Studies in Neural Data Science

1. Front Matter

   Pages i-xi

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2. Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data

   • Marta Crispino, Silvia D’Angelo, Saverio Ranciati, Antonietta Mira

   Pages 1-22

3. Hierarchical Graphical Model for Learning Functional Network Determinants

   • Emanuele Aliverti, Laura Forastiere, Tullia Padellini, Sally Paganin, Ernst Wit

   Pages 23-36

4. Three Testing Perspectives on Connectome Data

   • Alessandra Cabassi, Alessandro Casa, Matteo Fontana, Massimiliano Russo, Alessio Farcomeni

   Pages 37-55

5. An Object Oriented Approach to Multimodal Imaging Data in Neuroscience

   • Andrea Cappozzo, Federico Ferraccioli, Marco Stefanucci, Piercesare Secchi

   Pages 57-73

6. Curve Clustering for Brain Functional Activity and Synchronization

   • Gaia Bertarelli, Alice Corbella, Jacopo Di Iorio, Anastasia Gorshechnikova, Marian Scott

   Pages 75-90

7. Robust Methods for Detecting Spontaneous Activations in fMRI Data

   • Francesca Gasperoni, Alessandra Luati

   Pages 91-110

8. Hierarchical Spatio-Temporal Modeling of Resting State fMRI Data

   • Alessia Caponera, Francesco Denti, Tommaso Rigon, Andrea Sottosanti, Alan Gelfand

   Pages 111-130

9. Challenges in the Analysis of Neuroscience Data

   • Michele Guindani, Marina Vannucci

   Pages 131-156

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.

• Data Science
• Neuroscience
• Multimodality Imaging Data
• Statistics
• Complex Data
• Open Access

“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)

• Department of Statistical Sciences, University of Padova, Padua, Italy

  Antonio Canale, Bruno Scarpa

• Department of Decision Sciences, Bocconi University, Milan, Italy

  Daniele Durante

• Department of Statistical Sciences, Università Cattolica del Sacro Cuore, Milan, Italy

  Lucia Paci

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.

 

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