  In recent years, longitudinal neuroimaging study has become increasingly
popular in neuroscience research to investigate disease-related changes in
brain functions. In current neuroscience literature, one of the most commonly
used tools to extract and characterize brain functional networks is independent
component analysis (ICA). However, existing ICA methods are not suited for
modelling repeatedly measured imaging data. In this paper, we propose a novel
longitudinal independent component model (L-ICA) which provides a formal
modeling framework for extending ICA to longitudinal studies. By incorporating
subject-specific random effects and visit-specific covariate effects, L-ICA is
able to provide more accurate estimates of changes in brain functional networks
on both the population- and individual-level, borrow information across
repeated scans within the same subject to increase statistical power in
detecting covariate effects on the networks, and allow for model-based
prediction for brain networks changes caused by disease progression, treatment
or neurodevelopment. We develop a fully traceable exact EM algorithm to obtain
maximum likelihood estimates of L-ICA. We further develop a subspace-based
approximate EM algorithm which greatly reduce the computation time while still
retaining high accuracy. Moreover, we present a statistical testing procedure
for examining covariate effects on brain network changes. Simulation results
demonstrate the advantages of our proposed methods. We apply L-ICA to ADNI2
study to investigate changes in brain functional networks in Alzheimer disease.
Results from the L-ICA provide biologically insightful findings which are not
revealed using existing methods.
