% 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{Antonopoulos:1010399,
      author       = {Antonopoulos, Georgios and More, Shammi and Raimondo,
                      Federico and Eickhoff, Simon B. and Hoffstaedter, Felix and
                      Patil, Kaustubh R.},
      title        = {{A} systematic comparison of {VBM} pipelines and their
                      application to age prediction},
      journal      = {NeuroImage},
      volume       = {279},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-03039},
      pages        = {120292 -},
      year         = {2023},
      abstract     = {Voxel-based morphometry (VBM) analysis is commonly used for
                      localized quantification of gray matter volume (GMV).
                      Several alternatives exist to implement a VBM pipeline.
                      However, how these alternatives compare and their utility in
                      applications, such as the estimation of aging effects,
                      remain largely unclear. This leaves researchers wondering
                      which VBM pipeline they should use for their project. In
                      this study, we took a user-centric perspective and
                      systematically compared five VBM pipelines, together with
                      registration to either a general or a study-specific
                      template, utilizing three large datasets (n each).
                      Considering the known effect of aging on GMV, we first
                      compared the pipelines in their ability of individual-level
                      age prediction and found markedly varied results. To examine
                      whether these results arise from systematic differences
                      between the pipelines, we classified them based on their
                      GMVs, resulting in near-perfect accuracy. To gain deeper
                      insights, we examined the impact of different VBM steps
                      using the region-wise similarity between pipelines. The
                      results revealed marked differences, largely driven by
                      segmentation and registration steps. We observed large
                      variability in subject-identification accuracies,
                      highlighting the interpipeline differences in
                      individual-level quantification of GMV. As a biologically
                      meaningful criterion we correlated regional GMV with age.
                      The results were in line with the age-prediction analysis,
                      and two pipelines, CAT and the combination of fMRIPrep for
                      tissue characterization with FSL for registration, reflected
                      age information better.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37572766},
      UT           = {WOS:001070481100001},
      doi          = {10.1016/j.neuroimage.2023.120292},
      url          = {https://juser.fz-juelich.de/record/1010399},
}