits the acquisition of highresolution images with small repetition times. Consequently, a greater number of averages could be acquired thereby increasing the signal to noise per unit time. Second, as gradient-echo sequences are more sensitive to the effects of Magnevist® than spin-echo sequences, it allowed us to delineate a greater number of structures. The acquisition parameters for the gradient-echo scan were the following: TR = 80 ms, TE = 12 ms, flip angle = 30°, field of view = 5.2 × 3.0 × 3.0 mm. The number of averages was 16, and the total imaging time for the 3D data set was just over 13 h. The acquisition matrix was 512 × 202 × 202, which was symmetrically zero-filled to 512 × 300 × 300 prior to Fourier transformation resulting in an isotropic image resolution of 10 μm. Additionally, an 80% trapezoid windowing was applied. This filters out the first and last 10% of the refocusing echo thereby enhancing the signal-to-noise ratio.
Image post-processing Manual segmentation was performed by a single person (JFPU) using Amira (Visage Imaging, Inc.) software and verified by an expert in teleost neuroanatomy. Structures were initially segmented slice-byslice in the sagittal perspective. Boundaries were then confirmed by careful inspection in the two other orthogonal views (axial and coronal). The most distinct structures such as the bulbus olfactorius, tectum opticum, and crista cerebellaris were segmented first as their boundaries established the major divisions in the zebrafish central nervous system and made it relatively easier to identify smaller structures. Only structures with clear boundaries were segmented,
79
while structures without clear borders were grouped together (i.e., dorsal telencephalon). A total of approximately 1000 slices were manually traced and verified. In most cases, the tracing and correction process had to be repeated iteratively in order to obtain an acceptable result from a neuroanatomical perspective. A color code scheme was selected that allowed all structures to be easily viewed when reconstructed into three-dimensional surface renderings. Additionally, all ventricles were labeled white in color. The majority of the nomenclature used in the atlas was based on that of Northcutt and Davis (1983) and Wullimann et al. (1996), and the guidelines for the location and boundaries of structures were obtained from Wullimann et al. (1996).
Results and discussion In this study, we present the first three-dimensional atlas of the wild-type zebrafish brain. Using magnetic resonance histology (MRH), we were able to clearly identify 53 structures at an isotropic resolution of 10 μm, making it the highest resolution magnetic resonance (MR) atlas in any species examined thus far. The high resolution achieved allowed us to not only visualize large structures but also fiber tracts, e.g., fasciculus retroflexus, as small as 40 μm in diameter. Table 1 lists all structures, ventricles, and fiber tracts including their abbreviations in order of their location in the major and minor brain divisions of the teleost central nervous system (CNS). Although we obtained a slice thickness (10 μm) comparable or thinner to many histological atlases (Franklin and Paxinos, 2007; Wullimann et al., 1996), smaller structures, such as groups of adjacent nuclei, could still not be demarcated and were instead left as aggregates. For example, in the histological atlas by Wullimann et al. (1996), cytoarchitectonic differentiation allowed for the subdivision of the dorsal and ventral telencephalic areas into distinct groups of cell masses (e.g., the medial, dorsal, lateral, posterior, and central zones of the dorsal telencephalon). However, in our MRH atlas, only a boundary between the dorsal and ventral areas could be delineated. Two major technical challenges hinder anatomists from identifying structures in MR-based atlases: the optimization of the signal-tonoise ratio (SNR) and the contrast-to-noise ratio (CNR). To maximize
Fig. 2. Labeling of the zebrafish brain atlas. (A) Three-dimensional representation of the atlas from the dorsal view. (B) Labeled axial slice. (C) Labeled horizontal slice. (D) Labeled sagittal slice. All scale bars are 0.5 mm. Small inserts indicate orientation of slices.
80
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
Table 2 List of brain structures with the volumes, surface areas, x, y, and z coordinates and mean gray scale values. Structure names
Volume (mm3)
Surface area (mm2)
Center x (mm)
Center y (mm)
Center z (mm)
Mean gray scale values
Bulbus olfactorius Area ventralis telencephali Area dorsalis Telencephali Nuclues praeopticus parvocellaris, pars anterior Nuclues praeopticus parvocellaris, pars posterior Habenula Saccus dorsalis Thalamus Corpus mamillare Torus lateralis Hypothalamus Nucleus diffuses lobi inferioris hypothalami Glandula pituitaria Nucleus praetectalis Superficialis, pars parvocellularis Diencephalon Stratum marginale Stratum centrale Stratum periventriculare tecti optici Torus longitudinalis Nucleus centralis tori semicircularis Nucleus ventrolateralis tori semicircularis Nervus oculomotorius Nucleus interpeduncularis Mesencephalon Vestibulolateralis lobe of Ce Eminentia granularis Corpus cerebelli Valvula cerebelli, pars medialis Valvula cerebelli, pars medialis, granular layer Valvula cerebelli, pars lateralis Nucleus gustatorius secundarius Nucleus octavolateralis Lobus vagi Lobus facialis Crista cerebellaris Griseum central Formatio reticularis Rhombencephalon Tractus pretectomamillaris Tractus opticus Commissura horizontalis Tractus tectobulbaris Fasciculus longitudinalis lateralis Fasciculus longitudinalis medialis Comissura ansulata Tractus cerebellaris posterior Comissura anterior Tractus gustatorius secundarius Fasciculus retroflexus Ventriculus mesencephali Recessus Lateralis Ventriculus diencephali Ventriculus rhombencephali
0.0596 0.1474 0.4926 0.0129 0.0038 0.0106 0.0046 0.0191 0.0117 0.0275 0.2575 0.1524 0.0132 0.0131 0.0395 0.1416 0.5203 0.1675 0.0298 0.0142 0.0692 0.0010 0.0159 0.3759 0.1834 0.0569 0.0753 0.0165 0.0293 0.0168 0.0170 0.0081 0.0514 0.0082 0.0991 0.0267 0.5044 0.0404 0.0010 0.0316 0.0021 0.0639 0.0112 0.0365 0.0040 0.0018 0.0026 0.0027 0.0012 0.0647 0.0037 0.0018 0.0055
0.9110 2.1654 3.7980 0.4670 0.1746 0.2867 0.1800 0.5022 0.3714 0.6968 2.7125 2.2889 0.3398 0.4453 0.9830 9.4675 8.1651 5.1869 0.6901 0.5145 1.5533 0.1266 0.3670 5.5730 2.9664 1.1686 1.5002 0.4514 0.8791 0.5588 0.5509 0.2736 1.0051 0.2363 1.5124 0.7790 6.2307 1.1282 0.0854 1.0853 0.1897 1.5168 0.5688 1.2380 0.2555 0.1730 0.1248 0.2667 0.1211 2.7190 0.2314 0.1449 0.3266
0.6067 1.3708 1.1779 1.5791 1.7799 1.7816 1.6677 2.0490 2.6943 2.4059 2.2428 2.7210 2.4366 1.8642 1.8142 2.3174 2.3078 2.3789 2.2793 2.4497 2.5108 2.6157 2.7985 2.4074 3.0566 3.0514 3.4512 2.5671 2.5879 2.4627 2.9012 3.5321 4.0138 3.7495 2.9449 3.1238 3.6885 2.8780 2.2087 1.8274 2.1780 2.5995 2.9039 3.5167 2.6111 2.9459 1.3995 3.5166 2.2615 2.3106 2.5589 2.2233 3.4319
1.4106 1.4519 1.4175 1.4719 1.5019 1.4904 1.4332 1.5071 1.5098 1.6012 1.5083 1.5381 1.5547 1.5039 1.4958 1.5654 1.5276 1.4885 1.5120 1.5171 1.4764 1.4791 1.4759 1.4959 1.5453 1.5588 1.5381 1.5159 1.5146 1.5209 1.4390 1.4614 1.4575 1.4939 1.5550 1.4782 1.4321 1.4975 1.4960 1.4984 1.5104 1.5240 1.4685 1.4377 1.4939 1.5160 1.4302 1.4044 1.4899 1.5458 1.5333 1.5190 1.4937
1.4756 1.5000 1.8430 1.4176 1.3344 1.8496 2.0324 1.6173 1.2261 1.2536 1.1515 1.1472 0.7582 1.5271 1.5326 1.8683 1.8633 1.8860 1.9730 1.9649 1.8968 1.3906 1.4717 1.6080 2.1775 1.9634 2.0257 1.9462 1.8967 2.0045 1.7946 1.8940 1.9333 1.9143 2.1055 1.7704 1.6629 1.4551 1.4960 1.4184 1.3797 1.6395 1.6434 1.6514 1.3831 1.8690 1.6586 1.7196 1.6572 1.8306 1.1094 1.2041 1.8789
11,179.81 14,517.94 13,999.8 16,201.91 17,023.11 18,400.52 13,192.57 17,738.04 14,167.2 18,561.84 18,982.37 19,811.75 13,917.94 13,946.65 15,814.4 17,689.1 15,563.77 19,743.44 22,152.12 18,895.62 17,117.6 5819.18 17,464.46 14,756.75 18,837.36 19,774.68 20,571.72 21,747.34 17,049.34 19,575.36 16,636.52 10,191.28 13,701.33 15,736.64 19,927.87 13,548.52 10,274.04 12,726.64 8108.54 8043.56 10,096.41 14,878.36 5722.91 4707.6 7093.33 8310.79 13,095.57 5257.85 16,114.88 11,825.66 10,599.22 13,105.44 8637.85
the SNR, samples are frequently fixed to prevent cellular degradation during long scan times. However, studies have shown that the production of methylene bridges during the fixation process reduces the MR signal (Petiet et al., 2007; Ullmann et al., in press). Accordingly, the signal-to-noise ratio and hence image quality is diminished. To increase the CNR, numerous studies have successfully employed gadolinium-based contrast agents (Kim et al., 2008; Petiet et al., 2007; Spencer et al., 2006). Gadolinium alters the relaxation times of the water protons, thereby allowing for the identification of a greater number of anatomical structures. Despite the ability of gadolinium-based contrast agents to increase the CNR in ex vivo imaging, these agents were initially developed as non-specific, nontoxic, extracellular contrast agents for in vivo pathological imaging. Other paramagnetic elements such as, manganese and cromium (Lauffer, 2002), have the potential to target different anatomical
structures (Huang et al., 2009), which will further enhance the ability to differentiate nuclear structures using MRH. Despite these difficulties, three-dimensional MRH atlases exhibit several significant advantages. In the majority of conventional atlases, structures in the CNS are labeled by being overlaid with text or the structures are illustrated with a schematic drawing (e.g., Fig. 1). A three-dimensional MRH atlas allows easier visualization and labeling of each structure/voxel with a name and color. For example, Fig. 2 demonstrates a typical segmentation performed on the T2⁎-weighted images in each of the three orthogonal planes. By assigning each voxel to a brain structure, borders are no longer just boundaries between one type of tissue and another but now define entire structures, thereby allowing for the creation of volumetric and surface renderings (see Supplementary Material 1, 2, and 3 for surface renderings of the entire brain and the color coding for the different neuroanatomical
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
structures). Moreover, quantitative measurements including volume, surface area, and mean gray scale values can also be calculated (see Table 2). This could allow for future phenotypic comparisons of normal and diseased brains as previously demonstrated in mice (Bock et al., 2006; Chen et al., 2005). It is expected that the measurements of neuroanatomical structures in wildtype zebrafish will mirror those in other strains, i.e., AB, but care must be taken when comparing the absolute size of neurological features, where age and size must be matched. A second advantage of our approach is the ability to image an intact brain. Most conventional atlases, including the zebrafish brain atlas, are created using standard histological techniques including dehydration, sectioning, rehydration of tissue, and subsequent staining. This protocol makes three-dimensional calculations of distances and three-dimensional reconstruction difficult as each step in the protocol creates significant tissue distortion. Although we cannot completely discount the possible effects of fixation, the non-destructive nature of MRH allows for the visualization of the spatial relationships of a large number of neuroanatomical structures in situ and without the distortion usually associated with histological processing. A good illustration of this technical advantage can be seen when examining the horizontal commissure. In Fig. 3B, one can clearly visualize the horizontal commissure crossing the midline of the brain in the ventral hypothalamus. In Fig. 3A, one can then follow the tracts as they run caudally along each side of the brain, before turning dorsally and rostrally towards the anterior optic tectum (see Supplementary Material 4 for a three-dimensional animation). Finally, using the intrinsic three-axis nature of MRI-based atlases, we have established a stereotaxic coordinate system (Fig. 4) with x coordinates running from rostral to caudal, the y coordinates running medial to lateral, and the z coordinates running ventral to dorsal. The center x, y, and z coordinates for each structure can be found in Table 2. The mid-sagittal plane that divides the brain into the left and right hemispheres was defined as the coordinate plane y = 0. This system provides immense new opportunities as it provides a standard framework into which data obtained from other modalities can be incorporated. For example, by using our stereotaxic coordinate system, gene-expression data can be accurately plotted onto regions of the three-dimensional brain. Furthermore, as our atlas has a slice thickness comparable to histological atlases, higher resolution histological sections can also be easily overlaid onto the MR data. Additionally, this high-resolution atlas can serve as a reference system
81
Fig. 4. Stereotaxic coordinate system. (A) Three-dimensional view of the coordinate axis. (B) Axial slice at x = 2.55 overlaid with coordinate grid. (C) Sagittal slice at y = 0 overlaid with the coordinate grid. All coordinates are given in millimeters.
from which other maps can be created. For example, the atlas can be used to delineate boundaries in a lower resolution in vivo atlas or to locate nuclei in which to place seeds for fiber tracking using diffusion tensor imaging. Conclusion This atlas is a vital reference for the use of the zebrafish as an animal model for neuroscience. It not only provides the user with important information on the spatial relationships of neuronal structures but also provides quantitative information and an accurate stereotaxic coordinate system. The resolution obtained is significantly higher than other MR atlases. Although the slice thickness is comparable to conventional histology, the in-plane resolution and the number of structures obtained are still less than the zebrafish histological atlas. Nevertheless, new techniques to enhance resolution (Flint et al., 2009) and improve staining (Blackwell et al., 2009) could eventually enable a similar number of structures to be identified as is now possible with conventional histology. Acknowledgments All MRH work presented in this study was performed at the Centre for Magnetic Resonance at the University of Queensland. The 16.4-T scanner is supported by the Queensland Government through the Queensland NMR Network. This project was funded by the Australian Research Council (Grant LP0776985 to SPC) and Ridley Aquafeed. The authors would like to thank Angela Lawton and the Australian Zebrafish Phenomics Facility (NHMRC Grant 455871) for providing all the animals and Kylie O'Grady for critical comments and support. J.F.P. U. was supported by an Endeavour International Postgraduate Research Scholarship and a University of Queensland International Living Allowance Scholarship, and N.D.K. would like to thank the Eijkman Institute Jakarta for their support. Appendix A. Supplementary data
Fig. 3. Lateral (A) and dorsal (B) views of tectum opticum in blue, hypothalamus in yellow, and comrnissura horizontalis in purple.
Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2010.01.086.
82
J.F.P. Ullmann et al. / NeuroImage 51 (2010) 76–82
References Becker, C.G., Becker, T., 2008. Adult zebrafish as a model for successful central nervous system regeneration. Restor. Neurol. Neurosci. 26, 71–80. Blackwell, M.L., Farrar, C.T., Fischl, B., Rosen, B.R., 2009. Target-specific contrast agents for magnetic resonance microscopy. NeuroImage 46, 382–393. Bock, N.A., Kovacevic, N., Lipina, T.V., Roder, J.C., Ackerman, S.L., Henkelman, R.M., 2006. In vivo magnetic resonance imaging and semiautomated image analysis extend the brain phenotye for cdf/cdf mice. J. Neurosci. 26, 4455–4459. Chen, J.X., Kovacevic, N., Lobaugh, N.J., Sled, J.G., Henkelman, R.M., Henderson, J.T., 2005. Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI. NeuroImage 29, 99–105. Flint, J.J., Lee, C.H., Hansen, B., Fey, M., Schmidig, D., Bui, J.D., King, M.A., VestergaardPoulsen, P., Blackband, S.J., 2009. Magnetic resonance microscopy of mammalian neurons. NeuroImage 46, 1037–1040. Frahm, J., Haase, A., Matthaei, D., 1986. Rapid three-dimensional MR imaging using the FLASH technique. J. Comp. Assist. Tomogr. 10, 363–368. Franklin, K., Paxinos, G., 2007. The Mouse Brain in Stereotaxic Coordinates, 3 ed. Academic Press, San Diego. Grunwald, D.J., Eisen, J.S., 2002. Headwaters of the zebrafish—emergence of a new model vertebrate. Nat. Rev. Genet. 3, 717–724. Huang, S., Liu, C., Diai, G., Kim, Y.R., Rosen, B.R., 2009. Manipulation of tissue contrast using contrast agents for enhanced MR microscopy ex vivo mouse brain. NeuroImage 46, 589–599. Kabli, S., Alia, A., Spaink, H.P., Verbeek, F.J., de Groot, H.J.M., 2006. Magnetic resonance microscopy of adult zebrafish. Zebrafish 3, 2006.
Keller, E.T., Murtha, J.M., 2004. The use of mature zebrafish (Danio rerio) as a model for human aging and disease. Comp. Biochem. Physiol., C 138, 335–341. Kim, S., Pickup, S., Hsu, O., Poptani, H., 2008. Enhanced delineation of white matter structures of the fixed mouse brain using Gd-DTPA in microscopic MRI. NMR Biomed. 22, 303–309. Lauffer, R.B., 2002. Paramagnetic metal complexes as water proton relaxation agents for NMR imaging: theory and design. Chem. Rev. 87, 901–927. Lieschke, G.J., Currie, P.D., 2007. Animal models of human disease: zebrafish swim into view. Nat. Rev. 8, 353–367. Northcutt, R.G., Davis, R.E., 1983. Telencephalic Organization in Ray-Finned Fishes. University of Michigan Press, Ann Arbor. Petiet, A., Hedlund, L., Johnson, G.A., 2007. Staining methods for magnetic resonance microscopy of the rat fetus. J. Magn. Reson. Imaging 25, 1192–1198. Spencer, R.G., Fishbein, K.W., Cheng, A., Mattson, M.P., 2006. Compatibility of Gd-DTPA perfusion and histologic studies of the brain. Magn. Reson. Imaging 24, 27–31. Ullmann, J.F.P., Cowin, G., Kurniawan, N.D., Collin, S.P., in press. Magnetic resonance histology of the adult zebrafish brain: optimization of fixation and gadolinium enhancement. NMR in Biomedicine. Weinmann, H.-J., Brasch, R.C., Press, W.-R., Wesbey, G.E., 1984. Characteristics of gadolinium–DTPA complex: a potential NMR contrast agent. Am. J. Roentgenol. 142, 619–624. Wullimann, M.F., Rupp, B., Reichert, H., 1996. Neuroanatomy of the Zebrafish Brain: A Topological Atlas. Birkhäuser Verlag, Basel. Yu, L., Tucci, V., Kishi, S., Zhdanova, I.V., 2006. Cognitive aging in zebrafish. PLOS One 1, 1–13. Zupanc, G.K.H., 2009. Towards brain repair: insights from teleost fish. Semin. Cell Dev. Biol. 20, 683–690.