000910595 001__ 910595
000910595 005__ 20230123110710.0
000910595 0247_ $$2doi$$a10.5334/jors.394
000910595 0247_ $$2Handle$$a2128/33071
000910595 037__ $$aFZJ-2022-03973
000910595 082__ $$a004
000910595 1001_ $$0P:(DE-Juel1)164124$$aRosjat, Nils$$b0$$eCorresponding author
000910595 245__ $$aDST (Dynamic Synchronization Toolbox): A MATLAB Implementation of the Dynamic Phase-Locking Pipeline from Stimulus Transformation into Motor Action: Dynamic Graph Analysis Reveals a Posterior-to-Anterior Shift in Brain Network Communication of Older Subjects
000910595 260__ $$aLondon$$bUbiquity Press$$c2022
000910595 3367_ $$2DRIVER$$aarticle
000910595 3367_ $$2DataCite$$aOutput Types/Journal article
000910595 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1670930840_17645
000910595 3367_ $$2BibTeX$$aARTICLE
000910595 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000910595 3367_ $$00$$2EndNote$$aJournal Article
000910595 520__ $$aThe Dynamic Synchronization Toolbox allows the calculation of dynamic graphs based on phase synchronization in experimental data. This enables an analysis of the time-development of network connectivity between multiple recording sites (e.g. in electroencephalography (EEG) or magnetoencephalography (MEG) data) with a high temporal resolution. Optionally, the toolbox offers the possibility to compute several graph metrics (such as cluster dynamics, node degree, HUB nodes) via the Brain Connectivity toolbox.
000910595 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000910595 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000910595 7001_ $$0P:(DE-Juel1)162297$$aDaun, Silvia$$b1$$ufzj
000910595 773__ $$0PERI:(DE-600)2740435-3$$a10.5334/jors.394$$gVol. 10, p. 8$$p8$$tJournal of Open Research Software$$v10$$x2049-9647$$y2022
000910595 8564_ $$uhttps://juser.fz-juelich.de/record/910595/files/Invoice_UP-5823.pdf
000910595 8564_ $$uhttps://juser.fz-juelich.de/record/910595/files/394-5922-1-PB.pdf$$yOpenAccess
000910595 8564_ $$uhttps://juser.fz-juelich.de/record/910595/files/Post-print.pdf$$yOpenAccess
000910595 8767_ $$8UP-5823$$92022-09-20$$a1200185788$$d2022-11-11$$eAPC$$jZahlung erfolgt$$zGBP 350,-
000910595 909CO $$ooai:juser.fz-juelich.de:910595$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
000910595 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164124$$aForschungszentrum Jülich$$b0$$kFZJ
000910595 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)162297$$aForschungszentrum Jülich$$b1$$kFZJ
000910595 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5251$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000910595 9141_ $$y2022
000910595 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
000910595 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
000910595 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000910595 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000910595 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2020-08-28
000910595 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-08-28
000910595 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2022-11-09
000910595 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2022-11-09
000910595 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-06-01T06:50:39Z
000910595 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-06-01T06:50:39Z
000910595 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Peer review$$d2021-06-01T06:50:39Z
000910595 920__ $$lyes
000910595 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x0
000910595 980__ $$ajournal
000910595 980__ $$aVDB
000910595 980__ $$aUNRESTRICTED
000910595 980__ $$aI:(DE-Juel1)INM-3-20090406
000910595 980__ $$aAPC
000910595 9801_ $$aAPC
000910595 9801_ $$aFullTexts