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.