Assessing dynamic functional connectivity in heterogeneous samples

摘要: Several methods have been developed to measure dynamic functional connectivity (dFC) in fMRI data. These are often based on a sliding-window analysis, which aims capture how the brain9s organization varies over course of scan. The aim many studies is compare dFC across groups, such as younger versus older people. However, spurious group differences measured may be caused by other sources heterogeneity between For example, shape haemodynamic response function (HRF) and levels measurement noise found vary with age. We use generic simulation framework for data investigate effect estimates dFC. Our findings show that, despite no true dFC, individual can result from (non-dynamic) features data, neural autocorrelation, HRF shape, strength noise. also find that common k-means multilayer modularity approaches detect due inappropriate setting their hyperparameters. therefore need consider alternative individuals before concluding