Smooth Principal Component Analysis over two-dimensional manifolds with an application to Neuroimaging

TL;DR

This paper introduces a novel smoothing PCA method tailored for high-dimensional neuroimaging data on cortical surfaces, effectively handling manifold topology, missing data, and varying sampling grids, demonstrated on HCP fMRI data.

Contribution

The paper presents a new PCA technique for data on 2D manifolds with a geodesic-based smoothing penalty, applicable to any topology and capable of managing missing data and different sampling grids.

Findings

Method reveals differential brain region variations not seen with traditional approaches.

Algorithm outperforms classical methods in efficiency and accuracy.

Application to HCP data demonstrates practical utility in neuroimaging analysis.

Abstract

Motivated by the analysis of high-dimensional neuroimaging signals located over the cortical surface, we introduce a novel Principal Component Analysis technique that can handle functional data located over a two-dimensional manifold. For this purpose a regularization approach is adopted, introducing a smoothing penalty coherent with the geodesic distance over the manifold. The model introduced can be applied to any manifold topology, can naturally handle missing data and functional samples evaluated in different grids of points. We approach the discretization task by means of finite element analysis and propose an efficient iterative algorithm for its resolution. We compare the performances of the proposed algorithm with other approaches classically adopted in literature. We finally apply the proposed method to resting state functional magnetic resonance imaging data from the Human Connectome Project, where the method shows substantial differential variations between brain regions that were not apparent with other approaches.