Computational neurostimulation:
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| Format: | Book |
|---|---|
| Language: | English |
| Published: |
Amsterdam [u.a.]
Elsevier
2015
|
| Edition: | 1. ed. |
| Series: | Progress in brain research
222 |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Inhaltsverzeichnis |
| Physical Description: | XX, 295 S. Ill., graph. Darst. |
| ISBN: | 9780444635464 |
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MARC
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| 245 | 1 | 0 | |a Computational neurostimulation |c ed. by Sven Bestmann |
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| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier |c 2015 | |
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| _version_ | 1822792433651417088 |
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| adam_text |
Titel: Computational neurostimulation
Autor: Bestmann, Sven
Jahr: 2015
Contents
Contributors.v
Preface.xv
CHAPTER 1 Modeling Sequence and Quasi-Uniform
Assumption in Computational Neurostimulation.1
Marom Bikson, Dennis Q. Truong,
Antonios P. Mourdoukoutas, Mohamed Aboseria,
Niranjan Khadka, Devin Adair, Asif Rahman
1. A Sequential Multistep Modeling Process.1
2. Step 1: Forward Models of Current Flow.3
3. Step 2: Cellular Response Models of Polarization and the
Quasi-Uniform Assumption.6
4. Step 3: Information Processing and Network Changes.9
5. Step 4: From Network to Behavior.11
6. Dealing with Unknowns and Multiscale Approaches.13
References.15
CHAPTER 2 Multilevel Computational Models for
Predicting the Cellular Effects of Noninvasive
Brain Stimulation.25
Asif Rahman, Belen Lafon, Marom Bikson
1. Which Neural Elements Are Excited by Direct Current
Stimulation?.26
2. Modeling Electrical Stimulation.27
3. Quantifying Membrane Polarization.30
4. Polarization Profile of a Neuron in a Uniform
Electric Field.32
5. Cable Theory Formulation.33
6. Modeling Biphasic Polarization During DCS
in Hodgkin-Huxley-Based Neurons.34
7. Axon Terminal Polarization.35
8. A Quantitative Framework for Predicting Neuronal
Voltage Output.36
9. Numerical Methods.37
10. Conclusion.37
Acknowledgment/Conflict of Interest.37
References.38
ix
Contents
CHAPTER 3 Experiments and Models of Cortical Oscillations
as a Target for Noninvasive Brain Stimulation.41
Flavio Frohlich
1. Introduction.42
2. Dynamic Systems Theory: Periodic Forcing of Oscillators.44
3. Modulation of Cortical Oscillations in Humans.47
3.1. Transcranial Magnetic Stimulation.47
3.2. Transcranial Alternating Current Stimulation.50
4. Modulation of Oscillations in Animal Models.53
4.1. In Vitro Studies.54
4.2. In Vivo Studies.57
5. Computational Models.58
6. Synthesis and Outlook.67
Acknowledgments.70
References.70
CHAPTER 4 Understanding the Nonlinear Physiological
and Behavioral Effects of tDCS Through
Computational Neurostimulation.75
James J. Bonaiuto, Sven Bestmann
1. Introduction.76
2. A Biophysically Informed Neural Network Model
of Decision Making.78
2.1. Model Architecture.78
2.2. Synapse and Neuron Model.80
2.3. Simulating tDCS-Induced Currents in a Neural
Network Model.81
2.4. Modeling of Intensity-Dependent Changes
on Neural Dynamics and Behavior.83
2.5. Model Implementation and Analyses
of Model Behavior.84
3. Discussion.97
Acknowledgment.100
References.100
CHAPTER 5 Modeling TMS-lnduced l-Waves in Human
Motor Cortex.105
Jochen Triesch, Christoph Zrenner, Ulf Ziemann
1. Introduction.105
2. Description of the Rusu et al. (2014) Model.106
3. Key Findings from the Rusu et al. (2014) Model.106
Contents
4. Extension 1: Modeling the Effects of Ongoing
Brain Activity.110
5. Extension 2: Modeling the Effects of Pulse Waveform and
Direction, Coil Geometry, and Individual Brain Anatomy.113
6. Extension 3: Modeling Plasticity Induction.115
7. Conclusions.117
Acknowledgments.118
References.118
CHAPTER 6 Deep Brain Stimulation for Neurodegenerative
Disease: A Computational Blueprint Using
Dynamic Causal Modeling.125
Rosalyn Moran
1. Introduction.126
2. Modeling.129
2.1. Predicting Stimulation Effects Using DCM for fMRI.129
2.2. Augmenting Predictions Using DCM for EEG.131
2.3. Simulating DBS Effects Using DCM.134
3. Applications.136
3.1. Predicting Effects of DBS in AD.136
3.2. Testing the Origin of Effectiveness of DBS for PD.139
4. Discussion.140
References.142
CHAPTER 7 Model-Based Analysis and Design of Waveforms
for Efficient Neural Stimulation.147
Warren M. Grill
1. Introduction.147
2. Stimulation Waveforms for Neural Stimulation.148
3. Efficiency of Stimulation.150
4. The Importance of Energy-Efficient Neural Stimulation.151
5. Calculation of the Energy-Optimal Pulse Duration for
Rectangular Pulses.152
6. The Rising Exponential as an Energy-Optimal
Waveform Shape.154
7. Effect of Stimulation Waveform Shape of Energy
Efficiency of Stimulation.155
8. Optimized Pulse Shapes for Stimulation.157
9. Conclusion.158
Acknowledgment.158
References.159
Contents
CHAPTER 8 Computational Neurostimulation for Parkinson's
Disease.163
Simon Little, Sven Bestmann
1. Introduction.164
1.1. Biophysical and Computational Models of DBS.165
2. Biophysical Modeling.167
2.1. Modeling the Effects of DBS on Local Neural
Elements.167
2.2. Modeling the Effects of DBS on the Basal Ganglia
Network.168
2.3. Modeling the Effects of DBS on Phase
and Connectivity.173
3. Toward Computational Modeling for DBS.177
3.1. Computational Modeling of Basal Ganglia
Function.178
3.2. Computational Modeling of Neuronal Oscillations.181
4. Conclusions.184
Acknowledgments.184
References.185
CHAPTER 9 Computational Modeling of Neurostimulation
in Brain Diseases.191
Yujiang Wang, Frances Hutchings, Marcus Kaiser
1. Introduction.192
1.1. Modeling of Stimulation Modalities.194
1.2. Noninvasive Electric Stimulation.196
1.3. Noninvasive Magnetic Stimulation.198
1.4. Invasive Electrical Stimulation.200
1.5. Optogenetics.200
2. Computational Modeling of Stimulation
in Brain Disorders.201
2.1. Parkinson's Disease.201
2.2. Epilepsy.205
2.3. Cortical Spreading Depression.210
3. Discussion.212
Acknowledgments.216
References.216
Contents xiii
CHAPTER 10 Understanding the Biophysical Effects of
Transcranial Magnetic Stimulation on Brain
Tissue: The Bridge Between Brain Stimulation
and Cognition.229
Sebastiaan F.W. Neggers, Petar I. Petrov, Stefano Mandija,
Iris E.C. Sommer, Nico A.T. van den Berg
1. Introduction.230
2. Understanding and Predicting the Effects of TMS
on Cognition.235
2.1. Locally Induced Current Patterns and Neuronal
Computations.235
2.2. The Influence of Induced Action Potentials on
Networks of Brain Areas.238
3. The Path to Computing Local Currents: Models
and Validations.242
3.1. The TMS Coil: Influence of Orientation, Shape,
and Geometry on Induced Field.243
3.2. The Head Model: Tissue Classification, Meshing,
and Electromagnetic Properties.246
3.3. Computing Currents: FEM and BEM.247
3.4. Empirical Validation.248
4. Conclusion.252
Acknowledgments.253
References.253
CHAPTER 11 Modeling the Effects of Noninvasive Transcranial
Brain Stimulation at the Biophysical, Network,
and Cognitive Level.261
Gesa Hartwigsen, Til Ole Bergmann, Damian Marc Herz,
Steffen Angstmann, Anke Karabanov, Estelle Raff in,
Axel Thielscher, Hartwig Roman Siebner
1. Introduction.262
1.1. Online Transcranial Stimulation.263
1.2. Offline Transcranial Stimulation.264
1.3. Paradoxical TMS Effects on Cognitive Functions.264
2. Modeling the Distribution of the NTBS-Induced
Electrical Fields.265
xiv Contents
3. Modeling of NTBS-Induced Changes in Effective
Connectivity.269
3.1. The Psychophysiological Interaction Method.270
3.2. Dynamic Causal Modeling.271
4. Modeling the Behavioral Effects of NTBS.276
5. Future Perspectives on Computational Neurostimulation
in the Study of Cognition.280
References.281
Index.289
Other volumes in PROGRESS IN BRAIN RESEARCH.293 |
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| illustrated | Illustrated |
| indexdate | 2025-01-31T19:05:27Z |
| institution | BVB |
| isbn | 9780444635464 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028601855 |
| oclc_num | 934641526 |
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| owner | DE-19 DE-BY-UBM |
| owner_facet | DE-19 DE-BY-UBM |
| physical | XX, 295 S. Ill., graph. Darst. |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Elsevier |
| record_format | marc |
| series | Progress in brain research |
| series2 | Progress in brain research |
| spelling | Computational neurostimulation ed. by Sven Bestmann 1. ed. Amsterdam [u.a.] Elsevier 2015 XX, 295 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Progress in brain research 222 Computational neuroscience Nervenstimulation (DE-588)4732338-3 gnd rswk-swf Nervenstimulation (DE-588)4732338-3 s DE-604 Bestmann, Sven Sonstige (DE-588)1079504435 oth Progress in brain research 222 (DE-604)BV008000363 222 http://digitool.hbz-nrw.de:1801/webclient/DeliveryManager?pid=6526894&custom_att_2=simple_viewer Computational neurostimulation Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028601855&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Computational neurostimulation Progress in brain research Computational neuroscience Nervenstimulation (DE-588)4732338-3 gnd |
| subject_GND | (DE-588)4732338-3 |
| title | Computational neurostimulation |
| title_auth | Computational neurostimulation |
| title_exact_search | Computational neurostimulation |
| title_full | Computational neurostimulation ed. by Sven Bestmann |
| title_fullStr | Computational neurostimulation ed. by Sven Bestmann |
| title_full_unstemmed | Computational neurostimulation ed. by Sven Bestmann |
| title_short | Computational neurostimulation |
| title_sort | computational neurostimulation |
| topic | Computational neuroscience Nervenstimulation (DE-588)4732338-3 gnd |
| topic_facet | Computational neuroscience Nervenstimulation |
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