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Brain and Human Body Modeling 2020 : Computational Human Models Presented at EMBC 2019 and the BRAIN Initiative® 2019 Meeting.

Bibliographic Details
Main Author: Makarov, Sergey N.
Other Authors: Noetscher, Gregory M., Nummenmaa, Aapo.
Format: eBook
Language:English
Published: Cham : Springer International Publishing AG, 2020.
Edition:1st ed.
Subjects:
Electronic books.
Online Access:Click to View
Table of Contents:
  • Intro
  • Foreword
  • Contents
  • Part I: Tumor Treating Fields
  • Tumor-Treating Fields at EMBC 2019: A Roadmap to Developing a Framework for TTFields Dosimetry and Treatment Planning
  • 1 Introduction
  • 2 An Outline for TTFields Dosimetry and Treatment Planning
  • 3 TTFields Dosimetry
  • 4 Patient-Specific Model Creation
  • 5 Advanced Imaging for Monitoring Response to Therapy
  • 6 Discussion and Conclusions
  • References
  • How Do Tumor-Treating Fields Work?
  • 1 Introduction
  • 1.1 TTFields Affect Large, Polar Molecules
  • 1.2 The Need for a ``Complete ́́TTFields Theory
  • 2 Empirical Clues to TTFields MoA
  • 2.1 TTFields Only Kill Fast-Dividing Cells
  • 2.2 TTFields Require 2-4 V/cm Field Strength
  • 2.3 TTFields Are Frequency-Sensitive and Effective Only in the 100-300 KHz Range
  • 2.4 TTFields Are Highly Directional
  • 2.5 TTFields Have Their Strongest Effect in Prophase and Metaphase
  • 2.6 TTFields Increase Free Tubulin and Decrease Polymerized Tubulin in the Mitotic Spindle Region
  • 3 Candidate Mechanisms of Action (MoA)
  • 3.1 Dielectrophoretic (DEP) Effects
  • 3.2 Microtubule Effects
  • 3.3 Septin Effects
  • 3.4 Is Intrinsic Apoptosis the Key Signaling Pathway Triggered by TTFields?
  • 4 Conclusion
  • References
  • A Thermal Study of Tumor-Treating Fields for Glioblastoma Therapy
  • 1 Introduction
  • 1.1 Electromagnetic Radiation and Matter
  • 1.2 Tumor-Treating Fields
  • 1.3 The Optune Device
  • 2 Methods
  • 2.1 The Realistic Human Head Model
  • 2.2 Heat Transfer in TTFields: Relevant Mechanisms
  • 2.2.1 Conduction
  • 2.2.2 Convection
  • 2.2.3 Radiation
  • 2.2.4 Sweat
  • 2.2.5 Metabolism
  • 2.2.6 Blood Perfusion
  • 2.2.7 Joule Heating
  • 2.3 Heat Transfer in TTFields: Pennes ́Equation
  • 2.4 Simulations ́Conditions
  • 3 Results
  • 3.1 Duty Cycle and Effective Electric Field at the Tumor
  • 3.2 Improving the Duty Cycle.
  • 3.3 The Effect of Sweat
  • 3.4 Temperature Increases
  • 3.5 Prediction of the Thermal Impact
  • 3.6 Continuous Versus Intermittent Application of the Fields
  • 4 Limitations and Future Work
  • References
  • Improving Tumor-Treating Fields with Skull Remodeling Surgery, Surgery Planning, and Treatment Evaluation with Finite Element ...
  • 1 Introduction
  • 2 Glioblastoma
  • 3 Tumor Treating Fields
  • 4 TTFields Dosimetry
  • 5 Skull Remodeling Surgery and the Utility of FE Modeling
  • 6 The Aim and Motivation of Field Modeling in SR-Surgery Planning and Evaluation
  • 7 Physical Basis of the Field Calculations
  • 8 Creating the Head Models
  • 9 Placement of TTField Transducer Arrays
  • 10 Boundary Conditions and Tissue Conductivities
  • 11 SR-Surgery in the OptimalTTF-1 Trial
  • 12 Conclusion
  • References
  • Part II: Non-invasive Neurostimulation - Brain
  • A Computational Parcellated Brain Model for Electric Field Analysis in Transcranial Direct Current Stimulation
  • 1 Introduction
  • 2 Relation Between EF Magnitude and Orientation and tDCS-Physiological Effects
  • 3 A Computational Parcellated Brain Model in tDCS
  • 3.1 Head Anatomy
  • 3.2 Cortex Parcellation
  • 3.3 tDCS Electrode Montages
  • 3.4 The Physics of tDCS
  • 3.5 FEM Calculation
  • 4 Results
  • 4.1 Tangential and Normal EF Distribution Through the Cortex
  • 4.2 Mean and Peak Tangential and Normal EF Values over Different Cortical Areas
  • 5 Summary and Discussion
  • 6 Conclusion
  • References
  • Computational Models of Brain Stimulation with Tractography Analysis
  • 1 Introduction
  • 2 Methods
  • 2.1 Image Preprocessing
  • 2.2 White Matter Fibre Tractography
  • 2.2.1 Image Segmentation
  • 2.2.2 Fibre Orientation Distribution
  • 2.2.3 Anatomically Constrained Tractography
  • 2.2.4 Post-Processing
  • 2.3 Finite Element Analysis of ECT Brain Stimulation.
  • 2.3.1 Finite Element Model Reconstruction
  • 2.3.2 Tissue Conductivities
  • 2.3.3 White Matter Conductivity Anisotropy
  • 2.3.4 ECT Brain Stimulation Settings
  • 2.4 Model Combination
  • 3 Results
  • 3.1 White Matter Fibre Tractography Model
  • 3.2 Electric Field and Activating Function for Three White Matter Conductivity Settings
  • 3.3 White Matter Activation
  • 4 Discussion
  • References
  • Personalization of Multi-electrode Setups in tCS/tES: Methods and Advantages
  • 1 Introduction
  • 1.1 Biophysical Aspects of tCS
  • 2 Methods
  • 2.1 Subjects
  • 2.2 Head Model Generation
  • 2.3 Montage Optimization Algorithm
  • 2.4 Studies Performed
  • 3 Results
  • 3.1 Study A: Effect of Target Size
  • 3.2 Study B: Tissue Conductivity Values
  • 3.3 Study C: Intersubject Variability
  • 4 Discussion
  • 4.1 Interplay of Target Size, Cortical Geometry, and Optimization Constraints
  • 4.2 Influence of Skull Conductivity
  • 4.3 Montage Optimization and Intersubject Variability
  • 4.4 Study Limitations
  • 4.5 Consequences for Protocol Design
  • References
  • Part III: Non-invasive Neurostimulation - Spinal Cord and Peripheral Nervous System
  • Modelling Studies of Non-invasive Electric and Magnetic Stimulation of the Spinal Cord
  • 1 Relevance of Modelling Studies in Non-invasive Spinal Stimulation
  • 2 Creating a Realistic Human Volume Conductor Model
  • 3 Electric Field Calculation in Non-invasive Spinal Stimulation (NISS)
  • 3.1 Electrode Model and Stimulation Parameters in tsDCS
  • 3.2 Coil Model and Stimulation Parameters in tsMS
  • 4 Main Characteristics of the Electric Field in NISS
  • 4.1 Predictions in tsDCS
  • 4.2 Predictions in tsMS
  • 4.3 Implications of Modelling Findings in Clinical Applications of NISS
  • 5 What Lies Ahead in Non-invasive Spinal Stimulation Modelling Studies
  • References.
  • A Miniaturized Ultra-Focal Magnetic Stimulator and Its Preliminary Application to the Peripheral Nervous System
  • 1 Introduction
  • 2 Models and Methods
  • 2.1 μCoil Modeling
  • 2.2 Modeling Peripheral Nerve Stimulation: Titration Analysis
  • 3 Results
  • 3.1 Magnetic Field Generated by the μCoils
  • 3.2 Electric Field Induced by the μCoils
  • 3.3 Variation of the Peripheral Nerve Stimulation Threshold
  • 4 Discussion and Conclusion
  • References
  • Part IV: Modeling of Neurophysiological Recordings
  • Combining Noninvasive Electromagnetic and Hemodynamic Measures of Human Brain Activity
  • 1 Introduction
  • 2 Methods
  • 2.1 Minimum-Norm Estimates
  • 2.2 Example: MNE Analysis and the Effect of fMRI Weighting
  • 3 Discussion
  • 3.1 Developments of the fMRI-Weighted MNE
  • 3.2 Experimental Design, Model Comparison and Validation, and Neurovascular Coupling Models
  • 3.3 Neurovascular Coupling: The Physiological Bases of Integrating fMRI and MEG Source Modeling
  • References
  • Multiscale Modeling of EEG/MEG Response of a Compact Cluster of Tightly Spaced Pyramidal Neocortical Neurons
  • 1 Introduction
  • 2 Materials and Methods
  • 2.1 Gyrus Cluster Construction and Analysis
  • 2.2 Sulcus Cluster Construction and Analysis
  • 2.3 Modeling Algorithm
  • 3 Results
  • 3.1 Gyrus (Nearly Horizontal) Cluster
  • 3.2 Sulcus (Predominantly Vertical) Cluster
  • 3.3 Quantitative Error Measures
  • 4 Conclusions
  • References
  • Part V: Neural Circuits. Connectome
  • Robustness in Neural Circuits
  • 1 Introduction: Stability and Resilience - ``Robustness ́́
  • 2 Methods
  • 2.1 Node Parameters at Several Systems Levels Granularity
  • 2.2 Neuron Cell Parameters
  • 2.2.1 Dynamic Adjustment of Input Amplitude
  • 2.3 Simulation Duration, Time Step, and Calculation of Firing Rates
  • 2.4 Definition of ``Robustness ́́via Coefficient of Variance (CV).
  • 2.5 Definition of ``Robustness ́́via an Adapted Lyapunov Exponent
  • 2.6 Cumulative Firing Rate vs Momentary Firing Rate
  • 2.7 Limitations
  • 3 Results
  • 3.1 Sample Time Course of Firing Rate of Two Population-Group Configurations
  • 3.1.1 Plots of Firing Rate of All Sample Points vs Baseline Parameters
  • 3.1.2 Robustness vs Number of Elements as Measured by Coefficient of Variance (CV)
  • 3.1.3 Robustness vs Number of Elements as Measured by Lyapunov Exponent (LE)
  • 3.1.4 Robustness vs Number of Elements as Measured by Cumulative Firing Rate (CFR)
  • 4 Discussion
  • 4.1 Key Results
  • 4.2 Robustness and Degeneracy in Biological Systems
  • 4.3 Robustness and Degeneracy in Functional Connectivity Brain Networks
  • 4.4 Inadvertent Modeling Error Due to Scaling
  • 5 Conclusion
  • References
  • Insights from Computational Modelling: Selective Stimulation of Retinal Ganglion Cells
  • 1 Introduction
  • 2 Materials and Methods
  • 2.1 Computational Model of ON and OFF RGC Clusters
  • 2.2 ON and OFF Layer Simulation
  • 2.3 Extracellular Electrical Stimulation and Electrode Settings
  • 3 Results
  • 3.1 Differential Activation of Individual ON and OFF RGCs Using a Large HFS Parameter Space
  • 3.2 Simulating Population-Based RGC Activity Under Clinically Relevant Conditions
  • 4 Discussion and Conclusion
  • References
  • Functional Requirements of Small- and Large-Scale Neural Circuitry Connectome Models
  • 1 Introduction
  • 2 Goals and Means
  • 2.1 Electroceuticals and Neuromodulation
  • 2.2 Benefits of Numerical Modeling
  • 2.3 The Role of Simple Versus Complex Models
  • 2.4 Ockhamś Razor Drives All Modeling
  • 2.5 Capturing the Required Level of Detail
  • 2.6 Which Neural Circuitry Software?
  • 2.7 Initial Conditions
  • 2.8 Calibration and Validation
  • 3 The Functional Requirements
  • 4 Conclusion
  • References.
  • Part VI: High-Frequency and Radiofrequency Modeling.

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