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@ARTICLE{Deng:910588,
      author       = {Deng, Shikuang and Li, Jingwei and Thomas Yeo, B. T. and
                      Gu, Shi},
      title        = {{C}ontrol theory illustrates the energy efficiency in the
                      dynamic reconfiguration of functional connectivity},
      journal      = {Communications biology},
      volume       = {5},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2022-03966},
      pages        = {295},
      year         = {2022},
      abstract     = {The brain's functional connectivity fluctuates over time
                      instead of remaining steady in a stationary mode even during
                      the resting state. This fluctuation establishes the
                      dynamical functional connectivity that transitions in a
                      non-random order between multiple modes. Yet it remains
                      unexplored how the transition facilitates the entire brain
                      network as a dynamical system and what utility this
                      mechanism for dynamic reconfiguration can bring over the
                      widely used graph theoretical measurements. To address these
                      questions, we propose to conduct an energetic analysis of
                      functional brain networks using resting-state fMRI and
                      behavioral measurements from the Human Connectome Project.
                      Through comparing the state transition energy under distinct
                      adjacent matrices, we justify that dynamic functional
                      connectivity leads to $60\%$ less energy cost to support the
                      resting state dynamics than static connectivity when driving
                      the transition through default mode network. Moreover, we
                      demonstrate that combining graph theoretical measurements
                      and our energy-based control measurements as the feature
                      vector can provide complementary prediction power for the
                      behavioral scores (Combination vs. Control: t = 9.41, p =
                      1.64e-13; Combination vs. Graph: t = 4.92, p = 3.81e-6). Our
                      approach integrates statistical inference and dynamical
                      system inspection towards understanding brain networks.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35365757},
      UT           = {WOS:000777178500001},
      doi          = {10.1038/s42003-022-03196-0},
      url          = {https://juser.fz-juelich.de/record/910588},
}