TY - JOUR
T1 - Matrix decomposition for modeling lesion development processes in multiple sclerosis
AU - Hu, Menghan
AU - Crainiceanu, Ciprian
AU - Schindler, Matthew K.
AU - Dewey, Blake
AU - Reich, Daniel S.
AU - Shinohara, Russell T.
AU - Eloyan, Ani
N1 - Publisher Copyright:
© 2020 The Author 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
PY - 2022/1/1
Y1 - 2022/1/1
N2 - 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.
AB - 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.
KW - Analysis of variance
KW - Functional data
KW - Functional principal component analysis
KW - Hierarchical data
KW - Hypothesis testing
KW - Magnetic resonance imaging
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U2 - 10.1093/biostatistics/kxaa016
DO - 10.1093/biostatistics/kxaa016
M3 - Article
C2 - 32318692
AN - SCOPUS:85123806814
SN - 1465-4644
VL - 23
SP - 83
EP - 100
JO - Biostatistics
JF - Biostatistics
IS - 1
ER -