High Resolution Computational Modeling of Transcranial Stimulation using the MIDA Head Model

2021 ◽  
Author(s):  
William A. Wartrnan ◽  
Edward H. Burnham ◽  
Sergey N. Makarov ◽  
Mathias Davids ◽  
Mohammad Daneshzand ◽  
...  
Keyword(s):  
High Resolution ◽  
Computational Modeling ◽  
Head Model ◽  
Transcranial Stimulation

Computational Modeling of Blunt Impact to Head and Correlation of Biomechanical Measures With Medical Images

2018 ◽  
Author(s):  
X. Gary Tan ◽  
Maria M. D’Souza ◽  
Subhash Khushu ◽  
Raj K. Gupta ◽  
Virginia G. DeGiorgi ◽  
...  
Keyword(s):  
Brain Injury ◽  
High Resolution ◽  
Computational Modeling ◽  
Diffusion Tensor ◽  
Medical Images ◽  
Human Head ◽  
Biological Tissues ◽  
Brain Morphology ◽  
Head Model ◽  
Human Head Model

Mild traumatic brain injury (TBI) is a very common injury to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which can aid in the development of effective personal protective equipment. This paper attempts to correlate simulation results with clinical data from advanced techniques such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), MR spectroscopy and susceptibility weighted imaging (SWI), to identify TBI related subtle alterations in brain morphology, function and metabolism. High-resolution image data were obtained from the MRI scan of a young adult male, from a concussive head injury caused by a road traffic accident. The falling accident of human was modeled by combing high-resolution human head model with an articulated human body model. This mixed, multi-fidelity computational modeling approach can efficiently investigate such accident-related TBI. A high-fidelity computational head model was used to accurately reproduce the complex structures of the head. For most soft materials, the hyper-viscoelastic model was used to captures the strain rate dependence and finite strain nonlinearity. Stiffer materials, such as bony structure were simulated using an elasto-plastic material model to capture the permanent deformation. We used the enhanced linear tetrahedral elements to remove the parasitic locking problem in modeling such incompressible biological tissues. The bio-fidelity of human head model was validated from human cadaver tests. The accidental fall was reconstructed using such multi-fidelity models. The localized large deformation in the head was simulated and compared with the MRI images. The shear stress and shear strain were used to correlate with the post-accident medical images with respect to the injury location and severity in the brain. The correspondence between model results and MRI findings further validates the human head models and enhances our understanding of the mechanism, extent and impact of TBI.

scholarly journals A Software Toolkit for TMS Electric-Field Modeling with Boundary Element Fast Multipole Method: An Efficient MATLAB Implementation

2020 ◽  
Author(s):  
Sergey N. Makarov ◽  
William A. Wartman ◽  
Mohammad Daneshzand ◽  
Kyoko Fujimoto ◽  
Tommi Raij ◽  
...  
Keyword(s):  
High Resolution ◽  
Boundary Element ◽  
Electric Fields ◽  
Fast Multipole Method ◽  
Human Head ◽  
Head Model ◽  
Fast Multipole ◽  
Software Toolkit ◽  
Model Repository ◽  
Multipole Method

AbstractBackgroundTranscranial magnetic stimulation (TMS) is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields (E-fields) may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.ObjectiveTo present and disseminate our TMS modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model.MethodThe recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways.Results and ConclusionsThe improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development.

scholarly journals Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons

2020 ◽  
pp. 195-211
Author(s):  
Sergey N. Makarov ◽  
Jyrki Ahveninen ◽  
Matti Hämäläinen ◽  
Yoshio Okada ◽  
Gregory M. Noetscher ◽  
...  
Keyword(s):  
High Resolution ◽  
Boundary Element ◽  
Multiscale Modeling ◽  
Cluster Size ◽  
Fast Multipole Method ◽  
Head Model ◽  
Current Dipole ◽  
Neocortical Neurons ◽  
Dipole Sources ◽  
Compact Cluster

AbstractIn this study, the boundary element fast multipole method or BEM-FMM is applied to model compact clusters of tightly spaced pyramidal neocortical neurons firing simultaneously and coupled with a high-resolution macroscopic head model. The algorithm is capable of processing a very large number of surface-based unknowns along with a virtually unlimited number of elementary microscopic current dipole sources distributed within the neuronal arbor.The realistic cluster size may be as large as 10,000 individual neurons, while the overall computation times do not exceed several minutes on a standard server. Using this approach, we attempt to establish how well the conventional lumped-dipole model used in electroencephalography/magnetoencephalography (EEG/MEG) analysis approximates a compact cluster of realistic neurons situated either in a gyrus (EEG response dominance) or in a sulcus (MEG response dominance).

scholarly journals Functional physiology of the human terminal antrum defined by high-resolution electrical mapping and computational modeling

2016 ◽  
Vol 311 (5) ◽  
pp. G895-G902 ◽  
Author(s):  
Rachel Berry ◽  
Taimei Miyagawa ◽  
Niranchan Paskaranandavadivel ◽  
Peng Du ◽  
Timothy R. Angeli ◽  
...  
Keyword(s):  
High Resolution ◽  
Computational Modeling ◽  
Slow Wave ◽  
High Amplitude ◽  
Cfd Modeling ◽  
Maximal Velocity ◽  
Anatomical Landmarks ◽  
Electrode Arrays ◽  
Gastric Slow Wave ◽  
Resultant Data

High-resolution (HR) mapping has been used to study gastric slow-wave activation; however, the specific characteristics of antral electrophysiology remain poorly defined. This study applied HR mapping and computational modeling to define functional human antral physiology. HR mapping was performed in 10 subjects using flexible electrode arrays (128–192 electrodes; 16–24 cm2) arranged from the pylorus to mid-corpus. Anatomical registration was by photographs and anatomical landmarks. Slow-wave parameters were computed, and resultant data were incorporated into a computational fluid dynamics (CFD) model of gastric flow to calculate impact on gastric mixing. In all subjects, extracellular mapping demonstrated normal aboral slow-wave propagation and a region of increased amplitude and velocity in the prepyloric antrum. On average, the high-velocity region commenced 28 mm proximal to the pylorus, and activation ceased 6 mm from the pylorus. Within this region, velocity increased 0.2 mm/s per mm of tissue, from the mean 3.3 ± 0.1 mm/s to 7.5 ± 0.6 mm/s ( P < 0.001), and extracellular amplitude increased from 1.5 ± 0.1 mV to 2.5 ± 0.1 mV ( P < 0.001). CFD modeling using representative parameters quantified a marked increase in antral recirculation, resulting in an enhanced gastric mixing, due to the accelerating terminal antral contraction. The extent of gastric mixing increased almost linearly with the maximal velocity of the contraction. In conclusion, the human terminal antral contraction is controlled by a short region of rapid high-amplitude slow-wave activity. Distal antral wave acceleration plays a major role in antral flow and mixing, increasing particle strain and trituration.

scholarly journals Solving High-Resolution Forward Problems for Extra- and Intracranial Neurophysiological Recordings Using Boundary Element Fast Multipole Method

2019 ◽  
Author(s):  
Sergey N Makarov ◽  
Matti Hämäläinen ◽  
Yoshio Okada ◽  
Gregory M Noetscher ◽  
Jyrki Ahveninen ◽  
...  
Keyword(s):  
High Resolution ◽  
Boundary Element ◽  
Fast Multipole Method ◽  
Field Potential ◽  
Head Model ◽  
Practical Case ◽  
Source Modeling ◽  
Fast Multipole ◽  
Forward Solution ◽  
Multipole Method

AbstractWe present a general numerical approach for solving the forward problem in high-resolution. This approach can be employed in the analysis of noninvasive electroencephalography (EEG) and magnetoencephalography (MEG) as well as invasive electrocorticography (ECoG), stereoencephalography (sEEG), and local field potential (LFP) recordings. The underlying algorithm is our recently developed boundary element fast multipole method (BEM-FMM) that simulates anatomically realistic head models with unprecedented numerical accuracy and speed. This is achieved by utilizing the adjoint double layer formulation and zeroth-order basis functions in conjunction with the FMM acceleration. We present the mathematical formalism in detail and validate the method by applying it to the canonical multilayer sphere problem. The numerical error of BEM-FMM is 2-10 times lower while the computational speed is 1.5–20 times faster than those of the standard first-order FEM. We present four practical case studies: (i) evaluation of the effect of a detailed head model on the accuracy of EEG/MEG forward solution; (ii) demonstration of the ability to accurately calculate the electric potential and the magnetic field in the immediate vicinity of the sources and conductivity boundaries; (iii) computation of the field of a spatially extended cortical equivalent dipole layer; and (iv) taking into account the effect a fontanel for infant EEG source modeling and comparison of the results with a commercially available FEM. In all cases, BEM-FMM provided versatile, fast, and accurate high-resolution modeling of the electromagnetic field and has the potential of becoming a standard tool for modeling both extracranial and intracranial electrophysiological signals.

scholarly journals High-Resolution EEG Source Localization in Segmentation-Free Head Models Based on Finite-Difference Method and Matching Pursuit Algorithm

2021 ◽  
Vol 15 ◽  
Author(s):  
Takayoshi Moridera ◽  
Essam A. Rashed ◽  
Shogo Mizutani ◽  
Akimasa Hirata
Keyword(s):  
Inverse Problem ◽  
High Resolution ◽  
Finite Difference ◽  
Finite Difference Method ◽  
Source Localization ◽  
Difference Method ◽  
Computational Cost ◽  
Head Model ◽  
Eeg Source Localization ◽  
The Brain

Electroencephalogram (EEG) is a method to monitor electrophysiological activity on the scalp, which represents the macroscopic activity of the brain. However, it is challenging to identify EEG source regions inside the brain based on data measured by a scalp-attached network of electrodes. The accuracy of EEG source localization significantly depends on the type of head modeling and inverse problem solver. In this study, we adopted different models with a resolution of 0.5 mm to account for thin tissues/fluids, such as the cerebrospinal fluid (CSF) and dura. In particular, a spatially dependent conductivity (segmentation-free) model created using deep learning was developed and used for more realist representation of electrical conductivity. We then adopted a multi-grid-based finite-difference method (FDM) for forward problem analysis and a sparse-based algorithm to solve the inverse problem. This enabled us to perform efficient source localization using high-resolution model with a reasonable computational cost. Results indicated that the abrupt spatial change in conductivity, inherent in conventional segmentation-based head models, may trigger source localization error accumulation. The accurate modeling of the CSF, whose conductivity is the highest in the head, was an important factor affecting localization accuracy. Moreover, computational experiments with different noise levels and electrode setups demonstrate the robustness of the proposed method with segmentation-free head model.

scholarly journals Computational fluid dynamics of cerebral aneurysm coiling using high-resolution and high-energy synchrotron X-ray microtomography: comparison with the homogeneous porous medium approach

2016 ◽  
Vol 9 (8) ◽  
pp. 00.1-00 ◽  
Author(s):  
Michael R Levitt ◽  
Michael C Barbour ◽  
Sabine Rolland du Roscoat ◽  
Christian Geindreau ◽  
Venkat K Chivukula ◽  
...  
Keyword(s):  
Fluid Dynamics ◽  
Porous Medium ◽  
High Resolution ◽  
Computational Modeling ◽  
Intracranial Aneurysms ◽  
High Resolution Imaging ◽  
X Ray ◽  
Hemodynamic Variables ◽  
Homogeneous Porous Medium ◽  
Resolution Imaging

BackgroundComputational modeling of intracranial aneurysms provides insights into the influence of hemodynamics on aneurysm growth, rupture, and treatment outcome. Standard modeling of coiled aneurysms simplifies the complex geometry of the coil mass into a homogeneous porous medium that fills the aneurysmal sac. We compare hemodynamics of coiled aneurysms modeled from high-resolution imaging with those from the same aneurysms modeled following the standard technique, in an effort to characterize sources of error from the simplified model.MaterialsPhysical models of two unruptured aneurysms were created using three-dimensional printing. The models were treated with coil embolization using the same coils as those used in actual patient treatment and then scanned by synchrotron X-ray microtomography to obtain high-resolution imaging of the coil mass. Computational modeling of each aneurysm was performed using patient-specific boundary conditions. The coils were modeled using the simplified porous medium or by incorporating the X-ray imaged coil surface, and the differences in hemodynamic variables were assessed.ResultsX-ray microtomographic imaging of coils and incorporation into computational models were successful for both aneurysms. Porous medium calculations of coiled aneurysm hemodynamics overestimated intra-aneurysmal flow, underestimated oscillatory shear index and viscous dissipation, and over- or underpredicted wall shear stress (WSS) and WSS gradient compared with X-ray-based coiled computational fluid dynamics models.ConclusionsComputational modeling of coiled intracranial aneurysms using the porous medium approach may inaccurately estimate key hemodynamic variables compared with models incorporating high-resolution synchrotron X-ray microtomographic imaging of complex aneurysm coil geometry.

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