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@ARTICLE{Muganga:1045701,
      author       = {Muganga, Tobias and Sasse, Leonard and Larabi, Daouia and
                      Nieto, Nicolás and Caspers, Julian and Eickhoff, Simon B.
                      and Patil, Kaustubh R.},
      title        = {{V}oxel‐{W}ise or {R}egion‐{W}ise {N}uisance
                      {R}egression for {F}unctional {C}onnectivity {A}nalyses:
                      {D}oes {I}t {M}atter?},
      journal      = {Human brain mapping},
      volume       = {46},
      number       = {12},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2025-03561},
      pages        = {e70323},
      year         = {2025},
      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, 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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / JL
                      SMHB - Joint Lab Supercomputing and Modeling for the Human
                      Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1002/hbm.70323},
      url          = {https://juser.fz-juelich.de/record/1045701},
}