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@ARTICLE{Morawetz:878064,
      author       = {Morawetz, Carmen and Riedel, Michael C. and Salo, Taylor
                      and Berboth, Stella and Eickhoff, Simon B. and Laird, Angela
                      R. and Kohn, Nils},
      title        = {{M}ultiple large-scale neural networks underlying emotion
                      regulation},
      journal      = {Neuroscience $\&$ biobehavioral reviews},
      volume       = {116},
      issn         = {0149-7634},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-02608},
      pages        = {382 - 395},
      year         = {2020},
      abstract     = {Recent models suggest emotion generation, perception, and
                      regulation rely on multiple, interacting large-scale brain
                      networks. Despite the wealth of research in this field, the
                      exact functional nature and different topological features
                      of these neural networks remain elusive. Here, we addressed
                      both using a well-established data-driven meta-analytic
                      grouping approach. We applied k-means clustering to a large
                      set of previously published experiments investigating
                      emotion regulation (independent of strategy, goal and
                      stimulus type) to segregate the results of these experiments
                      into large-scale networks. To elucidate the functional
                      nature of these distinct networks, we used functional
                      decoding of metadata terms (i.e. task-level descriptions and
                      behavioral domains). We identified four large-scale brain
                      networks. The first two were related to regulation and
                      functionally characterized by a stronger focus on response
                      inhibition or executive control versus appraisal or language
                      processing. In contrast, the second two networks were
                      primarily related to emotion generation, appraisal, and
                      physiological processes. We discuss how our findings
                      corroborate and inform contemporary models of emotion
                      regulation and thereby significantly add to the
                      literature.Keywords: Distraction; Emotion regulation
                      strategies; Neuroimaging; Reappraisal; Suppression; fMRI.},
      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       = {32659287},
      UT           = {WOS:000557868200028},
      doi          = {10.1016/j.neubiorev.2020.07.001},
      url          = {https://juser.fz-juelich.de/record/878064},
}