000904401 001__ 904401 000904401 005__ 20220131120425.0 000904401 0247_ $$2doi$$a10.1016/j.neuroimage.2021.118533 000904401 0247_ $$2ISSN$$a1053-8119 000904401 0247_ $$2ISSN$$a1095-9572 000904401 0247_ $$2Handle$$a2128/29952 000904401 0247_ $$2altmetric$$aaltmetric:112593452 000904401 0247_ $$2pmid$$apmid:34469814 000904401 0247_ $$2WOS$$aWOS:000697098400004 000904401 037__ $$aFZJ-2021-05971 000904401 082__ $$a610 000904401 1001_ $$0P:(DE-HGF)0$$aBijsterbosch, Janine D.$$b0$$eCorresponding author 000904401 245__ $$aRecent developments in representations of the connectome 000904401 260__ $$aOrlando, Fla.$$bAcademic Press$$c2021 000904401 3367_ $$2DRIVER$$aarticle 000904401 3367_ $$2DataCite$$aOutput Types/Journal article 000904401 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1641469300_24280 000904401 3367_ $$2BibTeX$$aARTICLE 000904401 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000904401 3367_ $$00$$2EndNote$$aJournal Article 000904401 520__ $$aResearch into the human connectome (i.e., all connections in the human brain) with the use of resting state functional MRI has rapidly increased in popularity in recent years, especially with the growing availability of large-scale neuroimaging datasets. The goal of this review article is to describe innovations in functional connectome representations that have come about in the past 8 years, since the 2013 NeuroImage special issue on ‘Mapping the Connectome’. In the period, research has shifted from group-level brain parcellations towards the characterization of the individualized connectome and of relationships between individual connectomic differences and behavioral/clinical variation. Achieving subject-specific accuracy in parcel boundaries while retaining cross-subject correspondence is challenging, and a variety of different approaches are being developed to meet this challenge, including improved alignment, improved noise reduction, and robust group-to-subject mapping approaches. Beyond the interest in the individualized connectome, new representations of the data are being studied to complement the traditional parcellated connectome representation (i.e., pairwise connections between distinct brain regions), such as methods that capture overlapping and smoothly varying patterns of connectivity (‘gradients’). These different connectome representations offer complimentary insights into the inherent functional organization of the brain, but challenges for functional connectome research remain. Interpretability will be improved by future research towards gaining insights into the neural mechanisms underlying connectome observations obtained from functional MRI. Validation studies comparing different connectome representations are also needed to build consensus and confidence to proceed with clinical trials that may produce meaningful clinical translation of connectome insights. 000904401 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0 000904401 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de 000904401 7001_ $$0P:(DE-Juel1)173843$$aValk, Sofie L.$$b1 000904401 7001_ $$0P:(DE-HGF)0$$aWang, Danhong$$b2 000904401 7001_ $$0P:(DE-HGF)0$$aGlasser, Matthew F.$$b3 000904401 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2021.118533$$gVol. 243, p. 118533 -$$p118533 -$$tNeuroImage$$v243$$x1053-8119$$y2021 000904401 8564_ $$uhttps://juser.fz-juelich.de/record/904401/files/1-s2.0-S1053811921008065-main.pdf$$yOpenAccess 000904401 909CO $$ooai:juser.fz-juelich.de:904401$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire 000904401 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173843$$aForschungszentrum Jülich$$b1$$kFZJ 000904401 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)POF4-5252$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0 000904401 9141_ $$y2021 000904401 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-01-29 000904401 915__ $$0LIC:(DE-HGF)CCBYNCND4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0 000904401 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEUROIMAGE : 2019$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000904401 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEUROIMAGE : 2019$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-01-29 000904401 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-01-29$$wger 000904401 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-01-29 000904401 920__ $$lyes 000904401 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0 000904401 980__ $$ajournal 000904401 980__ $$aVDB 000904401 980__ $$aUNRESTRICTED 000904401 980__ $$aI:(DE-Juel1)INM-7-20090406 000904401 9801_ $$aFullTexts