% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Muganga:1034764,
      author       = {Muganga, Tobias and Sasse, Leonard and Larabi, Daouia I.
                      and Nieto, Nicolás and Caspers, Julian and Eickhoff, Simon
                      B. and Patil, Kaustubh R.},
      title        = {{V}oxel-wise or {R}egion-wise {N}uisance {R}egression for
                      {F}unctional {C}onnectivity {A}nalyses: {D}oes it matter?},
      reportid     = {FZJ-2024-07519},
      year         = {2024},
      abstract     = {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.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.12.10.627766},
      url          = {https://juser.fz-juelich.de/record/1034764},
}