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@ARTICLE{Kong:904409,
      author       = {Kong, Ru and Yang, Qing and Gordon, Evan and Xue, Aihuiping
                      and Yan, Xiaoxuan and Orban, Csaba and Zuo, Xi-Nian and
                      Spreng, Nathan and Ge, Tian and Holmes, Avram and Eickhoff,
                      Simon and Yeo, B T Thomas},
      title        = {{I}ndividual-{S}pecific {A}real-{L}evel {P}arcellations
                      {I}mprove {F}unctional {C}onnectivity {P}rediction of
                      {B}ehavior},
      journal      = {Cerebral cortex},
      volume       = {31},
      number       = {10},
      issn         = {1047-3211},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2021-05979},
      pages        = {4477 - 4500},
      year         = {2021},
      abstract     = {Resting-state functional magnetic resonance imaging
                      (rs-fMRI) allows estimation of individual-specific cortical
                      parcellations. We have previously developed a multi-session
                      hierarchical Bayesian model (MS-HBM) for estimating
                      high-quality individual-specific network-level
                      parcellations. Here, we extend the model to estimate
                      individual-specific areal-level parcellations. While
                      network-level parcellations comprise spatially distributed
                      networks spanning the cortex, the consensus is that
                      areal-level parcels should be spatially localized, that is,
                      should not span multiple lobes. There is disagreement about
                      whether areal-level parcels should be strictly contiguous or
                      comprise multiple noncontiguous components; therefore, we
                      considered three areal-level MS-HBM variants spanning these
                      range of possibilities. Individual-specific MS-HBM
                      parcellations estimated using 10 min of data generalized
                      better than other approaches using 150 min of data to
                      out-of-sample rs-fMRI and task-fMRI from the same
                      individuals. Resting-state functional connectivity derived
                      from MS-HBM parcellations also achieved the best behavioral
                      prediction performance. Among the three MS-HBM variants, the
                      strictly contiguous MS-HBM exhibited the best resting-state
                      homogeneity and most uniform within-parcel task activation.
                      In terms of behavioral prediction, the gradient-infused
                      MS-HBM was numerically the best, but differences among
                      MS-HBM variants were not statistically significant. Overall,
                      these results suggest that areal-level MS-HBMs can capture
                      behaviorally meaningful individual-specific parcellation
                      features beyond group-level parcellations.},
      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       = {pmid:33942058},
      UT           = {WOS:000695807700007},
      doi          = {10.1093/cercor/bhab101},
      url          = {https://juser.fz-juelich.de/record/904409},
}