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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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