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Functional network connectivity (FNC) is a useful measure for evaluating the temporal dependency among brain networks. Longitudinal changes of intrinsic function are of great interest, but to date there has been little focus on multivariate patterns of FNC changes with development. In this paper, we proposed a novel approach that uses FNC matrices to estimate multiple overlapping brain functional change patterns (FCPs). We applied this approach to the large-scale Adolescent Brain and Cognitive Development (ABCD) data. Results reveal several highly structured FCPs showing a significant change over a two-year period including brain functional connectivity between visual (VS) and sensorimotor (SM) domains. This pattern of FNC expression becomes stronger with age. We also found a differential pattern of changes between male and female individuals. Our approach provides a powerful way to evaluate whole brain functional changes in longitudinal data. 

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data‐driven source‐based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi‐blind extension of SBM, called constrained source‐based morphometry (constrained SBM), which enables the extraction of maximally independent reference‐alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source‐based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.