Abstract
We introduce a general framework for decomposing brain function into functional brain networks for multi-subject data with repeated measurements and covariate effects. This general method provides a rigorous and much needed tool for investigating brain networks and their differences in imaging studies with complex study designs including longitudinal and/or multi-center studies. Our approach incorporates effects corresponding to data collection sites to correct for site-level biases and incorporates subject-specific effects to accommodate within-subject repeated measures such as those from longitudinal studies. Through simulations, we show that the proposed method has considerably improved performance as compared to other potential ICA-based approaches. We apply our procedure to study internalization and externalization in the longitudinal ABCD study data and demonstrate functional brain network differences that supplement previous work showing age and sex related differences.
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