This volume presents a collection of peer-reviewed contributions arising from StartUp Research: a stimulating research experience in which twenty-eight early-career researchers collaborated with seven senior international professors in order to develop novel statistical methods for complex brain imaging data. During this meeting, which was held on June 25-27, 2017 in Siena (Italy), the research groups focused on recent multimodality imaging datasets measuring brain function and structure, and proposed a wide variety of methods for network analysis, spatial inference, graphical modeling, multiple testing, dynamic inference, data fusion, tensor factorization, object-oriented analysis and others. The results of their studies are gathered here, along with a final contribution by Michele Guindani and Marina Vannucci that opens new research directions in this field. The book offers a valuable resource for all researchers in Data Science and Neuroscience who are interested in the promising intersections of these two fundamental disciplines.
Rezensionen / Stimmen
"The book is clearly written, easy to read, and enables the expedient comprehension of the various discussed issues relating to the analysis and interpretation of neuro-imaging data. . This book may generally be useful to anyone who is dealing with neural data, particularly to biostatisticians involved in related research teams." (Sada Nand Dwivedi, ISCB News, Vol. 68, December, 2019)
"This book and provides an outlook over trends and new research directions in the analyses of brain imaging data. An excellent book! Congratulations for the way research has been done!" (Claudia Simionescu-Badea, zbMATH 1415.92006, 2019)
Reihe
Auflage
Sprache
Verlagsort
Verlagsgruppe
Springer International Publishing
Zielgruppe
Illustrationen
36
26 farbige Abbildungen, 36 s/w Abbildungen
XI, 156 p. 62 illus., 26 illus. in color.
Maße
Höhe: 241 mm
Breite: 160 mm
Dicke: 15 mm
Gewicht
ISBN-13
978-3-030-00038-7 (9783030000387)
DOI
10.1007/978-3-030-00039-4
Schweitzer Klassifikation
Antonio Canale is an Assistant Professor of Statistics at the Department of Statistical Sciences, University of Padova (Italy). His research covers Bayesian non-parametric methods, functional data analysis, statistical learning and data mining. He is the author of a number of papers on methodological and applied statistics, and has served on the scientific committees of national and international conferences. He was the coordinator of the young group of the Italian Statistical Society (y-SIS) in 2015.
Daniele Durante is an Assistant Professor of Statistics at the Department of Decision Sciences, Bocconi University (Italy), and a Research Affiliate at the Bocconi Institute for Data Science. His research is characterized by an interdisciplinary approach at the intersection of Bayesian methods, modern applications, and statistical learning to develop flexible and computationally tractable models for complex data. He is the coordinator of the young groupof the Italian Statistical Society (y-SIS).
Lucia Paci is an Assistant Professor of Statistics at the Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan (Italy). Her research focuses mainly on spatial and spatiotemporal modeling under the Bayesian framework, with applications in the environmental and economic sciences. She was the coordinator of the young group of the Italian Statistical Society (y-SIS) in 2016.
Bruno Scarpa is an Associate Professor of Statistics at the Department of Statistical Sciences, University of Padova (Italy). He teaches data mining at the master level and statistical methods for big data at the undergraduate level. His research interests include methodological developments motivated by real data applications. He is the author or coauthor of numerous papers and books in the fields of methodological and applied statistics and data mining.
