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@ARTICLE{ObersteFrielinghaus:1048790,
      author       = {Oberste-Frielinghaus, Jonas and Ito, Junji and Grün,
                      Sonja},
      title        = {{T}he effect of data preprocessing on spike correlation
                      analysis results},
      reportid     = {FZJ-2025-04905},
      year         = {2025},
      abstract     = {In recent years, an increasing number of large
                      electrophysiological data sets have become publicly
                      available, thereby providing researcherswith the opportunity
                      to analyze spike train data without conducting their own
                      experiments.While this is undoubtedly a positive
                      development, it increases the need for proper documentation
                      on how the data were collected and what preprocessing was
                      performed on the data, since interpreting analysis results
                      in ignorance of these pieces of information can lead to
                      wrong conclusions. An important preprocessing step is the
                      removal of artifacts from the recordings.
                      Electrophysiological recordings are particularly susceptible
                      to electrical cross-talks between recording channels,
                      resulting in artifact spikes that are coincident in multiple
                      channels on the time scale of the data sampling rate, i.e.,
                      1/30 ms in popular setups. The removal by signal whitening
                      is only possible if also the raw sampled data are available,
                      thus to eliminate this type of artifact is to remove all
                      coincident spikes on the recording time scale to definitely
                      avoid artifact spikes. However, given the lack of the
                      ``ground truth'' , this step has the potential to eliminate,
                      in conjunction with the artifacts, components of the data
                      that are pertinent to the research objective. In this study,
                      we use a modified version of the Unitary Event Analysis and
                      demonstrate that such preprocessing results in significantly
                      lower correlations than expected by chance even on longer
                      time scales. We also propose a method to correct for the
                      bias introduced by this preprocessing. Thus, slight changes
                      in the preprocessing have potentially strong impact on
                      analysis results and methods need to consider these
                      effects.},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / EBRAINS 2.0
                      - EBRAINS 2.0: A Research Infrastructure to Advance
                      Neuroscience and Brain Health (101147319) / GRK 2416 - GRK
                      2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung
                      neuronaler multisensorischer Integration (368482240) / DFG
                      project G:(GEPRIS)561027837 - Einbettung neuronaler
                      Ensembles und deren Signaturen (561027837)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)101147319 /
                      G:(GEPRIS)368482240 / G:(GEPRIS)561027837},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.1101/2025.11.28.691090},
      url          = {https://juser.fz-juelich.de/record/1048790},
}