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Semi-Individualized electrical models in deep brain stimulation: A variability analysis
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.
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Neurology.
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.
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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. p. 517-522
Keywords [en]
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: urn:nbn:se:uu:diva-347344DOI: 10.1109/CCTA.2017.8062514ISI: 000426981500084ISBN: 978-1-5090-2183-3 (print)ISBN: 978-1-5090-2182-6 (electronic)ISBN: 978-1-5090-2181-9 OAI: oai:DiVA.org:uu-347344DiVA, id: diva2:1194181
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
In thesis
1. Model-based optimization for individualized deep brain stimulation
Open this publication in new window or tab >>Model-based optimization for individualized deep brain stimulation
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
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:nbn:se:uu:diva-347353 (URN)978-91-513-0306-2 (ISBN)
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

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Cubo, RubénFahlström, MarkusJiltsova, ElenaAndersson, HelenaMedvedev, Alexander

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