Multimodal surface matching with higher-order smoothness constraints

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.

Microstructural and Functional Gradients are Increasingly Dissociated in Transmodal Cortices

“…Preprocessed images were nonlinearly registered to MNI152 space and cortical surfaces were extracted using FreeSurfer 5.3.0-HCP [110][111][112], with minor modifications to incorporate both T1w and T2w [18]. Cortical surfaces in individual participants were aligned using MSMAll [113,114] to the hemisphere-matched conte69 template [115]. T1w images were divided by aligned T2w images to produce a single volumetric T1w/T2w image per subject (Glasser and Van Essen, 2011).…”

A cortical wiring space links cellular architecture, functional dynamics and hierarchies in humans

“…BOLD timeseries were corrected for gradient nonlinearity, head motion, bias field and scanner drifts, then subjected to ICA-FIX for removal of additional noise 98 . The rs-fMRI data were transformed to native space and timeseries were sampled at each vertex of the MSMAll registered midthickness cortical surface 99,100 .…”

Determining the Hierarchical Architecture of the Human Brain Using Subject-Level Clustering of Functional Networks

“…The HCP minimal processing pipeline was used Smith et al, 2013). Briefly, this included projection to the surface space, 2 mm FWHM smoothing, ICA+FIX denoising with minimal high-pass filtering, and surface registration using MSMall (Jenkinson et al, 2002(Jenkinson et al, , 2012Fischl, 2012;Robinson et al, 2014Robinson et al, , 2018Griffanti et al, 2014;Salimi-Khorshidi et al, 2014). To define our ROIs, we used a newly-developed multimodal parcellation (MMP) (Glasser et al, 2016a).…”