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  • 1.
    Cubo, Rubén
    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.
    Mathematical modeling for optimization of Deep Brain Stimulation2016Licentiate 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.

    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. 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, p. 522-525Conference 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, p. 14-19Conference 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, 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
  • 2.
    Cubo, Rubén
    et al.
    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.
    Jiltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurosurgery.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Andersson, Helena
    Medvedev, Alexander
    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.
    Optimization of deep brain stimulation by means of a patient-specific mathematical model2016Conference paper (Refereed)
  • 3.
    Cubo, Rubén
    et al.
    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.
    Medvedev, Alexander
    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.
    Accuracy of the Finite Element Method in Deep Brain Stimulation Modelling2014In: Proc. International Conference on Control Applications: CCA 2014, Piscataway, NJ: IEEE , 2014, p. 1479-1484Conference paper (Refereed)
  • 4.
    Cubo, Rubén
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Medvedev, Alexander
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Individualization of a surrounding tissue model in Deep Brain Stimulation2017In: 2017 Ieee 56Th Annual Conference On Decision And Control (Cdc), IEEE, 2017, p. 5919-5924Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation is an established therapy that consists of sending mild electrical pulses to the brain via a surgically implanted electrode. It is used to treat the symptoms of neurodegenerative diseases such as Parkinson's Disease and Essential Tremor. The stimulation effect is however patient-specific and difficult to predict. Further, impedance measurements indicate changes in the medium around the lead over time that are generally challenging to account for. Although mathematical models to characterize the extent of the stimulation have been developed in recent years, it is imperative that the model parameters are realistic enough to produce correct results. This study aims at developing a system identification approach to capture the changes in the medium around the lead by measuring the potential on non-active contacts during stimulation. Due to infeasibility of assigning properties to the medium around the lead in vivo, synthetic data are used for analysis instead. Results suggest that a transfer function with one zero and one pole that corresponds to a circuit composed of two RC contours in cascade is sufficient to describe the measurements in silico. Furthermore, the identified parameters show an injective relation to the properties of the medium. Continuous Least Squares and Laguerre domain identification are utilized for obtaining parameter estimates.

  • 5.
    Cubo, Rubén
    et al.
    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.
    Medvedev, Alexander
    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.
    Andersson, Helena
    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.
    Deep Brain Stimulation therapies: a control-engineering perspective2017In: Proc. American Control Conference: ACC 2017, American Automatic Control Council , 2017, p. 104-109Conference paper (Refereed)
  • 6.
    Cubo, Rubén
    et al.
    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.
    Medvedev, Alexander
    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.
    Åström, Mattias
    Model-based optimization of individualized Deep Brain Stimulation therapy2016In: IEEE Design & Test, ISSN 2168-2356, Vol. 33, no 4, p. 74-81Article in journal (Refereed)
  • 7.
    Cubo, Rubén
    et al.
    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.
    Medvedev, Alexander
    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.
    Åström, Mattias
    Stimulation field coverage and target structure selectivity in field steering brain stimulation2014Conference paper (Refereed)
  • 8.
    Cubo, Rubén
    et al.
    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.
    Åström, Mattias
    Linkoping Univ, Dept Biomed Engn, S-58183 Linkoping, Sweden; Medtron Eindhoven Design Ctr, Medtron Neuromodulat, Eindhoven, Netherlands.
    Medvedev, Alexander
    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.
    Electric field modeling and spatial control in Deep Brain Stimulation2015In: Proc. 54th Conference on Decision and Control, Piscataway, NJ: IEEE , 2015, p. 3846-3851Conference 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.

  • 9.
    Cubo, Rubén
    et al.
    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.
    Åström, Mattias
    Medvedev, Alexander
    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.
    Model-based optimization of lead configurations in Deep Brain Stimulation2015In: Proc. 1st International Conference on Smart Portable, Wearable, Implantable and Disability-oriented Devices and Systems, International Academy, Research and Industry Association (IARIA), 2015, p. 14-19Conference paper (Refereed)
  • 10.
    Cubo, Rubén
    et al.
    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.
    Åström, Mattias
    Medvedev, Alexander
    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.
    Optimization-based contact fault alleviation in deep brain stimulation leads2018In: 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)
  • 11.
    Cubo, Rubén
    et al.
    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.
    Åström, Mattias
    Medvedev, Alexander
    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.
    Target coverage and selectivity in field steering brain stimulation2014In: Proc. 36th International Conference of the IEEE Engineering in Medicine and Biology Society, Piscataway, NJ: IEEE , 2014, p. 522-525Conference paper (Refereed)
1 - 11 of 11
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