Numerical investigation of transcranial direct current stimulation on cortical modulation

Transcranial direct current stimulation (tDCS) is a non-invasive and sub-convulsive functional stimulation technique with applications in both clinical therapy and neuro-science research. The technique provides researchers and clinicians with a unique tool capable of modulating the neural excitability in both the central and peripheral nervous system. On a clinical level, the procedure has been used quite extensively for its potential therapeutic applications in a number of neurological disorders. Despite the advantages of being safe, low cost and easy to administer, our limited under-standing on interaction mechanisms between the stimulation parameters and biologi-cal materials has impeded the development and optimisation of tDCS based therapies. The focus of this thesis is to develop a realistic finite element based human head model to address the problems involved in the forward modelling of transcranial direct current stimulation. The study explores the effects of model complexities and anisotropic material properties on field estimations. The sensitivity of electric field and current density on accurate modelling of cortical and non-cortical structures, and the influence of heterogeneously defined anisotropic electric conductivity on field parameters were analysed in an incremental manner. Using the averaged and the subject specific Magnetic Resonance Imaging (MRI) and Diffusion Tensor Imaging (DTI) data, the head models with detailed anatomical features and realistic tissue conductive properties, were developed and employed to specifically address the role of stimulation parameters, such as: morphological variations, structural details, tissue behaviour, inter-subject variations, electrode montages and neural fibre pathways for defining the site and strength of modulation/stimulation. This thesis demonstrates the importance of human head modelling in elucidating the complex electric field and current density profiles instigated by the non-invasive electric stimulation. The results of this study strongly support the initial hypothesis that model complexity and accurate conductivity estimation play a crucial role in determining the accurate predictions of field variables. The study also highlighted the inadequacy of scalar field maps to decipher the complex brain current flow patterns and axonal/neural polarization. With the proposed refinements, model based strategies can be employed to optimally select the required stimulation strength and electrode montage specific to individual dose requirements. Therefore, the work con-ducted in this study will bridge the gap between the current clinical practices and the subject specific treatments by providing accurate physiologically representative simulation

Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation

Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom über zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekürzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein Verstärken oder Dämpfen von Hirnaktivität zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur Unterstützung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe Variabilität im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollständiges Verständnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als Schlüsselkomponente für das Verständnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer größeren elektrischen Feldstärke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle für die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen Leitfähigkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. Außerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale Abschätzung der Ausbreitung des elektrischen Feldes anhand der bloßen Position der Stimulationselektroden ist daher nicht ausreichend genau für eine zielgerichtete Stimulation. Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und für ein breites Feld an Forschern zugänglich machen, wurden in den vergangenen Jahren für den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden Läsionen eine nicht-triviale Aufgabe. Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verändertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunächst für gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle überführt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden. Im Verlauf des normalen Alterns ist das Gehirn strukturellen Veränderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer Veränderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese Phänomene des normalen Alterns zu berücksichtigen. Die vordergründige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der veränderten Hirnmikrostruktur, da die resultierenden Veränderungen im Leitfähigkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen Leitfähigkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen Leitfähigkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der veränderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese Gewebsveränderungen hochgradig lokal und im Allgemeinen gering auswirken. Schließlich wurden in einem dritten Schritt Simulationen für Schlaganfallpatienten durchgeführt. Ihre großen, strukturzerstörenden Läsionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren Leitfähigkeiten gearbeitet, was zu unsicheren elektrischen Feldabschätzungen führte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter Berücksichtigung der inhärenten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergründen, ob die Hirnstimulation einen positiven Einfluss auf die Hirnaktivität der Patienten im Kontext von Rehabilitationstherapie ausüben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstützen kann. Während ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschließend zu klären.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation. Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task. Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation. Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact. Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung Abstract Contents 1 Overview 2 Anatomical structures in magnetic resonance images 2 Anatomical structures in magnetic resonance images 2.1 Neuroanatomy 2.2 Magnetic resonance imaging 2.3 Segmentation of MR images 2.4 Image morphology 2.5 Summary 3 Magnetic resonance image processing pipeline 3.1 Introduction to human body modeling 3.2 Description of the processing pipeline 3.3 Intermediate and final outcomes in two subjects 3.4 Discussion, limitations & future work 3.5 Conclusion 4 Numerical simulation of transcranial electric stimulation 4.1 Electrostatic foundations 4.2 Discretization of electrostatic quantities 4.3 The numeric solution process 4.4 Spatial discretization by volume meshing 4.5 Summary 5 Simulation workflow 5.1 Overview of tES simulation pipelines 5.2 My implementation of a tES simulation workflow 5.3 Verification & application examples 5.4 Discussion & Conclusion 6 Transcranial direct current stimulation in the aging brain 6.1 Handling age-related brain changes in tES simulations 6.2 Procedure of the simulation study 6.3 Results of the uncertainty analysis 6.4 Findings, limitations and discussion 7 Transcranial direct current stimulation in stroke patients 7.1 Bridging the gap between simulated electric fields and brain activation in stroke patients 7.2 Methodology for relating simulated electric fields to functional MRI data 7.3 Evaluation of the simulation study and correlation analysis 7.4 Discussion & Conclusion 8 Outlooks for simulations of transcranial electric stimulation List of Figures List of Tables Glossary of Neuroscience Terms Glossary of Technical Terms Bibliograph

Numerical human head modelling and investigation for precise tDCS applications

As a non-invasive and sub-convulsive functional stimulation technique, transcranial direct current stimulation (tDCS) generates a relatively weak current intensity and applies the moderate current to the brain to modulate the level of cortical excitability. This neuromodulatory technique has been extensively used as a potential clinical treatment for various neuropsychiatric conditions, ranging from depression, addition to schizophrenia and Parkinson’s disease. Recently, tDCS has also been researched as a promising alternative treatment to alleviate neuropathic pain of cancer patients. The focus of this project is to numerically investigate the precise applications of tDCS based on a series of high resolution realistic human head model using finite element methods. Specifically, the influence of brain shift caused by gravity was firstly pre-validated using real shaped human head model. After that, this study focuses on the investigation of tDCS applications on the brain cancer patients in order to treat their neuropsychiatric conditions and neuropathic pain caused by the brain tumors. Thirdly, the role of blood vessels in shaping the induced current distributions within the cortex during tDCS was thoroughly investigated and addressed. The outcomes of this project highlight the importance of head orientation during the clinical application of tDCS. The results also clear the safety concern in applying tDCS to the patients with brain cancer. In addition, this project provides positive supports on the introduction of brain blood vessels during the precise human head modelling for tDCS though considerable workload will be involved

Finite Element Study of Transcranial Direct Current Stimulation: customization of models and montages

Transcranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique that applies low amplitude current via electrodes placed on the scalp. Rather than directly eliciting a neuronal response, tDCS is believed to modulate excitability – encouraging or suppressing activity in regions of the brain depending on the polarity of stimulation. The particular application of tDCS is often determined by the electrode configuration and intensity of stimulation. MRI-derived finite element models have been developed to analyze the effect of these parameters allowing novel electrode configurations to be tested in subject specific models. By creating a subject specific model of an obese subject, the effect of fat on tDCS was examined. The inclusion of fat into the model led to an increase in cortical electric field intensity. To further investigate the influence of fat the conductivity was varied from that of skull to that of skin. Cortical electric field intensity did not change monotonically with fat conductivity. It was postulated that this may be due to a shunting effect both when the shell of fat surrounding the skull is too resistive for penetration and when the fat is so conductive as to lead current around rather than through the head. The effect of electrode positioning was then examined in a new 2x1 Hybrid montage utilizing both HD electrodes and sponge pads. Systematically varying the location of both the anode and cathode led to changes in the electric field distribution. This is in contrast to the old heuristic convention of placing the “active” electrode over a region of interest and neglecting the influence of the “return” electrode. Lastly the radial directionality of electric field was examined in a 4x1 ring configuration. Previous models have predicted the spatial focality of the 4x1 ring configuration. Polarity specificity, the ability to selectively apply either anodal or cathodal stimulation, was demonstrated in a 4x1 montage over the motor strip. The customization of models for specific populations and montages provides new avenues for clinical practice

Validation of Transcranial Electrical Stimulation (TES) Finite Element Modeling Against MREIT Current Density Imaging in Human Subjects

abstract: Transcranial electrical stimulation (tES) is a non-invasive brain stimulation therapy that has shown potential in improving motor, physiological and cognitive functions in healthy and diseased population. Typical tES procedures involve application of weak current (< 2 mA) to the brain via a pair of large electrodes placed on the scalp. While the therapeutic benefits of tES are promising, the efficacy of tES treatments is limited by the knowledge of how current travels in the brain. It has been assumed that the current density and electric fields are the largest, and thus have the most effect, in brain structures nearby the electrodes. Recent studies using finite element modeling (FEM) have suggested that current patterns in the brain are diffuse and not concentrated in any particular brain structure. Although current flow modeling is useful means of informing tES target optimization, few studies have validated tES FEM models against experimental measurements. MREIT-CDI can be used to recover magnetic flux density caused by current flow in a conducting object. This dissertation reports the first comparisons between experimental data from in-vivo human MREIT-CDI during tES and results from tES FEM using head models derived from the same subjects. First, tES FEM pipelines were verified by confirming FEM predictions agreed with analytic results at the mesh sizes used and that a sufficiently large head extent was modeled to approximate results on human subjects. Second, models were used to predict magnetic flux density, and predicted and MREIT-CDI results were compared to validate and refine modeling outcomes. Finally, models were used to investigate inter-subject variability and biological side effects reported by tES subjects. The study demonstrated good agreements in patterns between magnetic flux distributions from experimental and simulation data. However, the discrepancy in scales between simulation and experimental data suggested that tissue conductivities typically used in tES FEM might be incorrect, and thus performing in-vivo conductivity measurements in humans is desirable. Overall, in-vivo MREIT-CDI in human heads has been established as a validation tool for tES predictions and to study the underlying mechanisms of tES therapies.Dissertation/ThesisDoctoral Dissertation Biomedical Engineering 201

Improving Tumor Treating Fields Treatment Efficacy in Patients With Glioblastoma Using Personalized Array Layouts

PurposeTo investigate tumors of different size, shape, and location and the effect of varying transducer layouts on Tumor Treating Fields (TTFields) distribution in an anisotropic model.Methods and MaterialsA realistic human head model was generated from MR images of 1 healthy subject. Four different virtual tumors were placed at separate locations. The transducer arrays were modeled to mimic the TTFields-delivering commercial device. For each tumor location, varying array layouts were tested. The finite element method was used to calculate the electric field distribution, taking into account tissue heterogeneity and anisotropy.ResultsIn all tumors, the average electric field induced by either of the 2 perpendicular array layouts exceeded the 1-V/cm therapeutic threshold value for TTFields effectiveness. Field strength within a tumor did not correlate with its size and shape but was higher in more superficial tumors. Additionally, it always increased when the array was adapted to the tumor's location. Compared with a default layout, the largest increase in field strength was 184%, and the highest average field strength induced in a tumor was 2.21 V/cm.ConclusionsThese results suggest that adapting array layouts to specific tumor locations can significantly increase field strength within the tumor. Our findings support the idea of personalized treatment planning to increase TTFields efficacy for patients with GBM

Brain and Human Body Modeling 2020

​This open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book’s coverage of the latest models and techniques available to assess a given technology’s safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical

Human head temperature and electric field investigations under ECT

Electroconvulsive therapy (ECT) is a non-invasive technique used to treat psychiatric conditions. A high strength low frequency electrical stimulation is delivered through two electrodes. The aim of this work is to develop an ECT finite element human head model to investigate the electric field and the increase in temperature due to the electrical stimulation. The bio-heat transfer equation combined with Laplace equation and their initial and boundary conditions are used to define the physics of the models. Firstly, finite ele-ment spherical human head models are created in COMSOL Multiphysics and the behaviour of the thermal field due to ECT electrical stimulation is analysed. Hetero-geneity was considered and thermal anisotropy of the skull layer was applied to the finite element models. Secondly, a realistic human head model is created using magnetic resonance images (MRI). Similar physics is applied to define the thermal and electrical problems, and the anisotropic conductivity of the skull is considered. The realistic models contain anatomical features and realistic tissue conductive properties. Through these models we investigate the role of stimulation parameters such as: electrode montages, strength of stimulation, temperature behaviour, etc. Later on, another realistic human head model with a brain tumor is created and a diffusion tensor image is included. Based on this model the white matter anisotropy is considered and the effect on the electric field is analysed. The results show that high temperatures only occur on external areas of the head, such as scalp and fat. The thermal conductivity anisotropy is insignificant from a heat-transferring point of view. However, the electrical anisotropy does need to be included in order to get more accurate outcomes. If ECT was applied to a patient with a brain tumor, then factors such as tumor location, aggressiveness, electrode montage, etc would need to be considered. Further work can be undertaken through computational simulation to make personal ECT treatment feasible in clinical practice

A 3D Finite-Difference BiCG Iterative Solver with the Fourier-Jacobi Preconditioner for the Anisotropic EIT/EEG Forward Problem

The Electrical Impedance Tomography (EIT) and electroencephalography (EEG) forward problems in anisotropic inhomogeneous media like the human head belongs to the class of the three-dimensional boundary value problems for elliptic equations with mixed derivatives. We introduce and explore the performance of several new promising numerical techniques, which seem to be more suitable for solving these problems. The proposed numerical schemes combine the fictitious domain approach together with the finite-difference method and the optimally preconditioned Conjugate Gradient- (CG-) type iterative method for treatment of the discrete model. The numerical scheme includes the standard operations of summation and multiplication of sparse matrices and vector, as well as FFT, making it easy to implement and eligible for the effective parallel implementation. Some typical use cases for the EIT/EEG problems are considered demonstrating high efficiency of the proposed numerical technique