Brain and Human Body Modeling

Numerical simulation of the electric fields induced by non-invasive brain stimulation (NIBS), using realistic anatomical head models has gained interest in recent years for understanding the NIBS effects in individual subjects. Although automated tools for generating the head models and performing the electric field simulations have become available, individualized modelling is still not a standard practice in NIBS studies. This is likely partly explained by the lack of robustness and usability of the previously available software tools, and partly by the still developing understanding of the link between physiological effects and electric field distributions in the brain. To facilitate individualized modelling in NIBS, we have introduced the SimNIBS (Simulation of NIBS) software package, providing easy-to-use automated tools for electric field modelling. In this chapter, we give an overview of the modelling pipeline in SimNIBS 2.1, with step-by-step examples of how to run a simulation. Furthermore, we demonstrate a set of scripts for extracting average electric fields for a group of subjects, and finally demonstrate the accuracy of automated placement of standard electrode montages on the head model. SimNIBS 2.1 is freely available at www.​simnibs.​org.

Electroconvulsive therapy (ECT) is widely acknowledged as a highly effective treatment for major depressive disorder, and transcranial brain stimulation techniques in general are of great interest for therapeutic neuromodulation and neurostimulation. It is however difficult to determine the effect of electrical stimulation on the brain due to the complex current pathway between the electrodes, which cannot be readily visualized. Computational models of the human head, combined with a finite element implementation of the Laplace equation, can be used to provide information on the electrical stimulus, such as voltage, current density and electric field distributions, helping to understand the effect of transcranial stimulation on particular brain regions of interest. In this chapter, a detailed protocol for creating a finite element computational head model for transcranial electrical stimulation is provided. Procedures outlined include image segmentation, white matter anisotropy extraction, meshing and finite element model implementation. The computational modelling methods described here can be used, for example, for future novel designs of improved ECT protocols.

Transcranial magnetic stimulation (TMS) is increasingly used as a diagnostic and therapeutic tool for neuropsychiatric disorders. TMS for treatment of depression during pregnancy is an appealing alternative to fetus-threatening drugs. However, there are no studies to date that evaluate the safety of TMS for a pregnant mother and her fetus. Two scenarios are possible in practice: (i) pregnant woman as a patient and (ii) pregnant woman as an operator. The goal of the present study is to estimate maximum field exposures for the fetus in both scenarios. A full-body finite element method (FEM) compatible model of a pregnant woman with about 100 tissue parts has been developed for the present study. This model allows detailed computations of induced current/electric field in every tissue given different locations of a figure-eight coil, a biphasic pulse, common TMS pulse durations, and using different values of the TMS intensity measured in standard motor threshold (SMT) units. Along with the numerical simulations, we use a simple analytical estimation model; both approaches confirm and augment each other. Our simulation/analytical results estimate the maximum peak values of the electric field in the fetal area and beyond in 48 (operator/patient) representative cases, for every fetal tissue separately and for a TMS intensity of one SMT unit. This study provides the first detailed data on risk to fetal exposure to induced fields by TMS in pregnant patients and pregnant operators. It is expandable to any patient/operator configuration by applying a simple analytical upper estimate of field strength/eddy current density.

Major depressive disorder (MDD) is a highly prevalent condition and present first-line treatment options are inadequate. Among nonpharmacological treatment alternatives, noninvasive brain stimulation shows tremendous promise. There are two approved brain stimulation techniques for the treatment of MDD: electroconvulsive therapy (ECT) and repetitive transcranial magnetic stimulation (rTMS). The goal of this study is to quantify the induced electric field (E-field) at the dorsolateral prefrontal cortex (DLPFC) in a group of depressed patients. This study aimed to characterize the induced electric field distributions in a population of patients who received bilateral and/or unilateral electroconvulsive therapy and in a group of patients who received rTMS for the treatment of depression. We also investigated an alternative magnetic stimulation approach, using rotating permanent magnets.

Chronic pain represents a major health problem. Approximately 100 million US adults suffer from common chronic pain conditions, more than the number affected by heart disease, diabetes, and cancer combined. The economic cost of chronic pain in adults is $560–630 billion annually. Chronic low back pain accounts for 22% of all cases of chronic pain and for 35% of most persistent pain sites. The most common diffuse low back neuropathic pain is classified as nonspecific low back pain.

Evidence suggests that electrical stimulation may modify both cause and perception of chronic pain. The objective of this study is to describe the design of a novel, noncontact, large-scale electrostimulation device, which utilizes the concept of a spacious, high-quality electromagnetic resonator operating in the kHz range and at mild subthreshold stimulation levels. Our new resonant neurostimulation approach could probably combine the best of transcutaneous electric nerve stimulation, or TENS, and transcranial magnetic stimulation, or TMS. In the first case, it is a continuous and flexible operation, while in the second case, it is painless, noncontact, and provides deep field penetration.

This study is driven by the limitations of TENS. We introduce a conceptually different electromagnetic stimulation device. Instead of local high-intensity and suprathreshold TENS, we suggest to stimulate the PNS (peripheral nervous system) and muscular system of the entire lower body in a noncontact, patient-friendly way. At the same time, we suggest to use low or subthreshold power levels. In other words, we propose mild yet more broad electromagnetic treatment potentially beneficial for nonspecific chronic pain. The proposed device would primarily affect peripheral nerves, spinal cord, muscles, joints, and bone. Simultaneously, it could influence the somatosensory cortex via many affected pathways, in line with the modern concept of central control of pain.

Our goal is to uncover the mechanism underlying tumour-treating fields’ efficacy in killing cancer cells. Modelling the effects of these 200 kHz alternating current electric fields on tumour cell sub-structures has led us to focus on the microtubules (MTs), C-termini and the motor protein kinesin, which are integral to the critical functions of MT transport of proteins during the delicate orchestration of cell division (mitosis). Leading hypotheses of the TTFields’ mechanism that we are modelling include disruption of mitosis functions (such as the ‘kinesin walk’ along MTs), C-termini state transitions and MT polymerization.

Here we describe preliminary results of a simulation-based study investigating the connection between tumor-treating fields (TTFields) distribution in the brain and glioblastoma patient outcomes. In order to perform this study, we developed a semiautomatic method for creating realistic head models from glioblastoma patient MRI using a deformable template and atlas-based registration. This method, which is described in detail in this chapter, is robust and fast, making it suitable for rapid creation of multiple realistic head models. Using this method, we created 119 head models of newly diagnosed glioblastoma patients that were treated with tumor-treating fields. Finite element simulations were used to simulate delivery of TTFields to these patients, and the connection between field intensity distribution at the tumor bed and patient outcome was analyzed. The result of this analysis support the hypothesis that increasing field intensity at the tumor bed improves patient outcome.

The clinical efficacy of tumor-treating fields (TTFields) against glioblastoma has been established by randomized phase III clinical trials. TTFields work by disrupting intracellular macromolecules that have high dipole moments whose functions are critical for tumor cells progressing through mitosis. However, the distribution of electric fields and the deposition of energies within the brain are poorly understood. Using finite element computer modeling, the electric fields and specific absorption rate can be mapped and represented graphically in volume histograms. Factors that influence the electric field penetration into the gross tumor volume include the amount of cerebrospinal fluid on the convexity of the brain and the presence or absence of a necrotic core within the glioblastoma. The utilization of the electric field-volume histograms and specific absorption rate-volume histograms can help to quantify the adminstered TTFields to the tumor and potentially guide the development of personalized treatments for glioblastoma patients.

Glioblastoma (GBM) is the most common malignant brain tumor and accounts for 70% of all primary brain tumors in adults. Despite aggressive multimodal therapy including surgery, radiation, and chemotherapy, the prognosis remains poor with a median survival of around 2 years. Tumor-treating fields (TTFields) is a new frontier in cancer therapy and has been recently approved for the treatment of GBM. This chapter discusses emerging concepts of brain tumor management, with special emphasis toward novel therapeutic approaches. Recent neuroimaging advances including novel physiologic and metabolic neuroimaging techniques and their role in monitoring treatment-related temporal characteristics and assessing response to this unique treatment modality will also be reviewed.

Tumor-treating fields (TTFields) are a new cancer treatment that inhibits tumor growth with alternating electrical fields. Finite element (FE) methods are used to calculate the intensity of TTFields as a measure of therapeutic “dose.” However, the antitumor efficacy also depends on the direction and exposure time of the induced fields. We recently proposed a new FE approach for TTFields dosimetry that incorporates all these parameters in order to estimate both the unwanted directional field correlation (fractional anisotropy, FA) and the average field intensity. The method uses singular value decomposition to decompose the sequential TTFields over one duty cycle into principal components. Using this method, we showed that significant unwanted FA occurs in many brain regions, potentially affecting therapeutic efficacy. The distribution of FA in the brain varies between different transducer array layouts, and in fact the FA estimate indicates a different order of array performance than predicted from the more conventional estimate of field intensity. Furthermore, we found that resection of the tumor tends to nullify field distribution differences between array layouts while also significantly increasing FA. Contrary to the current practice, these results suggest that it may be more effective to place the TTFields arrays to maximize the field intensity in the tumor, rather than trying to maintain macroscopic orthogonality between the layout pairs. This principal component analysis framework has a number of potential applications, including technology development, outcome prognostication, improved treatment planning, and activation cycle optimization. This chapter describes the framework for the principal component calculations and recapitulates the current results. Furthermore, it provides a detailed outline of how patient-specific head models can be used to calculate the field distribution for individual patients.

This chapter presents an overview of electric conduction in living cells when viewed as a composition of bioelectric circuits. We review the cell’s components that are known to exhibit electric conduction properties and represent them as parts of a complex circuitry. In particular, we discuss conductivity of the membrane, ion channels, actin filaments, DNA, and microtubules, each of which play important roles in the biological functioning of the cell. A new picture emerges where electrical conduction within the cell is taking place in an integrated fashion and may explain synchronization and orchestration of the cell dynamics.

A brain haemorrhage constitutes a serious medical scenario with a need for rapid, accurate detection to facilitate treatment initiation. Machine learning (ML) techniques applied to such medical diagnostic problems can improve the rate and accuracy of bleed detection leading to improved patient outcomes. In this chapter we examine the potential role of support vector machine (SVM) type classifiers in detecting such haemorrhagic lesions (bleeds) using electrical impedance tomography (EIT) measurement frames as the source of training and test data. A two-layer computational model of the head is designed, with EIT frame generation simulated from electrodes placed on the surface of the head model. A wide variety of test scenarios are modelled, including variations in measurement noise, bleed size and location, electrode position, and anatomy. Initial results using a linear SVM classifier applied to test scenarios, with and without pre-processing of the EIT measurement frame, are summarised. The classifier returned detection accuracies >90% with signal-to-noise ratios of ≥60 dB; was independent of bleed location, capable of detecting bleeds as small as 10 ml; and was unaffected by slight variances of ±2 mm in electrode position. However, the performance was degraded with anatomical variations. Options for improvement of performance, including selection of a different kernel and pre-processing of the frames prior to implementing the classifier, are then examined. This analysis demonstrated that using the radial basis function as the kernel for the SVM classifier and principal component analysis (PCA) to select specific features leads to the most accurate and robust performance. The analysis and results indicate that the coupling of EIT with ML has potential for improvement in the detection of bleeds such as brain haemorrhages.

The interaction of electromagnetic (EM) fields with the human body during magnetic resonance imaging (MRI) is complex and subject specific. MRI radiofrequency (RF) coil performance and safety assessment typically includes numerical EM simulations with a set of human body models. The dimensions of mesh elements used for discretization of the EM simulation domain must be adequate for correct representation of the MRI coil elements, different types of human tissue, and wires and electrodes of additional devices. Examples of such devices include those used during electroencephalography, transcranial magnetic stimulation, and transcranial direct current stimulation, which record complementary information or manipulate brain states during MRI measurement. The electrical contact within and between tissues, as well as between an electrode and the skin, must also be preserved. These requirements can be fulfilled with anatomically correct surface-based human models and EM solvers based on unstructured meshes. Here, we report (i) our workflow used to generate the surface meshes of a head and torso model from the segmented AustinMan dataset, (ii) head and torso model mesh optimization for three-dimensional EM simulation in ANSYS HFSS, and (iii) several case studies of MRI RF coil performance and safety assessment.

Despite its importance as a diagnostic tool, patients with active implantable medical devices (AIMDs) are generally denied access to magnetic resonance imaging (MRI). Numerous scan parameters and a multiplicity of fields yield a complex MR environment for device safety assessment. Computational human models are used to quantify the RF-induced energy at the AIMD ports during the evaluation of RF-induced malfunction and unintended stimulation hazards. This work discusses this process, with an in-depth investigation of the RF-induced voltage at the AIMD antenna.

When accidents by industrial radiography sources occur, it is necessary to accurately and quickly estimate radiation doses for the effective treatment of those individuals with acute radiation syndrome (ARS). In the present study, a comprehensive set of absorbed dose coefficients (DCs) was obtained by performing Monte Carlo simulations using computational human phantoms of different body sizes. These DCs provide an “initial and rapid” dose estimation for individuals accidentally exposed to industrial radiography sources. The adult mesh-type ICRP reference computational phantoms (MRCPs) and the adult 10th and 90th percentile computational phantoms, constructed by deforming the MRCPs, were implemented in the Geant4 Monte Carlo code. We subsequently simulated the most commonly used industrial radiography sources (i.e., ¹⁹²Ir and ⁶⁰Co) placed in 72 different locations near the human body. It was found that body size significantly influences the DCs, especially when the source is closer than 1 m to the human body, which is a case frequently encountered during industrial radiography accidents. Acknowledging the significance of these results, the ICRP is planning to include the full set of the calculated DCs from this study in a forthcoming ICRP Publication, which is being prepared by the ICRP Committee 2 Task Group 103 “Mesh-type Reference Computational Phantoms.”

Microwave ablation is used clinically to thermally ablate cancerous tissue in the liver and other organs. Physicians may use multiple applicators simultaneously when treating large tumor volumes. Preclinical simulation and experimental studies most often presume parallel insertion of applicators. However, due to anatomical constraints, such as the presence of ribs and the diaphragm, it may be challenging to insert applicators in a parallel configuration. Here, we describe computational models of dual-antenna microwave ablation that account for the effects of temperature-dependent changes in tissue properties. We have also implemented a system for experimental assessment of dual-antenna microwave ablation profiles in ex vivo tissues. Utilizing 3D printing, we have constructed a device that precisely positions antennas within experimental tissue samples and allows for accurate sectioning of the ablation zone relative to the plane of antenna insertion. Furthermore, we implemented image processing techniques for quantifying the size and shape of experimental ablation zones. This enables more accurate and repeatable comparisons of ablation profiles between simulations and experiments. We found that for an inter-antenna spacing ranging from 10 to 20 mm, simulations and experiments indicated that non-parallel antenna insertion results in ablation zone volumes that may change by up to 30%.

Over the past 20 years, the coupling of computational human phantoms within existing Monte Carlo radiation transport codes has required phantoms to be in a voxelized format. Very recently, however, several popular radiation transport codes such as MCNP6, GEANT4, and PHITS now facilitate direct radiation transport in phantoms that are represented by either polygon or tetrahedral mesh structures – surfaces and volumes, respectively, that define both the phantom’s body contour and its array of internal organs. While both voxel-based and mesh-based phantoms provide a high degree of anatomic realism as compared to first-generation stylized (or mathematical) phantoms, mesh-type phantoms are now considered the state of the art as they permit re-sculpting of individual organs, body regions, and overall body size and shape. They also allow for extremity articulations and inclusion of thin tissue layers of radiobiological importance. These are all features that are either not permitted in or are not readily available to voxel-based phantoms. Nevertheless, since the late 1980s, a tremendous number of voxel-based computational human phantoms have been developed from image segmentation of patient CT or MR data. As a result, there is a need for conversion of these existing voxel phantoms to mesh-type formats. The present work describes an efficient and accurate algorithm to convert voxel-based phantoms to mesh-based formats, thus permitting the user to take full advantage of these additional modeling features. For this conversion, a boundary detection algorithm was implemented in conjunction with polygon detection to form high-quality mesh data suitable for radiation transport simulation or finite element analysis. This conversion can result in a significant reduction of required simulation time and can allow current voxel data to be used in modern CAD software.

An approximate method to model respiratory motion in a CAD human model subject to electromagnetic (or acoustic, thermal) finite-element analysis is suggested and described. Its concept implies using affine transformations, which are implemented in commercial FEM software packages, in the form of a parametric sweep. This method does not require multiple copies of the CAD model or multiple project files. It enables use of arbitrary sampling times and an automatic reposition of on-body and in-body devices. The method was applied to the platform-independent full-body electromagnetic computational model Visible Human Project® (VHP)-Female v. 3.1. Examples of scattering calculations and antenna modeling are provided.

Radio frequency wave propagation near the surface of a human body is highly sensitive to a number of items, including skin geometry, material composition, and proximity to internal air-filled cavities. This study develops an anatomically realistic model of a human ear canal that is integrated into a full-body computational phantom to address this final factor and examines, through numerical simulation, the impact of this level of detail on the power transmission of two-port and larger networks operating near or on the human body in the UHF band. Numerical results are validated with experimental measurements.

Electrical properties tomography (EPT) has been studied in order to non-invasively map the conductivity and permittivity of brain tissues. Various approaches have been investigated for predicting the electrical properties (EPs) at either very low or high Larmor frequencies. An example of the latter is water-based EPT (wEPT), which derives the EP maps from the image ratio of two T1-weighted images with different repetition times. The rationale is based on the fact that EPs can be modeled as monotonic functions of water content, which in turn can be estimated from the image ratio. The objective of this study is to examine if wEPT can be adapted to accurately create conductivity maps of the brain in the frequency range of 200–1000 kHz, and if EPs of pathological tissues can be estimated using the same approach. These frequencies are of particular interest as a cancer treatment modality, Tumor Treating Fields, utilizes electric fields with 200 kHz to treat glioblastoma. The treatment efficacy depends on the delivered field intensity at the target, which is determined by the EP distribution within the brain and specifically within the heterogeneous tumor. Experimental measurements and wEPT imaging estimations were performed in animal brain samples and tumor-bearing rats. These studies suggest that wEPT-estimated water content maps are reliable. In calf samples, the average error of wEPT-estimated water content is between 2.5% and 3.5%, and in rat samples between 2.2% and 6.4% (measurement error ± 1%). For wEPT estimations of conductivity at 200 kHz, the average errors range from 13.2% to 13.6% in calf tissue samples and from 20.7% to 22.1% rat tissue samples (measurement error ± 10%). Testing various brain and tumor tissues demonstrates a clear trend for wEPT estimations of water content and EPs. The approach might be enhanced by including adaptions according to additional radiological features derived from other imaging modalities, such as T2-weighted imaging or diffusion imaging.