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  • 1.
    Andersson, Helena
    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.
    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.
    The impact of deep brain stimulation on a simulated neuron: Inhibition, excitation, and partial recovery2018In: Proc. 16th European Control Conference, IEEE, 2018, p. 2034-2039Conference paper (Refereed)
  • 2.
    Binggeli, Christian
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Zackrisson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Pelckmans, Kristiaan
    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.
    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.
    Jensen, Hannes
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Shimizu, Ikko
    Osaka Univ, Dept Earth & Space Sci, Theoret Astrophys, 1-1 Machikaneyama, Toyonaka, Osaka 5600043, Japan.
    Lyman continuum leakage versus quenching with the James Webb Space Telescope: the spectral signatures of quenched star formation activity in reionization-epoch galaxies2018In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 479, no 1, p. 368-376Article in journal (Refereed)
    Abstract [en]

    In this paper, we study the effects of a recent drop in star formation rate (SFR) on the spectra of epoch of reionization (EoR) galaxies, and the resulting degeneracy with the spectral features produced by extreme Lyman continuum leakage. In order to study these effects in the wavelength range relevant for the upcoming James Webb Space Telescope (JWST), we utilize synthetic spectra of simulated EoR galaxies from cosmological simulations together with synthetic spectra of partially quenched mock galaxies. We find that rapid declines in the SFR of EoR galaxies could seriously affect the applicability of methods that utilize the equivalent width of Balmer lines and the ultraviolet spectral slope to assess the escape fraction of EoR galaxies. In order to determine if the aforementioned degeneracy can be avoided by using the overall shape of the spectrum, we generate mock NIRCam observations and utilize a classification algorithm to identify galaxies that have undergone quenching. We find that while there are problematic cases, JWST/NIRCam or NIRSpec should be able to reliably identify galaxies with redshifts z similar to 7 that have experienced a significant decrease in the SFR (by a factor of 10-100) in the past 50-100 Myr with a success rate greater than or similar to 85 per cent. We also find that uncertainties in the dust-reddening effects on EoR galaxies significantly affect the performance of the results of the classification algorithm. We argue that studies that aim to characterize the dust extinction law most representative in the EoR would be extremely useful.

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  • 3.
    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
    Download full text (pdf)
    fulltext
  • 4.
    Cubo, Rubén
    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.
    Model-based optimization for individualized deep brain stimulation2018Doctoral 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.

    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
    2020 (English)In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 149-162Article in journal (Refereed) Published
    National Category
    Control Engineering
    Identifiers
    urn:nbn:se:uu:diva-347346 (URN)10.1109/TCST.2018.2862397 (DOI)000505786600012 ()
    Available from: 2018-08-16 Created: 2018-03-29 Last updated: 2020-01-29Bibliographically approved
    Download full text (pdf)
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    presentationsbild
  • 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.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.
    Jiltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    Andersson, Helena
    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, Automatic control.
    Calculating Deep Brain Stimulation Amplitudes and Power Consumption by Constrained Optimization2019In: Journal of Neural Engineering, ISSN 1741-2560, E-ISSN 1741-2552, Vol. 16, no 1, article id 016020Article in journal (Refereed)
    Abstract [en]

    Objective: Deep brain stimulation (DBS) consists of delivering electrical stimuli to a brain target via an implanted lead to treat neurological and psychiatric 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.

    Approach: 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 were 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.

    Significance: 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 safety constraints based on brain anatomy depend on the patient and further research into it is needed. 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 considering power consumption in DBS optimization, since it increases with the square of the stimuli amplitude and also critically affects battery life through pulse frequency and duty cycle.

  • 6.
    Cubo, Rubén
    et al.
    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.
    Fahlström, Markus
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology. Uppsala Univ Hosp, Dept Biomed Technol Med Phys & IT, Uppsala, Sweden.
    Jiltsova, Elena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
    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.
    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.
    Semi-Individualized electrical models in deep brain stimulation: A variability analysis2017In: 2017 IEEE Conference on Control Technology and Applications (CCTA), IEEE, 2017, p. 517-522Conference 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.

  • 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.
    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)
  • 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.
    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)
  • 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.
    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.
    Individualization of a surrounding tissue model in Deep Brain Stimulation2017In: Proc. 56th Conference on Decision and Control, Piscataway, NJ: IEEE, 2017, p. 5919-5924Conference 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.
    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.
    Online tissue conductivity estimation in Deep Brain Stimulation2020In: IEEE Transactions on Control Systems Technology, ISSN 1063-6536, E-ISSN 1558-0865, Vol. 28, no 1, p. 149-162Article 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.
    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, IEEE, 2017, p. 104-109Conference paper (Refereed)
    Abstract [en]

    Deep Brain Stimulation (DBS) is an established therapy for treating e.g. Parkinson's disease, essential tremor, as well as epilepsy. In DBS, chronic pulsatile electrical stimulation is administered to a certain target area of the brain through a surgically implanted lead. The stimuli parameters have to be properly tuned in order to achieve therapeutical effect that in most cases is alleviation of motor symptoms. Tuning of DBS currently is a tedious task since it is performed manually by medical personnel in a trial-and-error manner. It can be dramatically improved and expedited by means of recently developed mathematical models together with control and estimation technology. This paper presents a control engineering perspective on DBS, viewing it as a control system for minimizing the severity of the symptoms through coordinated manipulation of the stimuli parameters. The DBS model structure comprises a stimuli model, an activation model, and a symptoms model. Each of those is individualized from patient data obtained through medical imaging, electrical measurements, and objective symptom quantification. The proposed approach is illustrated by simulation and clinical data from an individualized DBS model being developed by the authors.

  • 12.
    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)
  • 13.
    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)
  • 14.
    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.

  • 15.
    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)
  • 16.
    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)
  • 17.
    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)
  • 18.
    Giri, Sambit K.
    et al.
    Stockholm Univ, Dept Astron, SE-10691 Stockholm, Sweden;Stockholm Univ, Oskar Klein Ctr, SE-10691 Stockholm, Sweden.
    Zackrisson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Binggeli, Christian
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Pelckmans, Kristiaan
    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.
    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.
    Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys2020In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 491, no 4, p. 5277-5286Article in journal (Refereed)
    Abstract [en]

    The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into the intergalactic medium, resulting in faint nebular emission lines. We present a machine learning framework for identifying cases of very high hydrogen-ionizing photon escape from galaxies based on the data quality expected from potential NIRSpec observations of EoR galaxies in lensed fields. We train our algorithm on mock samples of JWST/NIRSpec data for galaxies at redshifts z = 6-10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for z greater than or similar to 6 objects of a brightness similar to EoR galaxy candidates uncovered in Frontier Fields observations of galaxy cluster Abell-2744 and MACS-J0416. We find that ionizing escape fractions (f(esc)) of galaxies brighter than m(AB,1500) approximate to 27 mag may be retrieved with mean absolute error Delta f(esc) approximate to 0.09(0.12) for 24 h (1.5 h) JWST/NIRSpec exposures at resolution R = 100. For 24 h exposure time, even fainter galaxies (m(AB,1500) < 28.5 mag) can be processed with Delta f(esc) approximate to 0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24 (m(AB,1500) < 28.5 mag) and 1.5 h (m(AB,1500) < 27 mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high f(esc) and present the conditions required for detecting a subpopulation of high-f(esc) galaxies within the data set.

  • 19. Giri, Sambit K.
    et al.
    Zackrisson, Erik
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Binggeli, Christian
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Observational Astronomy.
    Pelckmans, Kristiaan
    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.
    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.
    Mellema, Garrelt
    Constraining Lyman continuum escape using Machine Learning2018In: Peering towards Cosmic Dawn, Cambridge University Press, 2018, Vol. 333, p. 254-258Conference paper (Refereed)
    Abstract [en]

    The James Webb Space Telescope (JWST) will observe the rest-frame ultraviolet/optical spectra of galaxies from the epoch of reionization (EoR) in unprecedented detail. While escaping into the intergalactic medium, hydrogen-ionizing (Lyman continuum; LyC) photons from the galaxies will contribute to the bluer end of the UV slope and make nebular emission lines less prominent. We present a method to constrain leakage of the LyC photons using the spectra of high redshift (z greater than or similar to 6) galaxies. We simulate JWST/NIRSpec observations of galaxies at z = 6-9 by matching the fluxes of galaxies observed in the Frontier Fields observations of galaxy cluster MACS-J0416. Our method predicts the escape fraction f(esc) with a mean absolute error Delta f(esc) approximate to 0.14. The method also predicts the redshifts of the galaxies with an error approximate to 0.0003.

  • 20.
    Medvedev, Alexander
    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.
    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.
    Olsson, Fredrik
    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.
    Bro, Viktor
    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.
    Control-Engineering Perspective on Deep Brain Stimulation: Revisited2019Conference paper (Refereed)
    Abstract [en]

    Deep brain stimulation (DBS) is a an established therapy in neurological and mental disorders making use of electrical pulses chronically delivered to a certain disease-specific neural target through surgically implanted electrodes. The therapeutical effect of DBS is highly individual and depends on the target coverage by the stimuli and the amount of spill beyond it. This can be suitably formulated as an optimization problem. Since the biological mechanism underlying the DBS therapy is mainly unknown, and due to high inter-patient and intra-patient variability of the DBS effect, a pragmatic approach to the DBS programming is to consider the process as tuning of a control system for the symptoms. Such a technology assumes that the symptoms are accurately quantified. The paper summarizes the progress in the individualized DBS and presents the results of a limited clinical study making use of the proposed DBS programming approach.

1 - 20 of 20
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