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Brain and Human Body Modeling : Computational Human Modeling at EMBC 2018.

Bibliographic Details
Main Author: Makarov, Sergey.
Other Authors: Horner, Marc., Noetscher, Gregory.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2019.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Preface to Computation Human Models and Brain Modeling: EMBC 2018
  • Contents
  • Part I: Human Body Models for Non-invasive Stimulation
  • Chapter 1: SimNIBS 2.1: A Comprehensive Pipeline for Individualized Electric Field Modelling for Transcranial Brain Stimulation
  • 1.1 Introduction
  • 1.2 Overview of the SimNIBS Workflow
  • 1.2.1 Structural Magnetic Resonance Imaging Scans
  • 1.2.2 Volume Conductor Modelling
  • 1.2.3 Simulation Setup
  • 1.2.4 Finite Element Method Calculations
  • 1.2.5 Mapping Fields
  • 1.3 Practical Examples and Use Cases
  • 1.3.1 Hello SimNIBS: How to Process a Single Subject
  • Generating the Volume Conductor Model
  • Setting Up a Simulation
  • Visualizing Fields
  • 1.3.2 Advanced Usage: Group Analysis
  • Head Meshing
  • Write a Python or MATLAB Script
  • Visualizing Results
  • 1.4 The Accuracy of Automatic EEG Positioning
  • 1.5 Conclusion
  • References
  • Chapter 2: Finite Element Modelling Framework for Electroconvulsive Therapy and Other Transcranial Stimulations
  • 2.1 Introduction
  • 2.2 Methods
  • 2.2.1 Image Pre-processing
  • Bias Field Correction
  • Image Registration
  • Image Segmentation
  • Manual Segmentation
  • Surface Smoothing
  • Cortical Structure Labelling
  • Challenges and Tips in Segmentation
  • 2.2.2 White Matter Anisotropy
  • 2.2.3 FE Meshing
  • 2.2.4 Physics and Property Settings
  • Tissue Conductivity
  • Electrode Placement
  • Other Boundary Conditions
  • Numerical Solver Settings
  • 2.3 Simulation Results
  • 2.3.1 Electric Feld for Three ECT Electrode Configurations
  • 2.4 Discussion
  • 2.4.1 Model Extensions
  • Subject-Specific Tissue Conductivity
  • 2.5 Conclusion
  • References
  • Chapter 3: Estimates of Peak Electric Fields Induced by Transcranial Magnetic Stimulation in Pregnant Women as Patients or Operators Using an FEM Full-Body Model
  • 3.1 Introduction
  • 3.2 Methods and Materials.
  • 3.2.1 Existing Computational Models of a Pregnant Woman
  • 3.2.2 Construction of FEM (CAD) Full-Body Pregnant Woman Model and Model Topology
  • 3.2.3 Tissue Properties
  • 3.3 Study Design
  • 3.3.1 TMS Coil
  • 3.3.2 Pulse Form and Duration
  • 3.3.3 Coil Current
  • 3.3.4 Coil Positions
  • 3.3.5 Accidental Coil Discharge
  • 3.3.6 Frequency-Domain Computations
  • 3.3.7 Time-Domain Computations
  • 3.3.8 Finding Maximum Peak Current Density/Electric Field Strength in Individual Tissues
  • 3.4 Results: Pregnant Patient
  • 3.4.1 Qualitative Behavior of Induced Currents in the Body of a Pregnant Patient at Different Frequencies (Pulse Durations)
  • 3.4.2 Quantitative Results for Maximum Peak Electric Field at One SMT Unit
  • 3.4.3 Comparison with the Recommended Safe Value of Electric Field
  • 3.4.4 Observations from the Quantitative Solution
  • 3.4.5 Comparison with Upper Analytical Estimate for Electric Fields/Eddy Currents
  • 3.4.6 Using the Analytical Estimate for Predicting Maximum Fields for Different Patients
  • 3.5 Results: Pregnant Operator and Accidental Coil Discharge
  • 3.5.1 Quantitative Results for Maximum Peak Electric Field at One SMT Unit
  • 3.5.2 Accidental Coil Discharge
  • 3.6 Conclusion
  • Japanese Virtual Model (JVM) Finite-Element Model Version 1.1 (6 months)
  • References
  • Chapter 4: Electric Field Modeling for Transcranial Magnetic Stimulation and Electroconvulsive Therapy
  • 4.1 Introduction
  • 4.2 Modeling Methods
  • 4.2.1 ECT Modeling
  • 4.2.2 rTMS Modeling
  • 4.2.3 sTMS Modeling
  • 4.3 Results
  • 4.3.1 Electric Field Induced by ECT
  • 4.3.2 Electric Field Induced by rTMS
  • 4.3.3 Electric Field Induced by sTMS
  • 4.4 Discussion
  • 4.5 Conclusion
  • References
  • Chapter 5: Design and Analysis of a Whole-Body Noncontact Electromagnetic Subthreshold Stimulation Device with Field Modulation Targeting Nonspecific Neuropathic Pain.
  • 5.1 Introduction
  • 5.2 Materials and Methods
  • 5.2.1 Suprathreshold Versus Subthreshold Stimulation
  • 5.2.2 Concept of the Magnetic Stimulator. Two-Dimensional Analytical Solution for Solenoidal E-Field
  • 5.2.3 Three-Dimensional Coil Resonator Design. Solenoidal E-Field
  • 5.2.4 Solenoidal Electric Field Distribution with and without a Simple Conducting Object
  • 5.2.5 Contribution of Unpaired Electric Charges
  • 5.2.6 Power Amplifier/Driver
  • 5.2.7 Coupling and Matching the Power Amplifier to the Resonating Coil
  • 5.2.8 Tuning Procedure
  • 5.2.9 Coil Assembly, Device Setup, and Operation
  • 5.2.10 Quality Factor of the Resonator and the Magnetic Field Strength
  • 5.3 Device Safety Estimates
  • 5.3.1 Peripheral Nervous System (PNS) Stimulation Threshold
  • 5.3.2 Specific Absorption Rate (SAR)
  • 5.3.3 Method of Analysis
  • 5.3.4 Electric Field Levels
  • 5.3.5 SAR Levels
  • 5.4 Discussion
  • 5.4.1 Efficacy of Stimulation
  • 5.4.2 Integrated Effect of Stimulation
  • 5.4.3 Operation as an EMAT
  • 5.4.4 Variation of Resonant Frequency
  • 5.5 Conclusion
  • Appendix A: Derivation of Eq. (5.7) and Coil Q
  • References
  • Part II: Tumor Treating Fields (TTFs)
  • Chapter 6: Simulating the Effect of 200 kHz AC Electric Fields on Tumour Cell Structures to Uncover the Mechanism of a Cancer Therapy
  • 6.1 Introduction
  • 6.2 Overview of the Models
  • 6.2.1 Why Computer Modelling?
  • 6.2.2 Axiomatizing the Underlying Systems Level
  • 6.3 Clues to the Mechanisms Are Constraints on the Models
  • 6.4 Candidates for TTFields Mechanisms
  • 6.5 Disruption Metrics Derived from Signal-to-Noise Ratio
  • 6.6 Models and Results
  • 6.6.1 MT Resonance
  • Electromechanical Model
  • 6.6.2 MT Conductivity
  • MT as a Multi-Layered Cable
  • 6.6.3 C-Termini State Disruption
  • Model Calibration
  • 6.6.4 Kinesin Walk Diffusion Hypothesis
  • 6.7 Conclusion
  • References.
  • Chapter 7: Investigating the Connection Between Tumor-Treating Fields Distribution in the Brain and Glioblastoma Patient Outcomes. A Simulation-Based Study Utilizing a Novel Model Creation Technique
  • 7.1 Introduction
  • 7.2 Methods
  • 7.2.1 MRI Data Used for the Study
  • 7.2.2 Image Preprocessing
  • 7.2.3 MRI Full Head Completion
  • 7.2.4 High-Resolution Reconstruction
  • 7.2.5 Background Noise Reduction
  • 7.2.6 Patient Model Creation
  • 7.2.7 Placement of Transducer Arrays on the Model
  • Automatic Identification of Landmarks and Determination of the Array Positions
  • Positioning of Anchor Points to Assist with Array Placement
  • Finding the Center of All Disks in an Array
  • Creating Cylinders Representing the Ceramic Disks and the Medical Gel
  • 7.2.8 Simulations
  • 7.2.9 Analysis
  • 7.3 Results
  • 7.4 Discussion and Conclusion
  • References
  • Chapter 8: Insights from Computer Modeling: Analysis of Physical Characteristics of Glioblastoma in Patients Treated with Tumor-Treating Fields
  • 8.1 Introduction
  • 8.2 TTFields Is Another Treatment Modality from the Electromagnetic Spectrum
  • 8.3 Quantifying Electric Field Delivery in the Brain
  • 8.4 Clinical Outcome from TTFields Treatment
  • 8.5 Conclusion
  • References
  • Chapter 9: Advanced Multiparametric Imaging for Response Assessment to Tumor-Treating Fields in Patients with Glioblastoma
  • 9.1 Introduction
  • 9.2 Tumor-Treating Fields: Scientific Basis
  • 9.3 Tumor-Treating Fields: Clinical Application in GBM Patients
  • 9.4 Tumor-Treating Fields: Advanced Neuroimaging Techniques
  • 9.5 Tumor-Treating Fields: Initial Experience
  • 9.6 Conclusion
  • References
  • Chapter 10: Estimation of TTFields Intensity and Anisotropy with Singular Value Decomposition: A New and Comprehensive Method for Dosimetry of TTFields
  • 10.1 Introduction.
  • 10.2 Preparation of Computational Models and Calculation of the Electrical Field
  • 10.2.1 Laplace's Equation: The Electro-quasistatic Approximation of Maxwell's Equations
  • 10.2.2 The Finite Element Framework for TTFields
  • 10.2.3 Creation of Personalized Head Models
  • 10.2.4 Placement of Transducer Arrays
  • 10.2.5 Assignment of Tissue Conductivity
  • 10.3 Dosimetry of TTFields
  • 10.3.1 The Problem
  • 10.3.2 The Basic Framework
  • 10.3.3 Estimation of the TTFields Intensity
  • 10.3.4 Estimating the Spatial Correlation of TTFields Using the Fractional Anisotropy (FA) Measure
  • 10.3.5 Step-by-Step Framework for Calculation of FA and Eavr
  • 10.4 Results from Example Calculations
  • 10.4.1 Topographical Distributions of FA and Eavr
  • 10.4.2 Variations in FA and Eavr for Different Array Layouts
  • 10.4.3 Optimization of the TTFields Activation Cycle to Reduce Unwanted Field Anisotropy
  • 10.5 Summary
  • References
  • Chapter 11: The Bioelectric Circuitry of the Cell
  • 11.1 Introduction
  • 11.2 Ion Channel Conduction Effects
  • 11.3 Actin Filament Conductivity
  • 11.4 Microtubule Conductivity
  • 11.5 Conclusions
  • References
  • Part III: Electromagnetic Safety
  • Chapter 12: Brain Haemorrhage Detection Through SVM Classification of Electrical Impedance Tomography Measurements
  • 12.1 Introduction
  • 12.2 Technologies
  • 12.2.1 Electrical Impedance Tomography
  • 12.2.2 Support Vector Machine (SVM) Classifiers
  • 12.2.3 Computational Modelling Techniques
  • 12.3 SVM Applied to Raw EIT Measurement Frames with Analysis of the Effect of Individual Variables on SVM Performance
  • 12.3.1 The Effect of Noise
  • 12.3.2 Effect of Bleed Location
  • 12.3.3 Effect of Bleed Size
  • 12.3.4 Effect of Electrode Positioning
  • 12.3.5 Effect of Normal Variation in Between-Patient Anatomy
  • 12.4 SVM Applied to EIT Processed Measurement Frames.
  • 12.4.1 Radial Basis Function Kernel Compared to Linear Kernel.

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