TY  - JOUR
AU  - Muganga, Tobias
AU  - Sasse, Leonard
AU  - Larabi, Daouia
AU  - Nieto, Nicolás
AU  - Caspers, Julian
AU  - Eickhoff, Simon B.
AU  - Patil, Kaustubh R.
TI  - Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?
JO  - Human brain mapping
VL  - 46
IS  - 12
SN  - 1065-9471
CY  - New York, NY
PB  - Wiley-Liss
M1  - FZJ-2025-03561
SP  - e70323
PY  - 2025
AB  - 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.
LB  - PUB:(DE-HGF)16
DO  - DOI:10.1002/hbm.70323
UR  - https://juser.fz-juelich.de/record/1045701
ER  -