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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 | 1034764 | ||
| 005 | 20250203103359.0 | ||
| 024 | 7 | _ | |a 10.1101/2024.12.10.627766 |2 doi |
| 024 | 7 | _ | |a 10.34734/FZJ-2024-07519 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2024-07519 |
| 100 | 1 | _ | |a Muganga, Tobias |0 P:(DE-Juel1)190780 |b 0 |
| 245 | _ | _ | |a Voxel-wise or Region-wise Nuisance Regression for Functional Connectivity Analyses: Does it matter? |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1736234776_13269 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 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, 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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| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Sasse, Leonard |0 P:(DE-Juel1)190306 |b 1 |
| 700 | 1 | _ | |a Larabi, Daouia I. |0 P:(DE-Juel1)180372 |b 2 |
| 700 | 1 | _ | |a Nieto, Nicolás |0 P:(DE-Juel1)194707 |b 3 |
| 700 | 1 | _ | |a Caspers, Julian |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Eickhoff, Simon B. |0 P:(DE-Juel1)131678 |b 5 |
| 700 | 1 | _ | |a Patil, Kaustubh R. |0 P:(DE-Juel1)172843 |b 6 |e Corresponding author |
| 773 | _ | _ | |a 10.1101/2024.12.10.627766 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1034764/files/Voxel-wise%20or%20Region-wise%20Nuisance%20Regression.pdf |y OpenAccess |
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