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@ARTICLE{Sasse:1050533,
      author       = {Sasse, Leonard and Paquola, Casey and Dukart, Juergen and
                      Hoffstaedter, Felix and Eickhoff, Simon B. and Patil,
                      Kaustubh R.},
      title        = {{I}ndividual identifiability following {P}rocrustes
                      alignment of functional gradients: effect of subspace
                      dimensionality},
      journal      = {Communications biology},
      volume       = {.},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2026-00295},
      pages        = {.},
      year         = {2026},
      note         = {Grants and funding SPP2041/Deutsche Forschungsgemeinschaft
                      (German Research Foundation)},
      abstract     = {Functional connectivity (FC) gradients derived from fMRI
                      provide valuable insights into individual differences in
                      brain organisation, yet aligning these gradients across
                      individuals poses challenges for meaningful group
                      comparisons. Procrustes alignment is often employed to
                      standardize gradients, but the choice of the number of
                      gradients used in alignment introduces complexities that may
                      affect the validity of individual-level analyses. In this
                      study, we systematically investigate the impact of varying
                      gradient counts in Procrustes alignment on the principal FC
                      gradient, using data from four high-quality fMRI datasets,
                      including the Human Connectome Project (HCP-YA), Amsterdam
                      Open MRI Collection (AOMIC) PIOP1 and PIOP2, and Cambridge
                      Centre for Ageing and Neuroscience (Cam-CAN). We find that
                      increasing the number of gradients used in alignment
                      enhances subject identification. To further probe these
                      effects, we use machine learning to predict fluid
                      intelligence and age, and a motion prediction analysis,
                      revealing that higher alignment gradient counts may
                      introduce information from lower gradients into the
                      principal gradient with implications for the interpretation
                      of individual-level analyses.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5251 -
                      Multilevel Brain Organization and Variability (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1038/s42003-025-09509-3},
      url          = {https://juser.fz-juelich.de/record/1050533},
}