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Abstract:
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In recent years, longitudinal neuroimaging study has become increasingly popular in neuroscience research to investigate disease-related changes in brain functions. One important goal in longitudinal imaging analysis is to study temporal changes in brain functional networks (BFN) and its association with subjects' clinical or demographical covariates. In neuroscience literature, one of the most commonly used tools to study BFN is independent component analysis (ICA), which separates multivariate signals into linear mixture of independent components. However, existing ICA methods are not suited for modelling repeatedly measured imaging data. In this paper, we proposed a novel longitudinal independent component model (L-ICA) as the first formal statistical modeling framework that extends ICA to longitudinal setting. By incorporating subject-specific random effects and visit-specific covariate effects, L-ICA is able to provide more accurate estimates of BFNs, borrow information within the same subject to increase statistical power, and allow for model-based prediction. Simulation and real data analysis results demonstrate the advantages of our proposed methods.
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