If the skull fits: magnetic resonance imaging and microcomputed tomography for combined analysis of brain and skull phenotypes in the mouse

Abstract

The mammalian brain and skull develop concurrently in a coordinated manner, consistently producing a brain and skull that fit tightly together. It is common that abnormalities in one are associated with related abnormalities in the other. However, this is not always the case. A complete characterization of the relationship between brain and skull phenotypes is necessary to understand the mechanisms that cause them to be coordinated or divergent and to provide perspective on the potential diagnostic or prognostic significance of brain and skull phenotypes. We demonstrate the combined use of magnetic resonance imaging and microcomputed tomography for analysis of brain and skull phenotypes in the mouse. Co-registration of brain and skull images allows comparison of the relationship between phenotypes in the brain and those in the skull. We observe a close fit between the brain and skull of two genetic mouse models that both show abnormal brain and skull phenotypes. Application of these three-dimensional image analyses in a broader range of mouse mutants will provide a map of the relationships between brain and skull phenotypes generally and allow characterization of patterns of similarities and differences.

Fig. 1.

Schematic showing the decomposition of displacement vectors on the skull. A sketch of the outside of the skull surface is shown at left. At each location on the surface (square at left, expanded at right), the normal vector pointing out of the skull surface was defined. Displacements resulting from the registrations were then separated into 3 components: 1) displacements along the skull normal vector; 2) displacements both in the plane of the skull (that is, perpendicular to the normal vector) and pointing in the rostral-caudal direction [labeled as inplane (1) at right]; and 3) the residual displacements (perpendicular to each of the previous two vectors and thus including left-right and dorsal-ventral components in the plane of the skull). The first 2 of these, the normal component and the rostral-caudal in-plane component, are presented in detail in subsequent figures for both Gja1^Jrt and Zic1^+/−;Zic4^+/−;Shh^+/− mice.

Fig. 2.

MRI-detected brain phenotypes in the Gja1^Jrt mouse. Voxelwise volumetric changes (where FDR <0.05) are shown overlaid on the unbiased average image (shown without overlay at left for each slice). Blue corresponds to decreases in size for the mutant and red to increases. Horizontal images (top left) include green lines indicating the location of the coronal slices (shown at right). Integration of voxelwise volume changes over segmented structure volumes provided a measure of volumetric changes. Thirty-seven (37) structures of the 63 tested showed significant differences (FDR <0.05). Four structures of interest are shown in the bar graph (bottom left) with error bars showing standard SE. Volumes are shown normalized to the average control volume for each structure (shown in parentheses).

Fig. 3.

MRI-detected brain phenotypes in the Zic1^+/−;Zic4^+/−;Shh^+/− mouse. Voxelwise volumetric changes (where FDR <0.05) are shown overlaid on the unbiased average image (shown without overlay at left for each slice). Horizontal images (top left) include green lines indicating the location of the coronal slices (shown at right). Integration of voxelwise volume changes over segmented structure volumes provided a measure of volumetric changes. Thirty-seven (37) structures of the 63 tested showed significant differences (FDR <0.05). Four structures of interest are shown in the bar graph (bottom left) with error bars showing SE. Volumes are shown normalized to the control volume for each structure (shown in parentheses).

Fig. 4.

CT skull phenotypes in Gja1^Jrt and Zic1^+/−;Zic4^+/−;Shh^+/− mice. Both Gja1^Jrt (A, B, C) and Zic1^+/−;Zic4^+/−;Shh^+/− (D, E, F) mice showed significant phenotypes in the shape of the skull. Visualizations of the mutant to wild-type differences are shown mapped onto the surface of the average skull image in regions where FDR <0.05. The magnitude of average vector displacements between the control and mutant mice are shown in A and D. Average displacements normal to the skull surface are shown in B and E, with positive defined in the extracranial direction. Average displacements both in the plane of the skull surface and in the sagittal plane (predominantly rostral-caudal in direction) are shown in C and F, with positive defined in the rostral direction. The differences were more widespread in the Gja1^Jrt mice, notably including large changes in the facial region (A–C). More localized changes were evident in Zic1^+/−;Zic4^+/−;Shh^+/− mice (D–F), mostly in the vicinity of the interparietal bone and in the rostral-caudal (in-plane) direction (F).

Fig. 5.

Correspondence of MRI-brain and CT-skull phenotypes in Gja1^Jrt and Zic1^+/−;Zic4^+/−;Shh^+/− mice. Visualization of the magnitude (A, D), normal (B, E) and in-plane (C, F) displacements on the intracranial skull surface (top of each panel) and on the brain surface (bottom of each panel) show that morphometric phenotypes in the skull and brain closely correspond. This correlation is evident in both the Gja1^Jrt (A, B, C) and Zic1^+/−;Zic4^+/−;Shh^+/− (D, E, F) mutants. Positive directions are defined as extracranial (B, E) and rostral (C, F). The skull view is shown after removing one lateral half to enable view of the inside surface (the cut plane is colored a solid gray). In the Zic1^+/−;Zic4^+/−;Shh^+/− mutant, close similarities between brain and skull phenotypes are evident in all panels (D, E, F). In the Gja1^Jrt mutant, the magnitudes displacements show little change (A, FDR <0.15 and FDR <0.10 for the CT and MR data, respectively) and the normal displacements closely correspond. Two differences in the skull and brain phenotypes appear on the in-plane displacements of the Gja1^Jrt mutant (C): on the lateral side of the skull (green arrows) the CT data suggests a caudal shift larger than is present in the MRI data; and at the caudal edge of the cerebellum (red arrows) the shift in the skull and brain are in opposite directions. The latter difference evidences a shift in the cerebellum relative to the skull, such that lobe IX (inset C, purple arrowheads) extends more ventrally from the edge of the occipital plate in the mutant (inset C, blue arrowheads). All colored regions in B–F are shown with FDR <0.05. White arrows (E) indicate the location of analysis plotted in Fig. 6.

Fig. 6.

Modeling CT-skull displacements by genotype, brain structure volume, and brain surface displacement in Zic1^+/−;Zic4^+/−;Shh^+/− mice. Skull displacements were modeled as a function of brain surface displacements, brain structure volumes, and genotype. Although each independently showed significance, a model based solely on genotype was not improved by addition of MR-derived brain structure measurements. Lines of best fit are shown with 95% confidence intervals.

Fig. 7.

Structure volume comparisons. In A, the intracranial skull volume as measured by CT is compared with the brain volume measured by MRI for all mice imaged in the study, showing the 2 measures are correlated (R² = 0.8). The correlation remained in both combined and separate analyses of Gja1^Jrt and Zic1^+/−;Zic4^+/−;Shh^+/− mice (with their respective controls). In this case, MRI-derived brain volume was a stronger predictor of CT-derived intracranial volume than genotype. The line of best fit is shown with 95% confidence intervals. In B, the relative volume of the cerebellum is plotted vs. the whole brain volume for the Zic1^+/−;Zic4^+/−;Shh^+/− mice and their controls. A line of best fit with 95% confidence intervals is shown for each genotype. The notable break between the 2 genotypes indicates that the differences in relative cerebellum size cannot be attributed to allometric differences (i.e., cannot be attributed to the differences in the overall brain size).