Riemannian Nonlinear Mixed Effects Models: Analyzing Longitudinal Deformations in Neuroimaging

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Abstract

Statistical machine learning models that operate on manifold-valued data are being extensively studied in vision, motivated by applications in activity recognition, feature tracking and medical imaging. While non-parametric methods have been relatively well studied in the literature, efficient formulations for parametric models (which may offer benefits in small sample size regimes) have only emerged recently. So far, manifold-valued regression models (such as geodesic regression) are restricted to the analysis of cross-sectional data, i.e., the so-called ``fixed effects'' in statistics. But in most ``longitudinal analysis'' (e.g., when a participant provides multiple measurements, over time) the application of fixed effects models is problematic. In an effort to answer this need, this paper generalizes non-linear mixed effects model to the regime where the response variable is manifold-valued, i.e., f:R^d -> M. We derive the underlying model and estimation schemes and demonstrate the immediate benefits such a model can provide --- both for group level and individual level analysis --- on longitudinal brain imaging data. The direct consequence of our results is that longitudinal analysis of manifold-valued measurements (especially, the symmetric positive definite manifold) can be conducted in a computationally tractable manner.

Acknowledgments

This research was supported in part by NIH grants AG040396, AG021155, BRAIN Initiative R01-EB022883, UW ADRC AG033514, UW ICTR 1UL1RR025011, Waisman IDDRC U54-HD090256, UW CPCP AI117924, and NSF grants: CAREER award 1252725 (VS), IIS 1525431 (BCV). We like to thank Andrew Schoen for providing modifications of ANTs software for execution on Condor.