% 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”.

@INPROCEEDINGS{Gutzen:885608,
      author       = {Gutzen, Robin and De Bonis, Giulia and Pastorelli, Elena
                      and Capone, Cristiano and De Luca, Chiara and Mattheisen,
                      Glynis and Allegra Mascaro, Anna Letizia 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        = {{B}uilding adaptable and reusable pipelines for
                      investigating the features of slow cortical rhythms across
                      scales, methods, and species},
      reportid     = {FZJ-2020-03960},
      year         = {2020},
      abstract     = {Making progress in neuroscience is increasingly a
                      collaborative effort that requires insights obtained across
                      data, methodologies, and models. For various spatial and
                      temporal scales, there exist sophisticated analysis
                      approaches capturing the observed phenomena. These puzzle
                      pieces must eventually be properly related to one another
                      and integrated to frame the research in a coherent picture.
                      In this process, often three questions arise: How to make
                      results comparable? How to relate complementary, yet
                      different methodological approaches to one another? How to
                      incorporate and validate models based on these findings?A
                      prominent example of such a scenario are slow cortical waves
                      which are persistently observed in sleeping and anesthetized
                      subjects of various species and across various measurement
                      techniques [1,2], as well as being expressed by various
                      models. While slow waves are an ubiquitous phenomenon with a
                      rich literature basis, and have been proposed to be the
                      default activity pattern of the cortical network [3], the
                      diversity of the experimental data and the numerous
                      different analytical methods, tools, and even terminologies
                      makes it next to impossible to rigorously compare and
                      interrelate results from the various sources to form a
                      coherent understanding.Here, we present an analysis pipeline
                      for the study of slow wave dynamics to support quantitative,
                      statistical comparisons of analysis results across data
                      sources and algorithms. A key concept of the pipeline is
                      modularity at the correct level of granularity to flexibly
                      adapt, reuse, and extend it to a wide range of datasets, and
                      research questions. In the spirit of reproducibility,
                      individual analysis blocks are built on open-source software
                      tools, e.g., the workflow manager Snakemake
                      $(RRID:SCR_003475),$ the Neo $(RRID:SCR_000634)$ library for
                      data representation [4], and the Elephant
                      $(RRID:SCR_003833)$ analysis toolbox.This pipeline design
                      enables multi-scale analyses of measured slow wave activity,
                      which we demonstrate using ECoG [5,6] and Calcium Imaging
                      [7] data of anesthetized mice. Furthermore, the integration
                      of model data provides a basis for rigorous validation
                      testing [8,9]. While the ‘same method - different data’
                      approach enables fair comparisons, the pipeline equally
                      enables ‘same data - different methods’ benchmarking.
                      Finally, we discuss how the re-usable and adaptable
                      conceptual design helps to derive analysis pipelines for
                      similar research endeavours to amplify collaborative
                      research.References 1. 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 2. 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 3. Sanchez-Vives, M. V., Massimini, M.,
                      and Mattia, M. (2017) “Shaping the default activity
                      pattern of the cortical network”. Neuron, 94(5), 993-1001.
                      doi: 10.1016/j.neuron.2017.05.015 4. Grewe, J., Wachtler T,
                      and Benda J. (2011) "A bottom-up approach to data annotation
                      in neurophysiology." Frontiers in Neuroinformatics 5:16.
                      doi: 10.3389/fninf.2011.00016 5. 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 6.
                      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 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. Gutzen, R. et al. (2018) "Reproducible
                      neural network simulations: statistical methods for model
                      validation on the level of network activity data." Frontiers
                      in Neuroinformatics 12:90. doi: 10.3389/fninf.2018.00090 9.
                      Trensch, G. et al. (2018) "Rigorous neural network
                      simulations: a model substantiation methodology for
                      increasing the correctness of simulation results in the
                      absence of experimental validation data." Frontiers in
                      Neuroinformatics 12:81. doi: 10.3389/fninf.2018.00090},
      month         = {Sep},
      date          = {2020-09-29},
      organization  = {Bernstein Conference, online
                       (Germnay), 29 Sep 2020 - 29 Oct 2020},
      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          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.12751/NNCN.BC2020.0030},
      url          = {https://juser.fz-juelich.de/record/885608},
}