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Depression is a highly prevalent disorder, affecting more than 121 million people worldwide (Reines et al. 2008). Chronic stress acts as a triggering factor to induce dysfunction of the hypothalamic-pituitary-adrenocortical axis and the subsequent release of glucocorticoids. This results in increased Received January 12, 2011; revised receptor antagonist RS 39604 alone; whereas, the simultaneous application of both antagonists completely reversed the effect. Curcumin was also found to regulate corticosteroneinduced morphological changes such as increases in soma size, dendritic branching and dendritic spine density, as well as elevate synaptophysin expression in cortical neurons. p-MPPI and RS 39604 reversed the effect of curcumin-induced change in neuronal morphology and synaptophysin expression of corticosterone-treated neurons. In addition, an increase in cyclic adenosine monophosphate (cAMP) level was observed after curcumin treatment, which was further prevented by RS 39604, but not by p-MPPI. However, curcumin-induced elevation in protein kinase A activity and phosphorylation of cAMP response element-binding protein levels were inhibited by both p-MPPI and RS 39604. These findings suggest that the neuroprotection and modulation of neuroplasticity exhibited by curcumin might be mediated, at least in part, via the 5-HT receptor-cAMP-PKA-CREB signal pathway.

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The application of data-driven time series analysis techniques such as Granger causality, partial directed coherence and phase dynamics modeling to estimate effective connectivity in brain networks has recently gained significant prominence in the neuroscience community. While these techniques have been useful in determining causal interactions among different regions of brain networks, a thorough analysis of the comparative accuracy and robustness of these methods in identifying patterns of effective connectivity among brain networks is still lacking. In this paper, we systematically address this issue within the context of simple networks of coupled spiking neurons. Specifically, we develop a method to assess the ability of various effective connectivity measures to accurately determine the true effective connectivity of a given neuronal network. Our method is based on decision tree classifiers which are trained using several time series features that can be observed solely from experimentally recorded data. We show that the classifiers constructed in this work provide a general framework for determining whether a particular effective connectivity measure is likely to produce incorrect results when applied to a dataset.