Abstract
We present a novel group-wise registration method for cortical correspondence for local cortical thickness analysis in human and non-human primate neuroimaging studies. The proposed method is based on our earlier template based registration that estimates a continuous, smooth deformation field via sulcal curve-constrained registration employing spherical harmonic decomposition of the deformation field. This pairwise registration though results in a well-known template selection bias, which we aim to overcome here via a group-wise approach. We propose the use of an unbiased ensemble entropy minimization following the use of the pairwise registration as an initialization. An individual deformation field is then iteratively updated onto the unbiased average. For the optimization, we use metrics specific for cortical correspondence though all of these are straightforwardly extendable to the generic setting: The first focused on optimizing the correspondence of automatically extracted sulcal landmarks and the second on that of sulcal depth property maps. We further propose a robust entropy metric and a hierarchical optimization by employing spherical harmonic basis orthogonality. We also provide the detailed methodological description of both our earlier work and the proposed method with a set of experiments on a population of human and non-human primate subjects. In the experiment, we have shown that our method achieves superior results on consistency through quantitative and visual comparisons as compared to the existing methods.
Highlights
In neuroimaging studies, group analysis of local, cortical properties is a key step to investigate disease patterns, brain growth, and group variability (Rosas et al, 2008; Chouinard-Decorte et al, 2014; Zielinski et al, 2014)
We proposed cortical registration via spherical harmonic decomposition of the deformation field (Lyu et al, 2013a)
Our work presented here is based on our previous pairwise cortical correspondence method using spherical harmonic decomposition (Lyu et al, 2013a), where we use spherical harmonic decomposition to continuously represent a smooth deformation field over the sulcal landmarks and sulcal depth maps
Summary
Group analysis of local, cortical properties is a key step to investigate disease patterns, brain growth, and group variability (Rosas et al, 2008; Chouinard-Decorte et al, 2014; Zielinski et al, 2014). Group-wise cortical correspondence human and non-human primates are both complex as well as highly variable across subjects. Due to its high inter-subject variability, for consistent cortical correspondence it is critical to choose invariant anatomical/geometric features. With an inclusion of anatomical characteristics, anatomical landmarks often provide well established correspondence with little ambiguity, as compared to the intrinsic geometric properties such as local curvature on the cortex. In this sense, sulcal fundic regions are relatively invariant and stable across a population so have been widely used as robust features for cortical registration. Several studies proposed sulcal fundic region recognition in recent studies (Mangin et al, 2004; Lyu et al, 2010; Seong et al, 2010), which is further employed as critical features on cortical correspondence in several articles (Thompson and Toga, 1996; Durrleman et al, 2007; Lui et al, 2010; Joshi et al, 2012; Auzias et al, 2013; Tsui et al, 2013; Lyu et al, 2013a)
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