Biasing neural network dynamics using non-invasive brain stimulation

Recently, non-invasive brain stimulation (NBS) has been discovered as a tool to improve human performance on a wide variety of tasks. Although these observations are highly intriguing, the underlying mechanisms of such enhancements are still poorly understood. Here, we argue that in order to advance understanding of these mechanisms, it is necessary to focus on intrinsic network dynamics in the brain. Taking into account well-known network dynamics, increased excitation in one particular network or brain region may necessarily lead to inhibition of an opposing network (and vice versa). As a consequence, observed behavioral improvements due to NBS emerge from a shift in the balance between (competing) neural networks in the brain, implicating that behavioral enhancement due to stimulation most likely comes with a cost or side effect. We conclude that more elaborate experimental designs are essential for a better understanding of the relationship between network interactions and the behavioral effects of NBS.