國立虎尾科技大學 |

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Studies in Neural Data Science/ edited by Antonio Canale, Daniele Durante, Lucia Paci, Bruno Scarpa.
Reminder of title:	StartUp Research 2017, Siena, Italy, June 25–27 /
other author:	Canale, Antonio.
Description:	XI, 156 p. 62 illus., 26 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-030-00039-4
ISBN:	9783030000394

Studies in Neural Data Science = StartUp 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, Daniele Durante, Lucia Paci, Bruno Scarpa. - 1st ed. 2018. - XI, 156 p. 62 illus., 26 illus. in color.online resource. - Springer Proceedings in Mathematics & Statistics,2572194-1009 ;. - Springer Proceedings in Mathematics & Statistics,125.

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

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

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Studies in Neural Data Science = StartUp Research 2017, Siena, Italy, June 25–27 /
LDR:03280nam a22003975i 4500 001 990642
003 DE-He213
005 20200705210640.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783030000394 $9 978-3-030-00039-4
024 7 $a 10.1007/978-3-030-00039-4 $2 doi
035 $a 978-3-030-00039-4
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
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.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XI, 156 p. 62 illus., 26 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Proceedings in Mathematics & Statistics, $x 2194-1009 ; $v 257
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.
650 0 $a Statistics . $3 1253516
650 0 $a Neurosciences. $3 593561
650 0 $a Biostatistics. $3 783654
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
700 1 $a Canale, Antonio. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1211845
700 1 $a Durante, Daniele. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282332
700 1 $a Paci, Lucia. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282333
700 1 $a Scarpa, Bruno. $e editor. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1282334
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030000387
776 0 8 $i Printed edition: $z 9783030000400
830 0 $a Springer Proceedings in Mathematics & Statistics, $x 2194-1009 ; $v 125 $3 1253690
856 4 0 $u https://doi.org/10.1007/978-3-030-00039-4
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)