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@INPROCEEDINGS{Muganga:1010401,
      author       = {Muganga, Tobias and Sasse, Leonard and Larabi, Daouia and
                      Eickhoff, Simon and Patil, Kaustubh},
      title        = {{V}oxel- versus {ROI}-level {N}uisance {R}egression for
                      {F}unctional {C}onnectivity {A}nalyses},
      reportid     = {FZJ-2023-03041},
      year         = {2023},
      abstract     = {Introduction: Individual-level analysis of functional
                      connectivity (FC) using large datasets demandsconsiderations
                      of computational efficiency. The extraction of FC involves
                      two mainpreparatory steps: 1) cleaning the voxel-level time
                      series from nuisance variables, and 2)aggregation of the
                      voxel-level time series into region-level time series.
                      Computationalefficiency can be improved by cleaning the
                      region-level instead of the voxel-level time series.When
                      using mean aggregation, the two approaches should be
                      numerically equivalent(Friston et al., 2006). However, the
                      two approaches have not been systematically comparedfor
                      another popular option of aggregation: the first
                      eigenvariate. While these two aggregationschemes are similar
                      for regions with homogenous voxel-level time series, the
                      firsteigenvariate better captures the signal of
                      heterogeneous regions (Friston et al., 2006). In thecurrent
                      study, we evaluated individual specificity and the capacity
                      to predict cognitive scoresof FC obtained from both cleaning
                      approaches and aggregation schemes.MethodWe used FIX-cleaned
                      resting-state data acquired with RL- and LR-phase
                      encodingdirections (Smith et al., 2013) from two scan
                      sessions (one day apart) from the HCP-YAS1200 release (N =
                      376; NFemale = 182; Mage = 28.75; SDage = 3.82; Van Essen et
                      al., 2013).The 400 region version of the Schaefer atlas
                      (Schaefer et al., 2018) was used to computeaggregate
                      region-level time series as the mean or first eigenvariate
                      of all voxel time serieswithin each region. Signal cleaning
                      was applied either voxelwise with subsequentaggregation into
                      the region-level time series or aggregation was performed
                      first followed bycleaning. Cleaning steps included linear
                      detrending, bandpass filtering (0.01-0.08 Hz) andlinear
                      nuisance regression. Nuisance variables included mean white
                      matter (WM), meancerebral spinal fluid (CSF), mean gray
                      matter (GM), 24 motion regressors (Friston et al.,1996), and
                      21 aCompCor regressors from WM, CSF and GM (Muschelli et
                      al., 2014). Next,FC was computed as Pearson’s correlation
                      between all pairs of regional time series and thetwo FCs
                      from the same session acquired with two phase encoding
                      directions wereaveraged. We assessed individual specificity
                      in the resulting FCs by computing identificationaccuracy
                      (Finn et al., 2015) and differential identifiability (Amico
                      $\&$ Goni, 2018). Thepredictive capacity of interindividual
                      differences from FC was assessed using kernel
                      ridgeregression in a 5-fold cross-validation scheme with 5
                      repeats to predict age and fivecognitive scores; cognitive
                      flexibility, working memory, fluid intelligence, vocabulary
                      andreading.Results: We found that moving from voxelwise
                      cleaning to regionwise cleaning led to a 2-folddecrease in
                      computation time per FC (Msaving = 110 sec.). While
                      individual specificity betweenthe two cleaning approaches
                      was similar in the case of mean aggregation,
                      regionwisecleaning resulted in higher individual specificity
                      for first eigenvariate aggregation (Fig. 1). Asimilar yet
                      more diverse picture was found for the prediction analysis.
                      Here, for meanaggregation both cleaning approaches performed
                      similarly for all predicted scores. Whenusing eigenvariate
                      aggregation, both cleaning approaches performed equally for
                      ageprediction. However, regionwise cleaning resulted in
                      better prediction performance of fluidintelligence,
                      vocabulary and reading while voxelwise cleaning led to
                      higher predictionperformance for cognitive flexibility, and
                      working memory scores (Fig. 2).Conclusion: With our
                      findings, we encourage a more efficient default of
                      regionwise cleaning for FCextraction when mean aggregation
                      is used. In the first eigenvariate aggregation,
                      theindividual specificity was better with regionwise
                      cleaning but prediction results were mixed.Thus, further
                      investigation is advised before extending the regionwise
                      cleaning for firsteigenvariate aggregation.},
      month         = {Jul},
      date          = {2023-07-22},
      organization  = {Organization for Human Brain Mapping
                       (OHBM), Montreal (Canada), 22 Jul 2023
                       - 26 Jul 2023},
      subtyp        = {After Call},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2023-03041},
      url          = {https://juser.fz-juelich.de/record/1010401},
}