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| Hauptseite > Publikationsdatenbank > Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter? > print |
| Hauptseite > Publikationsdatenbank > Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter? > print |
| 001 | 1045701 | ||
| 005 | 20250827202242.0 | ||
| 024 | 7 | _ | |a 10.1002/hbm.70323 |2 doi |
| 024 | 7 | _ | |a 1065-9471 |2 ISSN |
| 024 | 7 | _ | |a 1097-0193 |2 ISSN |
| 024 | 7 | _ | |a 10.34734/FZJ-2025-03561 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-03561 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Muganga, Tobias |0 P:(DE-Juel1)190780 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter? |
| 260 | _ | _ | |a New York, NY |c 2025 |b Wiley-Liss |
| 336 | 7 | _ | |a article |2 DRIVER |
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| 520 | _ | _ | |a 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. |
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| 588 | _ | _ | |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de |
| 700 | 1 | _ | |a Sasse, Leonard |0 P:(DE-Juel1)190306 |b 1 |u fzj |
| 700 | 1 | _ | |a Larabi, Daouia |0 P:(DE-Juel1)180372 |b 2 |
| 700 | 1 | _ | |a Nieto, Nicolás |0 P:(DE-Juel1)194707 |b 3 |u fzj |
| 700 | 1 | _ | |a Caspers, Julian |0 P:(DE-HGF)0 |b 4 |
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| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 6 |e Corresponding author |
| 773 | _ | _ | |a 10.1002/hbm.70323 |g Vol. 46, no. 12, p. e70323 |0 PERI:(DE-600)1492703-2 |n 12 |p e70323 |t Human brain mapping |v 46 |y 2025 |x 1065-9471 |
| 856 | 4 | _ | |u https://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 |y OpenAccess |
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