Multimodal surface matching with higher-order smoothness constraints

• Published in: NeuroImage (Orlando, Fla.) Vol. 167; pp. 453 - 465
• Main Authors: Robinson, Emma C., Garcia, Kara, Glasser, Matthew F., Chen, Zhengdao, Coalson, Timothy S., Makropoulos, Antonios, Bozek, Jelena, Wright, Robert, Schuh, Andreas, Webster, Matthew, Hutter, Jana, Price, Anthony, Cordero Grande, Lucilio, Hughes, Emer, Tusor, Nora, Bayly, Philip V., Van Essen, David C., Smith, Stephen M., Edwards, A. David, Hajnal, Joseph, Jenkinson, Mark, Glocker, Ben, Rueckert, Daniel
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.02.2018; Elsevier Limited
• Subjects: Biomechanical priors; Brain mapping; Cerebral Cortex - anatomy & histology; Cerebral Cortex - diagnostic imaging; Cerebral Cortex - growth & development; Cortex; Deformation; Discrete optimisation; Fines & penalties; Functional magnetic resonance imaging; Humans; Image Processing, Computer-Assisted - methods; Infant, Newborn; Longitudinal registration; Longitudinal Studies; Magnetic Resonance Imaging - methods; Mathematical models; Menstruation; Models, Theoretical; Myelin; Neonatal brain development; Neonates; Neuroimaging; Neuroimaging - methods; NMR; Nuclear magnetic resonance; Registration; Statistical analysis; Statistics; Surface-based cortical registration; Washington DC; United States > US; Biomechanical priors; Longitudinal registration; Neonatal brain development; Discrete optimisation; Surface-based cortical registration
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.10.037

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. •Advances the Multimodal Surface Matching (MSM) method, for cortical surface registration of cortical surfaces, by improving control over the smoothness of the deformation.•Enhances alignment of multimodal features, including the feature set used for the Human Connectome Project’s parcellation of the human cerebral cortex.•Also allows statistical modelling of longitudinal patterns of cortical growth.