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@ARTICLE{Kong:848149,
      author       = {Kong, Ru and Li, Jingwei and Orban, Csaba and Sabuncu, Mert
                      R and Liu, Hesheng and Schaefer, Alexander and Sun, Nanbo
                      and Zuo, Xi-Nian and Holmes, Avram J and Eickhoff, Simon and
                      Yeo, B T Thomas},
      title        = {{S}patial {T}opography of {I}ndividual-{S}pecific
                      {C}ortical {N}etworks {P}redicts {H}uman {C}ognition,
                      {P}ersonality, and {E}motion},
      journal      = {Cerebral cortex},
      volume       = {29},
      number       = {6},
      issn         = {1460-2199},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2018-03421},
      pages        = {2533–2551},
      year         = {2019},
      abstract     = {Resting-state functional magnetic resonance imaging
                      (rs-fMRI) offers the opportunity to delineate
                      individual-specific brain networks. A major question is
                      whether individual-specific network topography (i.e.,
                      location and spatial arrangement) is behaviorally relevant.
                      Here, we propose a multi-session hierarchical Bayesian model
                      (MS-HBM) for estimating individual-specific cortical
                      networks and investigate whether individual-specific network
                      topography can predict human behavior. The multiple layers
                      of the MS-HBM explicitly differentiate intra-subject
                      (within-subject) from inter-subject (between-subject)
                      network variability. By ignoring intra-subject variability,
                      previous network mappings might confuse intra-subject
                      variability for inter-subject differences. Compared with
                      other approaches, MS-HBM parcellations generalized better to
                      new rs-fMRI and task-fMRI data from the same subjects. More
                      specifically, MS-HBM parcellations estimated from a single
                      rs-fMRI session (10 min) showed comparable generalizability
                      as parcellations estimated by 2 state-of-the-art methods
                      using 5 sessions (50 min). We also showed that behavioral
                      phenotypes across cognition, personality, and emotion could
                      be predicted by individual-specific network topography with
                      modest accuracy, comparable to previous reports predicting
                      phenotypes based on connectivity strength. Network
                      topography estimated by MS-HBM was more effective for
                      behavioral prediction than network size, as well as network
                      topography estimated by other parcellation approaches. Thus,
                      similar to connectivity strength, individual-specific
                      network topography might also serve as a fingerprint of
                      human behavior.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29878084},
      UT           = {WOS:000482132400017},
      doi          = {10.1093/cercor/bhy123},
      url          = {https://juser.fz-juelich.de/record/848149},
}