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

• Author: Makarov, Sergey N.
• TeilnehmendeR: Noetscher, Gregory M.; Nummenmaa, Aapo.
• Place / Publishing House: Cham : : Springer International Publishing AG,, 2020.; {copy}2021.
• Year of Publication: 2020
• Edition: 1st ed.
• Language: English
• Physical Description: 1 online resource (395 pages)

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.