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@ARTICLE{Kong:1005642,
      author       = {Kong, Ru and Tan, Yan Rui and Wulan, Naren and Ooi, Leon Qi
                      Rong and Farahibozorg, Seyedeh-Rezvan and Harrison, Samuel
                      and Bijsterbosch, Janine D. and Bernhardt, Boris C. and
                      Eickhoff, Simon and Yeo, B. T. Thomas},
      title        = {{C}omparison {B}etween {G}radients and {P}arcellations for
                      {F}unctional {C}onnectivity {P}rediction of {B}ehavior},
      journal      = {NeuroImage},
      volume       = {273},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-01585},
      pages        = {120044 -},
      year         = {2023},
      abstract     = {Resting-state functional connectivity (RSFC) is widely used
                      to predict behavioral measures. To predict behavioral
                      measures, representing RSFC with parcellations and gradients
                      are the two most popular approaches. Here, we compare
                      parcellation and gradient approaches for RSFC-based
                      prediction of a broad range of behavioral measures in the
                      Human Connectome Project (HCP) and Adolescent Brain
                      Cognitive Development (ABCD) datasets. Among the
                      parcellation approaches, we consider group-average
                      “hard” parcellations (Schaefer et al., 2018),
                      individual-specific “hard” parcellations (Kong et al.,
                      2021a), and an individual-specific “soft” parcellation
                      (spatial independent component analysis with dual
                      regression; Beckmann et al., 2009). For gradient approaches,
                      we consider the well-known principal gradients (Margulies et
                      al., 2016) and the local gradient approach that detects
                      local RSFC changes (Laumann et al., 2015). Across two
                      regression algorithms, individual-specific hard-parcellation
                      performs the best in the HCP dataset, while the principal
                      gradients, spatial independent component analysis and
                      group-average “hard” parcellations exhibit similar
                      performance. On the other hand, principal gradients and all
                      parcellation approaches perform similarly in the ABCD
                      dataset. Across both datasets, local gradients perform the
                      worst. Finally, we find that the principal gradient approach
                      requires at least 40 to 60 gradients to perform as well as
                      parcellation approaches. While most principal gradient
                      studies utilize a single gradient, our results suggest that
                      incorporating higher order gradients can provide significant
                      behaviorally relevant information. Future work will consider
                      the inclusion of additional parcellation and gradient
                      approaches for comparison.},
      cin          = {INM-7},
      ddc          = {610},
      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)16},
      pubmed       = {36940760},
      UT           = {WOS:000981253100001},
      doi          = {10.1016/j.neuroimage.2023.120044},
      url          = {https://juser.fz-juelich.de/record/1005642},
}