Deep brain stimulation (DBS) is a technique in which mild electrical pulses are applied to brain tissue to disrupt pathological activity. Existing DBS devices have four large (typically 6 mm2), cylindrically shaped, electrodes to deliver the pulses to the tissue. This results in a very non-specific delivery of the electrical energy to the tissue. The DBS targets can be as small as 1 mm or less and the stimulation currents cannot be accurately targeted to such small features with these existing DBS devices. This non-specific delivery has several drawbacks. It may, for example, lead to induction of side-effects due to current exciting tissues adjacent to target areas. Additionally, it may result in sub-optimal therapeutic effects due to current not optimally covering the target structures. New DBS devices under development may address this issue by providing large numbers of electrodes (e.g. 16-128) distributed along the probe in an array-like fashion (axially and azimuthally). By having such a high-density array of stimulation electrodes in principle very accurate delivery of electrical stimuli is possible, improving the mentioned short-comings of the larger electrodes.
With so many stimulation electrodes it is important to distribute the electrical stimuli optimally. The optimization problem for finding the best distribution of stimuli over the array is highly complex due to the enormous degrees of freedom offered by the new high-resolution DBS devices. Therefore, there is a need for a practical and reliable method to quickly determine the optimum settings. Model-based optimization approaches are being developed for this purpose. However, these approaches suffer from intrinsic limitations since not all parameters are and can be known in sufficient detail (most notably the local inhomogeneous and anisotropic conductivity distributions strongly influence the field but are very hard to accurately measure). Hence inaccuracy remains with these approaches.