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Calculating Directional Deep Brain Stimulation Settings by Constrained Optimization
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurosurgery.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
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(English)Article in journal (Refereed) Submitted
Abstract [en]

Objective: Deep Brain Stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurodegenerative conditions. Individualized stimulation is vital to ensure therapeutic results, since DBS may otherwise become ineffective or cause undesirable side effects. Since the DBS pulse generator is battery-driven, power consumption incurred by the stimulation is important. In this study, target coverage and power consumption are compared over a patient population for clinical and model-based patient-specific settings calculated by constrained optimization. Methods: Brain models for five patients undergoing bilateral DBS were built. Mathematical optimization of activated tissue volume was utilized to calculate stimuli amplitudes, with and without specifying the volumes, where stimulation was not allowed to avoid side effects. Power consumption was estimated using measured impedance values and battery life under both clinical and optimized settings. Results: It was observed that clinical settings are generally less aggressive than the ones suggested by unconstrained model-based optimization, especially under asymmetrical stimulation. The DBS settings satisfying the constraints were close to the clinical values. Conclusion: The use of mathematical models to suggest optimal patient-specific DBS settings that observe technological and safety constraints can save time in clinical practice. It appears though that the considered anatomy-related safety constraints depend on the patient and further research is needed in this regard. Power consumption is important to consider since it increases with the square of the stimuli amplitude and critically affects battery life. Significance: This work highlights the need of specifying the brain volumes to be avoided by stimulation while optimizing the DBS amplitude, in contrast to minimizing general stimuli overspill, and applies the technique to a cohort of patients. It also stresses the importance of taking power consumption into account.

Keywords [en]
Neuromodulation, Deep Brain Stimulation, inverse problems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-347345OAI: oai:DiVA.org:uu-347345DiVA, id: diva2:1194184
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-03-29
In thesis
1. Model-based optimization for individualized deep brain stimulation
Open this publication in new window or tab >>Model-based optimization for individualized deep brain stimulation
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Deep Brain Stimulation (DBS) is an established therapy that is predominantly  utilized 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 and Depression. Due to its invasive nature, DBS is considered as a last resort therapy.DBS is performed by transmitting electric pulses through an electrode implanted in the brain of the patient.

The stimulation is driven by a battery-powered Implanted Pulse Generator. The brain is a very delicate and complex organ and, therefore, accurate positioning the electrode is vital. To achieve a satisfactory therapeutical result, the stimulation targets a certain predefined brain structure that depends on the disease.

The effect of DBS depends on the individual, the chosen stimulating contact(s), and the pulse parameters, i.e. amplitude, frequency, width, and shape. Tuning these parameters to the best effect is currently done by a lengthy trial-and-error process. Insufficient stimulation does not properly alleviate the symptoms of the disease, while overstimulation or stimulation off target is prone to side effects.

This work envisions assisting physicians in DBS therapy by utilizing model-based estimation and optimization, maximizing stimulation of the target and minimizing stimulation in potentially problematic areas of the brain. This work focuses on amplitude and contact selection. Because of inter-patient differences, individualized models based on clinical imaging have to be created. Alternatively, semi-individualized models can be designed using atlases that save time but potentially introduce inaccuracies. Other optimization  applications to DBS are proposed in the thesis, e.g. fault alleviation and electrode design.

Electrical properties of the brain can change over time and alter the stimulation spread. A system identification approach has been proposed to quantify these changes.

The main aim of DBS is to alleviate the symptoms of the disease and quantifying symptoms is important. The ultimate vision of this work is to design a closed-loop system that can deliver optimal stimulation to the brain while automatically adapting to changes in the brain and the severity of symptoms.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 68
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1659
Keywords
Neuromodulation, Deep Brain Stimulation, Inverse Problems, Optimization, Finite Element Methods
National Category
Control Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
urn:nbn:se:uu:diva-347353 (URN)978-91-513-0306-2 (ISBN)
Public defence
2018-05-25, ITC 2446 (Polacksbacken), Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2018-05-03 Created: 2018-03-29 Last updated: 2018-10-08

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