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 downloadable resources 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.
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Zielgruppe
Für Beruf und Forschung
Academic and Professional Reference
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Broschur/Paperback
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Illustrationen
89 s/w Abbildungen
89 Illustrations, black and white
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Höhe: 234 mm
Breite: 156 mm
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ISBN-13
978-1-032-92375-8 (9781032923758)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Koichi Sameshima studied electrical engineering and medicine at the University of Sao Paulo. He was introduced to cognitive neuroscience, brain electrophysiology, and time-series analysis during doctoral and postdoctoral training at the University of Sao 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 Sao Paulo.
Luiz A. Baccala majored in electrical engineering and physics at the University of Sao 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.
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.
