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@ARTICLE{Sasse:1038546,
      author       = {Sasse, Leonard and Paquola, Casey and Dukart, Juergen and
                      Hoffstaedter, Felix and Eickhoff, Simon B. and Patil,
                      Kaustubh R.},
      title        = {{P}rocrustes {A}lignment in {I}ndividual-level {A}nalyses
                      of {F}unctional {G}radients},
      reportid     = {FZJ-2025-01529},
      year         = {2024},
      abstract     = {Functional connectivity (FC) gradients provide valuable
                      insights into individual differences in brain organization,
                      yet aligning these gradients across individuals poses
                      challenges. Procrustes alignment is often employed to
                      standardize gradients across multiple subjects, but the
                      choice of the number of gradients used in alignment
                      introduces complexities that may impact 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 resting
                      state 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 identification accuracy but can
                      reduce differential identifiability, as additional gradients
                      risk introducing nuisance signals such as motion back into
                      the principal gradient. To further probe these effects,
                      machine learning to predict fluid intelligence and age, and
                      a motion prediction analysis, revealing that higher
                      alignment gradient counts may leak information from lower
                      gradients into the principal gradient for individual-level
                      analyses. These findings highlight the trade-off between
                      alignment precision and the potential reintroduction of
                      noise.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2024.11.26.625368},
      url          = {https://juser.fz-juelich.de/record/1038546},
}