Computational Neuroscience (eBook)

Wanpracha Chaovalitwongse, Panos M. Pardalos, Petros Xanthopoulos (Herausgeber)

eBook Download: PDF

2010 | 2010
XVI, 396 Seiten
Springer New York (Verlag)
978-0-387-88630-5 (ISBN)

Infos

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.

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???????????????????????????????????????? ??????????????????. ??????????(460?. ?-360?. ?. ) Thereareinfacttwothings,scienceandopinion;theformerbegetsknowledge,the latterignorance. Hippocrates(460BC-360BC) This book represents a collection of recent advances in computational studies in neuroscience research that practically applies to a collaborative and integrative environment in engineering and medical domains. This work has been designed to address the explosion of interest by academic researchers and practitioners in highly-effective coordination between computational models and tools and quan- tative investigation of neuroscienti?c data. To bridge the vital gap between science and medicine, this book brings together diverse research areas ranging from me- cal signal processing, image analysis, and data mining to neural network modeling, regulation of gene expression, and brain dynamics. We hope that this work will also be of value to investigators and practitioners in academic institutions who become involved in computational modeling as an aid in translating information in neuroscienti?c data to their colleagues in medical - main. This volume will be very appealing to graduate (and advanced undergraduate) students, researchers, and practitioners across a wide range of industries (e. g. , ph- maceutical, chemical, biological sciences), who require a detailed overview of the practical aspects of computational modeling in real-life neuroscience problems. For this reason, our audience is assumed to be very diverse and heterogenous, including: vii viii Preface * researchers from engineering, computer science, statistics, and mathematics - mains as well as medical and biological scientists; *physicians working in scienti?c research to understand how basic science can be linked with biological systems.

Inhalt

Preface 7
Contents 10
List of Contributors 13
Part I Data Mining 19
1 Optimization in Reproducing Kernel Hilbert Spacesof Spike Trains 20
1.1 Introduction 21
1.2 Some Background on RKHS Theory 22
1.3 Inner Product for Spike Times 24
1.4 Inner Product for Spike Trains 25
1.5 Properties and Estimation of the Memoryless Cross-Intensity Kernel 27
1.5.1 Properties 27
1.5.2 Estimation 29
1.6 Induced RKHS and Congruent Spaces 30
1.6.1 Space Spanned by Intensity Functions 31
1.6.2 Induced RKHS 31
1.6.3 mCI Kernel and the RKHS Induced by 32
1.6.4 mCI Kernel as a Covariance Kernel 33
1.7 Principal Component Analysis 34
1.7.1 Optimization in the RKHS 34
1.7.2 Optimization in the Space Spanned by the Intensity Functions 37
1.7.3 Results 38
1.8 Conclusion 42
References 44
2 Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods 47
2.1 Introduction and Background 47
2.2 Graph Theory and Neuroscience 49
2.3 A Database of Imaging Experiments 51
2.4 The Usefulness of Co-activation Graphs 53
2.5 Relating fMRI to EEG 55
2.6 Conclusion 57
References 57
3 Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles 59
3.1 Introduction 60
3.2 Methods 61
3.2.1 Methodology Overview 61
3.2.2 Feature Extraction (Step 1) 62
3.2.3 Feature Selection (Step 2) 66
3.2.4 Feature Refinement (Steps 3 and 4) 66
3.3 Results 68
3.3.1 Simulation Test 68
3.4 Discussion 69
3.5 Conclusion 70
References 71
4 Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt--Jakob Disease for Assisting Early Diagnosis 73
4.1 Introduction 74
4.2 Patients and EEG Recordings 78
4.3 Methods 80
4.3.1 Independent Component Analysis and Extractionof CJD-Related Components 80
4.3.2 Bayesian Information Criterion 81
4.4 Results 82
4.4.1 Determination of the Number of Sources 82
4.4.2 CJD-Related Feature Extraction 83
4.4.3 Feature Extraction by PCA 85
4.5 Discussions 85
4.6 Conclusions 88
References 89
5 Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detectionin fMRI Data 91
5.1 Introduction 91
5.2 Data Set 92
5.3 Data Preprocessing 93
5.4 Pattern Recognition Methods 94
5.4.1 Fisher Linear Discriminant 95
5.4.2 Support Vector Machine 95
5.4.3 Gaussian Nave Bayes 96
5.4.4 Correlation Analysis 96
5.4.5 k-Nearest Neighbor 96
5.5 Results 97
5.6 Conclusions 98
References 98
6 Recent Advances of Data Biclustering with Application in Computational Neuroscience 100
6.1 Introduction 100
6.1.1 Motivation 100
6.1.2 Data Input 101
6.1.3 Objective of Task 102
6.1.4 History 103
6.1.5 Outline 104
6.2 Biclustering Types and Structures 104
6.2.1 Notations 104
6.2.2 Bicluster Types 105
6.2.3 Biclustering Structures 107
6.3 Biclustering Techniques and Algorithms 109
6.3.1 Based on Matrix Means and Residues 109
6.3.2 Based on Matrix Ordering, Reordering, and Decomposition 111
6.3.3 Based on Bipartite Graphs 115
6.3.4 Based on Information Theory 118
6.3.5 Based on Probability 119
6.3.6 Comparison of Biclustering Algorithms 121
6.4 Application of Biclustering in Computational Neuroscience 122
6.5 Conclusions 124
References 124
7 A Genetic Classifier Account for the Regulation of Expression 128
7.1 Introduction 128
7.1.1 Motivation 128
7.1.2 Background 129
7.2 Model and Methods 130
7.2.1 Basic Assumptions 130
7.2.2 Model Structure 130
7.2.3 Model Equations 131
7.2.4 Stability 132
7.3 Results 132
7.3.1 Composition by Overlap of Nodes 132
7.3.1.1 Complete Overlap 132
7.3.1.2 Incomplete Overlap 134
7.3.2 Multiple Gene Scenarios 134
7.3.2.1 Three Genes 134
7.3.3 Composition by Infinite Chains 135
7.3.3.1 Chain of Genes Including A 1-Product Gene 136
7.3.3.2 Chain of Genes Without A 1-Product Gene 136
7.3.4 Subchains 137
7.4 Discussion 137
References 138
Part II Modeling 139
8 Neuroelectromagnetic Source Imaging of Brain Dynamics 140
8.1 Introduction 140
8.1.1 Neuronal Origins of Electromagnetic Signals 141
8.2 Measurement Modalities 142
8.2.1 Magnetoencephalography (MEG) 142
8.2.2 Electroencephalography (EEG) 143
8.2.3 Electrocorticography (ECoG) 143
8.3 Data Preprocessing 143
8.4 Overview of Modeling Steps 145
8.4.1 Modeling of Neural Generators 145
8.4.2 Anatomical Modeling of Head Tissues and Neural Sources 146
8.4.3 Multimodal Geometric Registration 146
8.4.4 Forward Modeling 147
8.4.5 Inverse Modeling 147
8.5 Parametric Dipole Modeling 148
8.5.1 Uncorrelated Noise Model 148
8.5.2 Correlated Noise Model 149
8.5.3 Global Minimization 150
8.6 Source Space-Based Distributed and Sparse Methods 150
8.6.1 Bayesian Maximum a Posteriori (MAP) Estimates 151
8.6.2 Dynamic Statistical Parametric Mapping (dSPM) 154
8.6.3 Standardized Low Resolution Brain Electromagnetic Tomography (sLORETA) 155
8.6.4 Sparse Bayesian Learning (SBL) and Automatic Relevance Determination (ARD) 156
8.7 Spatial Scanning and Beamforming 158
8.7.1 Matched Filter 159
8.7.2 Multiple Signal Classification (MUSIC) 159
8.7.3 Linearly Constrained Minimum Variance (LCMV) Beamforming 160
8.7.4 Synthetic Aperture Magnetometry (SAM) 160
8.7.5 Dynamic Imaging of Coherent Sources (DICS) 161
8.7.6 Other Spatial Filtering Methods 161
8.8 Comparison of Methods 162
8.9 Conclusion 162
References 164
9 Optimization in Brain? -- Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms 169
9.1 Introduction 169
9.2 Modeling Behavioral and Brain Imaging Phenomena in Transcription Typing with Queuing Networks and Reinforcement Learning Algorithms 171
9.2.1 Behavioral Phenomena 171
9.2.2 Brain Imaging Phenomena 171
9.2.3 A Queuing Network Model with Reinforcement Learning Algorithms 172
9.2.3.1 The Static Portion of the Queuing Network Model 172
9.2.3.2 The Dynamic Portion of the Queuing Network Model: Self-Organization of the Queuing Network with Reinforcement Learning Algorithms 173
9.2.4 Model Predictions of three Skill Learning Phenomenaand two Brain Imaging Phenomena 176
9.2.4.1 Predictions of the three Skill Learning Phenomena 176
9.2.4.2 Predictions of the First Brain Imaging Phenomenon 177
9.2.4.3 Predictions of the Second Brain Imaging Phenomenon 177
9.2.5 Simulation of the three Skill Learning Phenomenaand the two Brain Imaging Phenomena 178
9.2.5.1 The First and the Second Skill Learning Phenomena 178
9.2.5.2 The Third Skill Learning Phenomena 178
9.2.5.3 The First Brain Imaging Phenomena 179
9.2.5.4 The Second Brain Imaging Phenomena 179
9.3 Modeling the Basic PRP and Practice Effect on PRP with Queuing Networks and Reinforcement Learning Algorithms 180
9.3.1 Modeling the Basic PRP and the Practice Effect on PRPwith Queuing Networks 180
9.3.1.1 Learning Process in Individual Servers 182
9.3.1.2 Learning Process in the Simplest Queuing Network with two Routes 183
9.3.2 Predictions of the Basic PRP and the Practice Effect on PRP with the Queuing Network Model 184
9.3.3 Simulation Results 184
9.4 Discussion 186
References 189
10 Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions 192
10.1 Introduction 192
10.2 Models and Theories of Motor Control 193
10.3 The Extended VITE--FLETE Models Without Dopamine 195
10.4 Conclusion 200
References 200
11 Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson's Disease 203
11.1 Introduction 203
11.2 Brain Anatomy in Parkinson's Disease 204
11.3 Empirical Signatures 206
11.4 Is There Dopaminergic Innervation of the Cortexand Spinal Cord? 206
11.5 Effects of Dopamine Depletion on Neuronal, Electromyographic, and Movement Parameters in PD Humans and MPTP Animals 207
11.5.1 Cellular Disorganization in Cortex 207
11.5.2 Reduction of Neuronal Intensity and of Rate of Development of Neuronal Discharge in the PrimaryMotor Cortex 208
11.5.3 Significant Increase in Mean Duration of Neuronal Discharge in Primary Motor Cortex Preceding and Following Onset of Movement 208
11.5.4 Prolongation of Behavioral Simple Reaction Time 209
11.5.5 Repetitive Triphasic Pattern of Muscle Activation 210
11.5.6 Electromechanical Delay Time Is Increased 210
11.5.7 Depression of Rate of Development and Peak Amplitudeof the First Agonist Burst of EMG Activity 210
11.5.8 Movement Time Is Significantly Increased 211
11.5.9 Reduction of Peak Velocity 212
11.5.10 Reduction of Peak Force and Rate of Force Production 212
11.5.11 Movement Variability 212
11.6 The Extended VITE--FLETE Models with Dopamine 213
11.7 Simulated Effects of Dopamine Depletion on the Cortical Neural Activities 216
11.8 Simulated Effects of Dopamine Depletion on EMG Activities 217
11.9 Conclusion 219
References 220
12 Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain 223
12.1 Introduction 224
12.2 Methods 226
12.2.1 Recording of Optical Signals and Preprocessing 226
12.2.2 Modeling 228
12.2.3 Classification of Optical Signals Based on Activation Timing 229
12.3 Results 231
12.3.1 Estimation of STF Model Parameters 231
12.3.2 Classification of Pixel Activity Patterns 232
12.4 Discussion 234
References 234
13 Advances Toward Closed-Loop Deep Brain Stimulation 236
13.1 Introduction 236
13.2 Nerve Stimulation 237
13.3 Local Field Potentials 238
13.4 Parkinson's Disease 239
13.4.1 Treatments 240
13.5 Deep Brain Stimulation 241
13.5.1 DBS Mechanism 241
13.5.2 Apparatus 241
13.5.3 Stimulus Specifications 242
13.5.4 DBS Programming 244
13.5.5 Side Effects 246
13.6 Biosignal Processing 246
13.6.1 Features 247
13.6.2 Classifiers 247
13.6.3 Feature Selection 248
13.7 Closed-Loop DBS 248
13.7.1 Demand-Controlled DBS 249
13.7.2 ALOPEX and DBS 250
13.7.3 Genetic Algorithms and DBS 251
13.7.4 Hardware Implementations 251
13.8 Related Advances in Other Neuroprosthetic Research 252
13.8.1 Closed-Loop Cardiac Pacemaker Technology 253
13.8.2 Brain-to-Computer Interface 253
13.9 Neural Network Modeling and the Basal Ganglia 254
13.10 Summary 255
References 255
14 Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization 263
14.1 Introduction 264
14.2 Biomolecular Computing In Vitro 265
14.3 Biomolecular Computing In Silico 266
14.4 Neural Nets in Biomolecules 268
14.5 Conclusions and Future Work 272
References 274
Part III Brain Dynamics/Synchronization 276
15 A Robust Estimation of Information Flow in Coupled Nonlinear Systems 277
15.1 Introduction 277
15.2 Methodology 279
15.2.1 Transfer Entropy (TE) 279
15.2.2 Improved Computation of Transfer Entropy 280
15.2.2.1 Selection of k 280
15.2.2.2 Selection of l 280
15.2.2.3 Selection of Radius r 281
15.2.3 Statistical Significance of Transfer Entropy 283
15.2.4 Detecting Causality Using Transfer Entropy 284
15.3 Simulation Example 284
15.3.1 Statistical Significance of TE and NTE 285
15.3.2 Robustness to Noise 287
15.4 Discussion and Conclusion 288
References 289
16 An Optimization Approach for Finding a Spectrum of Lyapunov Exponents 290
16.1 Introduction 290
16.2 Lyapunov Exponents 291
16.3 An Optimization Approach 293
16.3.1 Theory 294
16.3.2 Implementation Details 295
16.3.2.1 Phase Space Reconstruction 296
16.4 Models Used in the Computational Experiments 299
16.4.1 Lorenz Attractor 299
16.4.2 Rössler Attractor 300
16.4.3 Hénon Map 301
16.4.4 The Hénon--Heiles Equations 301
16.5 Computational Experiments 302
16.5.1 Numerical Computations 302
16.5.2 Sensitivity Analysis 304
16.6 Summary and Conclusion 306
References 306
17 Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs 309
17.1 Introduction 310
17.2 Materials and Methods 311
17.2.1 Recording Procedure and EEG Data 311
17.2.1.1 EEG from Barrow Neurological Institute, Phoenix, Arizona 312
17.2.1.2 EEG from Mayo Clinic Hospital, Scottsdale, Arizona 312
17.2.2 Measures of Brain Dynamics 313
17.2.2.1 Measure of Chaos(STLmax) 313
17.2.2.2 Measure of Dynamical Entrainment 315
17.3 Results 315
17.4 Conclusion 317
References 318
18 Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR 320
18.1 Introduction 320
18.2 Integrated and Cointegrated VAR 322
18.2.1 Augmented Dickey--Fuller Test for Testing the Null Hypothesis of a Unit Root 323
18.2.2 Estimation of Cointegrated VAR(p) Processes 325
18.2.3 Testing for the Rank of Cointegration 327
18.3 The Role of Phase Synchronization in Neural Dynamics 328
18.4 Phase Estimation Using Hilbert Transform 329
18.5 Multivariate Approach to Phase Synchrony via Cointegrated VAR 330
18.5.1 Cointegration Rank as a Measure of Synchronization among Different EEG Channels 331
18.5.2 Absence Seizures 334
18.5.3 Numerical Study of Synchrony in Multichannel EEG Recordings from Patients with Absence Epilepsy 334
18.6 Conclusion 338
18.6.1 Phillips--Ouliaris Cointegration Test 339
References 341
19 Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht--Lundborg Disease: A Pilot Study 343
19.1 Introduction 344
19.2 Data Information 347
19.3 Synchronization Measures 348
19.3.1 Mutual Information 348
19.3.2 Nonlinear Interdependencies 350
19.4 Statistical Tests and Data Analysis 351
19.5 Conclusion and Discussion 354
References 355
20 Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit 358
20.1 Introduction 359
20.2 Preictal Transition and Seizure Prediction 360
20.3 Methods 362
20.3.1 Chaos Theory and Epilepsy 362
20.3.2 Statistical Method for Pairwise Comparison of STLMAX 364
20.3.3 Finding Critical Sites by Quadratic Optimization Approach 365
20.4 Two Main Components of the Seizure Monitoring and Alert System 366
20.4.1 Algorithm for Generating Automatic Warnings about Impending Seizure from EEG 367
20.5 Conclusions 368
References 368

Produkt-Details

• Erscheint lt. Verlag: 3.7.2010
• Reihe/Serie: Springer Optimization and Its Applications
• Reihe/Serie: Springer Optimization and Its Applications
• Zusatzinfo: XVI, 396 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Analysis
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Medizin / Pharmazie ► Pflege
• Themenwelt: Medizin / Pharmazie ► Physiotherapie / Ergotherapie ► Orthopädie
• Themenwelt: Medizin / Pharmazie ► Studium
• Themenwelt: Naturwissenschaften ► Biologie ► Humanbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Zoologie
• Themenwelt: Technik ► Bauwesen
• Themenwelt: Technik ► Medizintechnik
• Schlagworte: biomedical engineering • Computational Neuroscience • Data Mining • human brain function • human neurophysiological systems • Master Patient Index • modern neuroscience problems • neuronal excitability • synaptic transmission
• ISBN-10: 0-387-88630-3 / 0387886303
• ISBN-13: 978-0-387-88630-5 / 9780387886305