E-Book, Englisch, Band 38, 370 Seiten
Chaovalitwongse / Pardalos / Xanthopoulos Computational Neuroscience
1. Auflage 2010
ISBN: 978-0-387-88630-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
E-Book, Englisch, Band 38, 370 Seiten
Reihe: Springer Optimization and Its Applications
ISBN: 978-0-387-88630-5
Verlag: Springer
Format: PDF
Kopierschutz: 1 - PDF Watermark
This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.
Autoren/Hrsg.
Weitere Infos & Material
1;Preface;7
2;Contents;10
3;List of Contributors;13
4;Part I Data Mining;19
4.1;1 Optimization in Reproducing Kernel Hilbert Spacesof Spike Trains;20
4.1.1;1.1 Introduction;21
4.1.2;1.2 Some Background on RKHS Theory;22
4.1.3;1.3 Inner Product for Spike Times;24
4.1.4;1.4 Inner Product for Spike Trains;25
4.1.5;1.5 Properties and Estimation of the Memoryless Cross-Intensity Kernel;27
4.1.5.1;1.5.1 Properties;27
4.1.5.2;1.5.2 Estimation;29
4.1.6;1.6 Induced RKHS and Congruent Spaces;30
4.1.6.1;1.6.1 Space Spanned by Intensity Functions;31
4.1.6.2;1.6.2 Induced RKHS;31
4.1.6.3;1.6.3 mCI Kernel and the RKHS Induced by ;32
4.1.6.4;1.6.4 mCI Kernel as a Covariance Kernel;33
4.1.7;1.7 Principal Component Analysis;34
4.1.7.1;1.7.1 Optimization in the RKHS;34
4.1.7.2;1.7.2 Optimization in the Space Spanned by the Intensity Functions;37
4.1.7.3;1.7.3 Results;38
4.1.8;1.8 Conclusion;42
4.1.9;References;44
4.2;2 Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods ;47
4.2.1;2.1 Introduction and Background;47
4.2.2;2.2 Graph Theory and Neuroscience;49
4.2.3;2.3 A Database of Imaging Experiments;51
4.2.4;2.4 The Usefulness of Co-activation Graphs;53
4.2.5;2.5 Relating fMRI to EEG;55
4.2.6;2.6 Conclusion;57
4.2.7;References;57
4.3;3 Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles;59
4.3.1;3.1 Introduction;60
4.3.2;3.2 Methods;61
4.3.2.1;3.2.1 Methodology Overview;61
4.3.2.2;3.2.2 Feature Extraction (Step 1);62
4.3.2.3;3.2.3 Feature Selection (Step 2);66
4.3.2.4;3.2.4 Feature Refinement (Steps 3 and 4);66
4.3.3;3.3 Results;68
4.3.3.1;3.3.1 Simulation Test;68
4.3.4;3.4 Discussion;69
4.3.5;3.5 Conclusion;70
4.3.6;References;71
4.4;4 Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt--Jakob Disease for Assisting Early Diagnosis ;73
4.4.1;4.1 Introduction;74
4.4.2;4.2 Patients and EEG Recordings;78
4.4.3;4.3 Methods;80
4.4.3.1;4.3.1 Independent Component Analysis and Extractionof CJD-Related Components;80
4.4.3.2;4.3.2 Bayesian Information Criterion;81
4.4.4;4.4 Results;82
4.4.4.1;4.4.1 Determination of the Number of Sources;82
4.4.4.2;4.4.2 CJD-Related Feature Extraction;83
4.4.4.3;4.4.3 Feature Extraction by PCA;85
4.4.5;4.5 Discussions;85
4.4.6;4.6 Conclusions;88
4.4.7;References;89
4.5;5 Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detectionin fMRI Data;91
4.5.1;5.1 Introduction;91
4.5.2;5.2 Data Set;92
4.5.3;5.3 Data Preprocessing;93
4.5.4;5.4 Pattern Recognition Methods;94
4.5.4.1;5.4.1 Fisher Linear Discriminant;95
4.5.4.2;5.4.2 Support Vector Machine;95
4.5.4.3;5.4.3 Gaussian Nave Bayes;96
4.5.4.4;5.4.4 Correlation Analysis;96
4.5.4.5;5.4.5 k-Nearest Neighbor;96
4.5.5;5.5 Results;97
4.5.6;5.6 Conclusions;98
4.5.7;References;98
4.6;6 Recent Advances of Data Biclustering with Application in Computational Neuroscience;100
4.6.1;6.1 Introduction;100
4.6.1.1;6.1.1 Motivation;100
4.6.1.2;6.1.2 Data Input;101
4.6.1.3;6.1.3 Objective of Task;102
4.6.1.4;6.1.4 History;103
4.6.1.5;6.1.5 Outline;104
4.6.2;6.2 Biclustering Types and Structures;104
4.6.2.1;6.2.1 Notations;104
4.6.2.2;6.2.2 Bicluster Types;105
4.6.2.3;6.2.3 Biclustering Structures;107
4.6.3;6.3 Biclustering Techniques and Algorithms;109
4.6.3.1;6.3.1 Based on Matrix Means and Residues;109
4.6.3.2;6.3.2 Based on Matrix Ordering, Reordering, and Decomposition;111
4.6.3.3;6.3.3 Based on Bipartite Graphs;115
4.6.3.4;6.3.4 Based on Information Theory;118
4.6.3.5;6.3.5 Based on Probability;119
4.6.3.6;6.3.6 Comparison of Biclustering Algorithms;121
4.6.4;6.4 Application of Biclustering in Computational Neuroscience;122
4.6.5;6.5 Conclusions;124
4.6.6;References;124
4.7;7 A Genetic Classifier Account for the Regulation of Expression;128
4.7.1;7.1 Introduction;128
4.7.1.1;7.1.1 Motivation;128
4.7.1.2;7.1.2 Background;129
4.7.2;7.2 Model and Methods;130
4.7.2.1;7.2.1 Basic Assumptions;130
4.7.2.2;7.2.2 Model Structure;130
4.7.2.3;7.2.3 Model Equations;131
4.7.2.4;7.2.4 Stability;132
4.7.3;7.3 Results;132
4.7.3.1;7.3.1 Composition by Overlap of Nodes;132
4.7.3.1.1;7.3.1.1 Complete Overlap;132
4.7.3.1.2;7.3.1.2 Incomplete Overlap;134
4.7.3.2;7.3.2 Multiple Gene Scenarios;134
4.7.3.2.1;7.3.2.1 Three Genes;134
4.7.3.3;7.3.3 Composition by Infinite Chains;135
4.7.3.3.1;7.3.3.1 Chain of Genes Including A 1-Product Gene;136
4.7.3.3.2;7.3.3.2 Chain of Genes Without A 1-Product Gene;136
4.7.3.4;7.3.4 Subchains;137
4.7.4;7.4 Discussion;137
4.7.5;References;138
5;Part II Modeling;139
5.1;8 Neuroelectromagnetic Source Imaging of Brain Dynamics ;140
5.1.1;8.1 Introduction;140
5.1.1.1;8.1.1 Neuronal Origins of Electromagnetic Signals;141
5.1.2;8.2 Measurement Modalities;142
5.1.2.1;8.2.1 Magnetoencephalography (MEG);142
5.1.2.2;8.2.2 Electroencephalography (EEG);143
5.1.2.3;8.2.3 Electrocorticography (ECoG);143
5.1.3;8.3 Data Preprocessing;143
5.1.4;8.4 Overview of Modeling Steps;145
5.1.4.1;8.4.1 Modeling of Neural Generators;145
5.1.4.2;8.4.2 Anatomical Modeling of Head Tissues and Neural Sources;146
5.1.4.3;8.4.3 Multimodal Geometric Registration;146
5.1.4.4;8.4.4 Forward Modeling;147
5.1.4.5;8.4.5 Inverse Modeling;147
5.1.5;8.5 Parametric Dipole Modeling;148
5.1.5.1;8.5.1 Uncorrelated Noise Model;148
5.1.5.2;8.5.2 Correlated Noise Model;149
5.1.5.3;8.5.3 Global Minimization;150
5.1.6;8.6 Source Space-Based Distributed and Sparse Methods;150
5.1.6.1;8.6.1 Bayesian Maximum a Posteriori (MAP) Estimates;151
5.1.6.2;8.6.2 Dynamic Statistical Parametric Mapping (dSPM);154
5.1.6.3;8.6.3 Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA);155
5.1.6.4;8.6.4 Sparse Bayesian Learning (SBL) and Automatic Relevance Determination (ARD);156
5.1.7;8.7 Spatial Scanning and Beamforming;158
5.1.7.1;8.7.1 Matched Filter;159
5.1.7.2;8.7.2 Multiple Signal Classification (MUSIC);159
5.1.7.3;8.7.3 Linearly Constrained Minimum Variance (LCMV) Beamforming;160
5.1.7.4;8.7.4 Synthetic Aperture Magnetometry (SAM);160
5.1.7.5;8.7.5 Dynamic Imaging of Coherent Sources (DICS);161
5.1.7.6;8.7.6 Other Spatial Filtering Methods;161
5.1.8;8.8 Comparison of Methods;162
5.1.9;8.9 Conclusion;162
5.1.10;References;164
5.2;9 Optimization in Brain? -- Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms ;169
5.2.1;9.1 Introduction;169
5.2.2;9.2 Modeling Behavioral and Brain Imaging Phenomena in Transcription Typing with Queuing Networks and Reinforcement Learning Algorithms;171
5.2.2.1;9.2.1 Behavioral Phenomena;171
5.2.2.2;9.2.2 Brain Imaging Phenomena;171
5.2.2.3;9.2.3 A Queuing Network Model with Reinforcement Learning Algorithms;172
5.2.2.3.1;9.2.3.1 The Static Portion of the Queuing Network Model;172
5.2.2.3.2;9.2.3.2 The Dynamic Portion of the Queuing Network Model: Self-Organization of the Queuing Network with Reinforcement Learning Algorithms;173
5.2.2.4;9.2.4 Model Predictions of three Skill Learning Phenomenaand two Brain Imaging Phenomena;176
5.2.2.4.1;9.2.4.1 Predictions of the three Skill Learning Phenomena;176
5.2.2.4.2;9.2.4.2 Predictions of the First Brain Imaging Phenomenon;177
5.2.2.4.3;9.2.4.3 Predictions of the Second Brain Imaging Phenomenon;177
5.2.2.5;9.2.5 Simulation of the three Skill Learning Phenomenaand the two Brain Imaging Phenomena;178
5.2.2.5.1;9.2.5.1 The First and the Second Skill Learning Phenomena;178
5.2.2.5.2;9.2.5.2 The Third Skill Learning Phenomena;178
5.2.2.5.3;9.2.5.3 The First Brain Imaging Phenomena;179
5.2.2.5.4;9.2.5.4 The Second Brain Imaging Phenomena;179
5.2.3;9.3 Modeling the Basic PRP and Practice Effect on PRP with Queuing Networks and Reinforcement Learning Algorithms;180
5.2.3.1;9.3.1 Modeling the Basic PRP and the Practice Effect on PRPwith Queuing Networks;180
5.2.3.1.1;9.3.1.1 Learning Process in Individual Servers;182
5.2.3.1.2;9.3.1.2 Learning Process in the Simplest Queuing Network with two Routes;183
5.2.3.2;9.3.2 Predictions of the Basic PRP and the Practice Effect on PRP with the Queuing Network Model;184
5.2.3.3;9.3.3 Simulation Results;184
5.2.4;9.4 Discussion;186
5.2.5;References;189
5.3;10 Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions ;192
5.3.1;10.1 Introduction;192
5.3.2;10.2 Models and Theories of Motor Control;193
5.3.3;10.3 The Extended VITE--FLETE Models Without Dopamine;195
5.3.4;10.4 Conclusion;200
5.3.5;References;200
5.4;11 Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson's Disease ;203
5.4.1;11.1 Introduction;203
5.4.2;11.2 Brain Anatomy in Parkinson's Disease;204
5.4.3;11.3 Empirical Signatures;206
5.4.4;11.4 Is There Dopaminergic Innervation of the Cortexand Spinal Cord?;206
5.4.5;11.5 Effects of Dopamine Depletion on Neuronal, Electromyographic, and Movement Parameters in PD Humans and MPTP Animals;207
5.4.5.1;11.5.1 Cellular Disorganization in Cortex;207
5.4.5.2;11.5.2 Reduction of Neuronal Intensity and of Rate of Development of Neuronal Discharge in the PrimaryMotor Cortex;208
5.4.5.3;11.5.3 Significant Increase in Mean Duration of Neuronal Discharge in Primary Motor Cortex Preceding and Following Onset of Movement;208
5.4.5.4;11.5.4 Prolongation of Behavioral Simple Reaction Time;209
5.4.5.5;11.5.5 Repetitive Triphasic Pattern of Muscle Activation;210
5.4.5.6;11.5.6 Electromechanical Delay Time Is Increased;210
5.4.5.7;11.5.7 Depression of Rate of Development and Peak Amplitudeof the First Agonist Burst of EMG Activity;210
5.4.5.8;11.5.8 Movement Time Is Significantly Increased;211
5.4.5.9;11.5.9 Reduction of Peak Velocity;212
5.4.5.10;11.5.10 Reduction of Peak Force and Rate of Force Production;212
5.4.5.11;11.5.11 Movement Variability;212
5.4.6;11.6 The Extended VITE--FLETE Models with Dopamine;213
5.4.7;11.7 Simulated Effects of Dopamine Depletion on the Cortical Neural Activities;216
5.4.8;11.8 Simulated Effects of Dopamine Depletion on EMG Activities;217
5.4.9;11.9 Conclusion;219
5.4.10;References;220
5.5;12 Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain;223
5.5.1;12.1 Introduction;224
5.5.2;12.2 Methods;226
5.5.2.1;12.2.1 Recording of Optical Signals and Preprocessing;226
5.5.2.2;12.2.2 Modeling;228
5.5.2.3;12.2.3 Classification of Optical Signals Based on Activation Timing;229
5.5.3;12.3 Results;231
5.5.3.1;12.3.1 Estimation of STF Model Parameters;231
5.5.3.2;12.3.2 Classification of Pixel Activity Patterns;232
5.5.4;12.4 Discussion;234
5.5.5;References;234
5.6;13 Advances Toward Closed-Loop Deep Brain Stimulation ;236
5.6.1;13.1 Introduction;236
5.6.2;13.2 Nerve Stimulation;237
5.6.3;13.3 Local Field Potentials;238
5.6.4;13.4 Parkinson's Disease;239
5.6.4.1;13.4.1 Treatments;240
5.6.5;13.5 Deep Brain Stimulation;241
5.6.5.1;13.5.1 DBS Mechanism;241
5.6.5.2;13.5.2 Apparatus;241
5.6.5.3;13.5.3 Stimulus Specifications;242
5.6.5.4;13.5.4 DBS Programming;244
5.6.5.5;13.5.5 Side Effects;246
5.6.6;13.6 Biosignal Processing;246
5.6.6.1;13.6.1 Features;247
5.6.6.2;13.6.2 Classifiers;247
5.6.6.3;13.6.3 Feature Selection;248
5.6.7;13.7 Closed-Loop DBS;248
5.6.7.1;13.7.1 Demand-Controlled DBS;249
5.6.7.2;13.7.2 ALOPEX and DBS;250
5.6.7.3;13.7.3 Genetic Algorithms and DBS;251
5.6.7.4;13.7.4 Hardware Implementations;251
5.6.8;13.8 Related Advances in Other Neuroprosthetic Research;252
5.6.8.1;13.8.1 Closed-Loop Cardiac Pacemaker Technology;253
5.6.8.2;13.8.2 Brain-to-Computer Interface;253
5.6.9;13.9 Neural Network Modeling and the Basal Ganglia;254
5.6.10;13.10 Summary;255
5.6.11;References;255
5.7;14 Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization ;263
5.7.1;14.1 Introduction;264
5.7.2;14.2 Biomolecular Computing In Vitro;265
5.7.3;14.3 Biomolecular Computing In Silico;266
5.7.4;14.4 Neural Nets in Biomolecules;268
5.7.5;14.5 Conclusions and Future Work;272
5.7.6;References;274
6;Part III Brain Dynamics/Synchronization;276
6.1;15 A Robust Estimation of Information Flow in Coupled Nonlinear Systems ;277
6.1.1;15.1 Introduction;277
6.1.2;15.2 Methodology;279
6.1.2.1;15.2.1 Transfer Entropy (TE);279
6.1.2.2;15.2.2 Improved Computation of Transfer Entropy;280
6.1.2.2.1;15.2.2.1 Selection of k;280
6.1.2.2.2;15.2.2.2 Selection of l;280
6.1.2.2.3;15.2.2.3 Selection of Radius r;281
6.1.2.3;15.2.3 Statistical Significance of Transfer Entropy;283
6.1.2.4;15.2.4 Detecting Causality Using Transfer Entropy;284
6.1.3;15.3 Simulation Example;284
6.1.3.1;15.3.1 Statistical Significance of TE and NTE;285
6.1.3.2;15.3.2 Robustness to Noise;287
6.1.4;15.4 Discussion and Conclusion;288
6.1.5;References;289
6.2;16 An Optimization Approach for Finding a Spectrum of Lyapunov Exponents ;290
6.2.1;16.1 Introduction;290
6.2.2;16.2 Lyapunov Exponents;291
6.2.3;16.3 An Optimization Approach;293
6.2.3.1;16.3.1 Theory;294
6.2.3.2;16.3.2 Implementation Details;295
6.2.3.2.1;16.3.2.1 Phase Space Reconstruction;296
6.2.4;16.4 Models Used in the Computational Experiments;299
6.2.4.1;16.4.1 Lorenz Attractor;299
6.2.4.2;16.4.2 Rössler Attractor;300
6.2.4.3;16.4.3 Hénon Map;301
6.2.4.4;16.4.4 The Hénon--Heiles Equations;301
6.2.5;16.5 Computational Experiments;302
6.2.5.1;16.5.1 Numerical Computations;302
6.2.5.2;16.5.2 Sensitivity Analysis;304
6.2.6;16.6 Summary and Conclusion;306
6.2.7;References;306
6.3;17 Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs ;309
6.3.1;17.1 Introduction;310
6.3.2;17.2 Materials and Methods;311
6.3.2.1;17.2.1 Recording Procedure and EEG Data;311
6.3.2.1.1;17.2.1.1 EEG from Barrow Neurological Institute, Phoenix, Arizona;312
6.3.2.1.2;17.2.1.2 EEG from Mayo Clinic Hospital, Scottsdale, Arizona;312
6.3.2.2;17.2.2 Measures of Brain Dynamics;313
6.3.2.2.1;17.2.2.1 Measure of Chaos(STLmax);313
6.3.2.2.2;17.2.2.2 Measure of Dynamical Entrainment;315
6.3.3;17.3 Results;315
6.3.4;17.4 Conclusion;317
6.3.5;References;318
6.4;18 Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR ;320
6.4.1;18.1 Introduction;320
6.4.2;18.2 Integrated and Cointegrated VAR;322
6.4.2.1;18.2.1 Augmented Dickey--Fuller Test for Testing the Null Hypothesis of a Unit Root;323
6.4.2.2;18.2.2 Estimation of Cointegrated VAR(p) Processes;325
6.4.2.3;18.2.3 Testing for the Rank of Cointegration;327
6.4.3;18.3 The Role of Phase Synchronization in Neural Dynamics;328
6.4.4;18.4 Phase Estimation Using Hilbert Transform;329
6.4.5;18.5 Multivariate Approach to Phase Synchrony via Cointegrated VAR;330
6.4.5.1;18.5.1 Cointegration Rank as a Measure of Synchronization among Different EEG Channels;331
6.4.5.2;18.5.2 Absence Seizures;334
6.4.5.3;18.5.3 Numerical Study of Synchrony in Multichannel EEG Recordings from Patients with Absence Epilepsy;334
6.4.6;18.6 Conclusion;338
6.4.6.1;18.6.1 Phillips--Ouliaris Cointegration Test;339
6.4.7;References;341
6.5;19 Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht--Lundborg Disease: A Pilot Study ;343
6.5.1;19.1 Introduction;344
6.5.2;19.2 Data Information;347
6.5.3;19.3 Synchronization Measures;348
6.5.3.1;19.3.1 Mutual Information;348
6.5.3.2;19.3.2 Nonlinear Interdependencies;350
6.5.4;19.4 Statistical Tests and Data Analysis;351
6.5.5;19.5 Conclusion and Discussion;354
6.5.6;References;355
6.6;20 Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit ;358
6.6.1;20.1 Introduction;359
6.6.2;20.2 Preictal Transition and Seizure Prediction;360
6.6.3;20.3 Methods;362
6.6.3.1;20.3.1 Chaos Theory and Epilepsy;362
6.6.3.2;20.3.2 Statistical Method for Pairwise Comparison of STLMAX;364
6.6.3.3;20.3.3 Finding Critical Sites by Quadratic Optimization Approach;365
6.6.4;20.4 Two Main Components of the Seizure Monitoring and Alert System;366
6.6.4.1;20.4.1 Algorithm for Generating Automatic Warnings about Impending Seizure from EEG;367
6.6.5;20.5 Conclusions;368
6.6.6;References;368
