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Mathematical modeling for optimization of Deep Brain Stimulation
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 Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
2016 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Deep Brain Stimulation (DBS) consists of sending mild electric stimuli to the brain via a chronically implanted lead. The therapy is used to alleviate the symptoms of different neurological diseases, such as Parkinson's Disease. However, its underlying biological mechanism is currently unknown. DBS patients undergo a lengthy trial-and-error procedure in order to tune the stimuli so that the treatment achieves maximal therapeutic benefits while limiting side effects that are often present with large stimulation values.

The present licentiate thesis deals with mathematical modeling for DBS, extending it towards optimization. Mathematical modeling is motivated by the difficulty of obtaining in vivo measurements from the brain, especially in humans. It is expected to facilitate the optimization of the stimuli delivered to the brain and be instrumental in evaluating the performance of novel lead designs. Both topics are discussed in this thesis.

First, an analysis of numerical accuracy is presented in order to verify the DBS models utilized in this study. Then a performance comparison between a state-of-the-art lead and a novel field-steering lead using clinical settings is provided. Afterwards, optimization schemes using intersection of volumes and electric field control are described, together with some simplification tools, in order to speed up the computations involved in the modeling.

Place, publisher, year, edition, pages
Uppsala University, 2016.
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2016-002
National Category
Control Engineering Medical Equipment Engineering
Research subject
Electrical Engineering with specialization in Automatic Control
Identifiers
URN: urn:nbn:se:uu:diva-284320OAI: oai:DiVA.org:uu-284320DiVA: diva2:920154
Supervisors
Available from: 2016-01-22 Created: 2016-04-16 Last updated: 2017-08-31Bibliographically approved
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, 1479-1484 p.Conference 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: 2016-04-16Bibliographically approved
2. Target coverage and selectivity in field steering brain stimulation
Open this publication in new window or tab >>Target coverage and selectivity in field steering brain stimulation
2014 (English)In: Proc. 36th International Conference of the IEEE Engineering in Medicine and Biology Society, Piscataway, NJ: IEEE , 2014, 522-525 p.Conference 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-252475 (URN)10.1109/EMBC.2014.6943643 (DOI)000350044700130 ()978-1-4244-7929-0 (ISBN)
Conference
EMBC 2014, August 26–30, Chicago, IL
Funder
EU, European Research Council, 247035
Available from: 2014-08-30 Created: 2015-05-07 Last updated: 2016-04-16Bibliographically approved
3. Model-based optimization of lead configurations in Deep Brain Stimulation
Open this publication in new window or tab >>Model-based optimization of lead configurations in Deep Brain Stimulation
2015 (English)In: Proc. 1st International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, International Academy, Research and Industry Association (IARIA), 2015, 14-19 p.Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
International Academy, Research and Industry Association (IARIA), 2015
National Category
Control Engineering Medical Equipment Engineering
Identifiers
urn:nbn:se:uu:diva-238214 (URN)978-1-61208-446-6 (ISBN)
Conference
SPWID 2015, June 21–26, Brussels, Belgium
Funder
EU, European Research Council, 247035
Available from: 2015-06-26 Created: 2014-12-10 Last updated: 2016-04-17Bibliographically approved
4. 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, 3846-3851 p.Conference 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: 2016-12-27Bibliographically approved

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