Studies in Neural Data Science

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.

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.