The effect of long-term corticosterone treatment on blood cell differentials and function in laboratory and wild-caught amphibian models.

The effect of long-term stress on amphibian immunity is not well understood. We modeled a long-term endocrine stress scenario by elevating plasma corticosterone in two species of amphibians and examined effects on white blood cell differentials and innate immune activity. Plasma corticosterone was elevated in American bullfrogs (Lithobates catesbeianus) by surgically implanting corticosterone capsules and in African clawed frogs (Xenopus laevis) by immersion in corticosterone-treated water. To provide a context for our results within endogenous corticosterone fluctuations, diurnal plasma corticosterone cycles were determined. A daily low of corticosterone was observed in X. laevis at 12:00, while a significant pattern was not observed in L. catesbeianus. Elevated plasma corticosterone levels increased the ratio of peripheral neutrophils to lymphocytes, in both species, and decreased eosinophil concentrations in L. catesbeianus over a long-term period. Whole blood oxidative burst generally correlated with neutrophil concentrations, and thus was increased with corticosterone treatment, significantly in L. catesbeianus. In L. catesbeianus, an endogenous response of eosinophils and lymphocytes to implanted empty (sham) capsules was observed, but this effect was attenuated by corticosterone. Peripheral monocyte and basophil concentrations were not significantly altered by corticosterone treatment in either species. Our results show that long-term stress can alter amphibian immune parameters for extended periods and may play a role in susceptibility to disease.

Information Flow Between Resting-State Networks.

UNLABELLED: The resting brain dynamics self-organize into a finite number of correlated patterns known as resting-state networks (RSNs). It is well known that techniques such as independent component analysis can separate the brain activity at rest to provide such RSNs, but the specific pattern of interaction between RSNs is not yet fully understood. To this aim, we propose here a novel method to compute the information flow (IF) between different RSNs from resting-state magnetic resonance imaging. After hemodynamic response function blind deconvolution of all voxel signals, and under the hypothesis that RSNs define regions of interest, our method first uses principal component analysis to reduce dimensionality in each RSN to next compute IF (estimated here in terms of transfer entropy) between the different RSNs by systematically increasing k (the number of principal components used in the calculation). When k=1, this method is equivalent to computing IF using the average of all voxel activities in each RSN. For k≥1, our method calculates the k multivariate IF between the different RSNs. We find that the average IF among RSNs is dimension dependent, increasing from k=1 (i.e., the average voxel activity) up to a maximum occurring at k=5 and to finally decay to zero for k≥10. This suggests that a small number of components (close to five) is sufficient to describe the IF pattern between RSNs. Our method--addressing differences in IF between RSNs for any generic data--can be used for group comparison in health or disease. To illustrate this, we have calculated the inter-RSN IF in a data set of Alzheimer's disease (AD) to find that the most significant differences between AD and controls occurred for k=2, in addition to AD showing increased IF w.r.t. CONTROLS: The spatial localization of the k=2 component, within RSNs, allows the characterization of IF differences between AD and controls.