Computational Neuroscience in Epilepsy
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Table of contents
- Cover
- Table of Contentsv
- Contributorsix
- Forewordxiii
- Rise of the Machines – On the Threshold of a New Era in Epilepsy Researchxiii
- Introduction: Applications and Emerging Concepts of Computational Neuroscience in Epilepsy Researchxv
- Part I Computational Modeling Techniques and Databases in Epilepsy Research1
- Chapter 1 Simulation of Large Networks: Technique and Progress3
- ABSTRACT3
- GOALS OF COMPUTER MODELING FOR CLINICAL DISEASE3
- DETAILED VERSUS SIMPLIFYING MODELING4
- NETWORK SIMULATION TECHNIQUE: CONTINUITY AND EVENTS5
- COMPARTMENT CELL TECHNIQUES: VARIABLE STEP METHODS5
- COMPARTMENT CELL TECHNIQUES: SUPERCOMPUTING APPROACHES7
- INTEGRATE-AND-FIRE REVISITED: COMPLEX ARTIFICIAL CELLS9
- EPILEPSY SIMULATION WITH COMPLEX ARTIFICIAL CELLS11
- SIMULATION DATA-MINING12
- RATIONAL PHARMACOTHERAPEUTICS15
- CONCLUSIONS16
- REFERENCES16
- Chapter 2 The NEURON Simulation Environment in Epilepsy Research18
- ABSTRACT18
- HOW MIGHT NEURON BE USEFUL IN EPILEPSY RESEARCH?18
- HOW NEURON FACILITATES MODELING OF NEURONS AND NETWORKS19
- MODEL SPECIFICATION AND MANAGEMENT20
- MODELING INDIVIDUAL NEURONS20
- MODELS THAT INCLUDE ELECTRONIC INSTRUMENTATION26
- MODELING NETWORKS27
- SUMMARY30
- ACKNOWLEDGMENTS30
- REFERENCES31
- Chapter 3 The CoCoDat Database: Systematically Organizing and Selecting Quantitative Data on Single34
- ABSTRACT34
- DATA REQUIREMENTS FOR REALISTIC MODELING OF NEURONS AND MICROCIRCUITS IN EPILEPSY34
- STRUCTURE OF THE DATABASE35
- MAPPING CONCEPTS37
- ACCESSING THE COCODAT DATABASE37
- EXTRACTING AND VISUALIZING DATASETS38
- INSPECTING AND EXPORTING DATASETS39
- ADDING CONTENT TO THE DATABASE41
- DISCUSSION41
- REFERENCES42
- Chapter 4 Validating Models of Epilepsy43
- ABSTRACT43
- REFERENCES46
- Chapter 5 Using NeuroConstruct to Develop and Modify Biologically Detailed 3D Neuronal Network Model48
- ABSTRACT48
- INTRODUCTION48
- OVERVIEW OF APPLICATION51
- ACCESSIBILITY AND TRANSPARENCY OF MODELS54
- METHODOLOGIES WITHIN NEUROCONSTRUCT57
- EXAMPLE OF NETWORK MODEL OF A NEUROLOGICAL DISORDER61
- OTHER NETWORK MODELS IN NEUROCONSTRUCT61
- DISCUSSION AND FUTURE DIRECTIONS65
- ACKNOWLEDGMENTS68
- REFERENCES68
- Chapter 6 Computational Neuroanatomy of the Rat Hippocampus: Implications and Applications to Epilep71
- ABSTRACT71
- INTRODUCTION71
- EXPERIMENTAL DESIGN73
- VOLUMETRIC ANALYSIS74
- CELLULAR EMBEDDING AND SPATIAL OCCUPANCY76
- INTRINSIC POTENTIAL CONNECTIVITY PATTERNS80
- INFLUENCE OF INHIBITORY SYNAPSE POSITION ON CA1 PYRAMIDAL CELL FIRING81
- IMPLICATIONS AND APPLICATIONS TO EPILEPSY81
- ACKNOWLEDGMENT82
- REFERENCES83
- Part II Epilepsy and Altered Network Topology87
- Chapter 7 Modeling Circuit Alterations in Epilepsy: A Focus on Mossy Cell Loss and Mossy Fiber Sprou89
- ABSTRACT89
- INTRODUCTION89
- SOFTWARE CONSIDERATIONS90
- UNDERSTANDING THE NETWORK TO BE MODELED90
- DECIDING THE DETAILS OF THE MODEL NETWORK96
- MULTICOMPARTMENTAL SINGLE CELL MODELS98
- WIRING UP THE HEALTHY DENTATE NETWORK101
- SIMULATING MOSSY FIBER SPROUTING AND CELL LOSS103
- EVALUATING EXCITABILITY AND OUTCOME IN THE MODEL106
- CONSIDERATION OF LIMITATIONS107
- FUTURE DIRECTIONS108
- REFERENCES108
- Chapter 8 Functional Consequences of Transformed Network Topology in Hippocampal Sclerosis112
- ABSTRACT112
- INTRODUCTION112
- CONSTRUCTION OF THE MODEL NETWORKS113
- ASSESSMENT OF THE TOPOLOGICAL AND FUNCTIONAL CHARACTERISTICS OF THE NETWORKS117
- RESULTS DERIVED FROM THE MODELS118
- MODEL LIMITATIONS, CONTROL SIMULATIONS AND TESTS FOR ROBUSTNESS125
- CONCLUSIONS AND FUTURE DIRECTIONS127
- ACKNOWLEDGMENTS128
- REFERENCES128
- Chapter 9 Multiple-Scale Hierarchical Connectivity of Cortical Networks Limits the Spread of Activit132
- ABSTRACT132
- INTRODUCTION132
- SPREADING IN HIERARCHICAL CLUSTER NETWORKS134
- DISCUSSION138
- OUTLOOK138
- ACKNOWLEDGMENTS139
- REFERENCES139
- Part III Destabilization of Neuronal Networks141
- Chapter 10 Computer Simulations of Sodium Channel Mutations that Cause Generalized Epilepsy with Feb143
- ABSTRACT143
- INTRODUCTION143
- EFFECTS OF GEFS+ MUTATIONS144
- APPROACH TO MODELING146
- COMPUTER SIMULATIONS147
- SHORTCOMINGS AND FUTURE DIRECTIONS152
- REFERENCES153
- Chapter 11 Gain Modulation and Stability in Neural Networks155
- ABSTRACT155
- INTRODUCTION155
- A MECHANISM FOR MULTIPLICATIVE GAIN MODULATION158
- GAIN MODULATION OF RESPONSES TO TIME-VARYING INPUTS162
- REFERENCES166
- Chapter 12 Neocortical Epileptiform Activity in Neuronal Models with Biophysically Realistic Ion Cha168
- ABSTRACT168
- INTRODUCTION168
- PRINCIPLES OF BIOPHYSICAL MODELING170
- BUILDING THE NEOCORTICAL MODEL173
- MODEL BEHAVIOR176
- EXPERIMENTAL SUPPORT179
- CONCLUSIONS181
- ACKNOWLEDGMENTS181
- REFERENCES181
- Chapter 13 Corticothalamic Feedback: A Key to Explain Absence Seizures184
- ABSTRACT184
- INTRODUCTION184
- THALAMIC MECHANISMS FOR HYPERSYNCHRONIZED OSCILLATIONS185
- CORTICAL MECHANISMS FOR SPIKE-AND-WAVE PATTERNS192
- THALAMOCORTICAL MECHANISMS FOR ABSENCE SEIZURES197
- TESTING THE MECHANISMS204
- CONCLUSIONS: A CORTICOTHALAMIC MECHANISM FOR ABSENCE SEIZURES208
- ACKNOWLEDGMENTS210
- REFERENCES210
- Chapter 14 Mechanisms of Graded Persistent Activity: Implications for Epilepsy215
- ABSTRACT215
- INTRODUCTION215
- RESULTS222
- DISCUSSION226
- ACKNOWLEDGMENTS229
- REFERENCES229
- Chapter 15 Small Networks, Large Networks, Experiment and Theory – Can We Bring Them Together with232
- ABSTRACT232
- IDEA 1: USING HETEROGENEITY TO COMPARE SINGLE COMPARTMENT NEURONAL MODELS233
- IDEA 2: USING TWO-CELL NETWORK MODEL PARAMETERS TO CONSTRAIN AND PREDICT N-CELL NETWORK MODEL OUTPUT235
- IDEA 3: ESTIMATING SYNAPTIC PARAMETERS FROM ACTUAL AND VIRTUAL MODEL NETWORKS237
- DISCUSSION, SUMMARY AND CONCLUSIONS239
- APPENDIX 15.1240
- ACKNOWLEDGMENTS242
- REFERENCES242
- Part IV Homeostasis and Epilepsy245
- Chapter 16 Stability and Plasticity in Neuronal and Network Dynamics247
- ABSTRACT247
- INTRODUCTION247
- CELLULAR HOMEOSTASIS MECHANISMS247
- SYNAPTIC MECHANISMS OF PLASTICITY AND STABILITY252
- PLASTICITY AND STABILITY AT THE NETWORK LEVEL253
- IMPLICATIONS FOR EPILEPSY AND CONCLUSIONS256
- ACKNOWLEDGMENTS256
- REFERENCES257
- Chapter 17 Homeostatic Plasticity and Post-Traumatic Epileptogenesis259
- ABSTRACT259
- INTRODUCTION259
- SHIFT OF BALANCE BETWEEN EXCITATION AND INHIBITION AS SEIZURE TRIGGERING FACTOR260
- LONG-LASTING HOMEOSTATIC ALTERATIONS OF EXCITABILITY AND CHRONIC EPILEPTOGENESIS261
- PARTIAL CORTICAL DEAFFERENTATION PROMOTES DEVELOPMENT OF PAROXYSMAL ACTIVITY IN VIVO261
- MODELING OF HOMEOSTATIC SYNAPTIC PLASTICITY PROCESSES LEADING TO EPILEPTOGENESIS266
- OTHER FACTORS PROMOTING EPILEPTOGENESIS274
- CONCLUSION275
- ACKNOWLEDGMENTS275
- REFERENCES275
- Part V Mechanisms of Synchronization279
- Chapter 18 Synchronization in Hybrid Neuronal Networks281
- ABSTRACT281
- INTRODUCTION: SYNCHRONIZED ACTIVITY IN THE HIPPOCAMPAL FORMATION281
- RESULTS282
- DISCUSSION285
- REFERENCES286
- Chapter 19 Complex Synaptic Dynamics of GABAergic Networks of the Hippocampus288
- ABSTRACT288
- INTRODUCTION288
- CELL TYPE-SPECIFIC GABAERGIC INPUT MODULATES SPIKE TIMING DURING HIPPOCAMPAL EPILEPTIFORM ACTIVITY289
- ELECTRICAL COUPLING AND PROPAGATION OF POSTSYNAPTIC CURRENTS AND POTENTIALS IN GABAERGIC NETWORKS295
- GENERAL CONCLUSIONS AND FUTURE APPROACHES299
- ACKNOWLEDGMENTS299
- REFERENCES300
- Chapter 20 Experimental and Theoretical Analyses of Synchrony in Feedforward Networks304
- ABSTRACT304
- INTRODUCTION304
- SYNCHRONY IN FEEDFORWARD NETWORKS305
- MODELING FEEDFORWARD NETWORKS308
- STRATEGIES FOR ELIMINATING SYNCHRONY312
- CONTROLLING THE SPREAD OF SYNCHRONY WITH INHIBITION313
- PATHOLOGICAL SYNCHRONY314
- SUMMARY315
- REFERENCES315
- Chapter 21 Modulation of Synchrony by Interneurons: Insights from Attentional Modulation of Response317
- ABSTRACT317
- INTRODUCTION317
- METHODS318
- RESULTS322
- DISCUSSION329
- ACKNOWLEDGMENT331
- REFERENCES331
- Part VI Interictal to Ictal Transitions333
- Chapter 22 Cellular and Network Mechanisms of Oscillations Preceding and Perhaps Initiating Epilepti335
- ABSTRACT335
- INTRODUCTION335
- THE ASSOCIATION OF VERY FAST OSCILLATIONS (VFO) WITH EPILEPTIFORM ACTIVITY336
- EVIDENCE FOR ELECTRICAL COUPLING BETWEEN AXONS, MEDIATED BY GAP JUNCTIONS, AND ITS LIKELY ROLE IN EP340
- AN IN VITRO EPILEPSY MODEL, IN WHICH EPOCHS OF GAMMA OSCILLATION ALTERNATE WITH EPILEPTIFORM BURSTS345
- A PRIMARILY GAP-JUNCTION-MEDIATED BETA OSCILLATION IN LAYER 5 OF SOMATOSENSORY CORTEX IN VITRO AND H349
- CONCLUSION352
- ACKNOWLEDGMENTS353
- REFERENCES353
- Chapter 23 Transition to Ictal Activity in Temporal Lobe Epilepsy: Insights from Macroscopic Models356
- ABSTRACT356
- INTRODUCTION356
- NEURONAL POPULATION MODELS357
- TRANSITION TO ICTAL ACTIVITY IN HUMAN TLE: DESCRIPTION OF ELECTROPHYSIOLOGICAL DATA OBTAINED FROM IN359
- INTERPRETATION OF INTRACEREBRAL EEG SIGNALS RECORDED DURING THE TRANSITION TO SEIZURE USING NEURONAL366
- GENERAL CONCLUSION383
- REFERENCES383
- Chapter 24 Unified Modeling and Analysis of Primary Generalized Seizures387
- ABSTRACT387
- INTRODUCTION387
- CORTICOTHALAMIC MODEL388
- STEADY STATES, LINEAR PROPERTIES AND STABILITY391
- ABSENCE SEIZURES393
- TONIC-CLONIC SEIZURES395
- UNIFIED PERSPECTIVE ON GENERALIZED SEIZURES398
- SUMMARY AND CONCLUSION399
- ACKNOWLEDGMENTS400
- REFERENCES401
- Chapter 25 A Neuronal Network Model of Corticothalamic Oscillations: The Emergence of Epileptiform A403
- ABSTRACT403
- INTRODUCTION403
- MATERIALS AND METHODS404
- RESULTS407
- DISCUSSION414
- ACKNOWLEDGMENTS417
- REFERENCES417
- Chapter 26 Extracellular Potassium Dynamics and Epileptogenesis419
- ABSTRACT419
- INTRODUCTION419
- CORTICAL ORIGIN OF PAROXYSMAL OSCILLATIONS GENERATED WITHIN THE THALAMOCORTICAL SYSTEM420
- CHANGES IN THE EXTRACELLULAR MILIEU AND EPILEPTOGENESIS421
- MODEL DESCRIPTION422
- SINGLE CELL MODEL423
- SINGLE CELL DYNAMICS MEDIATED BY ELEVATED EXTRACELLULAR K+425
- NETWORK DYNAMICS MEDIATED BY ELEVATED EXTRACELLULAR K+429
- CONCLUSION437
- ACKNOWLEDGMENT437
- REFERENCES437
- Chapter 27 Slow Waves Associated with Seizure Activity440
- ABSTRACT440
- INTRODUCTION440
- SLOW WAVES THAT APPEAR AT SEIZURE ONSET (INITIAL SLOW WAVES: ISWs)441
- SLOW WAVES THAT OCCUR DURING SEIZURE DEVELOPMENT443
- SLOW WAVES AT SEIZURE TERMINATION444
- POTENTIAL MECHANISMS OF SEIZURE ASSOCIATED SLOW WAVES446
- MECHANISMS OF DC SHIFT DURING SEIZURE DEVELOPMENT448
- MECHANISMS OF STSWs GENERATION449
- CONCLUSION450
- REFERENCES450
- Part VII Seizure Dynamics455
- Chapter 28 Dynamics of Epileptic Seizures during Evolution and Propagation457
- ABSTRACT457
- INTRODUCTION457
- METHODS FOR ANALYSIS OF THE EEG458
- SEIZURE DYNAMICS460
- RATIONALE FOR THE QUANTIFICATION OF SEIZURE DYNAMICS: POTENTIAL APPLICATIONS468
- REFERENCES469
- Chapter 29 Are Correlation Dimension and Lyapunov Exponents Useful Tools for Prediction of Epileptic471
- ABSTRACT471
- INTRODUCTION471
- CORRELATION DIMENSION, LYAPUNOV EXPONENTS AND THE KAPLAN-YORKE FORMULA472
- INABILITY FOR CORRELATION DIMENSION TO PREDICT SEIZURES475
- LYAPUNOV EXPONENTS DO NOT PREDICT EPILEPTIC SEIZURES481
- DISCUSSION491
- ACKNOWLEDGMENT493
- REFERENCES493
- Chapter 30 Towards a Dynamics of Seizure Mechanics496
- ABSTRACT496
- INTRODUCTION496
- THE POSSIBLE ROLE OF INHIBITION497
- EXCITATORY NEURON INTERACTIONS IN SEIZURES497
- HUMAN SEIZURE DYNAMICS498
- EXCITATORY AND INHIBITORY INTERPLAY IN SEIZURE PATTERNS499
- EXCITATORY AND INHIBITORY INTERPLAY DURING EPILEPTIFORM BURSTS503
- EXCITATORY AND INHIBITORY INTERPLAY DURING SEIZURES503
- GAP JUNCTIONS AND SEIZURES507
- DISCUSSION508
- ACKNOWLEDGMENTS510
- REFERENCES510
- Part VIII Towards Computer-Aided Therapy513
- Chapter 31 Principles and Practice of Computer-Aided Drug Design as Applied to the Discovery of Anti515
- ABSTRACT515
- INTRODUCTION515
- AN OVERVIEW OF COMPUTER-AIDED DRUG DESIGN FOR EPILEPSY516
- THE METHODS OF COMPUTER-AIDED DRUG DESIGN FOR EPILEPSY517
- APPLYING THE METHODS OF CADD TO ANTIEPILEPTIC DRUG DESIGN: BASIC PRINCIPLES521
- APPLYING THE METHODS OF CADD TO ANTICONVULSANT DRUG DESIGN: SPECIFIC EXAMPLES AND FUTURE CONSIDERATI524
- CONCLUSIONS527
- ACKNOWLEDGMENTS527
- REFERENCES527
- Chapter 32 Computation Applied to Clinical Epilepsy and Antiepileptic Devices530
- ABSTRACT530
- INTRODUCTION530
- CLINICAL DATA ACQUISITION531
- COMPUTATIONAL ANALYSIS532
- PRACTICAL APPLICATIONS544
- FUTURE WORK556
- REFERENCES556
- Chapter 33 Microelectrode-based Epilepsy Therapy: A Hybrid Neural Prosthesis Incorporating Seizure P559
- ABSTRACT559
- INTRODUCTION560
- PRINCIPLES OF IN VIVO ELECTROCHEMISTRY562
- ENZYME-BASED MULTISITE MICROELECTRODE ARRAYS563
- SECOND-BY-SECOND L-GLUTAMATE MEASUREMENTS IN THE MAMMALIAN CENTRAL NERVOUS SYSTEM567
- MICROELECTRODE-BASED EPILEPSY THERAPY COUPLED WITH NEURAL PROSTHESES FOR MAINTENANCE OF HIPPOCAMPAL571
- A NEURAL PROSTHESIS FOR THE CA3 REGION OF HIPPOCAMPUS573
- CONCLUSIONS584
- REFERENCES584
- Index587
- Color Plates605
Epilepsy is a neurological disorder that affects millions of patients worldwide and arises from the concurrent action of multiple pathophysiological processes. The power of mathematical analysis and computational modeling is increasingly utilized in basic and clinical epilepsy research to better understand the relative importance of the multi-faceted, seizure-related changes taking place in the brain during an epileptic seizure. This groundbreaking book is designed to synthesize the current ideas and future directions of the emerging discipline of computational epilepsy research. Chapters address relevant basic questions (e.g., neuronal gain control) as well as long-standing, critically important clinical challenges (e.g., seizure prediction). The book should be of high interest to a wide range of readers, including undergraduate and graduate students, postdoctoral fellows and faculty working in the fields of basic or clinical neuroscience, epilepsy research, computational modeling and bioengineering.
* Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures
* Contributors are top experts at the forefront of computational epilepsy research
* Chapter contents are highly relevant to both basic and clinical epilepsy researchers
* Covers a wide range of topics from molecular to seizure predictions and brain implants to control seizures
* Contributors are top experts at the forefront of computational epilepsy research
* Chapter contents are highly relevant to both basic and clinical epilepsy researchers