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Incorporating multidisciplinary work in applied mathematics, statistics, and animal and human experiments at the forefront of the field, this volume 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
List of contents
Brain Connectivity: An Overview.
Fundamental Theory. Directed Transfer Function: A Pioneering Concept in Connectivity Analysis. An Overview of Vector Autoregressive Models. Partial Directed Coherence. Information Partial Directed Coherence. Assessing Connectivity in the Presence of Instantaneous Causality. Asymptotic PDC Properties.
Extensions. Nonlinear Parametric Granger Causality in Dynamical Networks. Time-Variant Estimation of Connectivity and Kalman Filter.
Applications. Connectivity Analysis Based on Multielectrode EEG Inversion Methods with and without fMRI a Priori Information. Methods for Connectivity Analysis in fMRI. Assessing Causal Interactions among Cardiovascular Variability Series through a Time-Domain Granger Causality Approach.
Epilogue. Multivariate Time-Series Brain Connectivity: A Sum-Up.
Index.
About the author
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.
Summary
Incorporating multidisciplinary work in applied mathematics, statistics, and animal and human experiments at the forefront of the field, this volume 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
