Customizing deep brain stimulation to the patient using computational models.
McIntyre CC, Frankenmolle AM, Wu J, Noecker AM, Alberts JL. Customizing deep brain stimulation to the patient using computational models. , Conf Proc IEEE Eng Med Biol Soc. 2009; 2009:4228-9.
The purpose of this article was to compare DBS STN stimulator settings on cognitive and motor tasks in people with Parkinson’s disease (PWP) that were created on either a clinician’s skill and experience or settings generated by a computerized model. One problem with DBS stimulator settings is that there are 1000’s of possible settings and it is not feasible (time or discomfort) for clinicians or PWPs to go through all of the possibilities in order to find the “perfect” setting. Therefore, it would be ideal to find the least invasive and most efficient method to set stimulator parameters for the best possible motor improvement and minimization of cognitive dysfunction for PWP.
The study found that utilizing the computer based model resulted in less power used by the stimulator and the same amount of improvement in motor scores as found in the model created by the clinician. When they looked specifically at the cognitive tasks they found no difference between the two models when tasks were easy. However, as complexity increased, when the computer generated stimulator settings were used, the PWP did better than when the settings were generated by clinicians alone. The authors suggest that the difference was caused by the DBS stimulation spreading to areas adjacent to the motor portion of the STN, which then disrupts the full potential of one’s cognitive abilities. A goal of the computer based model would then be to prevent spreading of the DBS stimulation to areas that are involved in cognitive functions while at the same time stimulating areas to maximize motoric treatment efficacy. The authors suggest that based on their results, the computer based models should be used to supplement the stimulator parameter selection along with the clinician in order to best optimize the efficacy of the DBS STN.