Matrix decomposition for modeling lesion development processes in multiple sclerosis

Menghan Hu; Ciprian Crainiceanu; Matthew K Schindler; Blake Dewey; Daniel S Reich; Russell T Shinohara; Ani Eloyan

doi:10.1093/biostatistics/kxaa016

Matrix decomposition for modeling lesion development processes in multiple sclerosis

Biostatistics. 2022 Jan 13;23(1):83-100. doi: 10.1093/biostatistics/kxaa016.

Authors

Menghan Hu¹, Ciprian Crainiceanu², Matthew K Schindler³, Blake Dewey⁴, Daniel S Reich⁵, Russell T Shinohara⁶, Ani Eloyan¹

Affiliations

¹ Department of Biostatistics, Brown University, Providence, RI 02903, USA.
² Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA.
³ Translational Neuroradiology Section, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
⁴ Translational Neuroradiology Section, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA and Department of Electrical and Computer Engineering, Johns Hopkins Whiting School of Engineering, Baltimore, MD 21218, USA.
⁵ Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892, USA.
⁶ Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA and Department of Radiology, Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

Our main goal is to study and quantify the evolution of multiple sclerosis lesions observed longitudinally over many years in multi-sequence structural magnetic resonance imaging (sMRI). To achieve that, we propose a class of functional models for capturing the temporal dynamics and spatial distribution of the voxel-specific intensity trajectories in all sMRI sequences. To accommodate the hierarchical data structure (observations nested within voxels, which are nested within lesions, which, in turn, are nested within study participants), we use structured functional principal component analysis. We propose and evaluate the finite sample properties of hypothesis tests of therapeutic intervention effects on lesion evolution while accounting for the multilevel structure of the data. Using this novel testing strategy, we found statistically significant differences in lesion evolution between treatment groups.

Keywords: Analysis of variance; Functional data; Functional principal component analysis; Hierarchical data; Hypothesis testing; Magnetic resonance imaging.

Publication types

Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural

MeSH terms

Brain
Humans
Magnetic Resonance Imaging / methods
Multiple Sclerosis* / diagnostic imaging
Principal Component Analysis

Abstract

Publication types

MeSH terms

Grants and funding