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  • 1.
    Andersson, Helena
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
    Individualized mathematical modeling of neural activation in electric field2017Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Deep Brain Stimulation (DBS) is a treatment of movement disorders such as Parkinson's disease and essential tremor. Today it has been used in more than 80.000 patients. Electrical stimulation is administered by an implanted pulse generator through an electrode surgically placed in a target brain area specific to the treated disease. Opposed to alternative purely surgical treatment procedures, DBS is reversible and can be turned off.

    In this project, the aim is to individualise an already existing computational model of DBS, but also to look at optimisation of the treatment by developing a neuron model. It has been executed the following way. To localise the target area for the electrode, Magnetic Resonance Imaging (MRI) is used. An MRI image consists of volume elements called voxels. By analysing these voxels, it is possible to set up a coordinate system for the position of different parts of the brain. To build up an individualised model of the DBS, an MRI image is segmented into tissues of different conductivity thus resulting in a more accurate description of the electrical field around the electrode. To visualize the stimuli coverage for the medical staff, the MRI image of the target area, the electrode, and the electrical field produced by the stimuli are depicted in the same figure. From the results, we can draw the conclusion that this method works well for individualising the computational model of DBS, but it has only been used on one MRI scan so far so it needs further testing to obtain more data to compare with.

    The neuron model is a temporospatial mathematical model of a single neuron for the prediction of activation by a given electrically applied field generated by a DBS lead. The activation model is intended to be part of a patient-specific model of an already existing computational model of DBS. The model originate from a neuron model developed by Hodgkin and Huxley (HH). The original HH model only takes into account one compartment and, to make the neuron model more accurate, it is combined with a cable model. The simulation results obtained with the model have been validated against an established and widely accepted neuron model. The results correlated highly to each other with only minor differences. To see how position and orientation impact on activation, the developed HH model was tested for different pulse widths, distances from the lead, and rotations of the neuron relative to the lead. A larger pulse width makes activation more likely and so does a larger amplitude. Thicker neurons are more likely to get activated, neurons closer to the lead and also neurons perpendicular to the lead. From the results we can draw the conclusion that this method is a good way to stimulate neural activation of a single neuron. In future research, it might be possible to compare results from the neuron model with patient's response to treatment.

  • 2.
    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.

  • 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.
    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)
  • 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. 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.

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