Depression is a common and hazardous mental disorder, which has been pathophysiologically associated with alterations of neurocircuitries involving medial prefrontal cortex, hippocampus and thalamus. Recent progress in computational neuroscience, particularly in the field of in silico psychopharmacology suggests the increasing potential of mathematical modeling in providing insights on the dynamics of these neuronal networks, which in turn may lead to further develop and clarify the present models of the pathophysiology of depression. Moreover, computational approaches provide well-defined non-invasive frameworks for investigation of the clinically common poly-pharmacological treatment strategies, which take us one step closer to the development of novel agents that will potentially result in diagnostic and prognostic indicators to be used in individualized treatment strategies.
展开▼
