Finite element mesh generation for a multi-compartment head model in Zeffiro Interface

Modelling the human brain and simulating its functionality with computer programs introduces a great number of applications. These applications include for example applying finite element method for optimizing injection currents in transcranial direct electrical stimulation, a form of treatment used for example in depression and Parkinson’s disease, or localizing brain activity in different scenarios such as during an epileptic event to pinpoint the causing tissue in the brain. Zeffiro Interface is one software representing recent advancement in finite element-based forward and inverse modeling of brain activity. It is an open-source software built for multidisciplinary GPU accelerated finite element method based forward and inverse computations for intricate geometries in Matlab environment. Zeffiro Interface originally started as a project for enabling the use of finite element method in electro- and magnetoencephalography (EEG/MEG), but its modular parameter structure allows applications in other fields as well. Finite element mesh generation is an essential step in finite element method. This thesis is a literature review about the finite element mesh generation process for a multi-compartment head model in Zeffiro Interface. As background information, the thesis also introduces the finite element method, its applications in brain modelling, and related technologies such as EEG and MEG. In addition, the thesis introduces computing examples of mesh generation using Zeffiro Interface along with documented procedure for both user interface and script

On the Volume Conduction Model Validation with Stereo EEG Data

Volume conduction can be defined as the transmission of electric potential and magnetic field generated by a primary current source of brain activation in the surrounding medium, i.e., the human head. Volume conduction simulations are based on sophisticated models whose construction represents a current challenge within the neuroscientific community. Volume conduction models are used in various applications such as electroencephalography (EEG) or magnetoencephalography (MEG) source reconstruction, or in the optimization of the electrode placement in a transcranial electrical stimulation session. Clinical applications based on volume conduction models are, for example, the localization of the epileptogenic zone, i.e., the brain area responsible for the generation of seizures, in the presurgical assessment of focal drug-resistant epilepsy patients, and the antidepressant effects given by transcranial electrical stimulation. Increasing the accuracy of volume conduction simulations is therefore crucial. To the best of our knowledge, the accuracy of volume conduction models have never been validated directly with actual measurements in human patients. The main goal of this thesis is to describe a first attempt to validate volume conduction modeling using electric stimulation stereo-encephalografic (sEEG) data. This work therefore is focused on the research, investigation and test of tools and methods which can be used to describe the accuracy of volume conduction models used in both clinical and basic research. Given a dataset of one pharmaco-resistant epilepsy patient, composed by the anatomical T1 weighted magnetic resonance image (MRI), the electrophysiological signal recorded during electric brain stimulation sessions with sEEG technique and sEEG contact positions extracted by post-implantation CT image, the analysis conducted in this work can be split into three main steps. First, we built volume conduction head models and we simulated the electric potentials during the electric brain stimulations. In this step, we solved the so-called (s)EEG forward problem by means of the finite element method in its classical formulation, and we considered three different conductivity profile to assign to the computational domain, individually extracted by the T1-w MRI. Moreover we computed the solution in meshes with two different resolution, i.e., 1 mm and 2 mm, with three different ways to model the source term, i.e., the partial integration approach, the subtraction approach and Venant\u2019s approach. Second, we extracted the responses to the electric brain stimulations from the actual sEEG measurements. Particular emphasis in this step was given to the optimal referencing systems of sEEG electrodes. Third, we compared the simulated and measured potentials for each of the three volume conduction head models, both in a single shaft and global comparison. The comparison results in overall high relative differences, with only slight modulations given by the distance from the stimulation site, the underlying volume conduction head model used and the compartment where the dipolar source is located. Simulation results show that the computation of sEEG forward problem solution is feasible with the same scheme adopted for scalp EEG in the duneuro software (http:// duneuro.org/), and it is stable for different mesh resolutions and source models also for intracranial electrodes, i.e., for electrodes close to the source positions. From this first validation attempt, we can conclude that the distance contact-source modulates the relative error between measured and simulated potential; for the contacts in the white matter compartment we observed the most accurate results, and the results relative to the three and four compartment results were more accurate than the ones relative to the five compartment results. While we achieved topographical errors within 10% for most of the shafts, the amplitude of simulated and measured potentials notably differs

Individualisation of transcranial electric stimulation to improve motor function after stroke:Current challenges and future perspective

Transcranial electric stimulation (tES) is a non-invasive brain stimulation technique that could potentially improve motor rehabilitation after stroke. However, the effects of tES are in general stronger in healthy individuals compared to people with stroke. Interindividual variability in brain structure and function due to stroke potentially explain this difference in effects. This thesis describes the development of methods to facilitate the individualisation of tES in people with stroke and identifies objective neurophysiological correlates of motor learning that could potentially help to monitor the response to tES.In chapter 2, EEG correlates of explicit motor task learning were derived in healthy, young participants. Chapter 3 investigated the effects of 3 different tDCS configurations (sham, targeting contralateral M1 and targeting the full resting motor network) on corticospinal excitability. Both conventional and motor network tDCS did not increase corticospinal excitability relative to sham stimulation. Chapter 4 describes methods to create head models of people with stroke and assesses the effects of stroke lesions on the electric fields within stimulation targets. Chapter 5 describes a method to experimentally determine the electric conductivity of the stroke lesion. Finally, Chapter 6 analyses the electric fields generated by conventional tDCS in people with stroke and age-matched controls. It is shown that the one-size-fits-all approach results in more variable electric fields in people with stroke compared to controls. Optimisation of the electrode positions to maximise the electric field in stimulation targets increases the electric fields in people with stroke to the same level as found in healthy controls.This thesis shows anatomical and motor function variability exists between people with stroke due to differences in lesion characteristics. While there are several opportunities to individualise tES, more research is needed to investigate if this improves the effects of tES. As such, clinical implementation of tES seems unrealistic in the foreseeable future.<br/

Individualisation of transcranial electric stimulation to improve motor function after stroke:Current challenges and future perspective

Transcranial electric stimulation (tES) is a non-invasive brain stimulation technique that could potentially improve motor rehabilitation after stroke. However, the effects of tES are in general stronger in healthy individuals compared to people with stroke. Interindividual variability in brain structure and function due to stroke potentially explain this difference in effects. This thesis describes the development of methods to facilitate the individualisation of tES in people with stroke and identifies objective neurophysiological correlates of motor learning that could potentially help to monitor the response to tES.In chapter 2, EEG correlates of explicit motor task learning were derived in healthy, young participants. Chapter 3 investigated the effects of 3 different tDCS configurations (sham, targeting contralateral M1 and targeting the full resting motor network) on corticospinal excitability. Both conventional and motor network tDCS did not increase corticospinal excitability relative to sham stimulation. Chapter 4 describes methods to create head models of people with stroke and assesses the effects of stroke lesions on the electric fields within stimulation targets. Chapter 5 describes a method to experimentally determine the electric conductivity of the stroke lesion. Finally, Chapter 6 analyses the electric fields generated by conventional tDCS in people with stroke and age-matched controls. It is shown that the one-size-fits-all approach results in more variable electric fields in people with stroke compared to controls. Optimisation of the electrode positions to maximise the electric field in stimulation targets increases the electric fields in people with stroke to the same level as found in healthy controls.This thesis shows anatomical and motor function variability exists between people with stroke due to differences in lesion characteristics. While there are several opportunities to individualise tES, more research is needed to investigate if this improves the effects of tES. As such, clinical implementation of tES seems unrealistic in the foreseeable future.<br/

DUNEuro—A software toolbox for forward modeling in bioelectromagnetism

Accurate and efficient source analysis in electro- and magnetoencephalography using sophisticated realistic head geometries requires advanced numerical approaches. This paper presents DUNEuro, a free and open-source C++ software toolbox for the numerical computation of forward solutions in bioelectromagnetism. Building upon the DUNE framework, it provides implementations of modern fitted and unfitted finite element methods to efficiently solve the forward problems of electro- and magnetoencephalography. The user can choose between a variety of different source models that are implemented. The software’s aim is to provide interfaces that are extendable and easy-to-use. In order to enable a closer integration into existing analysis pipelines, interfaces to Python and MATLAB are provided. The practical use is demonstrated by a source analysis example of somatosensory evoked potentials using a realistic six-compartment head model. Detailed installation instructions and example scripts using spherical and realistic head models are appended

DUNEuro - A software toolbox for forward modeling in bioelectromagnetism

Accurate and efficient source analysis in electro- and magnetoencephalography using sophisticated realistic head geometries requires advanced numerical approaches. This paper presents DUNEuro, a free and open-source C++ software toolbox for the numerical computation of forward solutions in bioelectromagnetism. Building upon the DUNE framework, it provides implementations of modern fitted and unfitted finite element methods to efficiently solve the forward problems of electro- and magnetoencephalography. The user can choose between a variety of different source models that are implemented. The software's aim is to provide interfaces that are extendable and easy-to-use. In order to enable a closer integration into existing analysis pipelines, interfaces to Python and MATLAB are provided. The practical use is demonstrated by a source analysis example of somatosensory evoked potentials using a realistic six compartment head model. Detailed installation instructions and example scripts using spherical and realistic head models are appended.Peer reviewe