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  • Conference proceedings
  • © 2018

Studies in Neural Data Science

StartUp Research 2017, Siena, Italy, June 25–27

  • Outlines novel contributions on the statistical modeling of recent multimodality imaging data from Neuroscience

  • Includes a contribution by experts on Statistics for Neuroscience, discussing new and relevant research directions

  • Provides findings and new research questions to stimulate future promising research in Neural Data Science

Part of the book series: Springer Proceedings in Mathematics & Statistics (PROMS, volume 257)

Conference series link(s): START UP RESEARCH: START UP RESEARCH

Conference proceedings info: START UP RESEARCH 2017.

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  • ISBN: 978-3-030-00039-4
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  • Tax calculation will be finalised during checkout
Hardcover Book USD 179.99
Price excludes VAT (USA)

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Table of contents (8 papers)

  1. Front Matter

    Pages i-xi
  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

Other Volumes

  1. Studies in Neural Data Science

    StartUp Research 2017, Siena, Italy, June 25–27

About this book

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.

Keywords

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

Reviews

“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)

Editors and Affiliations

  • 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

About the editors

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, 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.

 


Bibliographic Information

Buying options

eBook USD 139.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-00039-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 179.99
Price excludes VAT (USA)