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Modern brain stimulation research partly relies on experiments to predict the effects of electric fields on the brain, and partly on computer models. The data gathered from experiments is used to set up the models, and the models can then be used to further study the behaviour of for example neuronal networks. Hence, the electric field shape and strength is of paramount importance to further develop clinical treatments. In one of our studies, we modeled the electric fields in experimental settings. We found that several assumptions that are often done in literature regarding the electric field distribution and strength were too optimistic. We made some suggestions to finetune the experimental methodologies that are often used in the lab. 

A second research focused on the influence of electric fields on neurons. Neurons talk in the form of action potentials, or spikes. Currently, state-of-the-art science only has a vague idea of what they are saying, or where the information is coded. We are not neurobiologists, so we simply cannot interpret the neuronal signals. However, we did want to know whether an electric field interferes with the signal processing of a neuron. Hence, we developed a descriptive statistical methodology that described the changes in neuronal signal processing without knowing what said changes meant. To do so, we injected neurons with electric currents to evoke a response, and compared the responses of a neuron in the presence and absence of an electric field.

We found that negative electric fields and positive electric fields have different effects on the activity of neurons. The effect of a negative electric field, for instance, appeared to be larger and more predictable than of a positive electric field. These findings help to finetune clinical brain stimulation treatments in the long run.