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Model-based optimization for individualized deep brain stimulation
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
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 [en]
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: urn:nbn:se:uu:diva-347353ISBN: 978-91-513-0306-2 (print)OAI: oai:DiVA.org:uu-347353DiVA, id: diva2:1194223
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
List of papers
1. Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
Open this publication in new window or tab >>Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling
2014 (English)In: Proc. International Conference on Control Applications: CCA 2014, Piscataway, NJ: IEEE , 2014, p. 1479-1484Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2014
National Category
Control Engineering Medical Equipment Engineering
Identifiers
urn:nbn:se:uu:diva-238211 (URN)10.1109/CCA.2014.6981533 (DOI)000366055800214 ()978-1-4799-7409-2 (ISBN)
Conference
CCA 2014, October 8–10, Antibes, France
Funder
EU, European Research Council, 247035
Available from: 2014-10-10 Created: 2014-12-10 Last updated: 2018-03-29Bibliographically approved
2. Optimization of lead design and electrode configuration in Deep Brain Stimulation
Open this publication in new window or tab >>Optimization of lead design and electrode configuration in Deep Brain Stimulation
2016 (English)In: International Journal On Advances in Life Sciences, ISSN 1942-2660, E-ISSN 1942-2660, Vol. 8, p. 76-86Article in journal (Refereed) Published
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-305224 (URN)
Available from: 2016-06-30 Created: 2016-10-13 Last updated: 2018-03-29Bibliographically approved
3. Electric field modeling and spatial control in Deep Brain Stimulation
Open this publication in new window or tab >>Electric field modeling and spatial control in Deep Brain Stimulation
2015 (English)In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 3846-3851Conference paper, Published paper (Refereed)
Abstract [en]

Deep Brain Stimulation (DBS) is an established treatment, in e.g. Parkinson's Disease, whose underlying biological mechanisms are unknown. In DBS, electrical stimulation is delivered through electrodes surgically implanted into certain regions of the brain of the patient. Mathematical models aiming at a better understanding of DBS and optimization of its therapeutical effect through the simulation of the electrical field propagating in the brain tissue have been developed in the past decade. The contribution of the present study is twofold: First, an analytical approximation of the electric field produced by an emitting contact is suggested and compared to the numerical solution given by a Finite Element Method (FEM) solver. Second, the optimal stimulation settings are evaluated by fitting the field distribution to a target one to control the spread of the stimulation. Optimization results are compared to those of a geometric approach, maximizing the intersection between the target and the activated volume in the brain tissue and reducing the stimulated area beyond said target. Both methods exhibit similar performance with respect to the optimal stimuli, with the electric field control approach being faster and more versatile.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2015
National Category
Control Engineering Medical Equipment Engineering
Identifiers
urn:nbn:se:uu:diva-284317 (URN)10.1109/CDC.2015.7402817 (DOI)000381554504006 ()9781479978847 (ISBN)
Conference
CDC 2015, December 15–18, Osaka, Japan
Funder
EU, European Research Council, 247035
Available from: 2015-12-18 Created: 2016-04-16 Last updated: 2018-03-29Bibliographically approved
4. Optimization-based contact fault alleviation in deep brain stimulation leads
Open this publication in new window or tab >>Optimization-based contact fault alleviation in deep brain stimulation leads
2018 (English)In: IEEE transactions on neural systems and rehabilitation engineering, ISSN 1534-4320, E-ISSN 1558-0210, Vol. 26, no 1, p. 69-76Article in journal (Refereed) Published
National Category
Medical Engineering
Identifiers
urn:nbn:se:uu:diva-342456 (URN)10.1109/TNSRE.2017.2769707 (DOI)000422939000008 ()29324404 (PubMedID)
Available from: 2017-11-03 Created: 2018-02-26 Last updated: 2018-03-29Bibliographically approved
5. Semi-Individualized electrical models in deep brain stimulation: A variability analysis
Open this publication in new window or tab >>Semi-Individualized electrical models in deep brain stimulation: A variability analysis
Show others...
2017 (English)In: 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2017, p. 517-522Conference paper, Published paper (Refereed)
Abstract [en]

Deep Brain Stimulation (DBS) is a well-established treatment in neurodegenerative diseases, e.g. Parkinson's Disease. It consists of delivering electrical stimuli to a target in the brain via a chronically implanted lead. To expedite the tuning of DBS stimuli to best therapeutical effect, mathematical models have been developed during recent years. The electric field produced by the stimuli in the brain for a given lead position is evaluated by numerically solving a Partial Differential Equation with the medium conductivity as a parameter. The latter is patient- and target-specific but difficult to measure in vivo. Estimating brain tissue conductivity through medical imaging is feasible but time consuming due to registration, segmentation and post-processing. On the other hand, brain atlases are readily available and processed. This study analyzes how alternations in the conductivity due to inter-patient variability or lead position uncertainties affect both the stimulation shape and the activation of a given target. Results suggest that stimulation shapes are similar, with a Dice's Coefficient between 93.2 and 98.8%, with a higher similarity at lower depths. On the other hand, activation shows a significant variation of 17 percentage points, with most of it being at deeper positions as well. It is concluded that, as long as the lead is not too deep, atlases can be used for conductivity maps with acceptable accuracy instead of fully individualized though medical imaging models.

Place, publisher, year, edition, pages
IEEE, 2017
Keywords
bioelectric phenomena, biological tissues, biomedical electrodes, brain, diseases, neurophysiology, partial differential equations, patient treatment, DBS stimuli, Parkinson disease, Partial Differential Equation, brain atlases, brain tissue conductivity, chronically implanted lead, deep brain stimulation, electric field, electrical stimuli, interpatient variability, medical imaging models, neurodegenerative diseases, semiIndividualized electrical models, variability analysis, Brain modeling, Computational modeling, Conductivity, Lead, Mathematical model, Satellite broadcasting
National Category
Control Engineering Other Medical Engineering
Identifiers
urn:nbn:se:uu:diva-347344 (URN)10.1109/CCTA.2017.8062514 (DOI)000426981500084 ()978-1-5090-2183-3 (ISBN)978-1-5090-2182-6 (ISBN)978-1-5090-2181-9 (ISBN)
Conference
1st Annual IEEE Conference on Control Technology and Applications, 27-30 Aug. 2017, Mauna Lani, HI, USA.
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-08-17Bibliographically approved
6. Calculating Directional Deep Brain Stimulation Settings by Constrained Optimization
Open this publication in new window or tab >>Calculating Directional Deep Brain Stimulation Settings by Constrained Optimization
Show others...
(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
Neuromodulation, Deep Brain Stimulation, inverse problems
National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-347345 (URN)
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-03-29
7. Online tissue conductivity estimation in Deep Brain Stimulation
Open this publication in new window or tab >>Online tissue conductivity estimation in Deep Brain Stimulation
(English)In: Article in journal (Refereed) Submitted
Abstract [en]

Deep Brain Stimulation (DBS) is an established therapy that consists of sending electrical pulses to the brain via a chronically implanted electrode. DBS is used to alleviate symptoms of neurological medical conditions such as Parkinson’s Disease and Essential Tremor. However, the stimulation effect is patient-specific and hard to predict. In addition, the brain tissue around the lead changes its electrical properties over time thus influencing the therapeutical effect. This paper proposes an approach to online conductivity estimation by sending a specially designed electrical probing signal into the brain from one contact of the electrode and measuring the response on another one. A conductivity estimate is then obtained from the parameters of an estimated linear time-invariant model. Both voltage-controlled and current-controlled DBS systems are treated. Continuous Least Squares and Laguerre domain identification are employed to estimate the involved models. Results suggest that a smooth Gaussian-shaped signal is sufficient to identify the model in a noise-free situation, but the resulting excitation might be not enough for accurate estimation in the face of disturbances, e.g. local field potential due to neuron firings. Better excitation is produced by input signals with designed Laguerre spectra. Due to the infeasibility of assigning electrical properties to the surroundings of the lead in vivo, synthetic data from an individualized high-fidelity mathematical model of DBS are used instead. The latter model is validated against clinically measured impedance values.

National Category
Control Engineering
Identifiers
urn:nbn:se:uu:diva-347346 (URN)
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-03-29

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