SMOOTH PRINCIPAL COMPONENT ANALYSIS OVER TWO-DIMENSIONAL MANIFOLDS WITH AN APPLICATION TO NEUROIMAGING

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 twodimensional manifold. For this purpose a regularization approach is adopted, introducing...

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Bibliographic Details
Published in:The annals of applied statistics 2016-12, Vol.10 (4), p.1854-1879
Main Authors: Lila, Eardi, Aston, John A. D., Sangalli, Laura M.
Format: Article
Language:English
Subjects:
Data smoothing
Magnetic resonance imaging
Mathematical functions
Mathematical manifolds
Mathematical surfaces
Mathematical vectors
Principal components analysis
Riemann manifold
Time series
Triangulation
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Summary: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 twodimensional 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, and 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.
ISSN:1932-6157
DOI:10.1214/16-AOAS975