001045701 001__ 1045701
001045701 005__ 20250827202242.0
001045701 0247_ $$2doi$$a10.1002/hbm.70323
001045701 0247_ $$2ISSN$$a1065-9471
001045701 0247_ $$2ISSN$$a1097-0193
001045701 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-03561
001045701 037__ $$aFZJ-2025-03561
001045701 082__ $$a610
001045701 1001_ $$0P:(DE-Juel1)190780$$aMuganga, Tobias$$b0$$eCorresponding author
001045701 245__ $$aVoxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?
001045701 260__ $$aNew York, NY$$bWiley-Liss$$c2025
001045701 3367_ $$2DRIVER$$aarticle
001045701 3367_ $$2DataCite$$aOutput Types/Journal article
001045701 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1756283723_10156
001045701 3367_ $$2BibTeX$$aARTICLE
001045701 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001045701 3367_ $$00$$2EndNote$$aJournal Article
001045701 520__ $$aRemoval of nuisance signals (such as motion) from the BOLD time series is an important aspect of preprocessing to obtain meaningful resting-state functional connectivity (rs-FC). The nuisance signals are commonly removed using denoising procedures at the finest resolution, that is the voxel time series. Typically, the voxel-wise time series are then aggregated into predefined regions or parcels to obtain an rs-FC matrix as the correlation between pairs of regional time series. Computational efficiency can be improved by denoising the aggregated regional time series instead of the voxel time series. However, a comprehensive comparison of the effects of denoising on these two resolutions is missing. In this study, we systematically investigate the effects of denoising at different time series resolutions (voxel-level and region-level) in 370 unrelated subjects from the HCP-YA dataset. Alongside the time series resolution, we considered additional factors such as aggregation method (Mean and first eigenvariate [EV]) and parcellation granularity (100, 400, and 1000 regions). To assess the effect of those choices on the utility of the resulting whole-brain rs-FC, we evaluated the individual specificity (fingerprinting) and the capacity to predict age and three cognitive scores. Our findings show generally equal or better performance for region-level denoising with notable differences depending on the aggregation method. Using Mean aggregation yielded equal individual specificity and prediction performance for voxel-level and region-level denoising. When EV was employed for aggregation, the individual specificity of voxel-level denoising was reduced compared to region-level denoising. Increasing parcellation granularity generally improved individual specificity. For the prediction of age and cognitive test scores, only fluid intelligence indicated worse performance for voxel-level denoising in the case of aggregating with the EV. Based on these results, we recommend the adoption of region-level denoising for brain-behavior investigations when using Mean aggregation. This approach offers equal individual specificity and prediction capacity with reduced computational resources for the analysis of rs-FC patterns.
001045701 536__ $$0G:(DE-HGF)POF4-5252$$a5252 - Brain Dysfunction and Plasticity (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001045701 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x1
001045701 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
001045701 7001_ $$0P:(DE-Juel1)190306$$aSasse, Leonard$$b1$$ufzj
001045701 7001_ $$0P:(DE-Juel1)180372$$aLarabi, Daouia$$b2
001045701 7001_ $$0P:(DE-Juel1)194707$$aNieto, Nicolás$$b3$$ufzj
001045701 7001_ $$0P:(DE-HGF)0$$aCaspers, Julian$$b4
001045701 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b5$$ufzj
001045701 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b6$$eCorresponding author
001045701 773__ $$0PERI:(DE-600)1492703-2$$a10.1002/hbm.70323$$gVol. 46, no. 12, p. e70323$$n12$$pe70323$$tHuman brain mapping$$v46$$x1065-9471$$y2025
001045701 8564_ $$uhttps://juser.fz-juelich.de/record/1045701/files/Human%20Brain%20Mapping%20-%202025%20-%20Muganga%20-%20Voxel%E2%80%90Wise%20or%20Region%E2%80%90Wise%20Nuisance%20Regression%20for%20Functional%20Connectivity%20Analyses%20.pdf$$yOpenAccess
001045701 909CO $$ooai:juser.fz-juelich.de:1045701$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
001045701 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190780$$aForschungszentrum Jülich$$b0$$kFZJ
001045701 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190306$$aForschungszentrum Jülich$$b1$$kFZJ
001045701 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)194707$$aForschungszentrum Jülich$$b3$$kFZJ
001045701 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131678$$aForschungszentrum Jülich$$b5$$kFZJ
001045701 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)131678$$a HHU Düsseldorf$$b5
001045701 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b6$$kFZJ
001045701 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$$x0
001045701 9141_ $$y2025
001045701 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001045701 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2024-12-19$$wger
001045701 915__ $$0LIC:(DE-HGF)CCBYNC4$$2HGFVOC$$aCreative Commons Attribution-NonCommercial CC BY-NC 4.0
001045701 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2024-08-08T17:07:28Z
001045701 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2024-08-08T17:07:28Z
001045701 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2024-12-19
001045701 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2024-12-19$$wger
001045701 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2024-12-19
001045701 9201_ $$0I:(DE-Juel1)INM-7-20090406$$kINM-7$$lGehirn & Verhalten$$x0
001045701 980__ $$ajournal
001045701 980__ $$aVDB
001045701 980__ $$aUNRESTRICTED
001045701 980__ $$aI:(DE-Juel1)INM-7-20090406
001045701 9801_ $$aFullTexts