Computational Neurostimulation
- 1st Edition, Volume 222 - October 21, 2015
- Editor: Sven Bestmann
- Language: English
- Hardback ISBN:9 7 8 - 0 - 4 4 4 - 6 3 5 4 6 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 4 - 6 3 5 4 7 - 1
Computational Neurostimulation, the latest volume in the Progress in Brain Research series provides an introduction to a nascent field with contributions from leading research… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteComputational Neurostimulation
, the latest volume in the Progress in Brain Research series 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 subfields
- Provides an introduction to a nascent field with contributions from leading researchers
Cognitive Psychologists, Neurologists, Psychiatrists, DBS clinicians, behavioural neuroscientists, engineers
- Preface: Computational neurostimulation in basic and translational research
- Chapter 1: Modeling sequence and quasi-uniform assumption in computational neurostimulation
- Abstract
- 1 A Sequential Multistep Modeling Process
- 2 Step 1: Forward Models of Current Flow
- 3 Step 2: Cellular Response Models of Polarization and the Quasi-Uniform Assumption
- 4 Step 3: Information Processing and Network Changes
- 5 Step 4: From Network to Behavior
- 6 Dealing with Unknowns and Multiscale Approaches
- Chapter 2: Multilevel computational models for predicting the cellular effects of noninvasive brain stimulation
- Abstract
- 1 Which Neural Elements Are Excited by Direct Current Stimulation?
- 2 Modeling Electrical Stimulation
- 3 Quantifying Membrane Polarization
- 4 Polarization Profile of a Neuron in a Uniform Electric Field
- 5 Cable Theory Formulation
- 6 Modeling Biphasic Polarization During DCS in Hodgkin–Huxley-Based Neurons
- 7 Axon Terminal Polarization
- 8 A Quantitative Framework for Predicting Neuronal Voltage Output
- 9 Numerical Methods
- 10 Conclusion
- Acknowledgment/Conflict of Interest
- Chapter 3: Experiments and models of cortical oscillations as a target for noninvasive brain stimulation
- Abstract
- 1 Introduction
- 2 Dynamic Systems Theory: periodic Forcing of Oscillators
- 3 Modulation of Cortical Oscillations in Humans
- 4 Modulation of Oscillations in Animal Models
- 5 Computational Models
- 6 Synthesis and Outlook
- Acknowledgments
- Chapter 4: Understanding the nonlinear physiological and behavioral effects of tDCS through computational neurostimulation
- Abstract
- 1 Introduction
- 2 A Biophysically Informed Neural Network Model of Decision Making
- 3 Discussion
- Acknowledgment
- Chapter 5: Modeling TMS-induced I-waves in human motor cortex
- Abstract
- 1 Introduction
- 2 Description of the Rusu et al. (2014) Model
- 3 Key Findings from the Rusu et al. (2014) Model
- 4 Extension 1: Modeling the Effects of Ongoing Brain Activity
- 5 Extension 2: Modeling the Effects of Pulse Waveform and Direction, Coil Geometry, and Individual Brain Anatomy
- 6 Extension 3: Modeling Plasticity Induction
- 7 Conclusions
- Acknowledgments
- Chapter 6: Deep brain stimulation for neurodegenerative disease: A computational blueprint using dynamic causal modeling
- Abstract
- 1 Introduction
- 2 Modeling
- 3 Applications
- 4 Discussion
- Chapter 7: Model-based analysis and design of waveforms for efficient neural stimulation
- Abstract
- 1 Introduction
- 2 Stimulation Waveforms for Neural Stimulation
- 3 Efficiency of Stimulation
- 4 The Importance of Energy-efficient Neural Stimulation
- 5 Calculation of the Energy-Optimal Pulse Duration for Rectangular Pulses
- 6 The Rising Exponential as an Energy-Optimal Waveform Shape
- 7 Effect of Stimulation Waveform Shape of Energy Efficiency of Stimulation
- 8 Optimized Pulse Shapes for Stimulation
- 9 Conclusion
- Acknowledgment
- Chapter 8: Computational neurostimulation for Parkinson's disease
- Abstract
- 1 Introduction
- 2 Biophysical Modeling
- 3 Toward Computational Modeling for DBS
- 4 Conclusions
- Acknowledgments
- Chapter 9: Computational modeling of neurostimulation in brain diseases
- Abstract
- 1 Introduction
- 2 Computational Modeling of Stimulation in Brain Disorders
- 3 Discussion
- Acknowledgments
- Chapter 10: Understanding the biophysical effects of transcranial magnetic stimulation on brain tissue: The bridge between brain stimulation and cognition
- Abstract
- 1 Introduction
- 2 Understanding and Predicting the Effects of TMS on Cognition
- 3 The Path to Computing Local Currents: Models and Validations
- 4 Conclusion
- Acknowledgments
- Chapter 11: Modeling the effects of noninvasive transcranial brain stimulation at the biophysical, network, and cognitive Level
- Abstract
- 1 Introduction
- 2 Modeling the Distribution of the NTBS-induced Electrical Fields
- 3 Modeling of NTBS-induced Changes in Effective Connectivity
- 4 Modeling the Behavioral Effects of NTBS
- 5 Future Perspectives on Computational Neurostimulation in the Study of Cognition
- Index
- Other volumes in Progress in Brain Research
- No. of pages: 316
- Language: English
- Edition: 1
- Volume: 222
- Published: October 21, 2015
- Imprint: Elsevier
- Hardback ISBN: 9780444635464
- eBook ISBN: 9780444635471
SB
Sven Bestmann
Dr Bestmann’s 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.
Affiliations and expertise
CL Institute of Neurology, London, UKRead Computational Neurostimulation on ScienceDirect