Studies in Neural Data Science StartUp Research 2017, Siena, Italy, June 25-27 /

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 ima...

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Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Canale, Antonio (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Durante, Daniele (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Paci, Lucia (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Scarpa, Bruno (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
Έκδοση:1st ed. 2018.
Σειρά:Springer Proceedings in Mathematics & Statistics, 257
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Studies in Neural Data Science  |h [electronic resource] :  |b StartUp Research 2017, Siena, Italy, June 25-27 /  |c edited by Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa. 
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505 0 |a 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. 
520 |a 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. 
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