INTRODUCTION

MR techniques have delivered images of brains from a wide array of species, ranging from invertebrates to birds to elephants and whales. However, their potential to serve as a basis for comparative brain morphometric investigations has rarely been tapped so far (Christidis and Cox, 2006; Van Essen & Dierker, 2007), which also hampers a deeper understanding of the mechanisms behind structural alterations in neurodevelopmental disorders (Kochunov et al., 2010). One of the reasons for this is the lack of computational tools suitable for morphometrci comparisons across multiple species. In this work, we aim to characterize this gap, taking primates as an example.

METHODS

Using a legacy dataset comprising MR scans from eleven species of haplorhine primates acquired on the same scanner (Rilling & Insel, 1998), we tested different automated processing streams, focusing on denoising and brain segmentation. Newer multi-species datasets are not currently available, so our experiments with this decade-old dataset (which had a very low signal-to-noise ratio by contemporary standards) can serve to highlight the lower boundary of the current possibilities of automated processing pipelines. After manual orientation into Talairach space, an automated bias correction was performed using CARET (Van Essen et al., 2001) before the brains were extracted with FSL BET (Smith, 2002; Fig. 1) and either smoothed by an isotropic Gaussian Kernel, FSL SUSAN (Smith, 1996), an anisotropic diffusion filter (Perona & Malik, 1990), an optimized Rician non-local means filter (Gaser & Coupé, 2010), or not at all (Fig. 2 & 3). Segmentation of the brains (Fig. 2 & 4) was performed separately by either FSL FAST (Zhang, 2001) without atlas priors, or using an Adaptive Maximum A Posteriori Approach (Rajapakse et al., 1997). Finally, the white matter surface was extracted with CARET, and inspected for anatomical and topological correctness. 

RESULTS

Figure 3 shows that noise reduction was generally necessary but, at least for these noisy data, anisotropic filtering (SUSAN, diffusion filter, Rician filter) provided little improvement over simple isotropic filtering. While several segmentations worked well in individual species, our focus was on cross-species optimization of the processing pipeline, and none of the tested segmentations performed uniformly well in all 11 species. The performance could be improved by some of the denoising approaches and by deviating systematically from the default parameters recommended for processing human brains (cf. Fig. 4). Depending on the size of the brains and on the processing path, it took a double-core 2.4GHz iMac from about two minutes (squirrel monkeys) to half an hour (humans) to generate the white matter surface from the T1 image. Nonetheless, the resulting surfaces always necessitated topology correction and - often considerable - manual cleanup. 


CONCLUSIONS

Automated processing pipelines for surface-based morphometry still require considerable adaptations to reach optimal performance across brains of multiple species, even within primates (cf. Fig. 5). However, most contemporary datasets have a better signal-to-noise ratio than the ones used here, which provides for better segmentations and cortical surface reconstructions. Considering further that cross-scanner variability is well below within-species differences (Stonnington, 2008), the prospects look good for comparative evolutionary analyses of cortical parameters, and gyrification in particular. In order to succeed, however, computational efforts on comparative morphometry depend on high-quality imaging data from multiple species being more widely available.

ACKNOWLEDGMENTS

D.M, R.D, & C.G are supported by the German BMBF grant 01EV0709.


REFERENCES

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