Multimodal Surface Matching with Higher-Order Smoothness Constraints☆

By Emma Claire Robinson, K. Garcia, Matthew F. Glasser, Zhengdao Chen, Timothy S. Coalson, Antonios Makropoulos, Jelena Bozek, Robert Wright, Andreas Schuh, Matthew A. Webster and 13 more. bioRxiv, 2017

312 citations recorded by the source corpus.

In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies; and cortical surface-based alignment has generally been accepted to be superior to volume-based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross-subject surface alignment, using areal features, such as resting state-networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSM's regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neu-robiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 32 and 45 weeks of post-menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population-based analysis relative to other spherical methods.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8ba63f59392a8dea06abede83f7e8c716cb8afc6

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The impact of traditional neuroimaging methods on the spatial localization of cortical areas (2018, methodology): “MSMAll has previously been shown to provide major improvements in independent task fMRI data that were not used in the registration (20, 21) over MSMSulc.”
neuromaps: structural and functional interpretation of brain maps (2022, background): “, registration fusion and multimodal surface matching) which have been rigorously assessed and validated on other datasets [62, 63, 90].”
The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction (2017, background): “Longitudinal deformation studies that have been performed using this data indicate that the patterns of deformation observed are biologically plausible Robinson et al. (2017).”