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@ARTICLE{Park:888030,
      author       = {Park, Bo-yong and Vos de Wael, Reinder and Paquola, Casey
                      and Larivière, Sara and Benkarim, Oualid and Royer, Jessica
                      and Tavakol, Shahin and Cruces, Raul R. and Li, Qiongling
                      and Valk, Sofie L. and Margulies, Daniel S. and Mišić,
                      Bratislav and Bzdok, Danilo and Smallwood, Jonathan and
                      Bernhardt, Boris C.},
      title        = {{S}ignal diffusion along connectome gradients and inter-hub
                      routing differentially contribute to dynamic human brain
                      function},
      journal      = {NeuroImage},
      volume       = {224},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2020-04608},
      pages        = {117429 -},
      year         = {2021},
      abstract     = {Human cognition is dynamic, alternating over time between
                      externally-focused states and more abstract, often
                      self-generated, patterns of thought. Although cognitive
                      neuroscience has documented how networks anchor particular
                      modes of brain function, mechanisms that describe
                      transitions between distinct functional states remain poorly
                      understood. Here, we examined how time-varying changes in
                      brain function emerge within the constraints imposed by
                      macroscale structural network organization. Studying a large
                      cohort of healthy adults (n = 326), we capitalized on
                      manifold learning techniques that identify low dimensional
                      representations of structural connectome organization and we
                      decomposed neurophysiological activity into distinct
                      functional states and their transition patterns using Hidden
                      Markov Models. Structural connectome organization predicted
                      dynamic transitions anchored in sensorimotor systems and
                      those between sensorimotor and transmodal states. Connectome
                      topology analyses revealed that transitions involving
                      sensorimotor states traversed short and intermediary
                      distances and adhered strongly to communication mechanisms
                      of network diffusion. Conversely, transitions between
                      transmodal states involved spatially distributed hubs and
                      increasingly engaged long-range routing. These findings
                      establish that the structure of the cortex is optimized to
                      allow neural states the freedom to vary between distinct
                      modes of processing, and so provides a key insight into the
                      neural mechanisms that give rise to the flexibility of human
                      cognition.Keywords: Hidden Markov Model; diffusion MRI;
                      functional dynamics; gradients; multimodal imaging;
                      structural connectome.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33038538},
      UT           = {WOS:000600796800043},
      doi          = {10.1016/j.neuroimage.2020.117429},
      url          = {https://juser.fz-juelich.de/record/888030},
}