000888032 001__ 888032
000888032 005__ 20210130010757.0
000888032 0247_ $$2doi$$a10.1038/s42003-020-0794-7
000888032 0247_ $$2Handle$$a2128/26239
000888032 0247_ $$2altmetric$$aaltmetric:77091037
000888032 0247_ $$2pmid$$apmid:32139786
000888032 0247_ $$2WOS$$aWOS:000519705500007
000888032 037__ $$aFZJ-2020-04610
000888032 082__ $$a570
000888032 1001_ $$0P:(DE-HGF)0$$aVos de Wael, Reinder$$b0
000888032 245__ $$aBrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets
000888032 260__ $$aLondon$$bSpringer Nature$$c2020
000888032 3367_ $$2DRIVER$$aarticle
000888032 3367_ $$2DataCite$$aOutput Types/Journal article
000888032 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1605806785_26137
000888032 3367_ $$2BibTeX$$aARTICLE
000888032 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000888032 3367_ $$00$$2EndNote$$aJournal Article
000888032 520__ $$aUnderstanding how cognitive functions emerge from brain structure depends on quantifying how discrete regions are integrated within the broader cortical landscape. Recent work established that macroscale brain organization and function can be described in a compact manner with multivariate machine learning approaches that identify manifolds often described as cortical gradients. By quantifying topographic principles of macroscale organization, cortical gradients lend an analytical framework to study structural and functional brain organization across species, throughout development and aging, and its perturbations in disease. Here, we present BrainSpace, a Python/Matlab toolbox for (i) the identification of gradients, (ii) their alignment, and (iii) their visualization. Our toolbox furthermore allows for controlled association studies between gradients with other brain-level features, adjusted with respect to null models that account for spatial autocorrelation. Validation experiments demonstrate the usage and consistency of our tools for the analysis of functional and microstructural gradients across different spatial scales.
000888032 536__ $$0G:(DE-HGF)POF3-572$$a572 - (Dys-)function and Plasticity (POF3-572)$$cPOF3-572$$fPOF III$$x0
000888032 588__ $$aDataset connected to CrossRef
000888032 7001_ $$00000-0003-3922-7643$$aBenkarim, Oualid$$b1
000888032 7001_ $$0P:(DE-HGF)0$$aPaquola, Casey$$b2
000888032 7001_ $$0P:(DE-HGF)0$$aLariviere, Sara$$b3
000888032 7001_ $$00000-0002-4448-8998$$aRoyer, Jessica$$b4
000888032 7001_ $$0P:(DE-HGF)0$$aTavakol, Shahin$$b5
000888032 7001_ $$0P:(DE-HGF)0$$aXu, Ting$$b6
000888032 7001_ $$00000-0002-1847-578X$$aHong, Seok-Jun$$b7
000888032 7001_ $$0P:(DE-HGF)0$$aLangs, Georg$$b8
000888032 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie$$b9
000888032 7001_ $$00000-0003-0307-2862$$aMisic, Bratislav$$b10
000888032 7001_ $$0P:(DE-HGF)0$$aMilham, Michael$$b11
000888032 7001_ $$0P:(DE-HGF)0$$aMargulies, Daniel$$b12
000888032 7001_ $$0P:(DE-HGF)0$$aSmallwood, Jonathan$$b13
000888032 7001_ $$0P:(DE-HGF)0$$aBernhardt, Boris C.$$b14$$eCorresponding author
000888032 773__ $$0PERI:(DE-600)2919698-X$$a10.1038/s42003-020-0794-7$$gVol. 3, no. 1, p. 103$$n1$$p103$$tCommunications biology$$v3$$x2399-3642$$y2020
000888032 8564_ $$uhttps://juser.fz-juelich.de/record/888032/files/s42003-020-0794-7.pdf$$yOpenAccess
000888032 909CO $$ooai:juser.fz-juelich.de:888032$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000888032 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173843$$aForschungszentrum Jülich$$b9$$kFZJ
000888032 9131_ $$0G:(DE-HGF)POF3-572$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$v(Dys-)function and Plasticity$$x0
000888032 9141_ $$y2020
000888032 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2020-09-03
000888032 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000888032 915__ $$0StatID:(DE-HGF)1040$$2StatID$$aDBCoverage$$bZoological Record$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000888032 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2020-09-03
000888032 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-09-03
000888032 920__ $$lyes
000888032 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000888032 980__ $$ajournal
000888032 980__ $$aVDB
000888032 980__ $$aUNRESTRICTED
000888032 980__ $$aI:(DE-Juel1)INM-7-20090406
000888032 9801_ $$aFullTexts