Studies in Neural Data Science
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Studies in Neural Data Science : StartUp Research 2017, Siena, Italy, June 25-27

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Description

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.
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Product details

  • Hardback | 156 pages
  • 155 x 235 x 11.18mm | 424g
  • Cham, Switzerland
  • English
  • 1st ed. 2018
  • 40 Tables, color; 26 Illustrations, color; 36 Illustrations, black and white; XI, 156 p. 62 illus., 26 illus. in color.
  • 3030000389
  • 9783030000387

Back cover copy

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.
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Table of contents

1 S. Ranciati et al, Understanding Dependency Patterns in Structural and Functional Brain Connectivity through fMRI and DTI Data.- 2 E. Aliverti et al, Hierarchical Graphical Model for Learning Functional Network Determinants.- 3 A. Cabassi et al, Three Testing Perspectives on Connectome Data.- 4 A. Cappozzo et al, An Object Oriented Approach to Multimodal Imaging Data in Neuroscience.- 5 G. Bertarelli et al, Curve Clustering for Brain Functional Activity and Synchronization.- 6 F. Gasperoni and A. Luati, Robust Methods for Detecting Spontaneous Activations in fMRI Data.- 7 A. Caponera et al, Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data.- 8 M. Guindani and M. Vannucci, Challenges in the Analysis of Neuroscience Data.
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About Antonio Canale

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 group of the Italian Statistical Society (y-SIS).



Lucia Paci is an Assistant Professor of Statistics at the Department of Statistical Sciences, Universita 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.
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