000907618 001__ 907618
000907618 005__ 20220512185434.0
000907618 0247_ $$2doi$$a10.25493/F9DP-WCQ
000907618 037__ $$aFZJ-2022-02107
000907618 1001_ $$0P:(DE-Juel1)179582$$aDomhof, Justin W. M.$$b0$$ufzj
000907618 245__ $$aParcellation-based resting-state blood-oxygen-level-dependent (BOLD) signals of a healthy cohort (v1.0)
000907618 260__ $$bEBRAINS$$c2022
000907618 3367_ $$2BibTeX$$aMISC
000907618 3367_ $$0PUB:(DE-HGF)32$$2PUB:(DE-HGF)$$aDataset$$bdataset$$mdataset$$s1652336704_27259
000907618 3367_ $$026$$2EndNote$$aChart or Table
000907618 3367_ $$2DataCite$$aDataset
000907618 3367_ $$2ORCID$$aDATA_SET
000907618 3367_ $$2DINI$$aResearchData
000907618 520__ $$aResting-state functional connectivity (FC) is frequently used to predict behavioral, clinical and demographic subject traits. This type of brain connectome can be derived from blood-oxygen-level-dependent (BOLD) signals that reflect the activation of individual brain regions parcellated according to a given brain atlas. Deriving FC from BOLD signals typically involves the estimation of the amount of synchronized coactivations between the BOLD time series of different brain regions. However, several measures of synchronization exist and which one of these metrics is suited best may deviate from study to study. In parallel, the appropriate selection of the brain parcellation is nowadays also still an open issue. This dataset hence comprises the region-based BOLD signals extracted from the resting-state functional magnetic resonance imaging (fMRI) data of 200 healthy subjects included in the Human Connectome Project. The time series were extracted for 20 different state-of-the-art parcellations. The neuroimaging community may use the data of this repository to study, for example, how different measures of synchronization affect the resting-state FC under various parcellation conditions.
000907618 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
000907618 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
000907618 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
000907618 588__ $$aDataset connected to DataCite
000907618 650_7 $$2Other$$aNeuroscience
000907618 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1$$ufzj
000907618 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b2$$ufzj
000907618 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr V.$$b3$$eCorresponding author$$ufzj
000907618 773__ $$a10.25493/F9DP-WCQ
000907618 909CO $$ooai:juser.fz-juelich.de:907618$$popenaire$$pVDB$$pec_fundedresources
000907618 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)179582$$aForschungszentrum Jülich$$b0$$kFZJ
000907618 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b1$$kFZJ
000907618 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b2$$kFZJ
000907618 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b3$$kFZJ
000907618 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
000907618 9141_ $$y2022
000907618 920__ $$lyes
000907618 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
000907618 980__ $$adataset
000907618 980__ $$aVDB
000907618 980__ $$aI:(DE-Juel1)INM-7-20090406
000907618 980__ $$aUNRESTRICTED