Patient-Specific Modeling of Deep Brain Stimulation

Deep Brain Stimulation

Deep brain stimulation (DBS) is a powerful clinical technology, positively affecting the lives of well over 100,000 patients worldwide. DBS currently has various forms of government regulatory approvals for the treatment of Parkinson disease (PD) ( ), essential tremor (ET) ( ), dystonia ( ), obsessive-compulsive disorder (OCD) ( ), and epilepsy ( ). However, for all of the clinical successes of DBS, numerous scientific questions remain on its therapeutic mechanisms and effects on the nervous system ( ).

It is commonly assumed that the fundamental purpose of DBS is to modulate pathological neural activity within targeted brain circuits ( ). However, quantitative details on which neurons are directly stimulated, the anatomical connections of those stimulated neurons, and the resulting synaptic effects of those stimulated neurons on the targeted brain circuits remain limited. In addition, DBS devices are capable of delivering thousands of different stimulation settings, where each parameter alteration can modify the neural response to the therapy. Fortunately, guidelines do exist for general stimulation parameter settings that are typically effective ( ), but it is infeasible to clinically evaluate the complete range of stimulation parameter combinations that may be useful to a given patient. As a result, the therapeutic benefit currently achievable with DBS is strongly dependent on the surgical placement accuracy of the DBS electrode and the intuitive skill of the clinician performing the stimulation parameter selection ( ).

An important and necessary step forward for more wide-scale use of DBS therapies is the development of assistive technologies that optimize and/or ease clinical implementation of the devices. Along that line, computational modeling is playing an important role in new developments to improve both electrode placement and stimulation parameter selection in DBS patients. Stereotactic neurosurgical navigation has a long history of relying on computational models to help identify target coordinates in the brain for electrode placement based on the patient’s medical imaging and intraoperative neurophysiological data (e.g., ). More recently, software technologies have been designed to assist clinicians in identifying therapeutic stimulation parameter settings customized to each patient (e.g., ). Such tools are leveraging the growing computational power available to DBS clinicians in the hospital, as well as the improved opportunities for data sharing across clinical divisions (e.g., radiology, neurosurgery, and neurology).