Coordinated Activity in the Brain (eBook)

J. L. Perez Velazquez was born in Zaragoza, Spain, and received the degree of 'Licenciado' in Chemistry (Biochemistry, universities of Zaragoza and Complutense of Madrid), and a PhD degree in 1992 from the Department of Molecular Physiology and Biophysics at Baylor College of Medicine (Houston), homologated to Doctorate in Chemistry by the Spanish Ministry of Culture in 1997. He is an associate scientist in the Neuroscience and Mental Health Programme and the Brain and Behaviour Centre at the Hospital For Sick Children in Toronto, and an associate professor at the University of Toronto. Richard Wennberg was born in Vancouver, Canada. He obtained the degree M.D. from the University of British Columbia in 1990 and completed a neurology residency at McGill University in 1994, followed by a fellowship in electroencephalography at the Montreal Neurological Institute. Currently, he is director of the clinical neurophysiology laboratory at the University Health Network, Toronto Western Hospital, associate professor of medicine at the University of Toronto, chair of the Royal College of Physicians and Surgeons of Canada examination board in neurology, and president of the Canadian League Against Epilepsy.

Preface 5
The Numerous Aspects of Coordinated Activityin the Nervous System 5
Contents 9
Contributors 11
Correlations of Cellular Activities in the Nervous System: Physiological and Methodological Considerations 14
1 Introduction 14
2 Neurophysiological Bases of the Correlation of Activities Among Brain Areas 16
3 On the Measurement of Correlations in Nervous System Activity 17
4 Expectations from Synchrony Analysis of Neural Signals 21
5 Notes on the Physical Interpretation of the Computed Synchrony in Neural Systems 22
6 The Significance of Considering Fluctuations in Coordinated Cellular Activity 24
7 Neural Information Processing and Coordination of Activity 26
8 Some Methodological Considerations on the Assessment of Phase Synchronization 28
9 Conclusions 32
References 32
Synchronization Between Sources: Emerging Methods for Understanding Large-Scale Functional Networks in the Human Brain 38
1 Introduction: Oscillatory Synchronization and Dynamic Functional Neural Assemblies 38
2 Methods for the Analysis of Oscillatory Synchronization 40
2.1 Wavelet Analysis: Application to Phase-Locking Analysis 41
2.2 The Hilbert Transform: Application to Phase-Locking Analysis 42
2.3 Phase-Locking Value 43
2.4 Phase Cross Coherence in the Assessment of Human Brain Synchronization 45
3 Dynamic Brain Networks: Synchronization Between Sources 46
3.1 Synchronization Between Sources Using ''Blind'' Source Separation 47
3.2 Synchronization Between Neural Sources Identified Using Anatomical Constraints 49
4 Summary and Conclusion 52
References 53
Approaches to the Detection of Direct Directed Interactions in Neuronal Networks 56
1 Introduction 56
2 Measuring Interactions: Theory and Methods 57
2.1 Undirected Interactions: Linear Spectral Analysis 57
2.1.1 The Spectral Matrix 58
2.1.2 Coherence 59
2.1.3 Partial Coherence 59
2.1.4 Nonparametric Estimation of the Spectral Matrix 60
2.1.5 Estimation of Coherence 62
2.1.6 Estimation of Partial Coherence 62
2.1.7 Testing for Significance 63
2.2 Directed Interactions: Linear Autoregressive Models 63
2.2.1 Granger-Causality and Vector Autoregressive Processes 63
2.2.2 Partial Directed Coherence 65
2.2.3 Estimation of Partial Directed Coherence 65
2.2.4 Testing for Significance 65
3 Simulations: What Can Be Inferred from Multivariate Analysis 66
3.1 Comparison of Coherence, Partial Coherence, and Partial Directed Coherence 67
3.2 Unobserved Processes 70
3.3 Application to Nonlinear Stochastic Systems 71
4 Application to Real-World Data 72
4.1 Application to Tremor Data 73
5 Discussion and Conclusion 74
References 76
The Phase Oscillator Approximation in Neuroscience: An Analytical Framework to Study Coherent Activity in Neural Networks 78
1 Introduction 78
2 Mathematical Description of Biological Rhythms 79
2.1 Time, Phase, and Phase Resetting 80
2.2 Phase Oscillator Approximation and the Kuramoto Model 81
2.3 Estimation of the Phase--Response Curve 84
3 Applications of the Phase Oscillator Approximation in Neuroscience 86
3.1 Dynamics of Oscillator Networks 86
3.2 Predicting the Emergence of Synchronized Assemblies 89
3.3 Encoding Models 91
3.4 Stability of Oscillations and Liapunov Exponent 92
3.5 Optimal Stimulus of a Neural Oscillator 94
3.6 Optimal Timescale for Spike-Time Reliability 95
3.7 Stochastic Synchronization 97
3.8 Response Dynamics of a Population of Neural Oscillators 98
3.9 Phase--Response Curves in EEG Studies of Epilepsy 99
4 Summary and Outlook 99
References 100
From Synchronisation to Networks: Assessment of Functional Connectivity in the Brain 103
1 Introduction 103
2 Synchronisation Analysis 103
2.1 The Concept of 'Functional Connectivity' 103
2.2 Linear Measures 104
2.3 Nonlinear Measures 105
2.4 Methodological Aspects and Pitfalls 107
3 Graph Analysis of Brain Networks 109
3.1 Modern Network Theory 109
3.2 Measures to Characterise Graphs 112
3.3 Application to Functional Connectivity: Principles and Problems 114
4 Examples of Applications 117
4.1 Synchronisation 117
4.2 Network Analysis 119
4.2.1 MRI 119
4.2.2 EEG/MEG 120
5 Summary and Conclusion 122
References 123
The Size of Neuronal Assemblies, Their Frequency of Synchronization, and Their Cognitive Function 128
1 Introduction 128
2 Hierarchy in the Brain 129
3 Synchronized Brain Activity Measured by LFP and EEG 130
3.1 Local Scale Synchronization 130
3.2 Measuring Electrical Dipole Fields from the Brain 131
3.3 Neuronal Synchronization as a Basis for LFP and EEG Measurements 132
3.4 Synchronization Between LFP and EEG Signals 133
3.5 What Does Synchronization of Neuronal Activity Mean for Cognitive Processing? 134
4 Methods of Spectral Analysis 135
4.1 Power Spectral Density 135
4.2 Coherence 136
4.3 Phase and Latency 137
4.4 Confidence Intervals 138
4.5 Frequency Bands 139
5 Experimental Evidence for the Inverse Relationship Between Assembly Size and Synchronization Frequency 139
5.1 Local Synchronization in High (Gamma) Frequencies 139
5.2 Interareal Interactions Mediated by Synchronization in Intermediate Frequencies 140
5.3 Top-Down Activity Mediated by Long-Distance Interactions in Low Frequencies 141
5.4 Alpha Waves as Markers of Top-Down Activity 143
6 Conclusions 143
References 144
Synchrony in Neural Networks Underlying Seizure Generationin Human Partial Epilepsies 148
1 Introduction 148
2 The Epileptogenic Zone: A Network of Hyperexcitable Structures 149
3 Synchronization of Ictal Activity: From Normal Brain Synchrony to Epileptic Binding 150
4 The Role of Synchronization/Desynchronization in Ictal Semiology 153
5 Involvement of Thalamo-cortical Synchrony in Temporal Lobe Epilepsy (TLE) Seizures 154
6 The Epileptogenic Zone Discloses Altered Synchrony During the Interictal State 155
7 A General Scheme for the Anatomo-functional Organization of Human Partial Seizures 156
References 157
Detection of Phase Synchronization in Multivariate Single Brain Signals by a Clustering Approach 159
1 Introduction 159
2 Methods 161
2.1 Phase Definitions 162
2.2 Phase Synchronization 162
2.3 Clustering 164
2.3.1 Quantifying MPS 165
2.3.2 The Number of Clusters 165
2.3.3 The Cluster Algorithm 166
2.4 Summary of the Algorithm 167
3 Applications 168
3.1 Experimental Data 168
3.2 Cluster Results 171
4 Discussion 171
References 172
Denoising and Averaging Techniques for Electrophysiological Data 175
1 Introduction 175
2 Noise in Electrophysiological Data 176
2.1 A Concept of Noise 176
2.2 Event-Related Potentials 176
2.3 Sources of Noise 178
2.4 Strategies to Handle Noise 179
3 Models for Event-Related Potentials 179
4 Methods for Signal Estimation 184
4.1 Single-Trial Estimation 184
4.2 Classification Methods 188
4.3 Averaging Procedures 191
5 Enhancing Averaging by Integrating Time Markers 194
6 Discussion and Conclusion 196
References 197
Dissection of Synchronous Population Discharges In Vitro 200
1 Introduction 175
2 In Vitro Approaches to Studying Population Activities 176
3 Spatiotemporal Resolution of Extracellular Recordings During Population Activities 203
4 Analytical Approach 205
5 Intermediate Levels of Firing Rate Fluctuations 207
6 Transition to Fully Synchronous Network States 209
7 Threshold Behaviour in the Initiation of Population Activity 179
8 Synaptic Processes Underlying Network Synchronization 184
9 Refractory Periods for the Initiation of Full Population Synchrony 216
10 Spatial Patterns of Activity During Partial and Full Synchronization 218
11 Cell-Type Dissection of Full Population Discharges: Leader Cells 221
12 Cell-Type Firing Dynamics of Pyramidal Cells and GABAergic Interneurons 194
13 Conclusions 196
References 228
Time-Frequency Methods and Brain Rhythm Signal Processing 234
1 Introduction 175
2 Mathematical Preliminaries and Wavelets 176
3 TimeFrequency Transforms 203
4 TimeFrequency Properties and Hippocampal Rhythms 205
5 Feature Extraction and Classification 207
6 Clustering and Associated Techniques 209
7 Conclusions 179
References 228
Complex Network Modeling: A New Approach to Neurosciences 249
1 Introduction 249
2 The Structure of Complex Networks 250
2.1 Definitions and Notations 251
2.2 Topological Properties 253
2.3 Topology of Functional Brain Networks 258
3 Brain-Like Dynamical Processes 259
3.1 Synchronization Processes 260
3.2 Synchronization of Kuramoto Oscillators 261
3.3 More Realistic Dynamical Models 265
4 Outlook 267
References 269
Index 272