Studies in Neural Data Science

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.

“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)

• 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.