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＜電子ブック＞
Studies in Neural Data Science : StartUp Research 2017, Siena, Italy, June 25–27 / edited by Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa
(Springer Proceedings in Mathematics & Statistics. ISSN:21941017 ; 257)

版	1st ed. 2018.
出版者	(Cham : Springer International Publishing : Imprint: Springer)
出版年	2018
大きさ	XI, 156 p. 62 illus., 26 illus. in color : online resource
著者標目	Canale, Antonio editor Durante, Daniele editor Paci, Lucia editor Scarpa, Bruno editor SpringerLink (Online service)
件　名	LCSH:Statistics  LCSH:Biometry LCSH:Neurosciences FREE:Statistical Theory and Methods FREE:Biostatistics FREE:Neuroscience
一般注記	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 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 HTTP:URL=https://doi.org/10.1007/978-3-030-00039-4

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