This page is a copy of research/systems_and_control/biomed/dbs (Wed, 31 Aug 2022 15:08:01)
Mathematical modeling of Deep Brain Stimulation
What is Deep Brain Stimulation?
Deep Brain Stimulation (DBS) is an established but still rapidly developing therapy that is mostly applied in treating the symptoms of neurodegenerative diseases such as Parkinson’s Disease and Essential Tremor, crippling diseases like Chronic Pain and Epilepsy, and psychiatric diseases such as Schizophrenia or Depression.
How DBS is done?
DBS is performed by generating high-frequency pulses through an electrode implanted inside the brain of the patient. A dedicated embedded computer system – the Implantable Pulse Generator (IPG) powering the electrode -- is surgically placed under the collarbone of the patient. The position of the electrode is vital since the stimulation targets a certain brain structure. The effect of DBS largely depends on the individual and the chosen pulse parameters, such as amplitude, frequency, duty cycle, and signal form. Tuning these parameters to the best effect is currently done by trial and error and can take a long time. Insufficient stimulation of the target area does not properly alleviate the symptoms of the disease while overstimulation or stimulation off target is prone to undesirable side effects.
DBS System (left) and MRI image with a segmented lead in blue (right)
The project aims at assisting medical personnel in DBS tuning by means of model-based optimization. Potentially effective setting are proposed by the software, visualized in a user-friendly way, and subsequently accepted or rejected by the physician.
An individualized model is created for each patient and based on pre-operative and post-operative medical imaging as well as electrical measurements obtained through the electrode and quantified symptoms.
Axial view of stimulated area (green) covering the caudal Zona Incerta (red) in unilateral stimulation. Subthalamic Nucleus (magenta) and Red Nucleus (blue) are depicted as well
- Researchers at UU: Anna Franziska Frigge, Helena Andersson, Alexander Medvedev
- Researchers at the Uppsala University Hospital: Elena Jiltsova, Markus Fahlström, Dag Nyholm
Graduated PhD students at UU: Rubén Cubo
Several collaborations have been established within the Department of IT and the University:
- Uppsala University Hospital: neurology, neurosurgery, DBS programming, medical imaging
- Scientific Computing (TDB): mathematical modeling of neural activation by external electric field. Contributors: Stefan Engblom, Pavol Bauer
- Visual Information and Visualization (Vi2): interactive 3D visualization tool for medical imaging and synthetic data. Contributors: Robin Strand
- R. Cubo, M. Åström, A. Medvedev, "Optimization-based Contact Fault Alleviation in Deep Brain Stimulation Leads", IEEE Transactions on Neural Systems & Rehabilitation Engineering, Volume: 26, Issue: 1, pp: 69 - 76, Jan. 2018.
- R. Cubo, M. Åström, A. Medvedev, "Optimization of Lead Design and Electrode Configuration in Deep Brain Stimulation", International Journal On Advances in Life Sciences, vol. 8, Issue 12, pp. 76-86, 2016.
- R. Cubo, M. Åström, A. Medvedev, "Model-based optimization of individualized deep brain stimulation therapy", IEEE Design & Test, vol. 33, Issue 4, pp. 74-81, 2016.
- H. Andersson, A. Medvedev, R. Cubo "The Impact of Deep Brain Stimulation on a Simulated Neuron: Inhibition, Excitation, and Partial Recovery", 2018 European Control Conference, Limassol, Cyprus.
- R. Cubo, M. Fahlström, E. Jiltsova, H. Andersson, A. Medvedev, "Semi-individualized electrical models in Deep Brain Stimulation: a variability analysis" in the 1st IEEE Conference on Control Technology and Applications, Hawaii, HI.
- R. Cubo, A. Medvedev, H. Andersson, "Deep Brain Stimulation therapies: a control-engineering perspective" in the 2017 American Control Conference, Seattle, WA.
- R. Cubo, M. Åström, A. Medvedev, "Electric Field Modeling and Spatial Control in Deep Brain Stimulation" in the 54th IEEE Conference on Decision and Control, pp. 3846-3851, December 15-18, 2015, Osaka, Japan, 2015.
- R. Cubo, M. Åström, A. Medvedev, "Model-based Optimization of Lead Configurations in Deep Brain Stimulation" in the 1st International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, pp.14-19, June 21-26, 2015, Brussels, Belgium. Best Paper Award
- R. Cubo, A. Medvedev, "Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling", IEEE Multi-Conference on Systems and Control, pp. 1479-1484, October 8-10, 2014, Antibes-Juan Les Pins, France
- R. Cubo, M. Åström, A. Medvedev, "Target coverage and selectivity in field steering brain stimulation", 36th Annual International Conference of the IEEE EMBS, pp. 522-525, August 27-30, 2014, Chicago, Illinois, USA
- E. Jiltsova, R. Cubo, M. Fahlström, A. Medvedev, E-M. Larsson, J. Wikström, D. Nyholm, "Bilateral directional lead stimulation of the subthalamic nucleus in patients with Parkinson's disease evaluated with the aid of patient-specific electric field estimations demonstrates good clinical outcome" in International Neuromodulation Society 13th World Congress, May 27-June 1, 2017, Edinburgh, United Kingdom.
- R. Cubo, E. Jiltsova, M. Fahlström, H. Andersson, A. Medvedev, "Optimization of deep brain stimulation by means of a patient-specific mathematical model" in XXIInd Congress of the European Society for Stereotactic and Functional Neurosurgery, September 28-30, 2016, Madrid, Spain.
- R. Cubo, M. Åström, A. Medvedev, "Model-based Optimization of Lead Configurations in Deep Brain Stimulation" in BRAIN Grand Challenges Conference, November 13-14, 2014, Washington, DC, USA.
- R. Cubo, M. Åström, A. Medvedev, "Stimulation field coverage and target structure selectivity in field steering brain stimulation", Movement Disorders, vol. 29, no. S1, pp. S198-S199, 2014.
- Cubo, R. . "Model-based optimization for individualized deep brain stimulation", PhD thesis, Uppsala University, 2018 Full Text
- R. Cubo, "Mathematical modeling for optimization of Deep Brain Stimulation", Licentiate Thesis, Department of Information Technology nr 2016-002, Uppsala University. Full Text