% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Gutzen:1020500,
      author       = {Gutzen, Robin and De Bonis, Giulia and De Luca, Chiara and
                      Pastorelli, Elena and Capone, Cristiano and Allegra Mascaro,
                      Anna Letizia and Resta, Francesco and Manasanch, Arnau and
                      Pavone, Francesco Saverio and Sanchez-Vives, Maria V. and
                      Mattia, Maurizio and Grün, Sonja and Paolucci, Pier
                      Stanislao and Denker, Michael},
      title        = {{A} modular and adaptable analysis pipeline to compare slow
                      cerebral rhythms across heterogeneous datasets},
      journal      = {Cell reports / Methods},
      volume       = {4},
      number       = {1},
      issn         = {2667-2375},
      address      = {Cambridge, MA},
      publisher    = {Cell Press},
      reportid     = {FZJ-2024-00219},
      pages        = {100681},
      year         = {2024},
      abstract     = {Neuroscience is moving toward a more integrative discipline
                      where understanding brain function requires consolidating
                      the accumulated evidence seen across experiments, species,
                      and measurement techniques. A remaining challenge on that
                      path is integrating such heterogeneous data into analysis
                      workflows such that consistent and comparable conclusions
                      can be distilled as an experimental basis for models and
                      theories. Here, we propose a solution in the context of
                      slow-wave activity (<1 Hz), which occurs during unconscious
                      brain states like sleep and general anesthesia and is
                      observed across diverse experimental approaches. We address
                      the issue of integrating and comparing heterogeneous data by
                      conceptualizing a general pipeline design that is adaptable
                      to a variety of inputs and applications. Furthermore, we
                      present the Collaborative Brain Wave Analysis Pipeline
                      (Cobrawap) as a concrete, reusable software implementation
                      to perform broad, detailed, and rigorous comparisons of
                      slow-wave characteristics across multiple, openly available
                      electrocorticography (ECoG) and calcium imaging datasets.},
      cin          = {INM-6 / IAS-6 / INM-10},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / JL SMHB -
                      Joint Lab Supercomputing and Modeling for the Human Brain
                      (JL SMHB-2021-2027) / DFG project 491111487 -
                      Open-Access-Publikationskosten / 2022 - 2024 /
                      Forschungszentrum Jülich (OAPKFZJ) (491111487) / Algorithms
                      of Adaptive Behavior and their Neuronal Implementation in
                      Health and Disease (iBehave-20220812)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-Juel1)JL SMHB-2021-2027 /
                      G:(GEPRIS)491111487 / G:(DE-Juel-1)iBehave-20220812},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {38183979},
      UT           = {WOS:001171298600001},
      doi          = {10.1016/j.crmeth.2023.100681},
      url          = {https://juser.fz-juelich.de/record/1020500},
}