Contributors |
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Preface |
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Chapter 1 Modeling Sequence and Quasi-Uniform Assumption in Computational Neurostimulation |
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1 | (24) |
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Antonios P. Mourdoukoutas |
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1 A Sequential Multistep Modeling Process |
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1 | (2) |
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2 Step 1: Forward Models of Current Flow |
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3 | (3) |
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3 Step 2: Cellular Response Models of Polarization and the Quasi-Uniform Assumption |
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6 | (3) |
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4 Step 3: Information Processing and Network Changes |
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9 | (2) |
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5 Step 4: From Network to Behavior |
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11 | (2) |
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6 Dealing with Unknowns and Multiscale Approaches |
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13 | (12) |
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15 | (10) |
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Chapter 2 Multilevel Computational Models for Predicting the Cellular Effects of Noninvasive Brain Stimulation |
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25 | (16) |
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1 Which Neural Elements Are Excited by Direct Current Stimulation? |
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26 | (1) |
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2 Modeling Electrical Stimulation |
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27 | (3) |
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3 Quantifying Membrane Polarization |
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30 | (2) |
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4 Polarization Profile of a Neuron in a Uniform Electric Field |
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32 | (1) |
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5 Cable Theory Formulation |
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33 | (1) |
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6 Modeling Biphasic Polarization During DCS in Hodgkin-Huxley-Based Neurons |
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34 | (1) |
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7 Axon Terminal Polarization |
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35 | (1) |
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8 A Quantitative Framework for Predicting Neuronal Voltage Output |
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36 | (1) |
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37 | (1) |
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37 | (4) |
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Acknowledgment/Conflict of Interest |
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37 | (1) |
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38 | (3) |
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Chapter 3 Experiments and Models of Cortical Oscillations as a Target for Noninvasive Brain Stimulation |
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41 | (34) |
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42 | (2) |
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2 Dynamic Systems Theory: Periodic Forcing of Oscillators |
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44 | (3) |
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3 Modulation of Cortical Oscillations in Humans |
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47 | (6) |
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3.1 Transcranial Magnetic Stimulation |
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47 | (3) |
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3.2 Transcranial Alternating Current Stimulation |
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50 | (3) |
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4 Modulation of Oscillations in Animal Models |
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53 | (5) |
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54 | (3) |
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57 | (1) |
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58 | (9) |
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67 | (8) |
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70 | (1) |
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70 | (5) |
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Chapter 4 Understanding the Nonlinear Physiological and Behavioral Effects of tDCS Through Computational Neurostimulation |
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75 | (30) |
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76 | (2) |
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2 A Biophysically Informed Neural Network Model of Decision Making |
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78 | (19) |
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78 | (2) |
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2.2 Synapse and Neuron Model |
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80 | (1) |
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2.3 Simulating tDCS-Induced Currents in a Neural Network Model |
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81 | (2) |
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2.4 Modeling of Intensity-Dependent Changes on Neural Dynamics and Behavior |
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83 | (1) |
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2.5 Model Implementation and Analyses of Model Behavior |
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84 | (13) |
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97 | (8) |
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100 | (1) |
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100 | (5) |
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Chapter 5 Modeling TMS-Induced I-Waves in Human Motor Cortex |
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105 | (20) |
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105 | (1) |
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2 Description of the Rusu et al. (2014) Model |
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106 | (1) |
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3 Key Findings from the Rusu et al. (2014) Model |
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106 | (4) |
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4 Extension 1: Modeling the Effects of Ongoing Brain Activity |
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110 | (3) |
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5 Extension 2: Modeling the Effects of Pulse Waveform and Direction, Coil Geometry, and Individual Brain Anatomy |
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113 | (2) |
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6 Extension 3: Modeling Plasticity Induction |
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115 | (2) |
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117 | (8) |
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118 | (1) |
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118 | (7) |
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Chapter 6 Deep Brain Stimulation for Neurodegenerative Disease: A Computational Blueprint Using Dynamic Causal Modeling |
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125 | (22) |
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126 | (3) |
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129 | (7) |
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2.1 Predicting Stimulation Effects Using DCM for fMRI |
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129 | (2) |
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2.2 Augmenting Predictions Using DCM for EEG |
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131 | (3) |
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2.3 Simulating DBS Effects Using DCM |
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134 | (2) |
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136 | (4) |
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3.1 Predicting Effects of DBS in AD |
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136 | (3) |
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3.2 Testing the Origin of Effectiveness of DBS for PD |
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139 | (1) |
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140 | (7) |
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142 | (5) |
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Chapter 7 Model-Based Analysis and Design of Waveforms for Efficient Neural Stimulation |
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147 | (16) |
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147 | (1) |
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2 Stimulation Waveforms for Neural Stimulation |
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148 | (2) |
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3 Efficiency of Stimulation |
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150 | (1) |
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4 The Importance of Energy-Efficient Neural Stimulation |
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151 | (1) |
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5 Calculation of the Energy-Optimal Pulse Duration for Rectangular Pulses |
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152 | (2) |
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6 The Rising Exponential as an Energy-Optimal Waveform Shape |
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154 | (1) |
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7 Effect of Stimulation Waveform Shape of Energy Efficiency of Stimulation |
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155 | (2) |
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8 Optimized Pulse Shapes for Stimulation |
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157 | (1) |
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158 | (5) |
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158 | (1) |
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159 | (4) |
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Chapter 8 Computational Neurostimulation for Parkinson's Disease |
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163 | (28) |
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164 | (3) |
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1.1 Biophysical and Computational Models of DBS |
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165 | (2) |
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167 | (10) |
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2.1 Modeling the Effects of DBS on Local Neural Elements |
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167 | (1) |
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2.2 Modeling the Effects of DBS on the Basal Ganglia Network |
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168 | (5) |
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2.3 Modeling the Effects of DBS on Phase and Connectivity |
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173 | (4) |
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3 Toward Computational Modeling for DBS |
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177 | (7) |
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3.1 Computational Modeling of Basal Ganglia Function |
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178 | (3) |
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3.2 Computational Modeling of Neuronal Oscillations |
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181 | (3) |
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184 | (7) |
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184 | (1) |
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185 | (6) |
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Chapter 9 Computational Modeling of Neurostimulation in Brain Diseases |
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191 | (38) |
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192 | (9) |
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1.1 Modeling of Stimulation Modalities |
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194 | (2) |
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1.2 Noninvasive Electric Stimulation |
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196 | (2) |
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1.3 Noninvasive Magnetic Stimulation |
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198 | (2) |
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1.4 Invasive Electrical Stimulation |
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200 | (1) |
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200 | (1) |
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2 Computational Modeling of Stimulation in Brain Disorders |
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201 | (11) |
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201 | (4) |
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205 | (5) |
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2.3 Cortical Spreading Depression |
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210 | (2) |
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212 | (17) |
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216 | (1) |
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216 | (13) |
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Chapter 10 Understanding the Biophysical Effects of Transcranial Magnetic Stimulation on Brain Tissue: The Bridge Between Brain Stimulation and Cognition |
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229 | (32) |
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230 | (5) |
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2 Understanding and Predicting the Effects of TMS on Cognition |
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235 | (7) |
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2.1 Locally Induced Current Patterns and Neuronal Computations |
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235 | (3) |
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2.2 The Influence of Induced Action Potentials on Networks of Brain Areas |
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238 | (4) |
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3 The Path to Computing Local Currents: Models and Validations |
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242 | (10) |
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3.1 The TMS Coil: Influence of Orientation, Shape, and Geometry on Induced Field |
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243 | (3) |
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3.2 The Head Model: Tissue Classification, Meshing, and Electromagnetic Properties |
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246 | (1) |
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3.3 Computing Currents: FEM and BEM |
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247 | (1) |
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248 | (4) |
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252 | (9) |
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253 | (1) |
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253 | (8) |
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Chapter 11 Modeling the Effects of Noninvasive Transcranial Brain Stimulation at the Biophysical, Network, and Cognitive Level |
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261 | (20) |
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262 | (3) |
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1.1 Online Transcranial Stimulation |
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263 | (1) |
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1.2 Offline Transcranial Stimulation |
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264 | (1) |
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1.3 Paradoxical TMS Effects on Cognitive Functions |
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264 | (1) |
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2 Modeling the Distribution of the NTBS-Induced Electrical Fields |
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265 | (4) |
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3 Modeling of NTBS-Induced Changes in Effective Connectivity |
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269 | (7) |
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3.1 The Psychophysiological Interaction Method |
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270 | (1) |
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3.2 Dynamic Causal Modeling |
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271 | (5) |
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4 Modeling the Behavioral Effects of NTBS |
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276 | (4) |
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5 Future Perspectives on Computational Neurostimulation in the Study of Cognition |
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280 | (1) |
References |
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281 | (8) |
Index |
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289 | (4) |
Other volumes in PROGRESS IN BRAIN RESEARCH |
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