| Summary: | Deep Brain Stimulation (DBS) is a treatment of movement disorders such as Parkinson's disease and essential tremor. Today it has been used in more than 80.000 patients. Electrical stimulation is administered by an implanted pulse generator through an electrode surgically placed in a target brain area specific to the treated disease. Opposed to alternative purely surgical treatment procedures, DBS is reversible and can be turned off. In this project, the aim is to individualise an already existing computational model of DBS, but also to look at optimisation of the treatment by developing a neuron model. It has been executed the following way. To localise the target area for the electrode, Magnetic Resonance Imaging (MRI) is used. An MRI image consists of volume elements called voxels. By analysing these voxels, it is possible to set up a coordinate system for the position of different parts of the brain. To build up an individualised model of the DBS, an MRI image is segmented into tissues of different conductivity thus resulting in a more accurate description of the electrical field around the electrode. To visualize the stimuli coverage for the medical staff, the MRI image of the target area, the electrode, and the electrical field produced by the stimuli are depicted in the same figure. From the results, we can draw the conclusion that this method works well for individualising the computational model of DBS, but it has only been used on one MRI scan so far so it needs further testing to obtain more data to compare with. The neuron model is a temporospatial mathematical model of a single neuron for the prediction of activation by a given electrically applied field generated by a DBS lead. The activation model is intended to be part of a patient-specific model of an already existing computational model of DBS. The model originate from a neuron model developed by Hodgkin and Huxley (HH). The original HH model only takes into account one compartment and, to make the neuron model more accurate, it is combined with a cable model. The simulation results obtained with the model have been validated against an established and widely accepted neuron model. The results correlated highly to each other with only minor differences. To see how position and orientation impact on activation, the developed HH model was tested for different pulse widths, distances from the lead, and rotations of the neuron relative to the lead. A larger pulse width makes activation more likely and so does a larger amplitude. Thicker neurons are more likely to get activated, neurons closer to the lead and also neurons perpendicular to the lead. From the results we can draw the conclusion that this method is a good way to stimulate neural activation of a single neuron. In future research, it might be possible to compare results from the neuron model with patient's response to treatment.
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