Compared to existing histology or MR-based atlases, the atlas presented here is unique in its characterization of developing mouse brains using high resolution DTI technique. DTI has become a useful phenotyping tool in neuroscience research, especially at early developmental stages. While conventional T1
based MRI show poor tissue contrast in immature mouse brains, probably due to lack of myelin, DTI was able to provide satisfactory and relatively consistent tissue contrasts in the developing mouse brains. Ex vivo
mouse brain DTI has been used to examine the white matter structures and their development in wild-type and mutant strains (Andrews et al., 2006
; Tyszka et al., 2006
; Verma et al., 2005
; Wang et al., 2006
; Zhang et al., 2005
; Zhang et al., 2003
). With these developments, this atlas will be a useful guide for locating anatomical structures in mouse DTI data, and our database may serve as a baseline for the study of abnormal mouse brain development.
The information in the atlas is presented in 2D and 3D formats, and the atlas is simple to use. The 2D atlas contains images from developing brains with structural labels, and can be used in the same ways as current histology-based atlases. We followed the structural nomenclature used in the two atlases as closely as possible. The 3D atlas provides visualization of the segmented white and gray matter structures of interest, thus allowing exploration of the 3D spatial relationships between these structures, e.g., the 3D trajectories of a particular white matter tract. By coregistering the atlas and MRI/DTI data to CT data for the external skull features, the 3D atlas may also be useful for surgical planning among other interventions (Aggarwal et al., 2009
). It is necessary to note that even though the images in the atlas have been rotated to the orientation defined in the Paxinos atlas (Paxinos and Franklin, 2003
) and Foster atlas (Foster, 1998
) for postnatal and embryonic mouse brains, respectively, small discrepancies in slice orientation may still present because accurate matching of our 3D MRI based atlases to the 2D section based atlases is difficult.
To avoid tissue deformation or damage during dissection and imaging, the images were acquired with the brains preserved in the skull. The disadvantages associated with imaging brains within the skull are in-skull susceptibility artifacts near the ear drum, and reduced SNR and resolution due to the use of larger, less sensitive RF coils. Y. Ma et al. (Ma et al., 2005
) reported significant differences in brain shapes between images obtained from brain specimens preserved inside and outside the skull, which was attributed to tissue relaxation and stretching after dissection. The volume measurements reported here are comparable with data from previous reports based on histology and/or MRI. A previous histology-based study on albino mice showed that the volumetric growth of the brain, neocortex, and cerebellum follow a sigmoidal model (Wingert, 1969
), with relatively slow growth in the late embryonic period (E16 ~ E18), followed by a rapid increase during the neonatal and early postnatal period (P0 ~ P20), subsequently slowly approaching their adult values. The data showed that the development of different brain compartments is not uniform. For example, while the volumetric growth of the neocortex and hippocampus followed the volumetric growth of the whole brain closely, the growth of the cerebellum is slower than the growth of the whole brain (). The estimated x0
for the cerebellum is around P8 to P9, compared to an estimated x0s
of P5 to P6 for the whole brain, neocortex, and hippocampus. The sigmoidal model, however, is a general model used here to characterize the brain growth throughout the embryonic and postnatal periods. For more detailed analysis on volume development, it is necessary to focus on a particular period with more sophisticated models (Uylings et al., 1990
) than the model adopted here.
While volume changes are prevalent in various types of human brain diseases, e.g., Huntington's disease (Nopoulos et al., 2007
; Rosas et al., 2004
) and Alzheimer's disease (Csernansky et al., 2005
; Thompson et al., 2003
) brain morphology has rarely been used as a phenotypic marker in mouse disease models. With the increasing availability of small animal imaging instruments, this situation is rapidly changing. The normal growth curves of C57BL/6 with 95% prediction bands will be a useful reference for future morphological phenotype studies.
In this study, population-averaged mouse brain images at several postnatal stages were generated by normalizing individual mouse brain image to a template using linear (rigid) and non-linear (LDDMM) transformations. After the transformations, we could obtain information about anatomical variability among individual mice. Recently, several groups have reported measurements of anatomical variation in mouse brains based on segmentation of MRI data (Badea et al., 2007
; Dorr et al., 2008
; Kovacevic et al., 2005
; Ma et al., 2005
). In this study, anatomical variation in adult mouse brains was characterized without segmentation. Instead, we relied on LDDMM, driven by the rich anatomical information in DTI data, to generate measurements of anatomical variation for each pixel. demonstrates that certain brain regions have greater variation than other regions. In addition, if the atlases are used for stereotactic operations, the variability data provide a level of confidence for the delivery of cells, chemicals, or electrodes, depending on the experiment. This type of information is difficult to obtain from histology-based morphology studies.
In this study, the maturation process of various white matter tracts was also quantitatively characterized in terms of their sizes. DTI can monitor only the macroscopic sizes of axonal bundles, which by no means provides precise cellular information, such as axonal counts, axonal calibers, and myelination. However, macroscopic axonal degeneration or misguidance is prevalent in various types of mutants, which are sometimes difficult to comprehend from serial histology sections. We believe that our atlases and quantitative growth curves of each white matter tract provide anatomical information in 0.1 – 1 mm scales, that is complementary to histological information (which is typically on a μm scale) and will serve as important references for axonal tract development studies.
Currently, the main limitation of our atlas is a lack of precise gray matter assignment based on correlations with histology. Although it is rather straightforward to identify major white matter tracts based on their trajectory information provided by DTI, assignment of gray matter structures is not always straightforward. MR-histology correlation studies will be an important future effort to establish more comprehensive atlases. Other limitations of our atlas are mainly DTI related. First, DTI is inherently a technique with low-signal-to-noise ratio, and therefore, it is more difficult to achieve a high spatial resolution image and is more time-consuming than conventional MRI and other 3D imaging modalities. The resolution achieved in this atlas ranges from 80 μm to 125 μm isotropic resolution. Previous reports based on T1
- and T2
*-weighted MRI were able to reach up to 20 ~ 30 μm resolution (Badea et al., 2007
; Dorr et al., 2008
; Ma et al., 2005
), while episcopic and microCT can achieve even higher resolution (Johnson et al., 2006
; Weninger and Mohun, 2002
). Second, DTI is based on a simplified model of water diffusion. When the anatomical structures are complex, for example in regions where two or more populations of fibers cross each other, DTI oversimplifies the underlying anatomy. Contrasts for such problematic areas could be improved by more sophisticated models, such as diffusion spectrum imaging, Q-ball imaging, and HARD (Frank, 2001
; Tuch et al., 2002
; Tuch et al., 2003
; Wedeen et al., 2000
In summary, an MRI-based atlas and a database of the developing mouse brain have been established. The atlas and database will be useful for the quantitative examination of brain phenotypes and their development in wildtype and mutant mouse strains. The atlas and database can also be used to study abnormal anatomy and lesions in mouse models of neurological diseases, and as a dataset for the development of novel analysis tools. In the future, we will continue to collect high-resolution data to add to our current database and pursue correlations between information provided by histology and MRI.
- Mouse brain development studied using MRI/DTI
- MRI/DTI atlas and database of developing mouse brain
- Characterization of anatomical changes in mouse brain development