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001034764 0247_ $$2doi$$a10.1101/2024.12.10.627766
001034764 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-07519
001034764 037__ $$aFZJ-2024-07519
001034764 1001_ $$0P:(DE-Juel1)190780$$aMuganga, Tobias$$b0
001034764 245__ $$aVoxel-wise or Region-wise Nuisance Regression for Functional Connectivity Analyses: Does it matter?
001034764 260__ $$c2024
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001034764 3367_ $$2BibTeX$$aARTICLE
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001034764 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, i.e. the voxel time series. Typically the voxel-wise time series are then aggregated into predefined regions or parcels to obtain a 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- and region-level) in 370 unrelated subjects from the  1HCP-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 1,000 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- 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.
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001034764 7001_ $$0P:(DE-Juel1)190306$$aSasse, Leonard$$b1
001034764 7001_ $$0P:(DE-Juel1)180372$$aLarabi, Daouia I.$$b2
001034764 7001_ $$0P:(DE-Juel1)194707$$aNieto, Nicolás$$b3
001034764 7001_ $$0P:(DE-HGF)0$$aCaspers, Julian$$b4
001034764 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b5
001034764 7001_ $$0P:(DE-Juel1)172843$$aPatil, Kaustubh R.$$b6$$eCorresponding author
001034764 773__ $$a10.1101/2024.12.10.627766
001034764 8564_ $$uhttps://juser.fz-juelich.de/record/1034764/files/Voxel-wise%20or%20Region-wise%20Nuisance%20Regression.pdf$$yOpenAccess
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001034764 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)172843$$aForschungszentrum Jülich$$b6$$kFZJ
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001034764 9141_ $$y2024
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