biostudies-literatureShappell HNIA NIH HHSNINDS NIH HHSNIGMS NIH HHS196-214https://www.ebi.ac.uk/biostudies/studies/S-EPMC7731975biostudies-literatureUnknown7(2)The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Much of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models (SAOMs) to the neurosciNetwork science (Cambridge University Press)A Paradigm for Longitudinal Complex Network Analysis over Patient Cohorts in Neuroscience.PMC7731975UH2 AG064704R01 NS095369T32 GM074905Killiany RJKolaczyk EDShappell HTripodis YfalseA Paradigm for Longitudinal Complex Network Analysis over Patient Cohorts in Neuroscience.The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Much of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models (SAOMs) to the neurosci2019-01-01T00:00:00Z2019 Jun2025-04-25T22:50:34.678Z2025-04-06T09:10:09.503ZS-EPMC77319753331256610.1017/nws.2019.9