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Link de la búsquedaComputational Neuroscience / SpringerLink (Online service) ; Chaovalitwongse, Wanpracha ; Pardalos, Panos M ; Xanthopoulos, Petros (2010)
Título : Computational Neuroscience Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Chaovalitwongse, Wanpracha ; Pardalos, Panos M ; Xanthopoulos, Petros Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Otro editor: Imprint: Springer Colección: Springer Optimization and Its Applications, ISSN 1931-6828 num. 38 Número de páginas: XVI, 396 p Il.: online resource ISBN/ISSN/DL: 978-0-387-88630-5 Idioma : Inglés (eng) Palabras clave: Medicine Neurosciences Health informatics Computer mathematics Mathematical models Biomedical engineering Biomedicine Informatics Engineering Computational Mathematics and Numerical Analysis Modeling Industrial Clasificación: 51 Matemáticas Resumen: The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain. 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. The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience Nota de contenido: Data Mining -- Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains -- Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods -- Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles -- Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis -- Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data -- Recent Advances of Data Biclustering with Application in Computational Neuroscience -- A Genetic Classifier Account for the Regulation of Expression -- Modeling -- Neuroelectromagnetic Source Imaging of Brain Dynamics -- Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms -- Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions -- Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson’s Disease -- Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain -- Advances Toward Closed-Loop Deep Brain Stimulation -- Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization -- Brain Dynamics/Synchronization -- A Robust Estimation of Information Flow in Coupled Nonlinear Systems -- An Optimization Approach for Finding a Spectrum of Lyapunov Exponents -- Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs -- Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR -- Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht–Lundborg Disease: A Pilot Study -- Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit En línea: http://dx.doi.org/10.1007/978-0-387-88630-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33519 Computational Neuroscience [documento electrónico] / SpringerLink (Online service) ; Chaovalitwongse, Wanpracha ; Pardalos, Panos M ; Xanthopoulos, Petros . - New York, NY : Springer New York : Imprint: Springer, 2010 . - XVI, 396 p : online resource. - (Springer Optimization and Its Applications, ISSN 1931-6828; 38) .
ISBN : 978-0-387-88630-5
Idioma : Inglés (eng)
Palabras clave: Medicine Neurosciences Health informatics Computer mathematics Mathematical models Biomedical engineering Biomedicine Informatics Engineering Computational Mathematics and Numerical Analysis Modeling Industrial Clasificación: 51 Matemáticas Resumen: The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain. 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. The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience Nota de contenido: Data Mining -- Optimization in Reproducing Kernel Hilbert Spaces of Spike Trains -- Investigating Functional Cooperation in the Human Brain Using Simple Graph-Theoretic Methods -- Methodological Framework for EEG Feature Selection Based on Spectral and Temporal Profiles -- Blind Source Separation of Concurrent Disease-Related Patterns from EEG in Creutzfeldt–Jakob Disease for Assisting Early Diagnosis -- Comparison of Supervised Classification Methods with Various Data Preprocessing Procedures for Activation Detection in fMRI Data -- Recent Advances of Data Biclustering with Application in Computational Neuroscience -- A Genetic Classifier Account for the Regulation of Expression -- Modeling -- Neuroelectromagnetic Source Imaging of Brain Dynamics -- Optimization in Brain? - Modeling Human Behavior and Brain Activation Patterns with Queuing Network and Reinforcement Learning Algorithms -- Neural Network Modeling of Voluntary Single-Joint Movement Organization I. Normal Conditions -- Neural Network Modeling of Voluntary Single-Joint Movement Organization II. Parkinson’s Disease -- Parametric Modeling Analysis of Optical Imaging Data on Neuronal Activities in the Brain -- Advances Toward Closed-Loop Deep Brain Stimulation -- Molecule-Inspired Methods for Coarse-Grain Multi-System Optimization -- Brain Dynamics/Synchronization -- A Robust Estimation of Information Flow in Coupled Nonlinear Systems -- An Optimization Approach for Finding a Spectrum of Lyapunov Exponents -- Dynamical Analysis of the EEG and Treatment of Human Status Epilepticus by Antiepileptic Drugs -- Analysis of Multichannel EEG Recordings Based on Generalized Phase Synchronization and Cointegrated VAR -- Antiepileptic Therapy Reduces Coupling Strength Among Brain Cortical Regions in Patients with Unverricht–Lundborg Disease: A Pilot Study -- Seizure Monitoring and Alert System for Brain Monitoring in an Intensive Care Unit En línea: http://dx.doi.org/10.1007/978-0-387-88630-5 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33519 Ejemplares
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Título : Information Systems and Neuroscience : Gmunden Retreat on NeuroIS 2015 Tipo de documento: documento electrónico Autores: SpringerLink (Online service) ; Davis, Fred D ; Riedl, René ; vom Brocke, Jan ; Léger, Pierre-Majorique ; Randolph, Adriane B Editorial: Cham : Springer International Publishing Fecha de publicación: 2015 Otro editor: Imprint: Springer Colección: Lecture Notes in Information Systems and Organisation, ISSN 2195-4968 num. 10 Número de páginas: XIII, 219 p. 39 illus., 2 illus. in color Il.: online resource ISBN/ISSN/DL: 978-3-319-18702-0 Idioma : Inglés (eng) Palabras clave: Business Management information systems Neurosciences Computers Computer science Neuropsychology and Information Systems Communication Service of Computing Clasificación: 658 Empresas. Organización de empresas Resumen: This book presents the proceedings of the Gmunden Retreat on NeuroIS 2015, reporting on topics at the intersection of Information Systems (IS) research, neurophysiology and the brain sciences. Readers will discover the latest findings from top scholars in the field of NeuroIS, which offer detailed insights on the neurobiology underlying IS behavior, essential methods and tools and their applications for IS, as well as the application of neuroscience and neurophysiological theories to advance IS theory Nota de contenido: NeuroIS Knowledge Discovery Approach to Prediction of Traumati Brain Injury Survival Rates: A Semantic Data Analysis Regression Feasibility Study -- The Status Quo of Neurophysiology in Organizational Technostress Research: A Review of Studies Published from 1978 to 2015 -- The Impact of Interruptions on Technology Usage: Exploring Interdependencies between Demands from Interruptions, Worker Control, and Role-based Stress -- An Investigation of the Nature of Information Systems from a Neurobiological Perspective -- A Hot Topic – Group Affect Live Biofeedback for Participation Platforms -- (Online)-buying Behavior and Personality Traits: Evolutionary Psychology and Neuroscience based -- Choice of a NeuroIS Tool: An AHP-based Approach -- Foreign Live Biofeedback: Using others’ neurophysiological data -- What does the skin tell us about information systems usage? A literature-based analysis of the utilization of electrodermal measurement for IS Research -- A Novel, Low-cost NeuroIS Prototype for Supporting Bio Signals Experimentation Based on BITalino -- The evaluation of different EEG sensor Technologies -- Choice Architecture: Using Fixation Patterns to Analyze the Effects of Form Design on Cognitive Biases -- Neurophysiological Analysis of Visual Syntax in Design -- The Influence of Cognitive Abilities and Cognitive Load on Business Process Models and their Creation -- An Evolutionary Explanation of Graph Comprehension using fMRI -- Investigation of the relationship between visual website complexity and users' mental workload: A NeuroIS perspective -- Measuring Cognitive Load During Process Model Creation -- Cognitive Differences and their Impact on Information Perception: An Empirical Study Combining Survey and Eye Tracking Data -- Using fMRI to Explain the Effect of Dual-Task Interference on Security Behavior -- Measuring Appeal in Human Computer Interaction: A Cognitive Neuroscience-Based Approach -- Mobile App Preferences: What Role Does Aesthetics and Emotions Play? -- Identifying Neurological Patterns Associated with Information Seeking: A Pilot fMRI Study -- Proposal for the Use of a Passive BCI to Develop a Neurophysiological Inference Model of IS Constructs -- Emotion is not what you think it is: Startle Reflex Modulation (SRM) as a measure of affective processing in NeuroIS -- Measuring flow using psychophysiological data in a multiplayer gaming context -- Using A Cognitive Analysis Grid to Inform Information Systems Design -- Research directions for methodological improvement of the statistical analysis of electroencephalography data collected in NeuroIS -- Measuring visual complexity using: neurophysiological data -- Using NeuroIS to Better Understand Activities Performed on Mobile Devices En línea: http://dx.doi.org/10.1007/978-3-319-18702-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35666 Information Systems and Neuroscience : Gmunden Retreat on NeuroIS 2015 [documento electrónico] / SpringerLink (Online service) ; Davis, Fred D ; Riedl, René ; vom Brocke, Jan ; Léger, Pierre-Majorique ; Randolph, Adriane B . - Cham : Springer International Publishing : Imprint: Springer, 2015 . - XIII, 219 p. 39 illus., 2 illus. in color : online resource. - (Lecture Notes in Information Systems and Organisation, ISSN 2195-4968; 10) .
ISBN : 978-3-319-18702-0
Idioma : Inglés (eng)
Palabras clave: Business Management information systems Neurosciences Computers Computer science Neuropsychology and Information Systems Communication Service of Computing Clasificación: 658 Empresas. Organización de empresas Resumen: This book presents the proceedings of the Gmunden Retreat on NeuroIS 2015, reporting on topics at the intersection of Information Systems (IS) research, neurophysiology and the brain sciences. Readers will discover the latest findings from top scholars in the field of NeuroIS, which offer detailed insights on the neurobiology underlying IS behavior, essential methods and tools and their applications for IS, as well as the application of neuroscience and neurophysiological theories to advance IS theory Nota de contenido: NeuroIS Knowledge Discovery Approach to Prediction of Traumati Brain Injury Survival Rates: A Semantic Data Analysis Regression Feasibility Study -- The Status Quo of Neurophysiology in Organizational Technostress Research: A Review of Studies Published from 1978 to 2015 -- The Impact of Interruptions on Technology Usage: Exploring Interdependencies between Demands from Interruptions, Worker Control, and Role-based Stress -- An Investigation of the Nature of Information Systems from a Neurobiological Perspective -- A Hot Topic – Group Affect Live Biofeedback for Participation Platforms -- (Online)-buying Behavior and Personality Traits: Evolutionary Psychology and Neuroscience based -- Choice of a NeuroIS Tool: An AHP-based Approach -- Foreign Live Biofeedback: Using others’ neurophysiological data -- What does the skin tell us about information systems usage? A literature-based analysis of the utilization of electrodermal measurement for IS Research -- A Novel, Low-cost NeuroIS Prototype for Supporting Bio Signals Experimentation Based on BITalino -- The evaluation of different EEG sensor Technologies -- Choice Architecture: Using Fixation Patterns to Analyze the Effects of Form Design on Cognitive Biases -- Neurophysiological Analysis of Visual Syntax in Design -- The Influence of Cognitive Abilities and Cognitive Load on Business Process Models and their Creation -- An Evolutionary Explanation of Graph Comprehension using fMRI -- Investigation of the relationship between visual website complexity and users' mental workload: A NeuroIS perspective -- Measuring Cognitive Load During Process Model Creation -- Cognitive Differences and their Impact on Information Perception: An Empirical Study Combining Survey and Eye Tracking Data -- Using fMRI to Explain the Effect of Dual-Task Interference on Security Behavior -- Measuring Appeal in Human Computer Interaction: A Cognitive Neuroscience-Based Approach -- Mobile App Preferences: What Role Does Aesthetics and Emotions Play? -- Identifying Neurological Patterns Associated with Information Seeking: A Pilot fMRI Study -- Proposal for the Use of a Passive BCI to Develop a Neurophysiological Inference Model of IS Constructs -- Emotion is not what you think it is: Startle Reflex Modulation (SRM) as a measure of affective processing in NeuroIS -- Measuring flow using psychophysiological data in a multiplayer gaming context -- Using A Cognitive Analysis Grid to Inform Information Systems Design -- Research directions for methodological improvement of the statistical analysis of electroencephalography data collected in NeuroIS -- Measuring visual complexity using: neurophysiological data -- Using NeuroIS to Better Understand Activities Performed on Mobile Devices En línea: http://dx.doi.org/10.1007/978-3-319-18702-0 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=35666 Ejemplares
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Título : Mathematical Foundations of Neuroscience Tipo de documento: documento electrónico Autores: Ermentrout, G. Bard ; SpringerLink (Online service) ; Terman, David H Editorial: New York, NY : Springer New York Fecha de publicación: 2010 Otro editor: Imprint: Springer Colección: Interdisciplinary Applied Mathematics, ISSN 0939-6047 num. 35 Número de páginas: XV, 422 p. 38 illus. in color Il.: online resource ISBN/ISSN/DL: 978-0-387-87708-2 Idioma : Inglés (eng) Palabras clave: Mathematics Neurosciences Neurobiology Biomathematics Mathematical and Computational Biology Clasificación: 51 Matemáticas Resumen: This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University Nota de contenido: The Hodgkin–Huxley Equations -- Dendrites -- Dynamics -- The Variety of Channels -- Bursting Oscillations -- Propagating Action Potentials -- Synaptic Channels -- Neural Oscillators: Weak Coupling -- Neuronal Networks: Fast/Slow Analysis -- Noise -- Firing Rate Models -- Spatially Distributed Networks En línea: http://dx.doi.org/10.1007/978-0-387-87708-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33514 Mathematical Foundations of Neuroscience [documento electrónico] / Ermentrout, G. Bard ; SpringerLink (Online service) ; Terman, David H . - New York, NY : Springer New York : Imprint: Springer, 2010 . - XV, 422 p. 38 illus. in color : online resource. - (Interdisciplinary Applied Mathematics, ISSN 0939-6047; 35) .
ISBN : 978-0-387-87708-2
Idioma : Inglés (eng)
Palabras clave: Mathematics Neurosciences Neurobiology Biomathematics Mathematical and Computational Biology Clasificación: 51 Matemáticas Resumen: This book applies methods from nonlinear dynamics to problems in neuroscience. It uses modern mathematical approaches to understand patterns of neuronal activity seen in experiments and models of neuronal behavior. The intended audience is researchers interested in applying mathematics to important problems in neuroscience, and neuroscientists who would like to understand how to create models, as well as the mathematical and computational methods for analyzing them. The authors take a very broad approach and use many different methods to solve and understand complex models of neurons and circuits. They explain and combine numerical, analytical, dynamical systems and perturbation methods to produce a modern approach to the types of model equations that arise in neuroscience. There are extensive chapters on the role of noise, multiple time scales and spatial interactions in generating complex activity patterns found in experiments. The early chapters require little more than basic calculus and some elementary differential equations and can form the core of a computational neuroscience course. Later chapters can be used as a basis for a graduate class and as a source for current research in mathematical neuroscience. The book contains a large number of illustrations, chapter summaries and hundreds of exercises which are motivated by issues that arise in biology, and involve both computation and analysis. Bard Ermentrout is Professor of Computational Biology and Professor of Mathematics at the University of Pittsburgh. David Terman is Professor of Mathematics at the Ohio State University Nota de contenido: The Hodgkin–Huxley Equations -- Dendrites -- Dynamics -- The Variety of Channels -- Bursting Oscillations -- Propagating Action Potentials -- Synaptic Channels -- Neural Oscillators: Weak Coupling -- Neuronal Networks: Fast/Slow Analysis -- Noise -- Firing Rate Models -- Spatially Distributed Networks En línea: http://dx.doi.org/10.1007/978-0-387-87708-2 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=33514 Ejemplares
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Título : Statistical Monitoring of Clinical Trials : Fundamentals for Investigators Tipo de documento: documento electrónico Autores: Moyé, Lemuel A ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2006 Número de páginas: XXII, 254 p Il.: online resource ISBN/ISSN/DL: 978-0-387-27782-0 Idioma : Inglés (eng) Palabras clave: Statistics Neurosciences Public health Epidemiology for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Statistical Monitoring of Clinical Trials: Fundamentals for Investigators introduces the investigator and statistician to monitoring procedures in clinical research. Clearly presenting the necessary background with limited use of mathematics, this book increases the knowledge, experience, and intuition of investigations in the use of these important procedures now required by the many clinical research efforts. The author provides motivated clinical investigators the background, correct use, and interpretation of these monitoring procedures at an elementary statistical level. He defines terms commonly used such as group sequential procedures and stochastic curtailment in non-mathematical language and discusses the commonly used procedures of Pocock, O’Brien–Fleming, and Lan–DeMets. He discusses the notions of conditional power, monitoring for safety and futility, and monitoring multiple endpoints in the study. The use of monitoring clinical trials is introduced in the context of the evolution of clinical research and one chapter is devoted to the more recent Bayesian procedures. Dr. Lemuel A. Moyé, M.D., Ph.D. is a physician and a biostatistician at the University of Texas School of Public Health. He is a diplomat of the National Board of Medical Examiners and is currently Professor of Biostatistics at the University of Texas School of Public Health in Houston where he holds a full time faculty position. Dr. Moyé has carried out cardiovascular research for twenty years and continues to be involved in the design, execution and analysis of clinical trials, both reporting to and serving on many Data Monitoring Committees. He has served in several clinical trials sponsored by both the U.S. government and private industry. In addition, Dr. Moyé has served as statistician/epidemiologist for six years on both the Cardiovascular and Renal Drug Advisory Committee to the Food and Drug Administration and the Pharmacy Sciences Advisory Committee to the FDA. He has published over 120 manuscripts in peer-reviewed literature that discuss the design, execution and analysis of clinical research. He authored Statistical Reasoning in Medicine: The Intuitive P-value Primer (Springer, 2000) and Multiple Analysis in Clinical Trials: Fundamentals for Investigators (Springer, 2003) Nota de contenido: Here, There be dragons… -- The Basis of Statistical Reasoning in Medicine -- Probability Tools for Monitoring Rules -- Issues and Intuition in Path Analysis -- Group Sequential Analysis Procedures -- Looking Forward: Conditional Power -- Safety and Futility -- Bayesian Statistical Monitoring En línea: http://dx.doi.org/10.1007/0-387-27782-X Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34744 Statistical Monitoring of Clinical Trials : Fundamentals for Investigators [documento electrónico] / Moyé, Lemuel A ; SpringerLink (Online service) . - New York, NY : Springer New York, 2006 . - XXII, 254 p : online resource.
ISBN : 978-0-387-27782-0
Idioma : Inglés (eng)
Palabras clave: Statistics Neurosciences Public health Epidemiology for Life Sciences, Medicine, Health Sciences Clasificación: 51 Matemáticas Resumen: Statistical Monitoring of Clinical Trials: Fundamentals for Investigators introduces the investigator and statistician to monitoring procedures in clinical research. Clearly presenting the necessary background with limited use of mathematics, this book increases the knowledge, experience, and intuition of investigations in the use of these important procedures now required by the many clinical research efforts. The author provides motivated clinical investigators the background, correct use, and interpretation of these monitoring procedures at an elementary statistical level. He defines terms commonly used such as group sequential procedures and stochastic curtailment in non-mathematical language and discusses the commonly used procedures of Pocock, O’Brien–Fleming, and Lan–DeMets. He discusses the notions of conditional power, monitoring for safety and futility, and monitoring multiple endpoints in the study. The use of monitoring clinical trials is introduced in the context of the evolution of clinical research and one chapter is devoted to the more recent Bayesian procedures. Dr. Lemuel A. Moyé, M.D., Ph.D. is a physician and a biostatistician at the University of Texas School of Public Health. He is a diplomat of the National Board of Medical Examiners and is currently Professor of Biostatistics at the University of Texas School of Public Health in Houston where he holds a full time faculty position. Dr. Moyé has carried out cardiovascular research for twenty years and continues to be involved in the design, execution and analysis of clinical trials, both reporting to and serving on many Data Monitoring Committees. He has served in several clinical trials sponsored by both the U.S. government and private industry. In addition, Dr. Moyé has served as statistician/epidemiologist for six years on both the Cardiovascular and Renal Drug Advisory Committee to the Food and Drug Administration and the Pharmacy Sciences Advisory Committee to the FDA. He has published over 120 manuscripts in peer-reviewed literature that discuss the design, execution and analysis of clinical research. He authored Statistical Reasoning in Medicine: The Intuitive P-value Primer (Springer, 2000) and Multiple Analysis in Clinical Trials: Fundamentals for Investigators (Springer, 2003) Nota de contenido: Here, There be dragons… -- The Basis of Statistical Reasoning in Medicine -- Probability Tools for Monitoring Rules -- Issues and Intuition in Path Analysis -- Group Sequential Analysis Procedures -- Looking Forward: Conditional Power -- Safety and Futility -- Bayesian Statistical Monitoring En línea: http://dx.doi.org/10.1007/0-387-27782-X Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34744 Ejemplares
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Título : The Statistical Analysis of Functional MRI Data Tipo de documento: documento electrónico Autores: Lazar, Nicole ; SpringerLink (Online service) Editorial: New York, NY : Springer New York Fecha de publicación: 2008 Colección: Statistics for Biology and Health, ISSN 1431-8776 Número de páginas: XIV, 299 p Il.: online resource ISBN/ISSN/DL: 978-0-387-78191-4 Idioma : Inglés (eng) Palabras clave: Medicine Neurosciences Radiology Bioinformatics Statistics Psychology Methodology Psychological measurement & Public Health Imaging / for Life Sciences, Medicine, Sciences Signal, Image and Speech Processing Methods/Evaluation Clasificación: 51 Matemáticas Resumen: One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data. Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed. Nicole A. Lazar is Professor of Statistics at the University of Georgia and affiliated faculty of the Center for Health Statistics, University of Illinois at Chicago. She is a prominent researcher in this area, a contributor to the FIASCO software for fMRI data analysis, and heads an fMRI statistics research group at the University of Georgia Nota de contenido: The Science of fMRI -- Design of fMRI Experiments -- Noise and Data Preprocessing -- Statistical Issues in fMRI Data Analysis -- Basic Statistical Analysis -- Temporal, Spatial, and Spatiotemporal Models -- Multivariate Approaches -- Basis Function Approaches -- Bayesian Methods in fMRI -- Multiple Testing in fMRI: The Problem of #x201C;Thresholding#x201D; -- Additional Statistical Issues -- Case Study: Eye Motion Data En línea: http://dx.doi.org/10.1007/978-0-387-78191-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34248 The Statistical Analysis of Functional MRI Data [documento electrónico] / Lazar, Nicole ; SpringerLink (Online service) . - New York, NY : Springer New York, 2008 . - XIV, 299 p : online resource. - (Statistics for Biology and Health, ISSN 1431-8776) .
ISBN : 978-0-387-78191-4
Idioma : Inglés (eng)
Palabras clave: Medicine Neurosciences Radiology Bioinformatics Statistics Psychology Methodology Psychological measurement & Public Health Imaging / for Life Sciences, Medicine, Sciences Signal, Image and Speech Processing Methods/Evaluation Clasificación: 51 Matemáticas Resumen: One of the most intriguing questions facing modern science is the inner workings of the human brain. Functional magnetic resonance imaging (fMRI) is a powerful tool used to study the human brain in action. The data produced from mapping the active processes within the brain present many challenges to statisticians, computer scientists, engineers and other data analysts, due to their complex structure and the ever-increasing sophistication of the scientific questions being posed by researchers. This book represents the first in-depth discussion of statistical methodology, which it couples with an introduction to the scientific background needed to understand the data. Starting from the basic science - where fMRI data come from, why they are so complicated, and the role statistics can play in designing and interpreting experiments - the book gives a detailed survey of the numerous methods that have been applied in the last fifteen years. The analysis of fMRI data features many of the major issues of concern in modern statistics, such as high dimensionality, multiple testing, and visualization. The array of techniques examined in the book ranges from the simple two-sample t-test and the general linear model to hierarchical spatiotemporal models, multivariate methods such as principal components analysis, and Bayesian approaches as they have been used in fMRI. Software, including descriptions of the most popular freeware packages and their capabilities, is also discussed. This book offers researchers who are interested in the analysis of fMRI data a detailed discussion from a statistical perspective that covers the entire process from data collection to the graphical presentation of results. The book is a valuable resource for statisticians who want to learn more about this growing field, and for neuroscientists who want to learn more about how their data can be analyzed. Nicole A. Lazar is Professor of Statistics at the University of Georgia and affiliated faculty of the Center for Health Statistics, University of Illinois at Chicago. She is a prominent researcher in this area, a contributor to the FIASCO software for fMRI data analysis, and heads an fMRI statistics research group at the University of Georgia Nota de contenido: The Science of fMRI -- Design of fMRI Experiments -- Noise and Data Preprocessing -- Statistical Issues in fMRI Data Analysis -- Basic Statistical Analysis -- Temporal, Spatial, and Spatiotemporal Models -- Multivariate Approaches -- Basis Function Approaches -- Bayesian Methods in fMRI -- Multiple Testing in fMRI: The Problem of #x201C;Thresholding#x201D; -- Additional Statistical Issues -- Case Study: Eye Motion Data En línea: http://dx.doi.org/10.1007/978-0-387-78191-4 Link: https://biblioteca.cunef.edu/gestion/catalogo/index.php?lvl=notice_display&id=34248 Ejemplares
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