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.
Table of Contents
Brain Connectivity: An Overview, Luiz A. Baccalá and Koichi Sameshima
Directed Transfer Function: A Pioneering Concept in Connectivity Analysis, Maciej Kaminski and Katarzyna J. Blinowska
An Overview of Vector Autoregressive Models, Pedro A. Morettin
Partial Directed Coherence, Luiz A. Baccala and Koichi Sameshima
Information Partial Directed Coherence, Daniel Y. Takahashi, Luiz A. Baccala and Koichi Sameshima
Assessing Connectivity in the Presence of Instantaneous Causality, Luca Faes
Asymptotic PDC Properties, Koichi Sameshima, Daniel Y. Takahashi and Luiz A. Baccala
Nonlinear Parametric Granger Causality in Dynamical Networks, Daniele Marinazzo, Wei Liao, Mario Pellicoro and Sebastiano Stramaglia
Time-Variant Estimation of Connectivity and Kalman Filter, Linda Sommerlade, Marco Thiel, Bettina Platt, Andrea Plano, Gernot Riedel, Celso Grebogi, Wolfgang Mader, Malenda Mader, Jens Timmer and Bjorn Schelter
Connectivity Analysis Based on Multielectrode EEG Inversion Methods with and without fMRI a Priori Information, Laura Astolfi and Fabio Babiloni
Methods for Connectivity Analysis in fMRI, Joao R. Sato, Philip A. Dean and Gilson Vieira
Assessing Causal Interactions among Cardiovascular Variability Series through a Time-Domain Granger Causality Approach, Alberto Porta, Anielle C. M. Takahashi, Aparecida M. Catai and Nicola Montano
Multivariate Time-Series Brain Connectivity: A Sum-Up, Luiz A. Baccala and Koichi Sameshima
Koichi Sameshima studied electrical engineering and medicine at the University of São Paulo. He was introduced to cognitive neuroscience, brain electrophysiology, and time-series analysis during doctoral and postdoctoral training at the University of São 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 São Paulo.
Luiz A. Baccalá majored in electrical engineering and physics at the University of São 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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