001038813 001__ 1038813
001038813 005__ 20250610131445.0
001038813 0247_ $$2doi$$a10.1162/netn_a_00447
001038813 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-01650
001038813 0247_ $$2WOS$$aWOS:001489278300003
001038813 037__ $$aFZJ-2025-01650
001038813 082__ $$a610
001038813 1001_ $$0P:(DE-Juel1)180326$$aZhang, Shufei$$b0
001038813 245__ $$aPredicting response speed and age from task-evoked effective connectivity
001038813 260__ $$aCambridge, MA$$bThe MIT Press$$c2025
001038813 3367_ $$2DRIVER$$aarticle
001038813 3367_ $$2DataCite$$aOutput Types/Journal article
001038813 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1740377029_15599
001038813 3367_ $$2BibTeX$$aARTICLE
001038813 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001038813 3367_ $$00$$2EndNote$$aJournal Article
001038813 520__ $$aRecent neuroimaging studies demonstrated that task-evoked functional connectivity (FC) may better predict individual traits than resting-state FC. However, the prediction properties of task-evoked effective connectivity (EC) remain unexplored. We investigated this by predicting individual reaction time (RT) performance in the stimulus-response compatibility task and age, using intrinsic EC (I-EC, calculated at baseline) and task-modulated EC (M-EC, induced by experimental conditions) with dynamic causal modeling (DCM) across various data-processing conditions, including different general linear model (GLM) designs, Bayesian model reduction, and different cross-validation schemes and prediction models. We report evident differences in predicting RT and age between I-EC and M-EC, as well as between event-related and block-based GLM and DCM designs. M-EC outperformed both I-EC and task-evoked FC in RT prediction, while all types of connectivity performed similarly for age. Event-related GLM and DCM designs performed better than block-based designs. Our findings suggest that task-evoked I-EC and M-EC may capture different phenotypic attributes, with performance influenced by data processing and modeling choices, particularly the GLM-DCM design. This evaluation of methods for behavior prediction from brain EC may contribute to a meta-scientific understanding of how data processing and modeling frameworks influence neuroimaging-based predictions, offering insights for improving their robustness and efficacy.Keywords: task fMRI, dynamic causal modeling, analytic flexibility, machine learning, brain-based prediction, stimulus-response compatibility, functional connectivity
001038813 536__ $$0G:(DE-HGF)POF4-5251$$a5251 - Multilevel Brain Organization and Variability (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001038813 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x1
001038813 588__ $$aDataset connected to DataCite
001038813 7001_ $$0P:(DE-Juel1)178611$$aJung, Kyesam$$b1
001038813 7001_ $$0P:(DE-Juel1)131693$$aLangner, Robert$$b2
001038813 7001_ $$0P:(DE-HGF)0$$aFlorin, Esther$$b3
001038813 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon$$b4
001038813 7001_ $$0P:(DE-Juel1)131880$$aPopovych, Oleksandr$$b5$$eCorresponding author
001038813 773__ $$0PERI:(DE-600)2900481-0$$a10.1162/netn_a_00447$$gp. 1 - 57$$p1-57$$tNetwork neuroscience$$v.$$x2472-1751$$y2025
001038813 8564_ $$uhttps://juser.fz-juelich.de/record/1038813/files/APC600625699.pdf
001038813 8564_ $$uhttps://juser.fz-juelich.de/record/1038813/files/Shufei%20Zhang_TaskDCM-MatAB_NetworkNeuroscience_R1_NN_Clean.docx$$yOpenAccess
001038813 8564_ $$uhttps://juser.fz-juelich.de/record/1038813/files/netn_a_00447.pdf$$yOpenAccess
001038813 8767_ $$8APC600625699$$92025-02-03$$a1200211232$$d2025-02-05$$eAPC$$jZahlung erfolgt$$z2250 USD
001038813 909CO $$ooai:juser.fz-juelich.de:1038813$$pdnbdelivery$$popenCost$$pVDB$$pdriver$$pOpenAPC$$popen_access$$popenaire
001038813 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)180326$$aForschungszentrum Jülich$$b0$$kFZJ
001038813 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)178611$$aForschungszentrum Jülich$$b1$$kFZJ
001038813 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131693$$aForschungszentrum Jülich$$b2$$kFZJ
001038813 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b4$$kFZJ
001038813 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b4
001038813 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131880$$aForschungszentrum Jülich$$b5$$kFZJ
001038813 9131_ $$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
001038813 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$$x1
001038813 9141_ $$y2025
001038813 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001038813 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001038813 915pc $$0PC:(DE-HGF)0002$$2APC$$aDFG OA Publikationskosten
001038813 915pc $$0PC:(DE-HGF)0003$$2APC$$aDOAJ Journal
001038813 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2025-01-01
001038813 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
001038813 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNETW NEUROSCI : 2022$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-02-09T16:05:29Z
001038813 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-02-09T16:05:29Z
001038813 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001038813 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Anonymous peer review$$d2023-02-09T16:05:29Z
001038813 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-01
001038813 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-01
001038813 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001038813 980__ $$ajournal
001038813 980__ $$aVDB
001038813 980__ $$aUNRESTRICTED
001038813 980__ $$aI:(DE-Juel1)INM-7-20090406
001038813 980__ $$aAPC
001038813 9801_ $$aAPC
001038813 9801_ $$aFullTexts