Velazquez / Wennberg Coordinated Activity in the Brain

Measurements and Relevance to Brain Function and Behavior
1. Auflage 2009
ISBN: 978-0-387-93797-7
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 2, 277 Seiten

Reihe: Springer Series in Computational Neuroscience

Increasing interest in the study of coordinated activity of brain cell ensembles reflects the current conceptualization of brain information processing and cognition. It is thought that cognitive processes involve not only serial stages of sensory signal processing, but also massive parallel information processing circuitries, and therefore it is the coordinated activity of neuronal networks of brains that give rise to cognition and consciousness in general. While the concepts and techniques to measure synchronization are relatively well characterized and developed in the mathematics and physics community, the measurement of coordinated activity derived from brain signals is not a trivial task, and is currently a subject of debate. Coordinated Activity in the Brain: Measurements and Relevance to Brain Function and Behavior addresses conceptual and methodological limitations, as well as advantages, in the assessment of cellular coordinated activity from neurophysiological recordings. The book offers a broad overview of the field for investigators working in a variety of disciplines (neuroscience, biophysics, mathematics, physics, neurology, neurosurgery, psychology, biomedical engineering, computer science/computational biology), and introduces future trends for understanding brain activity and its relation to cognition and pathologies. This work will be valuable to professional investigators and clinicians, graduate and post-graduate students in related fields of neuroscience and biophysics, and to anyone interested in signal analysis techniques for studying brain function.

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.

Autoren/Hrsg.

Wennberg, Richard

Velazquez, Jose Luis Perez

Inhaltsverzeichnis

1;Preface;5
1.1;The Numerous Aspects of Coordinated Activityin the Nervous System;5
2;Contents;9
3;Contributors;11
4;Correlations of Cellular Activities in the Nervous System: Physiological and Methodological Considerations;14
4.1;1 Introduction;14
4.2;2 Neurophysiological Bases of the Correlation of Activities Among Brain Areas;16
4.3;3 On the Measurement of Correlations in Nervous System Activity;17
4.4;4 Expectations from Synchrony Analysis of Neural Signals;21
4.5;5 Notes on the Physical Interpretation of the Computed Synchrony in Neural Systems;22
4.6;6 The Significance of Considering Fluctuations in Coordinated Cellular Activity;24
4.7;7 Neural Information Processing and Coordination of Activity;26
4.8;8 Some Methodological Considerations on the Assessment of Phase Synchronization;28
4.9;9 Conclusions;32
4.10;References;32
5;Synchronization Between Sources: Emerging Methods for Understanding Large-Scale Functional Networks in the Human Brain;38
5.1;1 Introduction: Oscillatory Synchronization and Dynamic Functional Neural Assemblies;38
5.2;2 Methods for the Analysis of Oscillatory Synchronization;40
5.2.1;2.1 Wavelet Analysis: Application to Phase-Locking Analysis;41
5.2.2;2.2 The Hilbert Transform: Application to Phase-Locking Analysis;42
5.2.3;2.3 Phase-Locking Value;43
5.2.4;2.4 Phase Cross Coherence in the Assessment of Human Brain Synchronization;45
5.3;3 Dynamic Brain Networks: Synchronization Between Sources;46
5.3.1;3.1 Synchronization Between Sources Using ''Blind'' Source Separation;47
5.3.2;3.2 Synchronization Between Neural Sources Identified Using Anatomical Constraints;49
5.4;4 Summary and Conclusion;52
5.5;References;53
6;Approaches to the Detection of Direct Directed Interactions in Neuronal Networks;56
6.1;1 Introduction;56
6.2;2 Measuring Interactions: Theory and Methods;57
6.2.1;2.1 Undirected Interactions: Linear Spectral Analysis;57
6.2.1.1;2.1.1 The Spectral Matrix;58
6.2.1.2;2.1.2 Coherence;59
6.2.1.3;2.1.3 Partial Coherence;59
6.2.1.4;2.1.4 Nonparametric Estimation of the Spectral Matrix;60
6.2.1.5;2.1.5 Estimation of Coherence;62
6.2.1.6;2.1.6 Estimation of Partial Coherence;62
6.2.1.7;2.1.7 Testing for Significance;63
6.2.2;2.2 Directed Interactions: Linear Autoregressive Models;63
6.2.2.1;2.2.1 Granger-Causality and Vector Autoregressive Processes;63
6.2.2.2;2.2.2 Partial Directed Coherence;65
6.2.2.3;2.2.3 Estimation of Partial Directed Coherence;65
6.2.2.4;2.2.4 Testing for Significance;65
6.3;3 Simulations: What Can Be Inferred from Multivariate Analysis;66
6.3.1;3.1 Comparison of Coherence, Partial Coherence, and Partial Directed Coherence;67
6.3.2;3.2 Unobserved Processes;70
6.3.3;3.3 Application to Nonlinear Stochastic Systems;71
6.4;4 Application to Real-World Data;72
6.4.1;4.1 Application to Tremor Data;73
6.5;5 Discussion and Conclusion;74
6.6;References;76
7;The Phase Oscillator Approximation in Neuroscience: An Analytical Framework to Study Coherent Activity in Neural Networks;78
7.1;1 Introduction;78
7.2;2 Mathematical Description of Biological Rhythms;79
7.2.1;2.1 Time, Phase, and Phase Resetting;80
7.2.2;2.2 Phase Oscillator Approximation and the Kuramoto Model;81
7.2.3;2.3 Estimation of the Phase--Response Curve;84
7.3;3 Applications of the Phase Oscillator Approximation in Neuroscience;86
7.3.1;3.1 Dynamics of Oscillator Networks;86
7.3.2;3.2 Predicting the Emergence of Synchronized Assemblies;89
7.3.3;3.3 Encoding Models;91
7.3.4;3.4 Stability of Oscillations and Liapunov Exponent;92
7.3.5;3.5 Optimal Stimulus of a Neural Oscillator;94
7.3.6;3.6 Optimal Timescale for Spike-Time Reliability;95
7.3.7;3.7 Stochastic Synchronization;97
7.3.8;3.8 Response Dynamics of a Population of Neural Oscillators;98
7.3.9;3.9 Phase--Response Curves in EEG Studies of Epilepsy;99
7.4;4 Summary and Outlook;99
7.5;References;100
8;From Synchronisation to Networks: Assessment of Functional Connectivity in the Brain;103
8.1;1 Introduction;103
8.2;2 Synchronisation Analysis;103
8.2.1;2.1 The Concept of 'Functional Connectivity';103
8.2.2;2.2 Linear Measures;104
8.2.3;2.3 Nonlinear Measures;105
8.2.4;2.4 Methodological Aspects and Pitfalls;107
8.3;3 Graph Analysis of Brain Networks;109
8.3.1;3.1 Modern Network Theory;109
8.3.2;3.2 Measures to Characterise Graphs;112
8.3.3;3.3 Application to Functional Connectivity: Principles and Problems;114
8.4;4 Examples of Applications;117
8.4.1;4.1 Synchronisation;117
8.4.2;4.2 Network Analysis;119
8.4.2.1;4.2.1 MRI;119
8.4.2.2;4.2.2 EEG/MEG;120
8.5;5 Summary and Conclusion;122
8.6;References;123
9;The Size of Neuronal Assemblies, Their Frequency of Synchronization, and Their Cognitive Function ;128
9.1;1 Introduction;128
9.2;2 Hierarchy in the Brain;129
9.3;3 Synchronized Brain Activity Measured by LFP and EEG;130
9.3.1;3.1 Local Scale Synchronization;130
9.3.2;3.2 Measuring Electrical Dipole Fields from the Brain;131
9.3.3;3.3 Neuronal Synchronization as a Basis for LFP and EEG Measurements;132
9.3.4;3.4 Synchronization Between LFP and EEG Signals;133
9.3.5;3.5 What Does Synchronization of Neuronal Activity Mean for Cognitive Processing? ;134
9.4;4 Methods of Spectral Analysis;135
9.4.1;4.1 Power Spectral Density;135
9.4.2;4.2 Coherence;136
9.4.3;4.3 Phase and Latency;137
9.4.4;4.4 Confidence Intervals;138
9.4.5;4.5 Frequency Bands;139
9.5;5 Experimental Evidence for the Inverse Relationship Between Assembly Size and Synchronization Frequency ;139
9.5.1;5.1 Local Synchronization in High (Gamma) Frequencies;139
9.5.2;5.2 Interareal Interactions Mediated by Synchronization in Intermediate Frequencies ;140
9.5.3;5.3 Top-Down Activity Mediated by Long-Distance Interactions in Low Frequencies ;141
9.5.4;5.4 Alpha Waves as Markers of Top-Down Activity;143
9.6;6 Conclusions;143
9.7;References;144
10;Synchrony in Neural Networks Underlying Seizure Generationin Human Partial Epilepsies;148
10.1;1 Introduction;148
10.2;2 The Epileptogenic Zone: A Network of Hyperexcitable Structures;149
10.3;3 Synchronization of Ictal Activity: From Normal Brain Synchrony to Epileptic Binding;150
10.4;4 The Role of Synchronization/Desynchronization in Ictal Semiology;153
10.5;5 Involvement of Thalamo-cortical Synchrony in Temporal Lobe Epilepsy (TLE) Seizures;154
10.6;6 The Epileptogenic Zone Discloses Altered Synchrony During the Interictal State;155
10.7;7 A General Scheme for the Anatomo-functional Organization of Human Partial Seizures ;156
10.8;References;157
11;Detection of Phase Synchronization in Multivariate Single Brain Signals by a Clustering Approach;159
11.1;1 Introduction;159
11.2;2 Methods;161
11.2.1;2.1 Phase Definitions;162
11.2.2;2.2 Phase Synchronization;162
11.2.3;2.3 Clustering;164
11.2.3.1;2.3.1 Quantifying MPS;165
11.2.3.2;2.3.2 The Number of Clusters;165
11.2.3.3;2.3.3 The Cluster Algorithm;166
11.2.4;2.4 Summary of the Algorithm;167
11.3;3 Applications;168
11.3.1;3.1 Experimental Data;168
11.3.2;3.2 Cluster Results;171
11.4;4 Discussion;171
11.5;References;172
12;Denoising and Averaging Techniques for Electrophysiological Data;175
12.1;1 Introduction;175
12.2;2 Noise in Electrophysiological Data;176
12.2.1;2.1 A Concept of Noise;176
12.2.2;2.2 Event-Related Potentials;176
12.2.3;2.3 Sources of Noise;178
12.2.4;2.4 Strategies to Handle Noise;179
12.3;3 Models for Event-Related Potentials;179
12.4;4 Methods for Signal Estimation;184
12.4.1;4.1 Single-Trial Estimation;184
12.4.2;4.2 Classification Methods;188
12.4.3;4.3 Averaging Procedures;191
12.5;5 Enhancing Averaging by Integrating Time Markers;194
12.6;6 Discussion and Conclusion;196
12.7;References;197
13;Dissection of Synchronous Population Discharges In Vitro;200
13.1;1 Introduction;175
13.2;2 In Vitro Approaches to Studying Population Activities;176
13.3;3 Spatiotemporal Resolution of Extracellular Recordings During Population Activities;203
13.4;4 Analytical Approach;205
13.5;5 Intermediate Levels of Firing Rate Fluctuations;207
13.6;6 Transition to Fully Synchronous Network States;209
13.7;7 Threshold Behaviour in the Initiation of Population Activity;179
13.8;8 Synaptic Processes Underlying Network Synchronization;184
13.9;9 Refractory Periods for the Initiation of Full Population Synchrony;216
13.10;10 Spatial Patterns of Activity During Partial and Full Synchronization;218
13.11;11 Cell-Type Dissection of Full Population Discharges: Leader Cells;221
13.12;12 Cell-Type Firing Dynamics of Pyramidal Cells and GABAergic Interneurons;194
13.13;13 Conclusions;196
13.14;References;228
14;Time-Frequency Methods and Brain Rhythm Signal Processing;234
14.1;1 Introduction;175
14.2;2 Mathematical Preliminaries and Wavelets;176
14.3;3 TimeFrequency Transforms;203
14.4;4 TimeFrequency Properties and Hippocampal Rhythms;205
14.5;5 Feature Extraction and Classification;207
14.6;6 Clustering and Associated Techniques;209
14.7;7 Conclusions;179
14.8;References;228
15;Complex Network Modeling: A New Approach to Neurosciences;249
15.1;1 Introduction;249
15.2;2 The Structure of Complex Networks;250
15.2.1;2.1 Definitions and Notations;251
15.2.2;2.2 Topological Properties;253
15.2.3;2.3 Topology of Functional Brain Networks;258
15.3;3 Brain-Like Dynamical Processes;259
15.3.1;3.1 Synchronization Processes;260
15.3.2;3.2 Synchronization of Kuramoto Oscillators;261
15.3.3;3.3 More Realistic Dynamical Models;265
15.4;4 Outlook;267
15.5;References;269
16;Index;272