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Studies in neural data scienceStartU...

(1998 :)

Studies in neural data scienceStartUp Research 2017, Siena, Italy, June 25-27 /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Studies in neural data scienceedited by Antonio Canale ... [et al.].
其他題名:	StartUp Research 2017, Siena, Italy, June 25-27 /
其他作者:	Canale, Antonio.
團體作者:
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xi, 156 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	NeurosciencesCongresses.Mathematical models
電子資源:	https://doi.org/10.1007/978-3-030-00039-4
ISBN:	9783030000394$q(electronic bk.)

Studies in neural data scienceStartUp Research 2017, Siena, Italy, June 25-27 /
Studies in neural data scienceStartUp Research 2017, Siena, Italy, June 25-27 /[electronic resource] :edited by Antonio Canale ... [et al.]. - Cham :Springer International Publishing :2018. - xi, 156 p. :ill., digital ;24 cm. - Springer proceedings in mathematics & statistics,v.2572194-1009 ;. - Springer proceedings in mathematics & statistics ;v.19..

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.

ISBN: 9783030000394$q(electronic bk.)

Standard No.: 10.1007/978-3-030-00039-4doiSubjects--Topical Terms:

825956
Neurosciences
--Mathematical models--Congresses.

LC Class. No.: QP351 / .S783 2017

Dewey Class. No.: 612.8233

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