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@INPROCEEDINGS{Gutzen:902442,
      author       = {Gutzen, Robin and Bonis, Giulia De and Pastorelli, Elena
                      and Capone, Cristiano and Luca, Chiara De and Mascaro, Anna
                      Letizia Allegra and Resta, Francesco and Pavone, Francesco
                      Saverio and Sanchez-Vives, Maria V. and Mattia, Maurizio and
                      Grün, Sonja and Davison, Andrew and Paolucci, Pier
                      Stanislao and Denker, Michael},
      title        = {{A}n adaptable analysis pipeline makes cortical wave
                      phenomena comparable across heterogeneous data sets},
      reportid     = {FZJ-2021-04264},
      year         = {2021},
      abstract     = {In the past decades, neuroscience excelled at accumulating
                      a rich, heterogeneous landscape of datasets and
                      methodologies. However, it is a challenge to effectively
                      leverage the benefits of such diversity by combining the
                      insights from across experiments, species, and measurement
                      techniques in order to build a cumulative understanding of
                      brain function. Integration of this heterogeneity requires
                      rigorous analysis workflows that offer to distill
                      consistent, reproducible, comparable, and reusable
                      conclusions. Aligning data from various sources enables not
                      only “big-data” analysis approaches, but also allows to
                      produce a broader experimental basis for validating models
                      and theories across the experimental and computational
                      scales of description. Here, we explore such an approach in
                      the context of slow cortical waves (up to 5 Hz). Slow waves
                      are persistently observed in sleep and anesthesia across
                      various species and various measurement techniques [1,2].
                      Furthermore, such activity is expressed by various models
                      [3]. First, we address the issue of providing a comparable
                      quantitative description of the wave dynamics to compare
                      such heterogeneous data on a conceptual level by designing a
                      general and modular analysis pipeline approach that is
                      adaptable to a variety of applications. Here, the key
                      objective is to interface existing methods, standards, and
                      tools in a flexible manner in order to serve the
                      requirements of a wide range of datasets and research
                      questions using a common set of analysis components.
                      Moreover, by building on and co-developing other specialized
                      open-source tools, we further emphasize the reusability and
                      extendability of each of the pipeline components.Based on
                      this design, we present the Slow Wave Analysis Pipeline
                      (SWAP) as a concrete, reusable implementation to perform
                      broad, detailed, and rigorous comparisons of slow wave
                      characteristics, such as velocities or directions. We apply
                      SWAP to a range of openly available ECoG [4,5,6] and calcium
                      imaging [7,8] datasets and investigate the influences of
                      experimental parameters such as the mice’s genetic strain,
                      the type and dosage of anesthetics, the measurement
                      technique and the spatial resolution. We also demonstrate
                      the pipeline’s capabilities to benchmark specific methods
                      within the analysis by switching them with another method
                      and by analyzing the same pool of data.Finally, we discuss
                      how the premise of reusability and modularity helps to
                      derive other analysis pipelines for similar research
                      endeavors to amplify collaborative research.References 1.
                      Adamantidis, A. R., Herrera C. G., and Gent T. C. (2019)
                      "Oscillating circuitries in the sleeping brain." Nature
                      Reviews Neuroscience 1-17. doi: 10.1038/s41583-019-0223-4 2.
                      Alkire, M. T., Hudetz A. G. , and Tononi G. (2008)
                      "Consciousness and anesthesia." Science 322.5903: 876-880.
                      doi: 10.1126/science.1149213 3. Sanchez-Vives, Maria V.,
                      Marcello Massimini, and Maurizio Mattia. (2017) "Shaping the
                      default activity pattern of the cortical network." Neuron
                      94.5: 993-1001. doi: 10.1016/j.neuron.2017.05.015 4. Dasilva
                      et al. (2020) “Altered Neocortical Dynamics in a Mouse
                      Model of Williams–Beuren Syndrome”. Molecular
                      Neurobiology, vol. 57, no 2, p. 765-777. doi:
                      10.1007/s12035-019-01732-4 5. Sanchez-Vives, M. (2019)
                      “Cortical activity features in transgenic mouse models of
                      cognitive deficits (Williams Beuren Syndrome)” [Data set].
                      EBRAINS. doi: 10.25493/DZWT-1T8 6. De Bonis, G. et al.
                      (2019) "Analysis pipeline for extracting features of
                      cortical slow oscillations". Frontiers in Systems
                      Neuroscience 13:70. doi: 10.3389/fnsys.2019.00070 7. Resta,
                      F., Allegra Mascaro, A. L., and Pavone, F. (2020). Study of
                      Slow Waves (SWs) propagation through wide-field calcium
                      imaging of the right cortical hemisphere of GCaMP6f mice
                      [Data set]. EBRAINS. doi: 10.25493/3E6Y-E8G 8. Celotto, M.
                      et al. (2020) “Analysis and Model of Cortical Slow Waves
                      Acquired with Optical Techniques”. Methods and Protocols
                      3.1:14. doi: 10.3390/mps3010014},
      month         = {Nov},
      date          = {2021-11-08},
      organization  = {Sfn Neuroscience, online (USA), 8 Nov
                       2021 - 11 Nov 2021},
      subtyp        = {After Call},
      cin          = {INM-6 / INM-10 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {5235 - Digitization of Neuroscience and User-Community
                      Building (POF4-523) / HBP SGA3 - Human Brain Project
                      Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/902442},
}