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E-grāmata: Computational Neurostimulation

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  • Formāts: EPUB+DRM
  • Sērija : Progress in Brain Research
  • Izdošanas datums: 16-Nov-2015
  • Izdevniecība: Elsevier Science Ltd
  • Valoda: eng
  • ISBN-13: 9780444635471
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  • Formāts: EPUB+DRM
  • Sērija : Progress in Brain Research
  • Izdošanas datums: 16-Nov-2015
  • Izdevniecība: Elsevier Science Ltd
  • Valoda: eng
  • ISBN-13: 9780444635471
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Computational Neurostimulation, the latest volume in the Progress in Brain Researchseries provides an introduction to a nascent field with contributions from leading researchers. In addition, it addresses a very timely and relevant issue which has long been known to require more treatment.Part of a well-established international series that examines major areas of basic and clinical research within neuroscience, as well as emerging subfieldsProvides an introduction to a nascent field with contributions from leading researchers

Papildus informācija

This latest volume in the Progress in Brain Research series explores novel ways to develop appropriate models that help mapping the physiological impact of stimulation onto its behavioral consequences.
Contributors v
Preface xv
Chapter 1 Modeling Sequence and Quasi-Uniform Assumption in Computational Neurostimulation
1(24)
Marom Bikson
Dennis Q. Truong
Antonios P. Mourdoukoutas
Mohamed Aboseria
Niranjan Khadka
Devin Adair
Asif Rahman
1 A Sequential Multistep Modeling Process
1(2)
2 Step 1: Forward Models of Current Flow
3(3)
3 Step 2: Cellular Response Models of Polarization and the Quasi-Uniform Assumption
6(3)
4 Step 3: Information Processing and Network Changes
9(2)
5 Step 4: From Network to Behavior
11(2)
6 Dealing with Unknowns and Multiscale Approaches
13(12)
References
15(10)
Chapter 2 Multilevel Computational Models for Predicting the Cellular Effects of Noninvasive Brain Stimulation
25(16)
Asif Rahman
Belen Lafon
Marom Bikson
1 Which Neural Elements Are Excited by Direct Current Stimulation?
26(1)
2 Modeling Electrical Stimulation
27(3)
3 Quantifying Membrane Polarization
30(2)
4 Polarization Profile of a Neuron in a Uniform Electric Field
32(1)
5 Cable Theory Formulation
33(1)
6 Modeling Biphasic Polarization During DCS in Hodgkin-Huxley-Based Neurons
34(1)
7 Axon Terminal Polarization
35(1)
8 A Quantitative Framework for Predicting Neuronal Voltage Output
36(1)
9 Numerical Methods
37(1)
10 Conclusion
37(4)
Acknowledgment/Conflict of Interest
37(1)
References
38(3)
Chapter 3 Experiments and Models of Cortical Oscillations as a Target for Noninvasive Brain Stimulation
41(34)
Flavio Frohlich
1 Introduction
42(2)
2 Dynamic Systems Theory: Periodic Forcing of Oscillators
44(3)
3 Modulation of Cortical Oscillations in Humans
47(6)
3.1 Transcranial Magnetic Stimulation
47(3)
3.2 Transcranial Alternating Current Stimulation
50(3)
4 Modulation of Oscillations in Animal Models
53(5)
4.1 In Vitro Studies
54(3)
4.2 In Vivo Studies
57(1)
5 Computational Models
58(9)
6 Synthesis and Outlook
67(8)
Acknowledgments
70(1)
References
70(5)
Chapter 4 Understanding the Nonlinear Physiological and Behavioral Effects of tDCS Through Computational Neurostimulation
75(30)
James J. Bonaiuto
Sven Bestmann
1 Introduction
76(2)
2 A Biophysically Informed Neural Network Model of Decision Making
78(19)
2.1 Model Architecture
78(2)
2.2 Synapse and Neuron Model
80(1)
2.3 Simulating tDCS-Induced Currents in a Neural Network Model
81(2)
2.4 Modeling of Intensity-Dependent Changes on Neural Dynamics and Behavior
83(1)
2.5 Model Implementation and Analyses of Model Behavior
84(13)
3 Discussion
97(8)
Acknowledgment
100(1)
References
100(5)
Chapter 5 Modeling TMS-Induced I-Waves in Human Motor Cortex
105(20)
Jochen Triesch
Christoph Zrenner
Ulf Ziemann
1 Introduction
105(1)
2 Description of the Rusu et al. (2014) Model
106(1)
3 Key Findings from the Rusu et al. (2014) Model
106(4)
4 Extension 1: Modeling the Effects of Ongoing Brain Activity
110(3)
5 Extension 2: Modeling the Effects of Pulse Waveform and Direction, Coil Geometry, and Individual Brain Anatomy
113(2)
6 Extension 3: Modeling Plasticity Induction
115(2)
7 Conclusions
117(8)
Acknowledgments
118(1)
References
118(7)
Chapter 6 Deep Brain Stimulation for Neurodegenerative Disease: A Computational Blueprint Using Dynamic Causal Modeling
125(22)
Rosatyn Moran
1 Introduction
126(3)
2 Modeling
129(7)
2.1 Predicting Stimulation Effects Using DCM for fMRI
129(2)
2.2 Augmenting Predictions Using DCM for EEG
131(3)
2.3 Simulating DBS Effects Using DCM
134(2)
3 Applications
136(4)
3.1 Predicting Effects of DBS in AD
136(3)
3.2 Testing the Origin of Effectiveness of DBS for PD
139(1)
4 Discussion
140(7)
References
142(5)
Chapter 7 Model-Based Analysis and Design of Waveforms for Efficient Neural Stimulation
147(16)
Warren M. Grill
1 Introduction
147(1)
2 Stimulation Waveforms for Neural Stimulation
148(2)
3 Efficiency of Stimulation
150(1)
4 The Importance of Energy-Efficient Neural Stimulation
151(1)
5 Calculation of the Energy-Optimal Pulse Duration for Rectangular Pulses
152(2)
6 The Rising Exponential as an Energy-Optimal Waveform Shape
154(1)
7 Effect of Stimulation Waveform Shape of Energy Efficiency of Stimulation
155(2)
8 Optimized Pulse Shapes for Stimulation
157(1)
9 Conclusion
158(5)
Acknowledgment
158(1)
References
159(4)
Chapter 8 Computational Neurostimulation for Parkinson's Disease
163(28)
Simon Little
Sven Bestmann
1 Introduction
164(3)
1.1 Biophysical and Computational Models of DBS
165(2)
2 Biophysical Modeling
167(10)
2.1 Modeling the Effects of DBS on Local Neural Elements
167(1)
2.2 Modeling the Effects of DBS on the Basal Ganglia Network
168(5)
2.3 Modeling the Effects of DBS on Phase and Connectivity
173(4)
3 Toward Computational Modeling for DBS
177(7)
3.1 Computational Modeling of Basal Ganglia Function
178(3)
3.2 Computational Modeling of Neuronal Oscillations
181(3)
4 Conclusions
184(7)
Acknowledgments
184(1)
References
185(6)
Chapter 9 Computational Modeling of Neurostimulation in Brain Diseases
191(38)
Yujiang Wang
Frances Hutchings
Marcus Kaiser
1 Introduction
192(9)
1.1 Modeling of Stimulation Modalities
194(2)
1.2 Noninvasive Electric Stimulation
196(2)
1.3 Noninvasive Magnetic Stimulation
198(2)
1.4 Invasive Electrical Stimulation
200(1)
1.5 Optogenetics
200(1)
2 Computational Modeling of Stimulation in Brain Disorders
201(11)
2.1 Parkinson's Disease
201(4)
2.2 Epilepsy
205(5)
2.3 Cortical Spreading Depression
210(2)
3 Discussion
212(17)
Acknowledgments
216(1)
References
216(13)
Chapter 10 Understanding the Biophysical Effects of Transcranial Magnetic Stimulation on Brain Tissue: The Bridge Between Brain Stimulation and Cognition
229(32)
Sebastiaan F.W. Neggers
Petar I. Petrov
Stefano Mandija
Iris E.C. Sommer
Nico A.T. van den Berg
1 Introduction
230(5)
2 Understanding and Predicting the Effects of TMS on Cognition
235(7)
2.1 Locally Induced Current Patterns and Neuronal Computations
235(3)
2.2 The Influence of Induced Action Potentials on Networks of Brain Areas
238(4)
3 The Path to Computing Local Currents: Models and Validations
242(10)
3.1 The TMS Coil: Influence of Orientation, Shape, and Geometry on Induced Field
243(3)
3.2 The Head Model: Tissue Classification, Meshing, and Electromagnetic Properties
246(1)
3.3 Computing Currents: FEM and BEM
247(1)
3.4 Empirical Validation
248(4)
4 Conclusion
252(9)
Acknowledgments
253(1)
References
253(8)
Chapter 11 Modeling the Effects of Noninvasive Transcranial Brain Stimulation at the Biophysical, Network, and Cognitive Level
261(20)
Gesa Hartwigsen
Til Ole Bergmann
Damian Marc Herz
Steffen Angstmann
Anke Karabanov
Estelle Raffin
Axel Thielscher
Hartwig Roman Siebner
1 Introduction
262(3)
1.1 Online Transcranial Stimulation
263(1)
1.2 Offline Transcranial Stimulation
264(1)
1.3 Paradoxical TMS Effects on Cognitive Functions
264(1)
2 Modeling the Distribution of the NTBS-Induced Electrical Fields
265(4)
3 Modeling of NTBS-Induced Changes in Effective Connectivity
269(7)
3.1 The Psychophysiological Interaction Method
270(1)
3.2 Dynamic Causal Modeling
271(5)
4 Modeling the Behavioral Effects of NTBS
276(4)
5 Future Perspectives on Computational Neurostimulation in the Study of Cognition
280(1)
References 281(8)
Index 289(4)
Other volumes in PROGRESS IN BRAIN RESEARCH 293
Dr Bestmanns research interests are centered around action control, and how our brain makes decisions that ultimately lead to controlled movements. He has approached this question using a combination of different research techniques with complementary strengths, including functional neuroimaging, electrophysiology, psychophysics, computational modeling, pharmacology, and neurostimulation. One prominent research activity throughout his career has been the use of interventional approaches to study brain-behaviour relationships. He has a long-standing interest in using non-invasive brain stimulation, and its combination with complementary research approaches such as functional neuroimaging. Recently, the research interest has included the use of computational models to understand the specific processes targeted by various neurostimulation techniques. He has recently coined the term computational neurostimulation, to describe this approach of using computational modeling to study the behavioural consequences of non-invasive brain stimulation.He is a Reader in Motor Neuroscience at the Sobell Department, UCL. His research has been supported by major funding sources, such as the European Research Council, the Biotechnology and Biological Sciences Research Council (BBSRC), and Wellcome Trust. He is a Board member and Chair of Life Science domain of the Young Academy of Europe.