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@INPROCEEDINGS{Gutzen:908473,
      author       = {Gutzen, R. and Bonis, G. D. and Pastorelli, E. and Capone,
                      C. and Luca, C. D. and Mascaro, A. L. A. and Resta, F. and
                      Pavone, F. S. and Sanchez-Vives, M. V. and Mattia, M. and
                      Grün, S. and Davison, A. and Paolucci, P. S. and Denker,
                      M.},
      title        = {{C}obrawap: a modular cortical wave analysis pipeline for
                      heterogeneous data},
      reportid     = {FZJ-2022-02626},
      year         = {2022},
      abstract     = {Introduction:An unprecedented richness of data and
                      methodologies enables more detailed access to neural
                      processes but also poses the challenge to combine insights
                      across experiments, species, and measurement techniques.
                      While different experimental recording modalities offer
                      complementary views onto the brain, their data analysis
                      approaches and workflows are often too specific to compare
                      the results rigorously. However, this challenge also
                      promises new avenues of scientific progress. By aligning
                      existing data and analyses from different sources in a
                      reusable workflow we can build a broader basis for
                      meta-studies, contextualization of individual studies, and
                      model validation.Here, we showcase such an analysis pipeline
                      with the application to cortical wave activity in the delta
                      (‘slow waves’) and beta range. Cortical waves can be
                      prominently observed in a variety of heterogeneous data
                      [1,2] and a plethora of analytical methods exist that we aim
                      to interface within a consistent framework: the
                      ‘collaborative brain wave analysis pipeline’
                      (CobraWap).Methods:The design of CobraWap is based on
                      modular building blocks that provide implementations of
                      analysis methods and processing steps. These blocks are
                      grouped in task-specific stages, e.g., data entry, data
                      processing, trigger detection, wave detection, wave
                      characterization. By letting the pipeline match the input
                      and output format requirements for each of these pipeline
                      components, defining a workflow becomes a matter of
                      selecting a combination of stages and blocks to be applied.
                      This flexibility is employed to converge the heterogeneous
                      data to a common description level of wave activity, from
                      which then common characteristic measures, such as velocity,
                      direction, inter-wave intervals, or wave type
                      classifications, can be derived and quantitatively compared
                      across the data. We demonstrate the versatility of the
                      pipeline with multiple datasets of ECoG [3] and calcium
                      imaging recordings [4] of anesthetized mice, and Utah-array
                      recordings of awake behaving macaques [e.g. 5]. Further, we
                      integrate standard analysis methods from the literature to
                      serve the requirements of a wide range of datasets and
                      research questions. To emphasize the reusability and
                      extendability of each of the pipeline components, the
                      pipeline builds entirely on open-source solutions, such as
                      the workflow manager Snakemake $(RRID:SCR_003475),$ the Neo
                      $(RRID:SCR_000634)$ library for data representation [6], the
                      Elephant $(RRID:SCR_003833)$ analysis toolbox, and the
                      EBRAINS Knowledge Graph (https://kg.ebrains.eu) for
                      capturing outputs of the pipeline execution.Results:The
                      pipeline design promotes the creation of
                      application-tailored and reproducible analysis workflows for
                      many datasets. We demonstrate this “big-data'' approach by
                      investigating dataset-specific parameters across different
                      experiments. For example, we evaluate the influences of the
                      type and dose of anesthesia or the measurement modality and
                      their temporal and spatial resolution on the characteristics
                      of slow waves (e.g., wave velocities) and show that we can
                      replicate corresponding findings from the literature
                      [7,8,9,10].Just as applying the same methods to different
                      data enables a fair comparison between datasets, the
                      pipeline equally enables analyzing the same data with
                      different methods to benchmark their influence on the
                      resulting wave detection and characterization. Finally, we
                      adapt the pipeline for the analysis of beta waves and
                      discuss how the individual elements can be reused,
                      rearranged, or extended to help derive analysis workflows
                      for similar research endeavors and amplify collaborative
                      research.Conclusions:While there are growing efforts in
                      formalizing how neuroscientific data is represented and
                      stored, we here present the benefits of furthermore
                      formalizing the analysis workflows, leveraging the benefits
                      of the diversity in data and methods towards easier
                      collaboration and a cumulative understanding of brain
                      function. 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] Muller, L. et al. (2018).
                      “Cortical Travelling Waves: Mechanisms and Computational
                      Principles.” Nature Reviews Neuroscience 19 (5): 255–68.
                      doi: 10.1038/nrn.2018.20.[3] 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; Sanchez-Vives, M. (2019)
                      "Cortical activity features in transgenic mouse models of
                      cognitive deficits (Williams Beuren Syndrome)" EBRAINS. doi:
                      10.25493/ANF9-EG3[4] Resta, F., Allegra Mascaro, A. L., $\&$
                      Pavone, F. (2020) "Study of Slow Waves (SWs) propagation
                      through wide-field calcium imaging of the right cortical
                      hemisphere of GCaMP6f mice" EBRAINS. doi: 10.25493/3E6Y-E8G;
                      Resta, F., Allegra Mascaro, A. L., $\&$ Pavone, F. (2021)
                      "Study of Slow Waves (SWs) propagation through wide-field
                      calcium imaging of the right cortical hemisphere of GCaMP6f
                      mice (v2)" EBRAINS. doi: 10.25493/QFZK-FXS; Resta, F., [5]
                      Allegra Mascaro, A. L., $\&$ Pavone, F. (2020) "Wide-field
                      calcium imaging of the right cortical hemisphere of GCaMP6f
                      mice at different anesthesia levels" EBRAINS. doi:
                      10.25493/XJR8-QCA[6] Brochier, T. et al. (2018) “Massively
                      Parallel Recordings in Macaque Motor Cortex during an
                      Instructed Delayed Reach-to-Grasp Task.” Scientific Data 5
                      (1): 180055. doi: 10.1038/sdata.2018.55.[7] Garcia, S. et
                      al. (2014) “Neo: an object model for handling
                      electrophysiology data in multiple formats.” Frontiers in
                      Neuroinformatics 8:10. doi: 10.3389/fninf.2014.00010[8] 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[9]
                      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[10] Dasilva, M.,
                      et al. (2020). Modulation of cortical slow oscillations and
                      complexity across anesthesia levels. NeuroImage, 224,
                      117415. doi: 10.1016/j.neuroimage.2020.117415[11] Liang, Y.
                      (2021). “Cortex-Wide Dynamics of Intrinsic Electrical
                      Activities: Propagating Waves and Their Interactions.”
                      Journal of Neuroscience 41 (16): 3665–78. doi:
                      10.1523/JNEUROSCI.0623-20.2021},
      month         = {Jun},
      date          = {2022-06-19},
      organization  = {OHBM Conference, Glasgow (Scottland),
                       19 Jun 2022 - 24 Jun 2022},
      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 SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / HAF -
                      Helmholtz Analytics Framework (ZT-I-0003)},
      pid          = {G:(DE-HGF)POF4-5235 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-HGF)ZT-I-0003},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/908473},
}