Cortical thickness measured from MRI in the YAC128 mouse model of Huntington's disease
Introduction
A recent study by our group examined differences between the YAC128 mouse model of Huntington's Disease (HD) and wild-type mice using deformation-based analyses of high-resolution post-mortem MR scans (Lerch et al., 2008). Along with the shrinkage of the striatum (Vonsattel et al., 1985), we found a volume increase in the sensorimotor cortex, a finding mirrored by a study showing that prodromal human HD also features increased cortical thickness (Paulsen et al., 2006) and exhibits compensatory functional responses in the thalamocortical circuit (Feigin et al., 2006).
The tools to investigate shape changes in the mouse cortex are, however, still limited. Histology and stereology-based techniques, while extremely powerful and capable of providing cellular level detail, are labour intensive and thus do not easily produce whole cortex coverage. They are also subject to tissue shrinkage and distortion from sectioning.
MR-based assessment of murine brain shape has recently gained increasing traction (Badea et al., 2007, Pitiot et al., 2007, Ma et al., 2005). The dominant methodology involves automated non-linear alignment of all examined mice towards a common space and is followed by either statistical analyses of the deformation fields or mapping an anatomical atlas back to each animal to measure structure volumes (Ma et al., 2005, Kovacevic et al., 2005, Chen et al., 2006). Among anatomical measurements, the thickness of the cortex in particular is endowed with biological significance, given that this measure can approximate the path taken by the cortical column, the prime computational unit of the cerebral cortex.
The last decade has produced a field of research based on accurately extracting shape models of the human cortex from MR images. As these techniques have become more sophisticated, intriguing new insights into the anatomy of the cortex have been gained, including mapping the pattern of thickening and thinning during development (Sowell et al., 2004) and ageing (Salat et al., 2004), in degenerative (Lerch et al., 2005) and psychiatric disorders (Shaw et al., 2006b), and in relationships between anatomy and behaviour (Shaw et al., 2006a, Walhovd et al., 2006).
Algorithms to measure cortical thickness from MRI follow one of four prototypes. The simplest, and rarest due to the labour intensive nature of the task, are manual measures using the digital equivalent of callipers (Ross et al., 2001, Meyer et al., 1996). A second set of methods which has gained increasing prominence involves the extraction of the inner and outer cortical surfaces using deformable models (MacDonald et al., 2000, Fischl and Dale, 2000, Kim et al., 2005). A third set of methods works without explicit polyhedral models, instead computing image boundaries to find the cortex then measuring the thickness of the cortex at the voxel level (Jones et al., 2000, Preul et al., 2005). The last type involves a mix of explicit surface parameterisation and voxel-based cortical thickness methods (Thompson et al., 2005, Miller et al., 2000).
The human cortex is convoluted with the folding pattern differing considerably between individuals. The mouse cortex, on the other hand, is lissencephalic and, given the similarities within inbred strains, almost identical among individuals. This key difference between mice and humans simplifies the creation of a cortical thickness algorithm considerably:
1. The complex deformable model process employed to map the convoluted human cortex is not necessary for the mouse.
2. The similarity across animals allows for near perfect image registration, a task still impossible within human populations.
3. The ability to compute accurate registrations implies that the definition of the cortex and its boundaries can be created by mapping a pre-existing atlas to each animal.
These points suggest that a volume-based cortical thickness algorithm with boundary definitions gained from registration towards an anatomical atlas will be the easiest solution. Explicit surface parameterisation does, however, provide one key advantage: the creation of a surface coordinate system with a controllable number of nodes. Rather than comparing cortical thickness at every voxel, thickness statistics can instead be computed on a much smaller set of vertices over the cortical surface, leading to obvious advantages in controlling for multiple comparisons. Moreover, it allows the data to be smoothed along the manifold, an anatomically more sensible operation than using volumetric Gaussian kernels. A combined method, using voxel-based cortical thickness measures together with a surface coordinate system, suggests itself as an ideal choice of algorithm. It has the further advantage that every voxel in the cortex is part of a path between the inner and outer cortical surface.
The goal of this study, motivated by the intriguing finding of shape differences in the YAC128 Huntington Disease model mice as well as the lack of available tools to investigate the mouse cortex, is to design a framework for automatically measuring cortical thickness from mouse MRI and to use these new tools to further investigate the YAC128 mouse model. These new methods encompass a combination of mouse registration algorithms with adaptations of tools designed to measure cortical thickness from human MRI scans.
Section snippets
Methods
The proposed method estimates the thickness of the entire cortical manifold. The basic procedure used follows: (1) MRIs of all mice in the study are acquired. (2) A model independent consensus representation of all mice in the study is created using linear and non-linear registration techniques. (3) A previously existing atlas with an associated segmentation of the cortex is then mapped to the result of step 2. (4) The remapped cortical boundaries are then in turn mapped to each of the
Variance and power analysis
In order to understand the cortical analysis system described herein, a group of 20 male C57BL/6 mice, first described in Spring et al. (2007), were processed. The goal was to gain an understanding of the variance inherent in the thickness and surface area measures and thus know their statistical power.
The results are shown in Fig. 3. The mean cortical thickness in these mice was 0.89 mm ± 0.016 mm. Given two groups of 10 mice, one can expect to recover a 0.05 mm difference in cortical thickness
YAC128 mouse model
HD is a neurodegenerative disorder characterised by motor dysfunction, psychiatric disturbances and cognitive impairment that is caused by a CAG trinucleotide expansion in the HD gene on chromosome 4 (Huntington's Collaborative Research Group, 1993). HD is classically associated with initial reduction in volume and neuronal loss primarily localised to the striatum (Vonsattel et al., 1985), but widespread brain atrophy has been described in early to mid-stage HD in humans using magnetic
Conclusions
Here we have presented an automated algorithm for measuring cortical thickness at every point of the cortex from mouse MRI. This technique can greatly compliment the existing set of tools for analysing differences in image deformations by providing a more biologically relevant index of cortical change. The application to the YAC128 mouse model of Huntington's Disease showed an increase in cortical thickness in response to striatal degeneration, a potential compensatory response also seen in
Acknowledgments
The authors would like to thank Dr. Claude Lepage of the Montreal Neurological Institute for valuable fixes to the code. This work was supported by grants from the Michael Smith Foundation for Health Research, the Canadian Institutes of Health Research, the Huntington's Disease Society of America and the High Q Foundation. J.P.L is supported by the Canadian institutes for Health Research. J.B.C is supported by the Huntington Society of Canada. M.R.H. is supported by the Canadian Institutes of
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