OBJECTIVE: Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. APPROACH: Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). MAIN RESULTS: The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. SIGNIFICANCE: This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.
OBJECTIVE: Deep brain stimulation (DBS) is a growing treatment option for movement and psychiatric disorders. As DBS technology moves toward directional leads with increased numbers of smaller electrode contacts, trial-and-error methods of manual DBS programming are becoming too time-consuming for clinical feasibility. We propose an algorithm to automate DBS programming in near real-time for a wide range of DBS lead designs. APPROACH: Magnetic resonance imaging and diffusion tensor imaging are used to build finite element models that include anisotropic conductivity. The algorithm maximizes activation of target tissue and utilizes the Hessian matrix of the electric potential to approximate activation of neurons in all directions. We demonstrate our algorithm's ability in an example programming case that targets the subthalamic nucleus (STN) for the treatment of Parkinson's disease for three lead designs: the Medtronic 3389 (four cylindrical contacts), the direct STNAcute (two cylindrical contacts, six directional contacts), and the Medtronic-Sapiens lead (40 directional contacts). MAIN RESULTS: The optimization algorithm returns patient-specific contact configurations in near real-time-less than 10 s for even the most complex leads. When the lead was placed centrally in the target STN, the directional leads were able to activate over 50% of the region, whereas the Medtronic 3389 could activate only 40%. When the lead was placed 2 mm lateral to the target, the directional leads performed as well as they did in the central position, but the Medtronic 3389 activated only 2.9% of the STN. SIGNIFICANCE: This DBS programming algorithm can be applied to cylindrical electrodes as well as novel directional leads that are too complex with modern technology to be manually programmed. This algorithm may reduce clinical programming time and encourage the use of directional leads, since they activate a larger volume of the target area than cylindrical electrodes in central and off-target lead placements.
Authors: Edgar Peña; Simeng Zhang; Remi Patriat; Joshua E Aman; Jerrold L Vitek; Noam Harel; Matthew D Johnson Journal: J Neural Eng Date: 2018-09-13 Impact factor: 5.379
Authors: Johannes Vorwerk; Andrea A Brock; Daria N Anderson; John D Rolston; Christopher R Butson Journal: J Neural Eng Date: 2019-11-06 Impact factor: 5.379
Authors: Collin J Anderson; Daria Nesterovich Anderson; Stefan M Pulst; Christopher R Butson; Alan D Dorval Journal: Brain Stimul Date: 2020-04-09 Impact factor: 8.955
Authors: Alexandre Boutet; Robert Gramer; Christopher J Steele; Gavin J B Elias; Jürgen Germann; Ricardo Maciel; Walter Kucharczyk; Ludvic Zrinzo; Andres M Lozano; Alfonso Fasano Journal: Curr Neurol Neurosci Rep Date: 2019-05-30 Impact factor: 5.081
Authors: Daria Nesterovich Anderson; Gordon Duffley; Johannes Vorwerk; Alan D Dorval; Christopher R Butson Journal: J Neural Eng Date: 2018-10-02 Impact factor: 5.379
Authors: Kara A Johnson; Gordon Duffley; Daria Nesterovich Anderson; Jill L Ostrem; Marie-Laure Welter; Juan Carlos Baldermann; Jens Kuhn; Daniel Huys; Veerle Visser-Vandewalle; Thomas Foltynie; Ludvic Zrinzo; Marwan Hariz; Albert F G Leentjens; Alon Y Mogilner; Michael H Pourfar; Leonardo Almeida; Aysegul Gunduz; Kelly D Foote; Michael S Okun; Christopher R Butson Journal: Brain Date: 2020-08-01 Impact factor: 13.501