%0 Journal Article
%A Muganga, Tobias
%A Sasse, Leonard
%A Larabi, Daouia
%A Nieto, Nicolás
%A Caspers, Julian
%A Eickhoff, Simon B.
%A Patil, Kaustubh R.
%T Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?
%J Human brain mapping
%V 46
%N 12
%@ 1065-9471
%C New York, NY
%I Wiley-Liss
%M FZJ-2025-03561
%P e70323
%D 2025
%X Removal 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.
%F PUB:(DE-HGF)16
%9 Journal Article
%R 10.1002/hbm.70323
%U https://juser.fz-juelich.de/record/1045701