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@ARTICLE{DeFilippi:911192,
      author       = {De Filippi, Eleonora and Escrichs, Anira and Càmara,
                      Estela and Garrido, César and Marins, Theo and
                      Sánchez-Fibla, Marti and Gilson, Matthieu and Deco,
                      Gustavo},
      title        = {{M}editation-induced effects on whole-brain structural and
                      effective connectivity},
      journal      = {Brain structure $\&$ function},
      volume       = {227},
      number       = {6},
      issn         = {0044-2232},
      address      = {Heidelberg},
      publisher    = {Springer},
      reportid     = {FZJ-2022-04504},
      pages        = {2087 - 2102},
      year         = {2022},
      abstract     = {In the past decades, there has been a growing scientific
                      interest in characterizing neural correlates of meditation
                      training. Nonetheless, the mechanisms underlying meditation
                      remain elusive. In the present work, we investigated
                      meditation-related changes in functional dynamics and
                      structural connectivity (SC). For this purpose, we scanned
                      experienced meditators and control (naive) subjects using
                      magnetic resonance imaging (MRI) to acquire structural and
                      functional data during two conditions, resting-state and
                      meditation (focused attention on breathing). In this way, we
                      aimed to characterize and distinguish both short-term and
                      long-term modifications in the brain’s structure and
                      function. First, to analyze the fMRI data, we calculated
                      whole-brain effective connectivity (EC) estimates, relying
                      on a dynamical network model to replicate BOLD signals’
                      spatio-temporal structure, akin to functional connectivity
                      (FC) with lagged correlations. We compared the estimated EC,
                      FC, and SC links as features to train classifiers to predict
                      behavioral conditions and group identity. Then, we performed
                      a network-based analysis of anatomical connectivity. We
                      demonstrated through a machine-learning approach that EC
                      features were more informative than FC and SC solely. We
                      showed that the most informative EC links that discriminated
                      between meditators and controls involved several large-scale
                      networks mainly within the left hemisphere. Moreover, we
                      found that differences in the functional domain were
                      reflected to a smaller extent in changes at the anatomical
                      level as well. The network-based analysis of anatomical
                      pathways revealed strengthened connectivity for meditators
                      compared to controls between four areas in the left
                      hemisphere belonging to the somatomotor, dorsal attention,
                      subcortical and visual networks. Overall, the results of our
                      whole-brain model-based approach revealed a mechanism
                      underlying meditation by providing causal relationships at
                      the structure-function level.},
      cin          = {INM-6 / INM-10 / IAS-6},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5232 - Computational Principles (POF4-523) / SDS005 -
                      Towards an integrated data science of complex natural
                      systems (PF-JARA-SDS005) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-Juel-1)PF-JARA-SDS005 /
                      G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35524072},
      UT           = {WOS:000791625400001},
      doi          = {10.1007/s00429-022-02496-9},
      url          = {https://juser.fz-juelich.de/record/911192},
}