Abstract:
: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 neurobiologically 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 31 and 43 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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Robinson EC,Garcia K,Glasser MF,Chen Z,Coalson TS,Makropoulos A,Bozek J,Wright R,Schuh A,Webster M,Hutter J,Price A,Cordero Grande L,Hughes E,Tusor N,Bayly PV,Van Essen DC,Smith SM,Edwards AD,Hajnal J,Jenkinson Mdoi
10.1016/j.neuroimage.2017.10.037subject
Has Abstractpub_date
2018-02-15 00:00:00pages
453-465eissn
1053-8119issn
1095-9572pii
S1053-8119(17)30864-9journal_volume
167pub_type
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