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@ARTICLE{Jiang:1023459,
      author       = {Jiang, Lin and Eickhoff, Simon B. and Genon, Sarah and
                      Wang, Guangying and Yi, Chanlin and He, Runyang and Huang,
                      Xunan and Yao, Dezhong and Dong, Debo and Li, Fali and Xu,
                      Peng},
      title        = {{M}ultimodal {C}ovariance {N}etwork {R}eflects {I}ndividual
                      {C}ognitive {F}lexibility},
      journal      = {International journal of neural systems},
      volume       = {34},
      number       = {4},
      issn         = {0129-0657},
      address      = {Singapore [u.a.]},
      publisher    = {World Scientific Publ. Co.},
      reportid     = {FZJ-2024-01698},
      pages        = {2450018},
      year         = {2024},
      abstract     = {Cognitive flexibility refers to the capacity to shift
                      between patterns of mental function and relies on functional
                      activity supported by anatomical structures. However, how
                      the brain's structural-functional covarying is preconfigured
                      in the resting state to facilitate cognitive flexibility
                      under tasks remains unrevealed. Herein, we investigated the
                      potential relationship between individual cognitive
                      flexibility performance during the trail-making test (TMT)
                      and structural-functional covariation of the large-scale
                      multimodal covariance network (MCN) using magnetic resonance
                      imaging (MRI) and electroencephalograph (EEG) datasets of
                      182 healthy participants. Results show that cognitive
                      flexibility correlated significantly with the
                      intra-subnetwork covariation of the visual network (VN) and
                      somatomotor network (SMN) of MCN. Meanwhile,
                      inter-subnetwork interactions across SMN and VN/default mode
                      network/frontoparietal network (FPN), as well as across VN
                      and ventral attention network (VAN)/dorsal attention network
                      (DAN) were also found to be closely related to individual
                      cognitive flexibility. After using resting-state MCN
                      connectivity as representative features to train a
                      multi-layer perceptron prediction model, we achieved a
                      reliable prediction of individual cognitive flexibility
                      performance. Collectively, this work offers new perspectives
                      on the structural-functional coordination of cognitive
                      flexibility and also provides neurobiological markers to
                      predict individual cognitive flexibility.Keywords: Cognitive
                      flexibility; EEG-MRI; multimodal covariance network;
                      response prediction; trail-making test.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38372035},
      UT           = {WOS:001164162800001},
      doi          = {10.1142/S0129065724500187},
      url          = {https://juser.fz-juelich.de/record/1023459},
}