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E-grāmata: Methods in Brain Connectivity Inference through Multivariate Time Series Analysis

Edited by (Department of Telecommunications and Control Engineering, University of Sćo Paulo, Brazil), Edited by (Department of Radiology and Oncology, University of Sćo Paulo, Brazil)
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Editors Sameshima and Baccal<’a> offer this research volume on brain connectivity. The first section covers theoretical fundamentals, beginning with directed transfer function (DTF), detailed coverage of multivariate autoregressive modeling, time-domain Granger causality, and model diagnostics. Partial directed coherence (PDC) and issues of information flow and instantaneous causality are carefully discussed. The second section presents a couple of extensions in nonlinear interactions and signal nonstationarity. In the last section, applications in EEG, fMRI, and multimodal biological variables are explored. The conclusion summarizes the contributions and proposes a new conception of connectivity beyond a conventional functional/effective model. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)

Interest in brain connectivity inference has become ubiquitous and is now increasingly adopted in experimental investigations of clinical, behavioral, and experimental neurosciences. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis gathers the contributions of leading international authors who discuss different time series analysis approaches, providing a thorough survey of information on how brain areas effectively interact.

Incorporating multidisciplinary work in applied mathematics, statistics, and animal and human experiments at the forefront of the field, the book addresses the use of time series data in brain connectivity interference studies. Contributors present codes and data examples to back up their methodological descriptions, exploring the details of each proposed method as well as an appreciation of their merits and limitations. Supplemental material for the book, including code, data, practical examples, and color figures is supplied in the form of a CD with directories organized by chapter and instruction files that provide additional detail.

The field of brain connectivity inference is growing at a fast pace with new data/signal processing proposals emerging so often as to make it difficult to be fully up to date. This consolidated panorama of data-driven methods includes theoretical bases allied to computational tools, offering readers immediate hands-on experience in this dynamic arena.

Series Preface vii
Preface ix
Software and Data CD xi
Editors xiii
Contributors xv
Chapter 1 Brain Connectivity: An Overview
1(12)
Luiz A. Baccala
Koichi Sameshima
Section I Fundamental Theory
Chapter 2 Directed Transfer Function: A Pioneering Concept in Connectivity Analysis
13(22)
Maciej Kaminski
Katarzyna Blinowska
Chapter 3 An Overview of Vector Autoregressive Models
35(22)
Pedro A. Morettin
Chapter 4 Partial Directed Coherence
57(18)
Luiz A. Baccala
Koichi Sameshima
Chapter 5 Information Partial Directed Coherence
75(12)
Daniel Y. Takahashi
Luiz A. Baccala
Koichi Sameshima
Chapter 6 Assessing Connectivity in the Presence of Instantaneous Causality
87(26)
Luca Faes
Chapter 7 Asymptotic PDC Properties
113(22)
Koichi Sameshima
Daniel Y. Takahashi
Luiz A. Baccala
Section II Extensions
Chapter 8 Nonlinear Parametric Granger Causality in Dynamical Networks
135(26)
Daniele Marinazzo
Wei Liao
Mario Pellicoro
Sebastiano Stramaglia
Chapter 9 Time-Variant Estimation of Connectivity and Kalman Filter
161(20)
Linda Sommerlade
Marco Thiel
Bettina Platt
Andrea Plano
Gernot Riedel
Celso Grebogi
Wolfgang Mader
Malenka Mader
Jens Timmer
Bjorn Schelter
Section III Applications
Chapter 10 Connectivity Analysis Based on Multielectrode EEG Inversion Methods with and without fMRI A Priori Information
181(16)
Laura Astolfi
Fabio Babiloni
Chapter 11 Methods for Connectivity Analysis in fMRI
197(26)
Joao Ricardo Sato
Philip J. A. Dean
Gilson Vieira
Chapter 12 Assessing Causal Interactions among Cardiovascular Variability Series through a Time-Domain Granger Causality Approach
223(22)
Alberto Porta
Anielle C. M. Takahashi
Aparecida M. Catai
Nicolo Montano
Section IV Epilogue
Chapter 13 Multivariate Time-Series Brain Connectivity: A Sum-Up
245(8)
Luiz A. Baccala
Koichi Sameshima
Index 253
Koichi Sameshima studied electrical engineering and medicine at the University of Sćo Paulo. He was introduced to cognitive neuroscience, brain electrophysiology, and time-series analysis during doctoral and postdoctoral training at the University of Sćo Paulo and the University of California, San Francisco, respectively. His research themes revolve around neural plasticity, cognitive function, and information processing aspects of mammalian brain through behavioral, electrophysiological, and computational neuroscience protocols. He holds an associate professorship at the Department of Radiology and Oncology, Faculty of Medicine, University of Sćo Paulo.

Luiz A. Baccalį majored in electrical engineering and physics at the University of Sćo Paulo and then furthered his study on time-series evolution of bacterial resistance to antibiotics in a nosocomial environment, obtaining an MSc at the same university. He has since been involved in statistical signal processing and analysis and obtained his PhD from the University of Pennsylvania by proposing new statistical methods of communication channel identification and equalization. His current research interests focus on the investigation of multivariate time-series methods for neural connectivity inference and for problems of inverse source determination using arrays of sensors that include fMRI imaging and multielectrode EEG processing.
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