Causality Analysis and Cell Network Modeling of Spatial Calcium Signaling Patterns in Liver Lobules

Dynamics as well as localization of Ca2+ transients plays a vital role in liver function under homeostatic conditions, repair, and disease. In response to circulating hormonal stimuli, hepatocytes exhibit intracellular Ca2+ responses that propagate through liver lobules in a wave-like fashion. Although intracellular processes that control cell autonomous Ca2+ spiking behavior have been studied extensively, the intra- and inter-cellular signaling factors that regulate lobular scale spatial patterns and wave-like propagation of Ca2+ remain to be determined. To address this need, we acquired images of cytosolic Ca2+ transients in 1300 hepatocytes situated across several mouse liver lobules over a period of 1600 s. We analyzed this time series data using correlation network analysis, causal network analysis, and computational modeling, to characterize the spatial distribution of heterogeneity in intracellular Ca2+ signaling components as well as intercellular interactions that control lobular scale Ca2+ waves. Our causal network analysis revealed that hepatocytes are causally linked to multiple other co-localized hepatocytes, but these influences are not necessarily aligned uni-directionally along the sinusoids. Our computational model-based analysis showed that spatial gradients of intracellular Ca2+ signaling components as well as intercellular molecular exchange are required for lobular scale propagation of Ca2+ waves. Additionally, our analysis suggested that causal influences of hepatocytes on Ca2+ responses of multiple neighbors lead to robustness of Ca2+ wave propagation through liver lobules

Predicting Brain Age Based on Spatial and Temporal Features of Human Brain Functional Networks

The organization of human brain networks can be measured by capturing correlated brain activity with functional MRI data. There have been a variety of studies showing that human functional connectivities undergo an age-related change over development. In the present study, we employed resting-state functional MRI data to construct functional network models. Principal component analysis was performed on the FC matrices across all the subjects to explore meaningful components especially correlated with age. Coefficients across the components, edge features after a newly proposed feature reduction method as well as temporal features based on fALFF, were extracted as predictor variables and three different regression models were learned to make prediction of brain age. We observed that individual's functional network architecture was shaped by intrinsic component, age-related component and other components and the predictive models extracted sufficient information to provide comparatively accurate predictions of brain age